Changes in Protein Expression Across Laboratory and Field Experiments in Geobacter bemidjiensis

Subscriber access provided by LAWRENCE BERKELEY NATL LAB

Journal of Proteome Research is published by the American Chemical Society. 1155Sixteenth Street N.W., Washington, DC 20036Published by American Chemical Society. Copyright © American Chemical Society.However, no copyright claim is made to original U.S. Government works, or worksproduced by employees of any Commonwealth realm Crown government in the courseof their duties.

Article

Changes in protein expression across laboratoryand field experiments in Geobacter bemidjiensis

Eric David Merkley, Kelly Wrighton, Cindy J. Castelle, Brian J. Anderson, Michael J. Wilkins, VegaShah, Tyler Arbour, Joseph N. Brown, Steven William Singer, Richard D Smith, and Mary S Lipton

J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr500983v • Publication Date (Web): 10 Dec 2014

Downloaded from http://pubs.acs.org on January 8, 2015

Just Accepted

“Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are postedonline prior to technical editing, formatting for publication and author proofing. The American ChemicalSociety provides “Just Accepted” as a free service to the research community to expedite thedissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscriptsappear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have beenfully peer reviewed, but should not be considered the official version of record. They are accessible to allreaders and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offeredto authors. Therefore, the “Just Accepted” Web site may not include all articles that will be publishedin the journal. After a manuscript is technically edited and formatted, it will be removed from the “JustAccepted” Web site and published as an ASAP article. Note that technical editing may introduce minorchanges to the manuscript text and/or graphics which could affect content, and all legal disclaimersand ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errorsor consequences arising from the use of information contained in these “Just Accepted” manuscripts.

1

Changes in protein expression across laboratory and field experiments in Geobacter bemidjiensis

Eric D. Merkley,a Kelly C. Wrighton

c, Cindy J. Castelle,

d,e Brian J. Anderson,

b Michael J. Wilkins,

c,f Vega

Shahg, Tyler Arbour

d, Joseph N. Brown,

b Steven W. Singer,

e Richard D. Smith,

b and Mary S. Lipton

b*

a Signature Sciences and Technology Division, Pacific Northwest National Laboratory, Richland, WA

99352

b Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352

c Department of Microbiology, The Ohio State University, Columbus, Ohio, 43210

d Department of Earth and Planetary Science, University of California Berkeley, Berkeley, CA, 94720

e Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720

f School of Earth Sciences, The Ohio State University, Columbus, Ohio, 43210

g Biological Oceanography, University of Washington, Seattle, WA 98105

*To whom correspondence should be addressed: Mary S. Lipton, Biological Sciences Division, Pacific

Northwest National Laboratory, PO Box 999 MS-IN K8-98, Richland, WA, 99352. Tel. 509-371-6589; Fax

509-371-6564; Email [email protected]

Page 1 of 37

ACS Paragon Plus Environment

Journal of Proteome Research

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

2

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 Geobacter 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

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

uranium-contaminated 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

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. sulfurreducens that have

Page 2 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

3

established multi-heme 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 c-type cytochromes, both single and multi-heme, 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 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 lab-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 <6 nm) to assess differences

in proteome complement in laboratory grown G. bemidjiensis. We then compared the proteome from

these laboratory conditions with the proteome of bacteria isolated from uranium-contaminated

groundwater that had been stimulated by acetate addition at the Department of Energy’s Integrated

Field Research Challenge (IFRC) site in Rifle, Colorado.19

We found several proteins that displayed differential expression between the soluble electron

acceptors and HFO growth conditions, although there were few differentially expressed proteins

between the two types of HFO. We identified several c-type cytochromes whose expression pattern is

suggestive of involvement in extracellular metal reduction. Some of these cytochromes have expression

patterns that differ from those of their G. sulfurreducens homologues. Comparisons between laboratory

and field proteomes revealed a greater proportion of proteins involved in chemotaxis, signal

transduction and motility in biomass recovered from the environment. We further describe a method of

analyzing AMT tag proteomics data that is robust with respect to interference from residual iron present

in cultures grown in HFO.

