Changes in Protein Expression Across Laboratory and Field

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Changes in Protein Expression Across Laboratory and Field Experiments in Geobacter bemidjiensis Eric D. Merkley,† Kelly C. Wrighton,§ Cindy J. Castelle,∥,⊥ Brian J. Anderson,‡ Michael J. Wilkins,§,# Vega Shah,○ Tyler Arbour,∥ Joseph N. Brown,‡ Steven W. Singer,⊥ Richard D. Smith,‡ and Mary S. Lipton*,‡ †

Signature Sciences and Technology Division, and ‡Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States § Department of Microbiology, and #School of Earth Sciences, The Ohio State University, Columbus, Ohio 43210, United States ∥ Department of Earth and Planetary Science, University of California Berkeley, Berkeley, California 94720 ⊥ Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States ○ Biological Oceanography, University of Washington, Seattle, Washington 98105, United States S Supporting Information *

ABSTRACT: Bacterial extracellular metal respiration, as carried out by members of the genus Geobacter, is of interest for applications including microbial fuel cells and bioremediation. Geobacter bemidjiensis is the major species whose growth is stimulated during groundwater amendment with acetate. We have carried out label-free proteomics studies of G. bemidjiensis grown with acetate as the electron donor and either fumarate, ferric citrate, or one of two hydrous ferric oxide mineral types as electron acceptor. The major class of proteins whose expression changes across these conditions is c-type cytochromes, many of which are known to be involved in extracellular metal reduction in other, better-characterized Geobacter species. Some proteins with multiple homologues in G. bemidjiensis (OmcS, OmcB) had different expression patterns than observed for their G. sulfurreducens homologues under similar growth conditions. We also compared the proteome from our study to a prior proteomics study of biomass recovered from an aquifer in Colorado, where the microbial community was dominated by strains closely related to G. bemidjiensis. We detected an increased number of proteins with functions related to motility and chemotaxis in the Colorado field samples compared to the laboratory samples, suggesting the importance of motility for in situ extracellular metal respiration. KEYWORDS: Geobacter bemidjiensis, c-type cytochromes, proteomics, electron acceptors

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INTRODUCTION Bacteria of the genus Geobacter use extracellular metals such as iron(III) and uranium(VI) as terminal electron acceptors for cellular respiration, a process known as dissimilatory metal reduction. The metals can be in a soluble form or incorporated in a solid mineral phase, such as hydrous ferric oxide (HFO). The ability to utilize soluble and insoluble electron acceptors has led to fundamental and applied research involving Geobacter, including electrical current generation in microbial fuel cells,1 identification of microbial nanowires derived from electrically conductive pili,2 and bioremediation of uraniumcontaminated aquifers.3−5 Geobacter bemidjiensis, an isolate obtained from an iron mine in Minnesota, is particularly important for potential bioremediation applications, since G. bemidjiensis and closely related strains are the cultured representatives of the predominant species coupling acetate utilization to the reduction of uranium in alluvial aquifers.4,5 © XXXX American Chemical Society

The detailed mechanism of extracellular electron transfer is the focus of recent research, and may differ between Geobacter species. In these mechanistic studies, there are numerous lines of genetic and biochemical evidence obtained from experiments with G. sulf urreducens that have established multiheme c-type cytochromes (proteins with one or more covalently bound heme c groups) as critical proteins for extracellular metal respiration.6−14 The genome of G. bemidjiensis encodes 84 ctype cytochromes, both single and multiheme, including some unique to the species and others homologous to c-type cytochromes in other Geobacter species.15 The exact nature of the in situ electron acceptor substrate in subsurface environments for dissimilatory metal reduction by populations related to G. bemidjiensis is also unclear. In isolation, G. bemidjiensis can Received: September 17, 2014

A

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Journal of Proteome Research

atmosphere, washed once, and reconcentrated in 60 mL of basal buffered medium (without vitamins and minerals). A total of 1.5 mL of this cell suspension was anoxically and aseptically added to 148.5 mL of growth media in experimental bottles. Each bottle was amended with 10 mM acetate and four different electron acceptor treatments. The electron acceptor treatments were designated (i) fumarate (∼40 mM), (ii) Fe(III) citrate (42.6 ± 1.3 mM Fe(III)), (iii) bulk HFO (67.21 ± 1.58 mM Fe(III)), and (iv) nanoparticle HFO (53.93 ± 1.57 mM Fe(III)). All four experimental electron acceptor treatments and accompanying killed controls were grown in triplicate. Additionally, for the bulk and nanoparticle HFO treatments an abiotic (no cell) control was also included. For ferric citrate, bulk HFO, and NP HFO, Fe(II) production over time was monitored regularly by the ferrozine assay with a 24 h extraction.24−26 Killed and abiotic controls demonstrated no change in Fe(II) production and/or cell density over time. Bulk and NP experiments were conducted over 35 days and ferric citrate and fumarate over 26 h. For ironcontaining media, samples were collected during “early” and “late” Fe(III) reduction, and compared to early and stationary phase fumarate control. Early reduction was denoted as the period up to when ∼25% of the bioavailable iron was reduced (NP 31% reduction, 6.25 ± 1.2 mM Fe(II); bulk 22% reduction, 3.75 ± 0.12 mM Fe(II); ferric citrate 25%, 10.79 ± 1.31 mM Fe (II)). Late iron reduction was denoted when Fe(II) production no longer increased logarithmically and stabilized for three consecutive time points (NP 15.86 ± 1.24 mM; bulk 13.24 ± 0.53 mM Fe(II)).

grow on either soluble iron(III) sources, such as iron(III) citrate, or insoluble iron-containing minerals such as HFO, as well as organic electron acceptors such as fumarate. To characterize the protein complement from G. bemidjiensis involved in extracellular metal respiration and to elucidate the role of c-type cytochromes, we have carried out peptide-based liquid chromatography−mass spectrometry proteomics measurements on laboratory-grown cultures of G. bemidjiensis using the accurate mass and time (AMT) tag approach.16−18 We used a variety of terminal electron acceptors, including fumarate, ferric citrate, and two different particle sizes of HFO (heterogeneous undefined HFO (bulk), and the other a chemically synthesized nanoparticle 1.96 is in a sense equivalent to a p-value of 0.05, which we have chosen as the critical value for assessing significance. Only proteins detected in two or more out of three replicates for each condition were used in calculating the regression line, but z-scores based on the distances from the regression line were calculated for any proteins detected in as few as two runs in one condition and one in the other in a given pairwise comparison. Thus, Method 2 is less stringent with respect to missing data than Method 1. Using eq 1, we calculated dcrit, the value of d corresponding to z = ± 1.96. Proteins with |z| < 1.96 and whose error bars were outside of the regression line ± dcrit were considered to have significantly changing abundances. We also tracked the occurrence of proteins seen only in one condition or the other. We filtered the data according to a series of heuristics meant to ensure that only proteins with qualitative agreement (i.e., same direction of change) between the two experiments were counted as differentially expressed. For instance, a protein was considered to change significantly if it was judged significant in both experiments by the z-score and error bar criteria above, but also if it was judged significant by zscore and error bar in one experiment and only by z-score in the other (that is, one of the two experiments had large error bars), or if it was judged significantly upregulated in condition 1 in one experiment and occurred only in condition 1 in the other experiment. The linear regression and statistical analysis were carried out in the R statistical computing environment33 using a custom script (available in the Supplemental Methods); collation of the data and comparison of the two experiments were done by a combination of custom Perl scripts and spreadsheet/database software. To evaluate the linear regression procedure (Method 2), we compared it to Method 1. Details of Method 1 can be found in D

