Available online at www.sciencedirect.com
Biochimica et Biophysica Acta 1778 (2008) 1698 – 1713
www.elsevier.com/locate/bbamem
Review
Proteome of the Escherichia coli envelope and technological
challenges in membrane proteome analysis
Joel H. Weiner a,b,⁎, Liang Li c
a
Membrane Protein Research Group and The Institute for Biomolecular Design, University of Alberta, Canada
Department of Biochemistry and Institute for Biomolecular Design, University of Alberta, Edmonton, Alberta, Canada T6G 2E7
Department of Chemistry and The Institute for Biomolecular Design, University of Alberta, Edmonton, Alberta, Canada T6G 2E1
b
c
Received 18 April 2007; received in revised form 19 July 2007; accepted 23 July 2007
Available online 11 August 2007
Abstract
The envelope of Escherichia coli is a complex organelle composed of the outer membrane, periplasm-peptidoglycan layer and cytoplasmic
membrane. Each compartment has a unique complement of proteins, the proteome. Determining the proteome of the envelope is essential for
developing an in silico bacterial model, for determining cellular responses to environmental alterations, for determining the function of proteins
encoded by genes of unknown function and for development and testing of new experimental technologies such as mass spectrometric methods for
identifying and quantifying hydrophobic proteins. The availability of complete genomic information has led several groups to develop computer
algorithms to predict the proteome of each part of the envelope by searching the genome for leader sequences, β-sheet motifs and stretches of αhelical hydrophobic amino acids. In addition, published experimental data has been mined directly and by machine learning approaches. In this
review we examine the somewhat confusing available literature and relate published experimental data to the most recent gene annotation of E.
coli to describe the predicted and experimental proteome of each compartment. The problem of characterizing integral versus membraneassociated proteins is discussed. The E. coli envelope proteome provides an excellent test bed for developing mass spectrometric techniques for
identifying hydrophobic proteins that have generally been refractory to analysis. We describe the gel based and solution based proteome analysis
approaches along with protein cleavage and proteolysis methods that investigators are taking to tackle this difficult problem.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Cytoplasmic membrane; Periplasm; Outer membrane; Mass spectrometry; Proteomics; Polyacrylamide gel electrophoresis
Contents
1.
2.
3.
4.
Introduction . . . . . . . . . . . . . . . . . . .
E. coli envelope composition . . . . . . . . . .
The outer membrane proteome . . . . . . . . .
3.1. Predicted outer membrane proteome . . .
3.2. Experimental outer membrane proteome .
The periplasmic proteome . . . . . . . . . . . .
4.1. Predicted periplasmic proteome . . . . .
4.2. Experimental periplasmic proteome . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1699
1699
1700
1700
1701
1702
1702
1703
Abbreviations: 2MEGA, dimethylation after lysine guanidination; ABC, ATP binding cassette; CNBr, cyanogen bromide; ESI, electrospray ionization; ICAT,
isotope-coded affinity tag; IEF, isoelectric focusing; IMP, inner membrane protein; LC, liquid chromatography; MAAH, microwave-assisted acid hydrolysis; MALDITOF, matrix assisted laser desorption/ionization-time of flight; MS, mass spectrometry; OMP, outer membrane protein; ORF, open reading frame; PAGE,
polyacrylamide gel electrophoresis; PMF, peptide mass fingerprinting; SDS, sodium dodecyl sulfate; TFA, trifluoroacetic acid; TMM, transmembrane
⁎ Corresponding author. Department of Biochemistry and Institute for Biomolecular Design, University of Alberta, Edmonton, Alberta, Canada T6G 2H7. Tel.: +1
780 492 2761; fax: +1 780 492 0886.
E-mail addresses: [email protected] (J.H. Weiner), [email protected] (L. Li).
0005-2736/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.bbamem.2007.07.020
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
5.
The cytoplasmic membrane proteome . . . . . . . . .
5.1. Predicted cytoplasmic membrane proteome . . .
5.2. Respiratory complexes. . . . . . . . . . . . . .
5.3. Experimental cytoplasmic membrane proteome .
6. Comparison with microarray . . . . . . . . . . . . . .
7. Methodology for membrane proteome analysis. . . . .
7.1. Gel-based proteome analysis platform. . . . . .
7.2. Solution-based proteome analysis platform . . .
8. Future perspectives . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1. Introduction
The availability of complete genomic sequences for hundreds
of microorganisms, as well as transcriptome measurements of
mRNA levels under a variety of physiologic conditions, has
spurred investigators to determine the complete protein composition, or proteome, of entire bacterial cells and how the proteome changes with physiologic shifts. Membrane proteins
which play essential roles in energetics, metabolism, signal
transduction and transport compose as much as 40% of the
entire proteome of a bacterial cell. The hydrophobic nature of
membrane proteins has hindered their investigation at the
functional and structural level. Similarly, characterizing the
membrane proteome has lagged studies of the soluble proteome. However, recent technical advances in separation methodologies by 2-dimensional electrophoretic techniques and 2dimensional HPLC methodologies, coupled with improved
proteolytic methodologies, and advanced mass spectrometry
instrumentation is opening the membrane proteome to experimental evaluation.