Methods

Synthesis of HFO

HFO was synthesized as previously described for undefined 20

and nanoparticulate (NP) forms with no

impurity phases and a mean particle diameter between 3-6 nm. 21-22

Briefly, for undefined HFO,

Page 3 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

4

amorphous Fe(III) oxyhydroxide was formed by neutralizing a 0.4 M solution of FeCl3 slowly to a pH of 7

with NaOH, followed by a series of washes to ensure the chloride is below inhibitory concentrations in

the final media. For NP, a ferrihydrite suspension was prepared by controlled addition of 0.48 M NaHCO3

to an equal volume of 0.40 M Fe(NO3)3·9H2O with vigorous stirring at 80 °C . After heating, samples were

immediately submerged in an ice bath to room temperature, microwave-annealed, and rapidly cooled in

an ice bath. Dialysis was performed for three days, changing water at least nine-times, and both

undefined and NP HFO suspensions were kept at 10 °C until use in this experiment.

Geobacter bemidjiensis culture conditions.

The inoculum contained 6 L of mid-log phase culture grown on fumarate (40 mM) and acetate

(10 mM) growth medium. Growth medium (pH 6.85) used here and throughout the experiment

contained the following (per liter): 0.1 g of KCl, 0.2 g of NH4Cl, 0.6 g of NaH2PO4, 2.5 g NaHCO3, 10 ml of

vitamin mix, and 10 ml of trace mineral mix.23-24

To obtain sufficient biomass for proteomic subsampling

at early time points on poorly crystalline iron minerals, bacterial cells were harvested by centrifugation

(6000g x 8min) under anoxic atmosphere, washed once, and reconcentrated in 60 ml basal buffered

medium (without vitamins and minerals). 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 run 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 hour 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 hours. For iron-containing 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)).

Biomass collection for proteomics

Page 4 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

5

Biomass samples for proteomics were harvested from each time point from each triplicate

culture. For each sample, 15 mL of cellular medium was anoxically removed from each bottle, with

biomass harvested by centrifugation (8000g, 3 minutes), and immediately frozen on liquid nitrogen and

stored at -80°C until protein isolation.

Cell pellets were washed with approximately 7-10 mL of 0.392 M oxalate (prepared as 0.225 M

oxalic acid and 0.167 M ammonium oxalate) in 2 mL aliquots.27

A change in color from colorless to

green/yellow, due to the iron-oxalate complex, indicated the presence of dissolved iron. 2 mL wash

volumes were repeated until the supernatant was nearly colorless, at which point the sample was

presumed to be iron-free. (This presumption was later proved to be incorrect--see below.) Sample cell

pellets were suspended in 100 mM ammonium bicarbonate pH ~8 and lysed by bead beating with 0.1

mm zirconia/silica beads (Biospec Products, Inc.) in a Bullet Blender homogenizer (Next Advance, Averill

Park, NY). Lysates were centrifuged for 5 min at 5,000 rpm to pellet visible insoluble iron, zirconia/silica

beads and cellular debris. Dry urea was added to each sample to reach 9 M final concentration, and DTT

was added to 5 mM. Samples were denatured at 60˚C for 30 minutes then diluted 10-fold with 100mM

ammonium bicarbonate. 1 M CaCl2 was added to 1 mM final concentration and trypsin was added in a

mass ratio of 1:50 (trypsin: protein). Samples were digested overnight (~18 hrs) at 37˚C. Samples were

briefly spun to pellet all remaining insoluble iron and zirconia/silica beads that may have remained, and

then supernatants were desalted by solid phase extraction using 1 mL/50 mg C18 columns (Supelco).

Eluents were concentrated to less than 10 µL in a vacuum centrifuge, then diluted to 50 µL using 50 mM

ammonium bicarbonate. After digestion and clean-up, protein concentration was measured by the BCA

assay, and samples were diluted to an apparent concentration of 0.25 µg/µL and subjected to LC-MS

analysis. Examination of the LC-MS total ion chromatograms indicated that samples from HFO-grown

cultures had much lower intensity than non-HFO-grown samples, probably due to residual iron

interfering with the BCA assay. We addressed this issue during data analysis; see below.