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Figure 1. Correlation of unscaled log2 peptide abundance values across all conditionsthe correlation value is represented by a red color scale (upper left). Data are Pearson correlation coefficients for the indicated comparisons, displayed as a heat map. Fumarate- and ferric citrate-grown samples of G. bemidjiensis closely resemble each other, as evidenced by the high correlation coefficients (for example, black box). Similarly, samples grown on bulk and nanoparticle iron mineral samples resemble each other (for example, gray box). By contrast, bulk/nanoparticle and fumarate/ ferric citrate samples have lower correlations (light red color, dashed boxes). The “checkerboard” appearance thus indicates that the peptide expression profiles split the samples into two groups, fumarate/ferric citrate and bulk/nanoparticle iron mineral. Hierarchical clustering analysis of these data (not shown) confirms that all ferric citrate/fumarate samples cluster together, as do all bulk/nanoparticle solid iron samples.

these samples are not amenable to proteomics analysis via the AMT tag approach. In addition, a quantitative statistical comparison between laboratory and field samples is confounded by the many differences between the two types of samples as well as the differences in sample handling. For this reason, we carried out a spectral counting analysis (reviewed in ref 41) of the field and laboratory data. Data were searched with MSGF+34,35,42 and filtered by mass measurement error and MSGF spectral probability to a peptide level of 0.25% for field samples and 0.29% for laboratory samples. Two peptides per protein and two spectral counts per peptide were required for confident protein identification. We used the spectral count data for comparison between lab and field samples by principal components analysis. For comparing lists of detected proteins, we used the results of spectral counting analysis for the field data and AMT tag analysis for the laboratory data.

Supplemental Methods. Where both methods could be applied, the agreement was acceptable (Figure 2 and Supporting Information). Because of the decreased sample loading for the insoluble iron-grown samples, we expect a greater number of protein identifications in the ferric citrate and fumarate data sets. Therefore, in order to provide the most conservative interpretation, we do not discuss proteins appearing only in ferric citrate or fumarate-grown samples (and not in the insoluble iron-grown samples), although they are reported in Table S2, Supporting Information. This heuristic provides the most conservative interpretation of the data. Where protein abundance could be estimated in both samples, the linear regression approach provides a confident comparison. Comparing Laboratory and Field Samples Using Spectral Counting. Because of the taxonomic complexity of samples derived from the Rifle aquifer microbial community, E

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Figure 2. Linear regression (method 2) comparison of early growth G. bemidjiensis cultures grown on fumarate or ferric citrate. The top and bottom figures represent technical replicates of the entire experiment. Data are the average log2 abundance values from two to three technical replicates for each condition. Gray points represent proteins that do not change significantly. Cyan points represent proteins that are judged to change significantly by the traditional median-centered normalization method (method 1). Orange points represent proteins that are judged to change significantly by the linear regression method (method 2) only (i.e., the points fall outside the dotted lines representing the |z| > 1.96 boundaries). Red points represent proteins judged to change significantly by both methods. If the points fall above the linear regression line, expression is higher in cultures grown on ferric citrate; if below the line, expression is higher in fumarate-grown cultures. Note that the proteins judged to have significantly different expression only by method 1 are in general borderline cases (cyan points near the dotted lines) in method 2, indicating that method 2 robustly detects large changes in protein expression, although some subtle changes may not be detected. The cyan point in (A) at (21.6, 22.0), lying very close to the regression line, represents Gbem_2232. This protein was called significant by method 1 but not by method 2. This apparent discrepancy is a result of using the data from both (A) and (B) in the method 1 analysis, so that method 1 used twice as many data points as method 2, and of the treatment of missing data in the R-Rollup procedure.37 Note that in (B), Gbem_2232 is in fact identified as significantly changing.

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RESULTS AND DISCUSSION

slightly greater. The requirement for agreement between two complete replicates of the entire experiment provides additional confidence that the FDR of significantly changing proteins is acceptably low. We conclude that the linear regression method is an effective option in cases where sample matrix effects make more rigorous data analysis methods impractical. Note that we have used the term “significant” to describe changes in protein abundance as described in Methods, which may not be equivalent to statistical significance in the strictest sense. In other words, the linear regresison method presented here is an approximation.

Performance of the Linear Regression Method

The linear regression method (described in Methods) was introduced to account for unintended differences in sample loading arising from matrix effects on protein concentration measurement in samples grown on solid iron mineral phases. We evaluated this method by comparison with a standard data analysis pipeline. Where both analysis methods could be used (i.e., where there was no HFO-related intensity difference, for instance when comparing fumarate and ferric citrate conditions), the overlap was good (Figure 2, red points). The standard method is more sensitive, identifying more significantly changing proteins than linear regression, but the additional differentially expressed proteins called out only by the standard method are borderline cases in the linear regression method (Figure 2, cyan points). The number of proteins detected by the linear regression method and not by the standard method in this comparison is small (Figures 2 and S2, gold points) and likely due to the inclusion of more proteins with missing data. These observations indicate that whereas the conventional method is more sensitive to small changes, the linear regression method effectively detects large changes in protein abundance. The false discovery rate (FDR) for significantly changing proteins in Method 1 is controlled at 5% or less by the q-value method. Given the high degree of overlap between method 1 and method 2, and given that the proteins called out by method 2 tend to show larger changes on average than those called out by method 1, we believe that the FDR of method 2 is likely similar to that of method 1 or only

Protein Expression: General Observations

A correlation matrix of protein abundance changes (Figure 1) based on the raw (un-normalized) peptide intensities reveals similarities and differences between the four electron acceptor treatments. Correlation matrices are similar for experiment 1 and experiment 2. The data in Figure 1 show that the identity of the electron acceptor, and not early or late growth, is the most important experimental factor influencing protein expression. Peptides collected from biomass grown on either form of poorly crystalline iron mineral (bulk or nanoparticle) are more similar to each other, as represented by higher Pearson correlation coefficients (mean Pearson correlation coefficient between all bulk and nanoparticle analyses R = 0.80 ± 0.08) than they are to the soluble treatments (mean R = 0.63 ± 0.06). Similarly, samples grown on soluble electron acceptors (ferric citrate, fumarate) are more similar to each other than they are to insoluble electron acceptors (mean R = 0.88 ± 0.05 and R = 0.63 ± 0.06, respectively). This analysis suggests that a F

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Journal of Proteome Research Table 1. Functional Classes of Proteins with Significant Changes in Expression Levelsd

“Bulk” designates undefined poorly hydrous ferric oxide. See text for details. b“Nanoparticle” designates synthesized hydrous ferric oxide mineral particles 1.96, indicating that expression was higher in the second condition listed, or green if z < −1.96, indicating that expression was higher in the first condition listed. Each z-score comes from the average of three analytical replicate experiments; to be judged significant, the error bars (standard deviations if n = 3, one-half the range if n = 2, and 10% of the value if n = 1) of the measurement also had to fall outside of |z| > 1.96. Hence, the symbols “+” and “^” indicate that the observed z-score was judged to be statistically significant in the positive (“+”, red) or negative (“^”, green) direction, based on the error bars. Cells with values of 10, 20, 30, or 40 are not z-scores but indicate that a protein was detected in only one condition (fumarate, ferric citrate, bulk HFO, or nanoparticle HFO, respectively). NA, protein was detected in too few runs to calculate a z-score; ND, protein was not detected. “No matches” indicates c-type cytochromes with no close homologues in G. sulfurreducens or G. metallireducens. Gbem_1116 and Gbem_2679 (*) and Gbem_3470 and Gbem_1153 (**) are pairs of proteins with high enough sequence identity to make assignment of observed peptides to one or the other protein uncertain. Significance notations: a, clear expression change in at least one conditions; b, no change in expression; c, no clear change in expression due to disagreement between experiments; d, no interpretation of expression pattern due to missing data.