This review will summarize current data on the predicted and
observed proteome of the Escherichia coli K-12 envelope along
with advances in methodology that allow more detailed exploration of this proteome.
The complete proteome of E. coli is being examined for
many reasons. The International E. coli Alliance [1] brings
together biochemists, system biologists and computer scientists
with the aim of developing a detailed understanding of the entire
transcriptome, proteome, interactome, metabolome and physiome of E. coli in response to physiologic, genetic or environmental variation with the aim of creating an in silico E. coli
where changes can be predicted and experimentally verified.
Whereas the transcriptome determined by microarray techniques gives a whole genome profile at the mRNA level, proteomics gives information on expression levels. Proteomics
provides information on post-translational modifications which
is not possible by microarray and proteomics can be used to
investigate subcellular localization such as the envelope.
The E. coli genome contains 4452 open reading frames
(ORF). Of these 2403 (54.1%) have an experimentally determined function and a further 1425 (32%) have a computationally
determined function. The remaining 663 (14.9%) are of unknown function. Interestingly, 238 ORFs (5.3%) do not have
homologues in other genomes [2]. About one third of the total
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1699
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1704
1704
1705
1705
1706
1706
1706
1708
1710
1710
1710
cellular proteome is associated with the envelope and at least one
half of these proteins are of unknown or putative function. It is
quite possible that the functionally uncharacterized proteins
catalyze unique metabolic transitions and these may prove
useful for the biotechnology industry. Thus, a full understanding
of E. coli requires the development of novel technologies
including proteomic analysis to determine the function of these
proteins. Proteomic studies under varying growth, regulatory
and stress conditions will help with the functional assignment of
unknown proteins. In addition, because of the hydrophobicity of
membrane proteins there are significant technical difficulties in
studying the membrane proteome and E. coli provides a standard
for evaluating and validating new technologies. E. coli offers an
excellent model system to develop new extraction, solubilization, proteolytic, electrophoretic and mass spectrometry techniques which will be applied to the more complex proteomes of
higher organisms.
2. E. coli envelope composition
The envelope of E. coli is a complex structure composed of
the outer membrane, periplasm/peptidoglycan layer and the
cytoplasmic membrane. The envelope is usually prepared by
lysozyme-EDTA lysis [3], French press lysis [4] or Avestin
EmulsiFlex [5] lysis of bacterial cultures. These processes form
vesicles that can be isolated by differential centrifugation. Inner
and outer membranes can be isolated by density centrifugation
comprising an 8000×g spin to sediment unbroken cells and
debris followed by a high speed spin (150,000×g) to sediment
inner and outer membrane vesicles [6]. Inner from outer membrane vesicles can then be separated by density centrifugation
using a sucrose gradient, with the outer membranes having a
higher density. In most cases vesicle integrity is such that
cytosolic proteins trapped in the vesicles can be removed by
buffer washing. Loosely-associated extrinsic proteins are often
removed by washing with chaotropic agents such as Na carbonate [7]. Fractions containing periplasmic proteins can be
prepared by the cold osmotic shock method [8].
One of the first attempts to characterize the envelope proteome was carried out by Ames and Nakaido in the mid-1970s
[9] using sodium dodecyl sulfate solubilization coupled with
O'Farrell two-dimensional polyacrylamide gels (isoelectric
focusing (IEF) in the first dimension and SDS polyacrylamide
gel electrophoresis (PAGE) in the second dimension). They
1700
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
Table 1
Predicted outer membrane proteome
Prediction
β-barrel
Lipoprotein
Reference
Molloy-SwissProt38
BOMP
TMB-HUNT
PSORTdb
Riley et al.
39
103
69
91
23
23
N/A
N/A
Does not list
101
[21]
[17]
[22]
[23]
[2]
noted the complexity of the sample and observed over 150 spots
on the gel. It is not clear that this represented 150 different
proteins as proteolysis, post-translational modification and isoelectric changes (e.g. deamidation) could have resulted in multiple spots for the same polypeptide. Technology at the time did
not allow them to identify the proteins. As will be described
below, recent advances in analytical techniques such as the
melding of two-dimensional liquid chromatography, improved
two dimensional gel methods, mass spectrometry, selective
proteolysis and bioinformatics allow relatively facile identification of the individual proteins.