As part of a strategy intended to increase the detection of c-type cytochromes in the AMT tag

workflow (see below), cultures of G. bemidjiensis were also grown in the laboratory on

fumarate/acetate medium. Protein extracts from these samples were analyzed by sodium

dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) and stained by the heme-specific reagent

3,3',5,5'-tetramethylbenzidine (TMBZ).28

Excised TMBZ-stained bands (containing c-type cytochromes)

were cut into approximate 1 mm3 pieces and de-stained, at room temperature, on a rotating rack for a

few hours in a 50:50 vol% mixture of acetonitrile and 50 mM ammonium bicarbonate. After this point,

the in-gel digestion procedure followed standard protocols.29

The solution was removed and the gel

Page 5 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

6

pieces were soaked in 100% acetonitrile for 10 min. The acetonitrile was then discarded and the pieces

were dried in a vacuum centrifuge for 10 minutes. Dried gel pieces were covered with a 12.5 ng/µL

solution of trypsin in 50 mM ammonium bicarbonate and incubated on ice for 45 minutes, after which

50 mM ammonium bicarbonate was added when necessary to ensure all pieces were covered by

solution. Samples were digested overnight (~18 hrs) at 37˚C. Peptides were extracted from each gel

with 5% formic acid/50% acetonitrile three times. Each extraction included three rounds of a 1 min spin

at 20,000 g, followed by 1 min at rest. The three extractions for each sample were pooled and desalted

by SPE as described above, and the samples analyzed by LC-MS as describe below. Data from these

samples, which are highly enriched in c-type cytochromes, was used only in construction of the AMT tag

database, and is expected to increase the number of heme-containing peptides in the database and

therefore the number of c-type cytochromes identified in subsequent LC-MS analyses.

LC-MS and LC-MS/MS analysis of samples.

To generate the AMT tag database, we pooled peptide samples from all of the culture

conditions and separated them off-line using a strong cation exchange chromatography (SCX) column

packed with 5-µm particle size Poly LC Polysulfoethyl A (PolyLC, Columbia, MD). The mobile phase

solvents A and B were 10 mM ammonium formate + 25% acetonitrile, and 500 mM ammonium formate

+ 25% acetonitrile, respectively. The gradient was 0-10 min, 0% B; 10-50 min, 0-50% B; 50-60 min, 50-

100% B; 60-70 min, 100% B; and finally a wash with 100% nanopure water from 70-110 min. Non-

adjacent SCX fractions were pooled to maintain high proteome coverage while reducing the number of

required runs.30

Pooled SCX fractions, as well as all other samples, were analyzed by reversed-phase LC-

MS, on a custom-built chromatography system31

using a 60 cm, 75 µm ID fused silica column packed

with Jupiter C18 stationary phase (3 µm particle size; Phenomenex, Torrance, CA). The two mobile

phases were (A) 0.1% formic acid in water, and (B) 80% acetonitrile/20% water/0.1% formic acid. The LC

column was coupled to either a Thermo Scientific LTQ (for analyses used in database generation only) or

Thermo Scientific LTQ Orbitrap (for production runs) mass spectrometer via an electrospray ionization

interface as previously described.31

Initially, samples were analyzed in triplicate. In order to overcome possible issues associated

with run order and column-to-column variation, the complete set of samples were run in triplicate a

second time. We refer to the resulting groups of datasets as experiments 1 and 2. We report the results

for both experiments, treating experiment 2 as an additional replicate of the whole of experiment 1, and

requiring agreement between the two for a protein’s expression change to be judged significant.

Page 6 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

7

Data Analysis for Proteomics

Generation of the accurate mass and time tag database. We analyzed our LC-MS data by the

accurate mass and time (AMT) tag approach.16-18

In this method, the LC retention time and accurate

mass of peptides detected and confidently identified in an initial LC-MS/MS experiment are stored in a

database as putative mass tags (PMTs). Peptides detected in subsequent LC-MS experiments are

identified by matching their elution times and accurate masses to the PMT database, even if the peptide

is not selected for MS/MS fragmentation. Detecting a peptide in a second analysis with a mass and

elution time that match the database also serves to confirm the PMT, which then becomes an AMT tag,

a signature in the data indicating the presence of a particular peptide. Statistical techniques were

applied to assess the confidence of the matches and control the false discovery rate (FDR) as described

previously.32

Identified peptides were then quantified by LC-MS peak intensities.