Figure 4. Comparison of proteomics results for Rifle acetate amendment field samples and laboratory cultures grown under the indicated conditions with acetate as the carbon source. (A) Principle components analysis (PCA). Black point represents the summed spectral counts for a series of strong cation exchange fractions of a sample of Rifle aquifer groundwater sampled at various times after acetate amendment. Other points represent the summed spectral counts for all technical replicates of analyses of cultures grown with the indicated electron acceptor (with early and late temporal samples grouped separately). Note that the Rifle groundwater samples form a cluster that is distinct from both the solid iron cluster (bulk and nanoparticle) and soluble electron acceptor (fumarate and ferric citrate) clusters. (B) Heatmap of correlation coefficients for the same samples, including a hierarchical clustering dendrogram at left. The clustering results support the groupings observed in A, indicating that the Rifle samples exhibit distinct protein profiles from laboratory-cultured samples. Input data for both A and B were the protein abundances measured by normalized spectral counts. H

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Figure 5. Overlap in detected proteins between laboratory cultures of G. bemidjiensis and environmental groundwater samples from the Rifle site. The Venn diagram shows the numbers of proteins common or unique to each combination of conditions. The Rifle field samples have slightly more proteins in common with ferric citrate/fumarate samples (705 proteins, or 93 outside the common core of 612 proteins) than with bulk/nanoparticle samples (675 proteins, or 63 outside the common core). The list of proteins from the laboratory samples was the list of confidently identified proteins from the AMT tag analysis. The list of proteins from the Rifle samples was based on spectral counting. For functional enrichment analysis of Rifle-only and common-core proteins, see Figure 6.

Figure 6. Functional category enrichment analysis of selected regions of the Venn diagram in Figure 5. (A) Proteins unique to Rifle groundwater samples compared to the G. bemidjiensis genome. (B) Proteins found in all samples, both Rifle groundwater and laboratory cultures. Red asterisks indicate statistical significance (p < 0.05, twosided test for equal proportions as described in Methods). Functional categories were based on the COG functional categories and are defined as follows: B, chromatin structure and dynamics; C, energy production/conversion and redox processes; D, cell cycle control and mitosis; E, amino acid metabolism and transport; F, nucleotide metabolism and transport; G, carbohydrate metabolism and transport; H, coenzyme metabolism; J, translation; K, transcription; L, replication and repair including nuclease activity; M, cell wall/membrane/ envelope biogenesis; N, cell motility; O, post-translational modification, protein turnover, and chaperone functions; P, inorganic ion transport and metabolism; Q, secondary metabolite biosynthesis, transport, and catabolism; T, signal transduction; U, intracelluar trafficking and secretion; V, defense mechanisms; Y, c-type cytochromes; Z, cytoskeleton. The set of proteins common to all samples is highly enriched in housekeeping and basic cellular processes. The set of proteins unique to the Rifle samples is enriched in cell motility and signal transduction proteins, many of which are involved in chemotaxis.

expression profile between the two conditions, but with differences in the expression of many individual proteins. To get a broad overview of the functions of proteins whose levels change significantly between electron acceptors at roughly equivalent time points, we grouped these proteins according to the Clusters of Orthologous Groups (COG) functional classes.45 To this list we added another class, c-type cytochromes (as annotated in ref 15). Functional classes that were enriched in at least one pairwise comparison of terminal electron acceptors at the same time point (early or late growth) according to the Method 2 (linear regression) analysis include coenzyme metabolism, post-translational modification, protein turnover, chaperone functions, signal transduction, and most frequently, c-type cytochromes, indicating that these cellular functions are important in responding to variation in the terminal electron acceptor (Table 1). Our results show some similarities with results from transcriptomics laboratory studies of G. uraniireducens grown on Rifle sediment or fumarate.46 In that study, transcript levels of c-type cytochromes, genes relating to protein fate (i.e., protein folding, degradation, and trafficking), and signal transduction genes were found to change significantly with the growth conditions. In this report, we have chosen to focus on c-type cytochromes as they showed the greatest differential protein expression patterns across the electron acceptor treatments (Table 1). The following section describes the expression patterns of the observed c-type cytochromes. Information about proteins in the other functional categories can be found in Table S1, Supporting Information.

peptides did not allow us to distinguish between paralogs. Because of this inability to distinguish between paralogs, we treat those proteins as identical in the subsequent discussion (Figure 3). Out of the detected c-type cytochromes, 12 showed a clearly significant change in abundance between at least one set of conditions. Three proteins showed no change in abundance across time or electron acceptor. Five cytochromes did not show a clear expression pattern due to lack of agreement between methods 1 and 2, and thus were not included in subsequent discussion. For the final seven proteins, the expression pattern could not be clearly described due to missing data (Figure 3). In this category we have included proteins detected only in ferric citrate or fumarate conditions, due to the sample loading issues discussed in Methods. Cytochromes with Missing Data or No Clear Expression Pattern. Deletion of G. sulf urreducens OmcH, an extracellular 24-heme cytochrome, decreases the efficiency of Fe(III) reduction by reducing the expression of OmcB.48 Two detected homologues of OmcH are listed in Figure 3: Gbem_1153, a 27-heme predicted to have an extracellular localization, and Gbem_3470, also predicted to have 27 hemes but with predicted periplasmic/outer membrane localization (subcellular prediction performed by PsortB49). These two

c-Type Cytochromes

c-Type cytochromes play a major role in electron transfer processes.47 Out of the 84 c-type cytochromes encoded in the G. bemidjiensis genome, we detected 27−29 in the eight samples (two growth phases, four treatments). In two cases the detected I

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Journal of Proteome Research Table 2. Proteins Related to Cell Motility or Signal Transduction (COG Functional Classes N or T) Found in Rifle Groundwater Biomass Samples but Not in Laboratory G. bemidjiensis Cultures by LC−MS Proteomicsa gene locus Gbem_0040 Gbem_0080 Gbem_0257 Gbem_0295 Gbem_0296 Gbem_0379 Gbem_0383 Gbem_0467 Gbem_0712 Gbem_0809 Gbem_0811 Gbem_0812 Gbem_0888 Gbem_1044 Gbem_1482 Gbem_1511 Gbem_1591 Gbem_1593 Gbem_1594 Gbem_1595 Gbem_1597 Gbem_1826 Gbem_2234 Gbem_2239 Gbem_2335 a

gene locus

annotation

methyl-accepting chemotaxis sensory transducer response regulator receiver modulated metal dependent phosphohydrolase methyl-accepting chemotaxis sensory transducer putative phytochrome sensor protein adenylate/guanylate cyclase with Chase sensor PAS/PAC sensor hybrid histidine kinase methyl-accepting chemotaxis sensory transducer methyl-accepting chemotaxis sensory transducer response regulator receiver modulated CheW protein methyl-accepting chemotaxis sensory transducer Hpt sensor hybrid histidine kinase

annotation

Gbem_2406 Gbem_2407

metal dependent phosphohydrolase inhibitor of MCP methylation-like protein methyl-accepting chemotaxis sensory transducer methyl-accepting chemotaxis sensory transducer UspA domain protein methyl-accepting chemotaxis sensory transducer response regulator receiver protein CheA signal transduction histidine kinase MCP methyltransferase, CheR-type response regulator receiver modulated CheB methylesterase methyl-accepting chemotaxis sensory transducer CheW protein two component, sigma54 specific, transcriptional regulator, Fis family multisensor signal transduction histidine kinase