The entire genomic sequence of E. coli MG1655 has been
available for many years [10], and multiple attempts have been
made to assign a function and location for each open reading
frame. Bioinformatic attempts to assign a function are often
carried out in parallel with experimental approaches to define
function and location and by inference those proteins associated
with the envelope. The challenge of such efforts is to provide
insightful information that is neither contradictory nor confusing. This is not a simple exercise.
3. The outer membrane proteome
3.1. Predicted outer membrane proteome
The outer membrane consists of phospholipids, membrane
proteins, lipoproteins and lipopolysaccharide. It forms the
outermost layer of the cell envelope and is covalently attached
to the peptidoglycan by an almost continuous layer of small
lipoprotein molecules (Lpp or Braun's lipoprotein) [11]. The
peptidoglycan (murein) layer is a lattice formed by repeating
disaccharides interconnected by peptides that make up the
bacterial cell wall. It is a polymer of N-acetylglucosamine
(gluNAc) alternating with molecules of N-acetylmuramic acid
(murNAc). In the eubacteria these molecules are cross-linked
by pentapetides (L-alanine-D-glutamate-meso-diaminopimelic
acid-D-alanine-D-alanine). These peptides are unusual in that
they contain the rare D-enantiomers of the Ala and Glu residues
[12].
The outer membrane of Gram-negative bacteria is the interface between the cell and the environment and this distinguishes
Gram-negative from Gram-positive organisms that lack this
layer. The double leaflet membrane is unlike a typical phospholipid bilayer as it has phospholipids on the internal leaflet
and lipopolysaccharide on the outer leaflet.
Integral membrane proteins of the inner and outer membrane
differ in structure. Cytoplasmic membrane proteins are hydrophobic and are composed of α-helices 15–25 amino acids in
length that cross the membrane. Many algorithms are available
to identify these, ranging from the original Kyte Doolittle
algorithm [13] to the TMHMM algorithm [14–17] which gives
the most reliable prediction of the presence of transmembrane
α-helices. The outer membrane encompasses a limited number
of proteins and structural studies of several of them [18,19] have
shown that they are comprised of β-barrel motifs and lack the αhelices found in cytoplasmic membrane proteins. The β-barrel
structures form mono, di and trimeric structures with 8–22 βTable 2
Outer membrane proteome comparison
Riley
Riley
Riley
Riley Molloy Fountoulakis LopezGevaert
Campistrous
acrE
bcsZ
blc
borD
btuB
cirA
csgG
cusC
ecnA
ecnB
emtA
fadL
fecA
fepA
fhuA
fhuE
fimD
fiu
flgH
fliL
hslJ
kefA
lamB
lepB
lolB
mipA
mltA
mltB
mltC
mltD
mulI
nfrA
nlpB
nlpC
nlpD
nlpE
nlpI
nmpC
nrfG
ompA
ompC
ompF
ompG
ompL
ompN
ompW
ompX
osmB
osmE
pal
panE
phoE
pldA
rcsF
rlpA
rzoD
rzoR
sfmD
slp
slyB
smpA
spr
srlD
tolC
tsx
uidC
vacJ
wza
yaeF
yaeT
yafT
yaiW
yajG
yajI
ybaY
ybbC
ybdA
ybeT
ybfM
ybfN
ybfP
ybgQ
ybhC
ybjP
ybjR
ycaL
ycbS
yccZ
ycdR
ycdS
yceB
ycfL
ycfM
ycjN
ydbA
ydbJ
ydcL
yddW
ydeK
yeaY
yecR
yedD
yegR
yehB
yfaZ
yfcT
yfeN
yfeY
yfgH
yfgL
yfhG
yfiB
yfiL
yfiM
yfiO
ygdI
ygdR
ygeR
yggG
yghG
ygiB
yhcD
yhdV
yhfL
yiaD
yidQ
yidX
yjaH
yjbF
yjcP
yjeI
yjfO
ymcC
ynbE
yncD
yoaF
yohG
ypdI
yqhH
yraJ
yraP
ysaB
btuB
cirA
fadL
fepA
fhuA
fhuE
flu
lamB
nlpB
ompA
ompC
ompF
ompP
ompT
ompW
ompX
pal
slp
slyB
tolC
tsx
ybhC
ybiL
yaeT
yeaF
btuB
cirA
fecA
fepA
fhuA
fimD
flu
lolB
lamB
mltA
nfrA
nlpB
ompA
ompC
ompF
ompT
pal
pldA
tolC
tsx
yaeT
yciD
yeaF
yncD
btuB
cirA
fadL
fecA
fhi
fhuA
hemM
imp
lamB
mulI
nlpB
ompC
ompF
ompT
ompX
osmE
pal
rlpA
slp
slyB
tolC
tsx
yajG
ybhC
ybjP
yciD
yeaF
yedD
yfgL
yfiO
yifL
ynfB
yraM
yraP
aceE
fimD
lolB
nlpB
ompA
ompC
ompF
ompP
ompX
rlpA
ycjI
yedD
yfeY
yfiO
ygdI
yjeI
yraP
The predicted outer membrane proteome from the Riley consortium annotation
[2]. This list includes those proteins listed as outer membrane by “cell
localization” as well as those proteins listed as outer membrane by “gene product
description” that are underlined. A comparison of the outer membrane proteins
determined by Molloy [21], Fountoulakis [25] and Lopez-Campistrous [28]
using 2D gel electrophoresis and Gevaert [26] using LC-MS was undertaken by
combining the Supplementary Data in their publications with the Riley et al.