The Geobacter bemidjiensis AMT tag database was populated with data from 115 individual LC-

MS analyses. To maximize the number of peptides in the AMT tag database, we prepared a pooled

sample of all the lab-grown G. bemidjiensis samples and fractionated it by off-line SCX chromatography.

Fractionation simplifies the mixture that is subjected to LC-MS analysis, allowing more proteins to be

detected. Twenty-eight LC-MS datasets were collected from the SCX fractions. Another 65 datasets

were derived from samples grown on fumarate, ferric citrate, or one of the solid iron(III) phases. This

category included all the runs used for protein quantitation. Finally, to improve the representation of c-

type cytochromes in the AMT tag database, 23 LC-MS analyses of in-gel digests of TMBZ-stained gel

bands from SDS-PAGE analysis of laboratory-grown G. bemidjiensis cultures were also included in the

library. Since these gel bands are stained for the presence of heme groups, the gel bands are enriched

for c-type cytochromes. All datasets were searched with TurboSequest v27.1233

using 3 Da precursor

mass tolerance for LTQ datasets and 50 ppm precursor mass tolerance for LTQ Orbitrap datasets, partial

tryptic peptide rules, up to 4 missed cleavages, and a dynamic modification of 615.1694 (heme c) on

cysteine residues. Sequest identifications were rescored with MS-GF.34-35

The maximum allowed MS-

GF spectral probability score allowed was 1x10-9

(lower scores represent more confident peptide-

spectrum matches). After filtering, the final database had 23,253 peptides, 16,610 of which were

identified and quantitated in the production LC-MS runs.

Note that MS/MS results from both high- and low-resolution instrumentation were combined to

generate the AMT tag database. Since the theoretical mass of an identified peptide (calculated from the

sequence) is used in the creation of a putative mass tag, high resolution MS data is not necessary. We

Page 7 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

8

did not investigate the effect of low-resolution instrumentation versus high-resolution instrumentation

on the identifications used to generate the AMT tag database.

Statistical Analysis of Protein Abundance

Overcoming Matrix Effects Using a Linear Regression Data Analysis Approach. In the course of

these studies, we discovered that sample matrix effects had a significant impact on our results for

samples grown on HFO (both bulk and nanoparticle treatments). HFO, as a poorly crystalline iron

mineral, cannot be separated from the cell pellet by simple centrifugation. We speculate that density

gradient centrifugation could successfully separate cells from nanoparticles and avoid the problems

associated with residual dissolved iron, but since we did not detect the problem until after cell lysis and

digestion, we were unable to apply this method. We applied the common method of dissolving

insoluble HFO with oxalate prior to centrifugation and downstream sample preparation. However, we

found that HFO and/or the products of its reaction with oxalate (i.e., residual iron ions) interfered with

the estimation of peptide concentration by the BCA assay. The interference resulted in a large

overestimation of the protein concentration and a dramatically decreased sample loading and measured

LC-MS intensity for these samples. It is also possible that dissolved iron could have other effects, such as

inhibiting digestion by stabilizing iron-binding proteins or promoting oxidative chemical modifications of

peptides, but we focused our attention on the effect of dissolved iron on the estimation of peptide

concentration. We tested a variety of methods for effectively estimating the peptide concentration.

None proved satisfactory; therefore we decided to address the issue at the level of data processing

rather than sample preparation.

Without inter-dataset normalization,36

the protein abundances in the HFO-grown samples were

systematically lower in abundance than in the samples grown on ferric citrate or fumarate. The median

peptide log2 abundance values for HFO-grown samples ranged from 14-19, whereas the ferric citrate

and fumarate median log2 abundance values ranged from 21-22.5. The overall LC-MS intensity judged

by total ion chromatograms was also much higher for the non-HFO samples. We first analyzed the data

using a standard procedure for our laboratory, which we will designate as method 1: central tendency

normalization 36

, R-rollup37

to convert peptide data to protein abundances, and ANOVA with multiple

hypothesis testing correction by the q-value method38

(see Supplemental Methods). Method 1 analysis

resulted in the classification of an unreasonably high number of proteins (20-30% of all detected) as

differentially expressed. Furthermore, the majority of these proteins were judged to be more highly

expressed in the non-HFO condition; that is, in the condition with higher LC-MS signal, which suggests

Page 8 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

9

that the combination of very different signal intensity and central tendency normalization introduced a

systematic bias.