Gbem_3744 Gbem_3752 Gbem_3759 Gbem_3802 Gbem_3826 Gbem_3828 Gbem_3835 Gbem_3837 Gbem_3838 Gbem_3846 Gbem_3941 Gbem_3942 Gbem_3943

multisensor hybrid histidine kinase response regulator receiver modulated metal dependent phosphohydrolase metal dependent phosphohydrolase integral membrane sensor hybrid histidine kinase antisigma-factor antagonist methyl-accepting chemotaxis sensory transducer CheW protein response regulator receiver and ANTAR domain protein methyl-accepting chemotaxis sensory transducer methyl-accepting chemotaxis sensory transducer response regulator receiver sensor signal transduction histidine kinase flagellar FlbT family protein MotA/TolQ/ExbB proton channel flagellar basal body P-ring protein putative sigma54 specific transcriptional regulator flagellar motor switch protein FliN flagellar basal body-associated protein FliL flagellar protein export ATPase FliI flagellar motor switch protein FliG flagellar M-ring protein FliF PAS/PAC sensor signal transduction histidine kinase CheW protein CheA signal transduction histidine kinase MCP methyltransferase, CheR-type

Gbem_2406

multisensor hybrid histidine kinase

Gbem_2438 Gbem_2495 Gbem_2649 Gbem_2942 Gbem_3155 Gbem_3279 Gbem_3297 Gbem_3298 Gbem_3634

See text for discussion.

a role in general electron transport processes and are not linked to a specific substrate. A BLAST search of the UniprotKB database with Gbem_2674 reveals that its closest homologue is Gbem_3059 (49.6% sequence identity), which we did not detect in this study. It also had significant sequence identity (20−22%) to proteins from G. lovleyi (Glov_1710), G. metallireducens (Gmet_0679), G. daltoni (Geob_0309), and G. sulf urreducens (GSU2801), all annotated simply as c-type cytochromes. The closest homologues of Gbem_3353 are uncharacterized and/or putative proteins from Geobacter strains M21 (GM21_0892), M18 (GM18_3432), Rf64 (Geob_1863), and G. uraniireducens (Gura_3283). The detection of these proteins (annotated as hypothetical proteins) in our proteomics data shows that these genes are indeed translated in G. bemidjiensis and suggests that the homologous genes are expressed proteins in other Geobacter strains as well. These species are a phylogenetically coherent group and have been identified as a clade predominating in subsurface aquifer ecosystems, with important ramifications for environmental biogeochemical cycling.52 Together these results highlight the yet undefined genomic potential that may catalyze functionally important electron transfer reactions in the subsurface. Gbem_3199, a seven-heme, predicted inner-membrane cytochrome c-nitrite reductase, is also expressed in all conditions studied. The close G. sulf urreducens homologue GSU3259 was observed in the cytosolic membrane fraction in the proteomics study of Ding et al.,44 but it was not more highly expressed during growth on Fe(III) citrate than on citrate. The later proteomics study comparing Fe(III) citrate and Fe(III) oxide by the same authors did not report

proteins have 99% sequence identity, and the two detected peptides are common between the two proteins, so either protein could be represented. Peptides from these OmcH homologues were detected too sporadically across electron acceptors and time points to draw any conclusions about their specific roles. Other cytochromes with no easily interpretable expression pattern due to missing data (that is, proteins not detected in all LC-MS data sets) or from differences in protein loading between experiments, include Gbem_0325 (1 heme group), Gbem_0679 (12 heme groups), Gbem_1100 (5 heme groups), Gbem_1234 (1 heme group), Gbem_1236 (2 heme groups), and Gbem_3371 (10 heme groups). Little is known about homologues of these cytochromes in other Geobacter spp., although Gbem_3371 belongs to the same family as Shewanella oneidensis MtrC, which, as part of the MtrCAB complex, plays a key role in extracellular metal reduction in that organism.50 Five additional cytochromes (Gbem_2070, Gbem_0095, Gbem_3352, Gbem_1249, and Gbem_0972) have expression patterns that are unclear due to disagreements between the two replicate experiments. Cytochromes with No Change in Expression. Gbem_2674 (five heme groups) and Gbem_3353 (one heme group) are both hypothetical proteins with predicted periplasmic/outer membrane localizations (there is no available experimental data on localization, but the SignalP 4.1 server51 identifies signal peptides in both proteins). These proteins were detected at all time points with all electron acceptors, but showed no significant differences in expression between any set of conditions. This observation suggests that these proteins play J

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Journal of Proteome Research GSU3259.43 At early stages of iron reduction, Gbem_3199 is more highly expressed in ferric citrate than in fumarate conditions, but the difference is not deemed significant by the criteria of the method 2 analysis because of data variability. However, this change is deemed significant by method 1 (Table S5, Supporting Information). Differentially Expressed Cytochromes. The remaining 12 cytochromes (out of 27 detected) were differentially expressed in at least one binary comparison. OmcF (GSU_2432) is a monoheme cytochrome with predicted outer membrane localization.12 Deletion of OmcF in G. sulfurreducens drastically impaired reduction of Fe(III) citrate and resulted in loss of expression of OmcB and OmcC, and increased expression of OmcS.12 Our data show that two G. bemidjiensis OmcF homologues, Gbem_2183 and Gbem_1585, have very different expression patterns. Gbem_1585 tended to be more highly expressed (or at least more frequently detected) during growth on soluble Fe(III) or fumarate than on HFO at early times. By contrast, Gbem_2183 was more highly expressed during growth on solid iron phases at both early and late reduction. While Gbem_2183 expression patterns showed no difference between ferric citrate and fumarate at an early stage, Gbem_2183 was more highly expressed in fumarate than in ferric citrate during late reduction. The differences in expression patterns suggest that in G. bemidjiensis, the two homologues have different roles, with Gbem_2183 being important for growth on insoluble electron acceptors. Gbem_1585 and Gbem_2183 have 46% and 37% sequence identity to G. sulfurreducens OmcF/GSU_2432, respectively, and 46% sequence identity to each other. In G. sulfurreducens, OmcF controls expression of OmcB.12 Like Gbem_2183/ OmcF, in our experiments Gbem_3379/OmcB was more highly expressed during early reduction of poorly crystalline iron substrates. On the basis of the loose correlation between expression of Gbem_2183/OmcF and Gbem_3779/OmcB, we speculate that Gbem_2183 plays a similar role to G. sulf urreducens OmcF (that is, influencing expression of OmcB) and that Gbem_1585 plays a divergent role. G. bemidjiensis has a total of five OmcS homologues (Gbem_1116, Gbem_1117, Gbem_1131, Gbem_2679, and Gbem_2680) that all contain six hemes15 and are predicted to localize to the extracellular space. They have between 45%− 56% sequence identity to G. sulf urreducens OmcS and 38%− 91% sequence identity to each other. Because of the high sequence identity, it is difficult to determine which gene product was detected in our LC−MS experiments. Four OmcS peptides were detected: one peptide unique to Gbem_1116, two common to Gbem_1116 and Gbem_2679, and one common to all of Gbem_1116, Gbem_2679, and Gbem_2680. Therefore, all of the peptide data can be explained by the presence of Gbem_1116, although we cannot rule out the presence of Gbem_2679 and Gbem_2680. In our data, OmcS is expressed in all conditions studied. OmcS expression was higher in ferric citrate samples than in fumarate at an early stage (although not significantly higher due to variability in the data) and significantly higher in ferric citrate samples than in insoluble (bulk) iron at early times, but this observation is based on a single significantly changing peptide. Interestingly, there was no significant difference between ferric citrate and nanoparticle iron. There are no significant changes at later reduction across the electron acceptors. In agreement with our data, G. sulf urreducens OmcS transcripts are detected during log-phase growth on solid Fe(III).11 G. sulf urreducens OmcS is