annotation. Proteins in underline italics are common to all three reports. The
unique proteins identified by Gevaert [26] by LC MS are in bold.
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
barrels. The β-barrel is composed of alternate polar and nonpolar amino acids. The non-polar amino acids point into the
lipid and protein interface, while the polar amino acids point
into the interior of the barrel. Because of this complexity, facile
prediction of the location of the transmembrane domains of
outer membrane proteins remains elusive.
In addition to the outer membrane proteins with the β-barrel
architecture, many lipoproteins [20] are found on the outer
membrane including the major outer membrane protein MulI
(Lpp, Braun's lipoprotein) [11] that links the outer membrane to
the peptidoglycan layer. It is estimated that each log phase E.
coli cell contains 1.6 × 105 copies of this protein, making it one
of the most abundant in the cell. Other major outer membrane
proteins include OmpA ∼ 105 copies per cell, OmpC ∼ 2 × 104
copies per cell and OmpF ∼ 104 copies per cell. These proteins
form hydrophilic channels allowing free diffusion of small
molecules across the outer membrane. The outer membrane also
contains specific channels such as PhoE for phosphate, LamB
for maltose and maltodextrins, Tsx for nucleosides. These
proteins also serve as attachment sites for colicins and
bacteriophages (e.g. Tsx for phage T6, LamB for phage λ)
from which their names derive. There are also high affinity
receptors for ferric iron (FepA, FhuA), vitamin B12 (BtuB), fatty
acids (FadL).
β-barrel structures are difficult to predict due to variable
properties and the short stretch of hydrophobic amino acids.
Molloy [21] carried out one of the earlier analyses of the outer
membrane proteome using the SwissProt Release 37 database that
had 58 potential outer membrane proteins based on a combination
of experiment, computer driven prediction and similarity to OMPs
from other organisms. Thirty-seven β-barrel integral proteins were
predicted to have a pI of 4–7, two integral proteins had a pI N 7.
There were 10 lipoprotein OMPs with pI of 4–7 and 9 proteins
with pI N7 (Tables 1 and 2). No OMPs had a predicted pI b4. The
more recent release of the SwissProt database Release 42 reveals
71 OMPs, but this list contains 12 proteins (FhiA, GspD, HlpA,
HofQ, Imp, PgpB, YiaT, YifL, YnfB, YpjA, YqiG, YraM) that are
probably not OMPs based on the Riley annotation [2].
BOMP [17] (Tables 1 and 2) was developed to predict
integral β-barrel OMPs. BOMP uses a C-terminal pattern
typical of internal β-barrel proteins and comparison to stretches
of amino acids typically found in transmembranal β-strands of
proteins with resolved crystal structures. These workers found
ninety-one β-barrel proteins using their algorithm and an
additional 12 proteins based on BLAST searches for a total of
103 of the 4346 polypeptides in E. coli to be possible integral
OMPs. Sixty-seven of the proteins were previously annotated as
OMP within SwissProt leaving thirty-six possible additional
OMPs. Using this algorithm on various prokaryotic genomes
gave a range of 1.8% to 3% OMPs.