To avoid this kind of artifact when comparing samples such as fumarate versus bulk HFO, for

which the iron interference/sample loading issue causes one of the samples (bulk or nanoparticle HFO)

to give a much higher overall signal than the other sample (fumarate or ferric citrate), we used a linear

regression procedure to make a series of pairwise comparisons between conditions. This approach,

which we designate Method 2, is similar to the linear regression normalization described by Callister et

al.,36

except that the linear regression was used to identify significant proteins directly, rather than to

normalize abundance values. In this respect, the method resembles the graphical displays of data used

by Beck et al.,39

and by Washburne and coworkers.40

Log2-transformed protein abundances were

calculated from peptide abundances by the R-rollup procedure as applied in DAnTE37

with no

normalization (other than the scaling procedure included in R-rollup). Peptides were required to be

present in at least 10% of datasets, and all other parameters were the defaults. This method exludes

proteins where only one peptide was detected, but due to missing data a protein result can be strongly

dependent on a single peptide’s abundance. Log2 protein abundances for each replicate dataset at a

given condition for a given experiment were averaged, and error bars were calculated as the standard

deviation (for n=3), half the range (for n=2, i.e., when one of the runs was an outlier or had missing

data). If data was missing in all but one run, then the error bar was taken as 10% of the non-missing

value. Only the datasets being compared were included in the rollup. For each observed protein, the

average log2 abundance values for condition 2 were plotted versus condition 1, and a regression line

calculated. Assuming a Gaussian distribution of residuals from the regression line, a z-score for each

protein was calculated as:

� =�� − �̅

��,

where the subscript p indicates a particular protein, dp is the residual from that protein, and d is the

mean of all dp (which is approximately zero, due to the assumptions of the linear regression). Only 5% of

the area under the curve occurs at more extreme values than z-score of ±1.96 in either direction, so

z=±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 2 or more out of 3 replicates for each condition were

used in calculating the regression line, but z-scores based on the distances from the regression line were

Page 9 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

10

calculated for any proteins detected in as few as two runs in one condition and one in the other in a

pairwise comparison. Thus, Method 2 is less stringent with respect to missing data than Method 1.

Using Equation 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 z-score 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 was carried out in the R statistical computing environment [33] 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 Supplemental Methods. Where both methods could be applied, the

agreement was acceptable (Supplementary Material).

Due to the decreased sample loading for the insoluble iron-grown samples, we expect a greater

number of protein identifications in the ferric citrate and fumarate datasets. 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. 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, 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 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

Page 10 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

11

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.

Results and Discussion

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 by the standard method only 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 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 derived here is an approximation.

Page 11 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

12

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 similar complement of proteins may be responsible for growth on HFO, regardless of

differences in particle size or properties, although there are a few proteins specific to one or the other

(see below). In analogy to a proteomics study of G. sulfurreducens by Ding et al.,43

our data also suggest

that this HFO-induced proteome is different than the proteome observed during reduction of soluble

Fe(III). Another study by these same authors found proteomics differences between G. sulfurreducens

grown on ferric citrate or fumarate.44

In our results, the values of the correlation coefficients between

ferric citrate and fumarate runs suggest broadly similar protein expression profile between the two

conditions, but with a 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 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,

Page 12 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

13

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.

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 8 samples (two growth

phases, 4 treatments). In two cases the detected peptides did not allow us to distinguish between

paralogs. Due to 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, twelve 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. sulfurreducens

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 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 datasets) 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 homologs 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

Page 13 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

14

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 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. sulfurreducens (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. sulfurreducens 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 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).