required for the reduction of solid Fe(III) phases but not soluble Fe(III) citrate, but in our G. bemidjiensis data we observed upregulation of OmcS in ferric citrate over fumarate.11 Recent transmission microscopy studies53 have shown that OmcS associates with electrically conductive pili, likely acting as terminal reductases for charge transfer between the microorganism and iron minerals. Although our data show that OmcS is expressed during growth on iron minerals, we do not observe any upregulation of OmcS in response to growth on iron mineral phases. OmcB is a decaheme outer-membrane cytochrome that, in G. sulf urreducens, is important for growth on ferric citrate, but not on fumarate.14 We detected two homologues of OmcB in G. bemidjiensis (Gbem_3354 and Gbem_3379), both with unknown localization, and with 10 and 12 hemes, respectively. Gbem_3354 is expressed in all conditions studied, with no differential expression except for significant up-regulation in nanoparticle iron medium over bulk solid iron medium at the early stage. However, the peptide-level evidence for this change is weak (not shown). If Gbem_3354 is in fact up-regulated in the presence of nanoparticles, expression may be regulated by minerological or redox factors or particle-size effects. Further research is necessary to validate this observation. Gbem_3379 was also expressed in all conditions and is up-regulated in both bulk and NP iron mineral phases over both ferric citrate and fumarate at early stages of iron reduction. The differences between ferric citrate and insoluble iron persisted later into the reduction process, but the differences between fumarate and insoluble iron phases did not. In keeping with this observation, Gbem_3379 was also more highly expressed in fumarate than in ferric citrate during late time points. One way to interpret this expression pattern is that poorly crystalline solid iron promotes the expression of G. bemidjiensis OmcB (Gbem_3379), whereas prolonged ferric citrate exposure inhibits its expression. Thus, Gbem_3379 could play a key role in reduction of iron minerals and is a good candidate for further study in subsurface strains. The observation that Gbem_3379 and Gbem_3354 have different expression patterns, along with the differing numbers of heme groups, suggests that these two proteins have distinct functional roles. Ding et al. reported that G. sulfurreducens OmcB protein is more highly expressed during growth on ferric citrate than during growth on fumarate at the late stage in batch cultures,44 the opposite of our result for Gbem_3379. We therefore speculate that Gbem_3354 may play a similar functional role to G. sulfurreducens OmcB and Gbem_3379 a more divergent role, but both may be important in the subsurface where organisms encounter both soluble and insoluble forms of iron. In G. sulf urreducens, the triheme periplasmic cytochrome PpcA is involved in reduction of Fe(III), humic substances, and U(VI) when acetate is the electron donor, but has not been implicated in reduction of fumarate.13,54 There are four ppc genes in G. sulf urreducens, each with slightly different functions.54 Of the three Ppc-family genes present in G. bemidjiensis,15 we detected only Gbem_3455 (PpcG). In our data, at the early stage, Gbem_3455 was more highly expressed in the presence of bulk HFO than in fumarate or ferric citrate. Interestingly, the PpcA knockout in G. sulf urreducens13 was suggested to play a role in electron transfer during growth on soluble Fe(III) citrate with acetate as the electron donor (growth on solid iron mineral was not investigated in that study). In our data, however, there was no clear evidence of increased Ppc protein expression during growth on soluble K

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Journal of Proteome Research ferric citrate. These results may indicate distinct functional roles for different ppc genes, or alternatively a functional difference between G. bemidjiensis and G. sulfurreducens.

ment methods apply: the Rifle samples were fractionated by strong cation exchange chromatography before LC−MS analysis, but no replicates were run. By contrast, the laboratory samples were run without prior fractionation, but with multiple biological and technical replicates. The laboratory medium was also supplemented with vitamins and minerals, whose concentrations may be much lower in the Rifle groundwater systems. For these reasons, we felt that a direct statistical comparison of field versus laboratory samples would not be valid. However, we have conducted a qualitative comparison based on the detection (at a high level of confidence) or nondetection of proteins in the various samples. This comparison used the list of confidently identified proteins from the Rifle spectral counting analysis. The list of proteins identified in the laboratory-grown samples was derived from the AMT tag analysis. Figure 5 shows the results of this analysis as a Venn diagram. Proteins common to all laboratory samples and to the Rifle samples form the largest set (612 proteins). These “common core” proteins are enriched in housekeeping genes, including 44 ribosomal proteins, aminoacyl tRNA synthetases for all 20 amino acids, TCA cycle enzymes, and proteins involved in carbohydrate, amino acid, and nucleotide metabolism (Figure 6A). The second-largest set consists of proteins common to all samples except the Rifle field samples. This group of proteins was also enriched for many of the same functional categories as the common core, but with the addition of c-type cytochromes (c-type cytochromes found in the laboratory-grown samples in this study are listed in Figure 3). We initially speculated that this observation reflected the differences between database searches used to construct the AMT tag database (which included heme c as a dynamic modification) and those used to analyze the Rifle data (which did not include a heme c dynamic modification). However, a search of the Rifle data using a dynamic modification of 615.1694 Da on cysteinyl residues55,56 revealed no heme-c modified peptides when filtered to ≤1% peptide-level FDR. This observation therefore likely reflects a real biological finding that more c-type cytochromes are expressed in our laboratory conditions than in the field. However, differences in identifying proteins and in estimating relative protein abundances due to differences between the AMT tag and spectral counting approaches cannot be ruled out, nor can matrix effects. The set of proteins unique to the Rifle samples is of particular interest. In keeping with the PCA analysis, the COG categories for cell motility (N, 31 proteins, p = 3.3 × 10−32) and signal transduction (T, 39 proteins, p = 3.2 × 10−17, including 21 proteins common to both functional classes, Figure 6B) are significantly enriched in this set. On the basis of their annotations, these proteins (Table 2) are likely related to chemotaxis and include proteins annotated as flagellar proteins, response regulators, histidine kinases, and methyl-accepting chemotaxis proteins. Despite the differences between the lab and field experiments, and considering that the high complexity of an environmental microbial community sample increases the difficulty of detecting a given protein, the fact that several proteins in this class were detected only in the field sample is notable. This suite of proteins specific to the field samples likely reflect the environmental conditions found in the subsurface, where electron acceptors (e.g., solid phase Fe(III)) and other nutrients are present at lower concentrations than in the laboratory batch incubations. Under these field conditions, a planktonic lifestyle may be advantageous, or even necessary, for

Comparison of Laboratory Cultures and Previously-Studied Rifle Groundwater Samples

Extensive proteomics experiments have previously been carried out on microbial biomass filtered from groundwater at an acetate amendment field site at Rifle, CO.3,4,19 Geobacter species, in particular species and strains closely resembling G. bemidjiensis, are the predominant species to be stimulated by acetate addition to the groundwater.3 Using the AMT tag approach with a database of G. bemidjiensis peptides is problematic for such microbial community samples, since the potential for incorrect database matches is high. Therefore, we reanalyzed the Rifle LC−MS/MS data sets collected in 2010,19 along with our laboratory data, using a spectral counting approach. Field and laboratory samples were then subjected to principal components analysis (PCA), a dimension-reduction technique that allows analysis of variation and clustering in the data. Figure 4A shows a scatter plot of the first two principle components of the spectral count data, which together account for 43% of the total variation in the input data. As with Figure 1 above, it is clear that the protein expression profiles of ferric citrate and fumarate samples closely resemble one another, as do the protein expression profiles of nanoparticle and bulk solid iron mineral samples. The Rifle field samples also form a distinct tight cluster, but they do not cluster with either the HFO or the non-HFO laboratory samples. This finding is not surprising since the complex conditions prevailing in the field are not completely replicated in any of our batch culture systems. To complement the PCA analysis and to provide a quantitative assessment of similarity between field and laboratory samples, we also calculated the correlation coefficients between each group of samples and performed a hierarchical clustering analysis (Figure 4B). The correlation analysis confirms that Rifle samples most closely resemble each other, with about the same degree of similarity to the laboratory samples regardless of culture conditions. Each principle component consists of contributions from hundreds of proteins, making detailed interpretation of the PCA results at the level of individual proteins difficult. Therefore, we have examined the correlations between the first principal component (PC1) and the individual protein abundances across the sample categories. A strong correlation indicates that a given protein contributes to the trend described by PC1. Although many COG functional categories had proteins that correlated highly with PC1, the COG functional classes with the most extreme median correlation coefficients were N (cell motility), T (signal transduction), and R (general functional prediction only). Spectral counts for proteins in the N and T categories were negatively correlated with PC1. This analysis suggests that proteins related to cell motility and signal transduction are among the important proteins defining PC1, and therefore among the proteins that differentiate between field samples and lab samples, at least in the data sets used here. The large differences between the Rifle and laboratory experiments make a direct proteomic comparison problematic. Importantly, the laboratory samples came from cultures of a single organism, whereas the field samples contained more complex microbial communities consisting of many different species and strains. Additionally, some differences in measureL

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Journal of Proteome Research

performing linear regression analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

continued growth and survival. The large number of proteins in this group annotated as “methyl-accepting chemotaxis sensory transducer,” identified by unique peptides, raises the question of what chemoattractants are being sensed and to what degree these proteins might be functionally redundant. This list of proteins therefore provides interesting targets for future study.

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Corresponding Author

*Tel. 509-371-6589. Fax 509-371-6564. E-mail mary.lipton@ pnnl.gov.

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CONCLUSIONS Using a linear-regression based procedure, we have analyzed label-free, intensity-based LC−MS proteomics data in the presence of large, systematic differences in signal between data sets. The method is more lenient with respect to missing data, and sometimes can be influenced by a single peptide observation, but it proved robust enough to allow biological insight into changes in the proteome of G. bemidjiensis under growth on fumarate, ferric citrate, or insoluble iron minerals as the terminal electron acceptor. We detected between 27 and 29 cytochromes (the observed peptides do not always allow distinction between closely related proteins). A number of cytochromes appeared to have constitutive expression or had no clear expression pattern. But we also found that the expression of several c-type cytochromes changed in response to these conditions, sometimes in ways that do not parallel prior work on G. sulf urreducens. Indeed, in at least two cases (OmcF, OmcB), a gene previously studied in G. sulfurreducens has more than one homologue in G. bemidjiensis, and the protein expression patterns hint at divergent function between the homologues. This finding suggests some degree of divergence in extracellular metal reduction pathways between the two species, and thus the possibility that there is not just one mechanism of microbial extracellular metal respiration, but several. It is our hope that the present protein expression data will encourage further research into these variations. By comparing our proteomics results on laboratory cultures with previous proteomic analyses of field samples, we found that field samples express many more proteins involved in motility, chemotaxis, and signal transduction. This finding suggests that in situ, G. bemidjiensis may actively travel to insoluble iron(III) electron acceptors. Recent cryo-transmission electron microscopy studies showed iron mineral nanoparticles bound to the surface of Geobacter cells.57 In that study, the authors suggested that cell-bound nanoparticles explain how iron-reducing bacteria can be both mineral-dependent and planktonic. The suggestion that motility and chemotaxis proteins are not as readily detected in laboratory culture, and the fact that the groundwater sampling methods are biased toward planktonic organisms leaves open the possibility that a population of G. bemidjiensis could also exist in a distinct physiological state in other ecological niches.

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AUTHOR INFORMATION

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was funded by a grant from the DOE/BER for PanOmics Technologies Development, Implementation and Applications, and by DOE/SBR Grant DE-SC-0004733. Portions of this research were conducted at the Environmental and Molecular Sciences Laboratory, a DOE/BER National Scientific User Facility located at Pacific Northwest National Laboratory in Richland, Washington. The authors would like to thank Ashoka Polpitiya for a helpful discussion, Karl Weitz and Justin Chambers for LC−MS analyses, and Sam Purvine and Matt Monroe for assistance with data management and analysis.

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REFERENCES

(1) Lovley, D. R. Bug juice: harvesting electricity with microorganisms. Nat. Rev. Microbiol. 2006, 4 (7), 497−508. (2) Lovley, D. R. Live wires: direct extracellular electron exchange for bioenergy and the bioremediation of energy-related contamination. Energy Environ. Sci. 2011, 4 (12), 4896−4906. (3) Wilkins, M. J.; VerBerkmoes, N. C.; Williams, K. H.; Callister, S. J.; Mouser, P. J.; Elifantz, H.; et al. Proteogenomic Monitoring of Geobacter Physiology during Stimulated Uranium Bioremediation. Appl. Environ. Microbiol. 2009, 75 (20), 6591−6599. (4) Callister, S. J.; Wilkins, M. J.; Nicora, C. D.; Williams, K. H.; Banfield, J. F.; VerBerkmoes, N. C.; et al. Analysis of Biostimulated Microbial Communities from Two Field Experiments Reveals Temporal and Spatial Differences in Proteome Profiles. Environ. Sci. Technol. 2010, 44 (23), 8897−8903. (5) Anderson, R. T.; Vrionis, H. A.; Ortiz-Bernad, I.; Resch, C. T.; Long, P. E.; Dayvault, R.; et al. Stimulating the In Situ Activity of Geobacter Species To Remove Uranium from the Groundwater of a Uranium-Contaminated Aquifer. Appl. Environ. Microbiol. 2003, 69 (10), 5884−5891. (6) Qian, X.; Mester, T.; Morgado, L.; Arakawa, T.; Sharma, M. L.; Inoue, K.; et al. Biochemical characterization of purified OmcS, a ctype cytochrome required for insoluble Fe(III) reduction in Geobacter sulfurreducens. Biochim. Biophys. Acta 2011, 1807 (4), 404−412. (7) Voordeckers, J. W.; Kim, B. C.; Izallalen, M.; Lovley, D. R. Role of Geobacter sulfurreducens Outer Surface c-Type Cytochromes in Reduction of Soil Humic Acid and Anthraquinone-2,6-Disulfonate. Appl. Environ. Microbiol. 2010, 76 (7), 2371−2375. (8) Inoue, K.; Qian, X. L.; Morgado, L.; Kim, B. C.; Mester, T.; Izallalen, M.; et al. Purification and Characterization of OmcZ, an Outer-Surface, Octaheme c-Type Cytochrome Essential for Optimal Current Production by Geobacter sulfurreducens. Appl. Environ. Microbiol. 2010, 76 (12), 3999−4007. (9) Inoue, K.; Franks, A. E.; Nevin, K. P.; Lovley, D. R. OmcZ, a mobile, extracellular, c-type cytochrome that accumulates at the anode in current-producing biofilms of Geobacter sulfurreducens. Abstr. Pap. Am. Chem. Soc. 2010, 239. (10) Holmes, D. E.; Mester, T.; O’Neil, R. A.; Perpetua, L. A.; Larrahondo, M. J.; Glaven, R.; et al. Genes for two multicopper proteins required for Fe(III) oxide reduction in Geobacter sulfurreducens have different expression patterns both in the subsurface and on energy-harvesting electrodes. Microbiology 2008, 154 (5), 1422−1435.