Differentially Expressed Cytochromes. The remaining twelve cytochromes (out of 27 detected)

were differentially expressed in at least one binary comparison. OmcF (GSU_2432) is a monoheme

Page 14 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

15

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 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. Based on the loose correlation between expression of Gbem_2183/OmcF and

Gbem_3779/OmcB we speculate that Gbem_2183 plays a similar role to G. sulfurreducens 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 6 hemes,15

and are predicted to localize to the

extracellular space. They have between 45%-56% sequence identity to G. sulfurreducens 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 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. sulfurreducens OmcS

transcripts are detected during log-phase growth on solid Fe(III).11

G. sulfurreducens OmcS is required

Page 15 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

16

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 up-regulation of OmcS in

response to growth on iron mineral phases.

OmcB is a decaheme outer-membrane cytochrome that, in G. sulfurreducens, 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 early stage..

However, the peptide-level evidence for this change is weak (not shown). If Gbem_3354 is in fact

upregulated in the presence of nanoparticles, expression may be regulated by minerological or redox

factors or particle size. 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 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. sulfurreducens, the tri-heme 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. sulfurreducens, each with slightly different

Page 16 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

17

functions.54

Of the three Ppc-family genes present in G. bemidjiensis,15

we detected only Gbem_3455

(PpcG). In our data, at 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. sulfurreducens13

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 ferric citrate. These

results may indicate distinct functional roles for different ppc genes, or alternatively a functional

difference between G. bemidjiensis and G. sulfurreducens.

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 datasets collected in

2010,55

along with our laboratory data, using a spectral counting approach.(ref) 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.

Page 17 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

18

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 datasets 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 measurement 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 non-detection 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

Page 18 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

19

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 residues56-57

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. Based on 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 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.

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 datasets. The

method is more lenient with respect to missing data than, 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

Page 19 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

20

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. sulfurreducens. 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.58

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 G. bemidjiensis could also exist with a distinct physiological state in other

ecological niches.

Supporting Information Available

This material is available free of charge via http://pubs.acs.org. 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 performing

linear regression analysis.

Acknowledgments

Page 20 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

21

This work was funded by a grant from the DOE/BER for Pan-Omics 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.

References

(1) Lovley, D. R. Bug juice: harvesting electricity with microorganisms. Nat Rev Micro 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).

(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 c-type cytochrome required for insoluble Fe(III) reduction in

Geobacter sulfurreducens. Biochimica et Biophysica Acta (BBA) - Bioenergetics 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. Abstracts of

Papers of the American Chemical Society 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.

(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.

Page 21 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

22

(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. Journal of Soil Science 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. Journal of Crystal 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. American Mineralogist 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.; Byrne-Bailey, 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. Zeitschrift für 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 P-450 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 Review of Proteomics 2012, 9 (2), 129-134.

Page 22 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

23

(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 Review of 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 sulfurreducens grown with Fe(III) oxide or Fe(III) citrate as the electron acceptor.

Biochimica et Biophysica Acta (BBA) - Proteins & Proteomics 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 sulfurreducens under various

growth conditions. Biochimica et Biophysica Acta (BBA) - Proteins & Proteomics 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 Journal 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 Optimal

Fe(III) Reduction in Geobacter sulfurreducens. J. Bacteriol. 2006, 188 (8), 3138-3142.

Page 23 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

24

(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 Meth 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 Journal 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: 2011; Vol. Volume 59, pp 1-100.

(55) 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), e57819.

(56) 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 Heme-Attachment Sites in

Shewanella oneidenis Cytochromes Using Mass Spectrometry. J. Proteome Res. 2005, 4 (3), 846-854.

(57) 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.

(58) 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 Journal 2013, 7 (2), 338-350.

(59) 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.

Page 24 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

25

Table 1. Functional classes of proteins with significant changes in expression levels. Functional

classification was based on the clusters of orthologous genes (COGs 59

) functional classes with minor

modifications (i.e., genes not belonging to a COG were manually assigned to a functional class, and c-

type cytochromes were added as a class). Functional classes that were enriched in a particular

comparison (one-sided binomial test with Yates correction applied, p<0.05) are underlined and

highlighted in red. Note that the list of significantly changing proteins used to generate this table does

not include proteins detected exclusively in fumarate or ferric citrate growth conditions (see Methods

for details). Number of Significantly Changing Proteins

Co

de

Mo

dif

ied

CO

G F

un

ctio

na

l Cla

ss

Ge

no

me

Ea

rly s

tag

e,

Fu

ma

rate

vs.