ASSOCIATED CONTENT

S Supporting Information *

Supplementary Methods, Supplementary Figures S1 (Boxplot of peptide abundances from AMT tag analysis), Figures S2−S7 (linear regression proteomics analysis scatter plots for each pair of laboratory culture conditions). Supplementary Tables (provided as Microsoft Excel spreadsheets) S1 (Summary of protein expression by linear regression analysis), S2 (Details of the functional enrichment analysis shown in Figure 6 in the main text), S3−S6 (Summary of protein expression by standard analysis, i.e., Method 1. Provided as a single Excel spreadsheet). LinearRegressionMethodExample.R.txt: Example R script for M

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Journal of Proteome Research (11) Mehta, T.; Coppi, M. V.; Childers, S. E.; Lovley, D. R. Outer Membrane c-Type Cytochromes Required for Fe(III) and Mn(IV) Oxide Reduction in Geobacter sulfurreducens. Appl. Environ. Microbiol. 2005, 71 (12), 8634−8641. (12) Kim, B.-C.; Leang, C.; Ding, Y.-H. R.; Glaven, R. H.; Coppi, M. V.; Lovley, D. R. OmcF, a Putative c-Type Monoheme Outer Membrane Cytochrome Required for the Expression of Other Outer Membrane Cytochromes in Geobacter sulfurreducens. J. Bacteriol. 2005, 187 (13), 4505−4513. (13) Lloyd, J. R.; Leang, C.; Hodges Myerson, A. L.; Coppi, M. V.; Cuifo, S.; Methe, B.; et al. Biochemical and genetic characterization of PpcA, a periplasmic c-type cytochrome in Geobacter sulfurreducens. Biochem. J. 2003, 369 (1), 153−161. (14) Leang, C.; Coppi, M. V.; Lovley, D. R. OmcB, a c-Type Polyheme Cytochrome, Involved in Fe(III) Reduction in Geobacter sulfurreducens. J. Bacteriol. 2003, 185 (7), 2096−2103. (15) Aklujkar, M.; Young, N.; Holmes, D.; Chavan, M.; Risso, C.; Kiss, H.; et al. The genome of Geobacter bemidjiensis, exemplar for the subsurface clade of Geobacter species that predominate in Fe(III)reducing subsurface environments. BMC Genomics 2010, 11 (1), 490. (16) Conrads, T. P.; Anderson, G. A.; Veenstra, T. D.; Paša-Tolić, L.; Smith, R. D. Utility of Accurate Mass Tags for Proteome-Wide Protein Identification. Anal. Chem. 2000, 72 (14), 3349−3354. (17) Smith, R. D.; Anderson, G. A.; Lipton, M. S.; Pasa-Tolic, L.; Shen, Y.; Conrads, T. P.; et al. An accurate mass tag strategy for quantitative and high-throughput proteome measurements. PROTEOMICS 2002, 2 (5), 513−523. (18) Jaitly, N.; Monroe, M. E.; Petyuk, V. A.; Clauss, T. R. W.; Adkins, J. N.; Smith, R. D. Robust Algorithm for Alignment of Liquid Chromatography−Mass Spectrometry Analyses in an Accurate Mass and Time Tag Data Analysis Pipeline. Anal. Chem. 2006, 78 (21), 7397−7409. (19) Wilkins, M. J.; Wrighton, K. C.; Nicora, C. D.; Williams, K. H.; McCue, L. A.; Handley, K. M.; et al. Fluctuations in Species-Level Protein Expression Occur during Element and Nutrient Cycling in the Subsurface. PLoS One 2013, 8 (3), 11. (20) McLaughlin, J. R.; Ryden, J. C.; Syers, J. K. Sorption of Inorganic Phosphate by Iron- and Aluminium- Containing Components. J. Soil Sci. 1981, 32 (3), 365−378. (21) Penn, R. L.; Erbs, J. J.; Gulliver, D. M. Controlled growth of alpha-FeOOH nanorods by exploiting-oriented aggregation. J. Cryst. Growth 2006, 293 (1), 1−4. (22) Gilbert, B.; Erbs, J. J.; Penn, R. L.; Petkov, V.; Spagnoli, D.; Waychunas, G. A. A disordered nanoparticle model for 6-line ferrihydrite. Am. Mineral. 2013, 98 (8−9), 1465−1476. (23) Lovley, D. R.; Phillips, E. J. P. Novel Mode of Microbial Energy Metabolism: Organic Carbon Oxidation Coupled to Dissimilatory Reduction of Iron or Manganese. Appl. Environ. Microbiol. 1988, 54 (6), 1472−1480. (24) Wrighton, K. C.; Thrash, J. C.; Melnyk, R. A.; Bigi, J. P.; ByrneBailey, K. G.; Remis, J. P.; et al. Evidence for Direct Electron Transfer by a Gram-Positive Bacterium Isolated from a Microbial Fuel Cell. Appl. Environ. Microbiol. 2011, 77 (21), 7633−7639. (25) Fredrickson, J. K.; Zachara, J. M.; Kennedy, D. W.; Dong, H.; Onstott, T. C.; Hinman, N. W.; et al. Biogenic iron mineralization accompanying the dissimilatory reduction of hydrous ferric oxide by a groundwater bacterium. Geochim. Cosmochim. Acta 1998, 62 (19−20), 3239−3257. (26) Lovley, D. R.; Phillips, E. J. P. Rapid Assay for Microbially Reducible Ferric Iron in Aquatic Sediments. Appl. Environ. Microbiol. 1987, 53 (7), 1536−1540. (27) Schwertmann, U. Differenzierung der Eisenoxide des Bodens durch Extraktion mit Ammoniumoxalat-Lösung. Z. Pflanzenernährung, Düngung, Bodenkunde 1964, 105 (3), 194−202. (28) Thomas, P. E.; Ryan, D.; Levin, W. An improved staining procedure for the detection of the peroxidase activity of cytochrome P450 on sodium dodecyl sulfate polyacrylamide gels. Anal. Biochem. 1976, 75 (1), 168−176.