Bu

lka

Ea

rly s

tag

e,

Fu

ma

rate

vs.

Na

no

pa

rtic

leb

Ea

rly s

tag

e,

Fe

rric

cit

rate

vs.

Bu

lk

Ea

rly s

tag

e,

Fe

rric

Cit

rate

vs.

Na

no

pa

rtic

le

Ea

rly s

tag

e,

Bu

lk v

s. N

an

op

art

icle

Ea

rly s

tag

e,

Fu

ma

rate

vs.

Fe

rric

cit

rate

Late

sta

ge

, F

um

ara

te v

s. B

ulk

Late

sta

ge

, F

um

ara

te v

s. N

an

op

art

icle

Late

sta

ge

, F

err

ic c

itra

te v

s. B

ulk

Late

sta

ge

, F

err

ic c

itra

te v

s. N

an

op

art

icle

Late

sta

ge

, B

ulk

vs.

Na

no

pa

rtic

le

Late

sta

ge

, F

um

ara

te v

s. F

err

ic c

itra

te

An

y C

om

pa

riso

n

C Energy production and conversion including redox

processes

330 1 1 0 1 1 3 2 3 1 2 0 2 9

D Cell cycle control and mitosis 29 0 0 0 0 0 0 1 1 0 0 0 2 2

E Amino acid metabolism and transport 195 3 1 2 1 2 0 2 0 2 1 4 0 10

F Nucleotide metabolism and transport 67 0 0 0 0 0 0 1 0 1 0 0 0 1

G Carbohydrate metabolism and transport 137 0 0 0 0 0 1 2 1 3 1 1 1 6

H Coenzyme metabolism 144 0 3 0 3 2 1 0 1 0 1 2 1 10

I Lipid metabolism 101 0 0 1 0 0 1 0 0 0 0 0 0 2

J Translation 242 0 0 1 1 1 7 0 1 0 1 2 4 13

K Transcription 163 3 1 3 1 1 1 0 1 0 1 2 1 9

L Replication and repair, including nuclease activity 134 0 0 0 0 0 0 0 0 1 0 1 0 2

M Cell wall/membrane/envelope biogenesis 221 0 0 0 0 2 1 0 0 1 0 0 1 5

N Cell motility 149 0 0 0 0 0 0 1 0 1 0 0 0 1

O Post-translational modification,protein

turnover,chaperone functions

135 3 2 3 2 1 0 0 1 1 2 3 4 12

P Inorganic ion transport and metabolism 169 0 0 0 0 0 1 2 2 2 3 1 0 6

Q Secondary metabolites biosynthesis, transport and

catabolism

63 0 0 1 0 0 0 0 0 0 0 0 1 2

R General functional prediction only 382 5 3 4 1 1 2 4 2 2 3 2 1 17

S Hypothetical or uncharacterized protein,protein of

unknown function, or no annotation

1235 14 3 12 2 2 2 9 8 8 10 9 13 44

T Signal transduction 373 3 0 3 0 0 0 3 0 4 0 2 3 11

U Intracellular trafficking and secretion 111 0 0 0 0 0 0 1 0 1 0 0 1 2

V Defense mechanisms 41 0 0 0 0 1 0 1 0 0 0 0 0 2

Y c-type cytochromes 86 3 2 6 1 1 0 3 3 5 3 1 3 12

Z Cytoskeleton 2 0 0 0 0 0 0 0 0 0 0 0 0 0

Page 25 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

26

Total of significantly changing proteins in samplec 4127 31 15 31 12 14 18 28 22 28 26 30 36 164

a “Bulk” designates undefined poorly hydrous ferric oxide. See text for details

b “Nanoparticle” designates synthesized hydrous ferric oxide mineral particles < 6 nm. See text for

details.

c Column sums are greater than the total number of significantly changing proteins in each column

because a protein can belong to more than one functional class.

Page 26 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

27

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 proteomics.

See text for discussion.

Gene Locus Annotation Gene Locus Annotation

Gbem_0040 methyl-accepting chemotaxis sensory transducer Gbem_2406 multi-sensor hybrid histidine kinase

Gbem_0080

response regulator receiver modulated metal

dependent phosphohydrolase Gbem_2407

response regulator receiver modulated metal

dependent phosphohydrolase

Gbem_0257 methyl-accepting chemotaxis sensory transducer Gbem_2438 metal dependent phosphohydrolase

Gbem_0295 putative phytochrome sensor protein Gbem_2495 integral membrane sensor hybrid histidine kinase

Gbem_0296 adenylate/guanylate cyclase with Chase sensor Gbem_2649 anti-sigma-factor antagonist

Gbem_0379 PAS/PAC sensor hybrid histidine kinase Gbem_2942 methyl-accepting chemotaxis sensory transducer

Gbem_0383 methyl-accepting chemotaxis sensory transducer Gbem_3155 CheW protein

Gbem_0467 methyl-accepting chemotaxis sensory transducer Gbem_3279

response regulator receiver and ANTAR domain

protein

Gbem_0712 response regulator receiver modulated CheW protein Gbem_3297 methyl-accepting chemotaxis sensory transducer

Gbem_0809 methyl-accepting chemotaxis sensory transducer Gbem_3298 methyl-accepting chemotaxis sensory transducer

Gbem_0811 Hpt sensor hybrid histidine kinase Gbem_3634

response regulator receiver sensor signal transduction

histidine kinase

Gbem_0812 metal dependent phosphohydrolase Gbem_3744 flagellar FlbT family protein

Gbem_0888 inhibitor of MCP methylation-like protein Gbem_3752 MotA/TolQ/ExbB proton channel

Gbem_1044 methyl-accepting chemotaxis sensory transducer Gbem_3759 flagellar basal body P-ring protein

Gbem_1482 methyl-accepting chemotaxis sensory transducer Gbem_3802 putative sigma54 specific transcriptional regulator

Gbem_1511 UspA domain protein Gbem_3826 flagellar motor switch protein FliN

Gbem_1591 methyl-accepting chemotaxis sensory transducer Gbem_3828 flagellar basal body-associated protein FliL

Gbem_1593 response regulator receiver protein Gbem_3835 flagellar protein export ATPase FliI

Gbem_1594 CheA signal transduction histidine kinase Gbem_3837 flagellar motor switch protein FliG

Gbem_1595 MCP methyltransferase, CheR-type Gbem_3838 flagellar M-ring protein FliF

Gbem_1597

response regulator receiver modulated CheB

methylesterase Gbem_3846 PAS/PAC sensor signal transduction histidine kinase

Gbem_1826 methyl-accepting chemotaxis sensory transducer Gbem_3941 CheW protein

Gbem_2234 CheW protein Gbem_3942 CheA signal transduction histidine kinase

Gbem_2239

two component, sigma54 specific, transcriptional

regulator, Fis family Gbem_3943 MCP methyltransferase, CheR-type

Gbem_2335 multi-sensor signal transduction histidine kinase Gbem_2406 multi-sensor hybrid histidine kinase

Page 27 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

28

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

Page 28 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

29

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.

Page 29 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

30

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 2-3 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

Page 30 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

31

|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.

Page 31 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

32

Figure 3. Results of method 2 analysis of c-type cytochromes, arranged as a set of pairwise comparisons between conditions. Each column

represents a pairwise comparison between conditions using 2-3 LC-MS replicates per condition. Data are the z-scores from the method 2 linear

regression analysis (see text), colored red if z > 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

Page 32 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

33

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.

Page 33 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

34

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 [reference]. 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

Page 34 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

35

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.

Page 35 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

36

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, two-sided 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.

Page 36 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

37

Table of Contents Figure

Page 37 of 37



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

Changes in Protein Expression Across Laboratory and Field Experiments in Geobacter bemidjiensis

Documents

Transcript of Changes in Protein Expression Across Laboratory and Field Experiments in Geobacter bemidjiensis