(29) Shevchenko, A.; Tomas, H.; Havlis, J.; Olsen, J. V.; Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protocols 2007, 1 (6), 2856−2860. (30) Yang, F.; Shen, Y. F.; Camp, D. G.; Smith, R. D. High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Rev. Proteomics 2012, 9 (2), 129−134. (31) Livesay, E. A.; Tang, K.; Taylor, B. K.; Buschbach, M. A.; Hopkins, D. F.; LaMarche, B. L.; et al. Fully Automated Four-Column Capillary LC−MS System for Maximizing Throughput in Proteomic Analyses. Anal. Chem. 2007, 80 (1), 294−302. (32) Stanley, J. R.; Adkins, J. N.; Slysz, G. W.; Monroe, M. E.; Purvine, S. O.; Karpievitch, Y. V.; et al. A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics. Anal. Chem. 2011, 83 (16), 6135−6140. (33) Eng, J. K.; McCormack, A. L.; Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5 (11), 976−989. (34) Kim, S.; Gupta, N.; Pevzner, P. A. Spectral Probabilities and Generating Functions of Tandem Mass Spectra: A Strike against Decoy Databases. J. Proteome Res. 2008, 7 (8), 3354−3363. (35) Kim, S.; Mischerikow, N.; Bandeira, N.; Navarro, J. D.; Wich, L.; Mohammed, S.; et al. The Generating Function of CID, ETD, and CID/ETD Pairs of Tandem Mass Spectra: Applications to Database Search. Mol. Cell. Proteomics 2010, 9 (12), 2840−2852. (36) Callister, S. J.; Barry, R. C.; Adkins, J. N.; Johnson, E. T.; Qian, W.-j.; Webb-Robertson, B.-J. M.; et al. Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics. J. Proteome Res. 2006, 5 (2), 277−286. (37) Polpitiya, A. D.; Qian, W.-J.; Jaitly, N.; Petyuk, V. A.; Adkins, J. N.; Camp, D. G.; et al. DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 2008, 24 (13), 1556−1558. (38) Storey, J. D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U. S. A. 2003, 100 (16), 9440−9445. (39) Beck, D. A. C.; Hendrickson, E. L.; Vorobev, A.; Wang, T.; Lim, S.; Kalyuzhnaya, M. G.; et al. An Integrated Proteomics/Transcriptomics Approach Points to Oxygen as the Main Electron Sink for Methanol Metabolism in Methylotenera mobilis. J. Bacteriol. 2011, 193 (18), 4758−4765. (40) Zybailov, B.; Mosley, A. L.; Sardiu, M. E.; Coleman, M. K.; Florens, L.; Washburn, M. P. Statistical Analysis of Membrane Proteome Expression Changes in Saccharomyces cerevisiae. J. Proteome Res. 2006, 5 (9), 2339−2347. (41) Lundgren, D. H.; Hwang, S.-I.; Wu, L.; Han, D. K. Role of spectral counting in quantitative proteomics. Expert Rev. Proteomics 2010, 7 (1), 39−53. (42) Kim, S.; Pevzner, P. A. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 2014, 5. (43) Ding, Y.-H. R.; Hixson, K. K.; Aklujkar, M. A.; Lipton, M. S.; Smith, R. D.; Lovley, D. R.; et al. Proteome of Geobacter sulf urreducens grown with Fe(III) oxide or Fe(III) citrate as the electron acceptor. Biochim. Biophys. Acta 2008, 1784 (12), 1935−1941. (44) Ding, Y.-H. R.; Hixson, K. K.; Giometti, C. S.; Stanley, A.; Esteve-Núñez, A.; Khare, T.; et al. The proteome of dissimilatory metal-reducing microorganism Geobacter sulf urreducens under various growth conditions. Biochim. Biophys. Acta 2006, 1764 (7), 1198−1206. (45) Tatusov, R.; Fedorova, N.; Jackson, J.; Jacobs, A.; Kiryutin, B.; Koonin, E.; et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 2003, 4 (1), 41. (46) Holmes, D. E.; O’Neil, R. A.; Chavan, M. A.; N’Guessan, L. A.; Vrionis, H. A.; Perpetua, L. A.; et al. Transcriptome of Geobacter uraniireducens growing in uranium-contaminated subsurface sediments. ISME J. 2008, 3 (2), 216−230. (47) Lovley, D. R. The microbe electric: conversion of organic matter to electricity. Curr. Opin. Biotechnol. 2008, 19 (6), 564−571. (48) Kim, B.-C.; Qian, X.; Leang, C.; Coppi, M. V.; Lovley, D. R. Two Putative c-Type Multiheme Cytochromes Required for the Expression of OmcB, an Outer Membrane Protein Essential for N

DOI: 10.1021/pr500983v J. Proteome Res. XXXX, XXX, XXX−XXX

Article

Journal of Proteome Research Optimal Fe(III) Reduction in Geobacter sulfurreducens. J. Bacteriol. 2006, 188 (8), 3138−3142. (49) Yu, N. Y.; Wagner, J. R.; Laird, M. R.; Melli, G.; Rey, S.; Lo, R.; et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010, 26 (13), 1608− 1615. (50) Hartshorne, R. S.; Reardon, C. L.; Ross, D.; Nuester, J.; Clarke, T. A.; Gates, A. J.; et al. Characterization of an electron conduit between bacteria and the extracellular environment. Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (52), 22169−22174. (51) Petersen, T. N.; Brunak, S.; von Heijne, G.; Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 2011, 8 (10), 785−786. (52) Holmes, D. E.; O’Neil, R. A.; Vrionis, H. A.; N’Guessan, L. A.; Ortiz-Bernad, I.; Larrahondo, M. J.; et al. Subsurface clade of Geobacteraceae that predominates in a diversity of Fe(III)-reducing subsurface environments. ISME J. 2007, 1 (8), 663−677. (53) Leang, C.; Qian, X.; Mester, T.; Lovley, D. R. Alignment of the c-Type Cytochrome OmcS along Pili of Geobacter sulfurreducens. Appl. Environ. Microbiol. 2010, 76 (12), 4080−4084. (54) Lovley, D. R.; Ueki, T.; Zhang, T.; Malvankar, N. S.; Shrestha, P. M.; Flanagan, K. A.; et al., Geobacter: The Microbe Electric’s Physiology, Ecology, and Practical Applications. In Adv. Microb. Physiol.; Robert, K. P., Ed.; Academic Press: New York, 2011; Vol. 59, pp 1−100. (55) Yang, F.; Bogdanov, B.; Strittmatter, E. F.; Vilkov, A. N.; Gritsenko, M.; Shi, L.; et al. Characterization of Purified c-Type Heme-Containing Peptides and Identification of c-Type HemeAttachment Sites in Shewanella oneidenis Cytochromes Using Mass Spectrometry. J. Proteome Res. 2005, 4 (3), 846−854. (56) Merkley, E. D.; Anderson, B. J.; Park, J.; Belchik, S. M.; Shi, L.; Monroe, M. E.; et al. Detection and Identification of Heme c-Modified Peptides by Histidine Affinity Chromatography, High-Performance Liquid Chromatography−Mass Spectrometry, and Database Searching. J. Proteome Res. 2012, 11 (12), 6147−6158. (57) Luef, B.; Fakra, S. C.; Csencsits, R.; Wrighton, K. C.; Williams, K. H.; Wilkins, M. J.; et al. Iron-reducing bacteria accumulate ferric oxyhydroxide nanoparticle aggregates that may support planktonic growth. ISME J. 2013, 7 (2), 338−350. (58) Tatusov, R. L.; Fedorova, N. D.; Jackson, J. D.; Jacobs, A. R.; Kiryutin, B.; Koonin, E. V.; et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 2003, 4.

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DOI: 10.1021/pr500983v J. Proteome Res. XXXX, XXX, XXX−XXX