The TMB-Hunt algorithm [22] (Tables 1 and 2) is designed
to find β-barrel proteins based on the presence of a signal
sequence and amino acid composition. Using the NCBI FTP
site, various strains of E. coli (including pathogens) have a total
of 5341 different proteins and, of these, 1032 polypeptides have
a signal sequence to direct them to the periplasm or outer
membrane. Eighty-seven proteins were identified as transmem-
1701
branal β-barrel. In E. coli K12, as many as 782 proteins have a
signal sequence and they predict that 69 are transmembranal βbarrel proteins.
Unlike transmembranal β-barrel proteins, outer membrane
lipoproteins are easier to identify. These proteins can be
identified by the presence of a leader with a common consensus
sequence [20]. The leader is typically between 15 and 40 amino
acid residues in length, and has at least one arginine or lysine in
the first seven residues. The leader is cleaved by signal peptidase
II, on the amino terminal side of the cysteine residue which is
then enzymatically modified by the addition of N-acyl and Sdiacyl glyceryl groups [20]. This lipid serves to anchor these
proteins to the outer leaflet of the inner membrane or inner leaflet
of outer membrane so that they can function in the aqueous
periplasmic interface.
PSORTdb is a database that combines experimentation and
computational prediction to determine the subcellular location
of a protein [23] (Tables 1 and 2). Their analysis of the E. coli
genome lists 91 outer membrane proteins, but does not distinguish between β-barrel proteins and lipoproteins.
Recently, a consortium of investigators has produced a new
annotation of the ORFs of E. coli [2]. The E. coli consortium
database relied more on experimental data for annotation and
found that only 23 β-barrel proteins are identified along with
142 potential outer membrane lipoproteins. This listing comes
from a combination of proteins annotated as “outer membrane”
by “cell location” (125 proteins) as well as 17 proteins annotated with an outer membrane location by “gene product description” (underlined in Table 2).
3.2. Experimental outer membrane proteome
Analysis of the outer membrane is complicated by a series of
technical problems including the extraction and solubilization
of outer membrane proteins prior to 2D PAGE, solubilization of
intractable proteins, trypsin digestion, artifacts due to the issue
of loading large amounts of protein onto 2D gels, protein
microanalysis by mass spectrometry and the databases used for
identification of peptides. These problems also hold for the
inner membrane proteome.
Molloy [21] used a combination of 2D PAGE and matrix
assisted laser desorption/ionization-time of flight (MALDITOF) MS to characterize the outer membrane proteome. E. coli
cells were broken by French press lysis to prepare a total
envelope fraction that was washed with 0.1 M Na carbonate to
remove peripheral proteins. The whole envelope was solubilized
in 7 M urea, 2 M thiourea, 1% ASB14 surfactant (amidosulfobetaine with an alkyl tail containing 14–16 carbons [24]
(Table 2). Apparently, this surfactant mixture did not solubilize
proteins from the inner membrane or proteolytic digestion of
integral peptides taken from gel plugs was not efficient. Molloy
identified 40 unique proteins, 75% of which were outer membrane or membrane-associated. They identified 21 of the 37
predicted integral OMPs (78% of OMP not including hypothetical) plus one probable OMP, 5 lipoprotein OMPs, 2 cytoplasmic
membrane associated polypeptides (AtpB, NuoC), 1 cytoplasmic membrane lipoprotein (AcrA), 4 periplasmic proteins,
1702
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
Fig. 1. Venn diagram noting the overlapping and uniquely identified proteins in the
experimentally identified outer membrane proteins by Molloy (M), Fountoulakis
(F) and Lopez-Campistrous (L) data as taken from Table 2 and [21,25,28].
flagellin, 2 cytoplasmic, 2 unknown, and 1 unknown lipoprotein.
Re-analysis of their list with the Riley listing indicates 25 OMPs
(Table 2).
Fountoulakis and Glasser [25] carried out a proteomic
analysis of E. coli envelopes composed of both inner and outer
membranes. They solubilized the membranes with mild
(CHAPS) or strong detergents (SDS, sodium cholate or sodium
deoxycholate), carried out 2D PAGE, in gel proteolysis and
MALDI-TOF MS. They identified a total of 394 different gene
products of which 25 were annotated to the outer membrane (24
using the Riley annotation list in Table 2).
In one of the first gel-free proteomic analysis of the E. coli
proteome, Gevaert et al. [26] identified more then 800 proteins.
They analyzed only methionine containing peptides in a total
peptide mixture of an unfractionated lysate. They identified 11
outer membrane proteins using the MASCOT search engine for
mass spectrometry [27] and a database of E. coli methionine
containing peptides and 14 proteins using the PROCORR
database at the 95% confidence limit (12 proteins at 98%
confidence). Re-analysis of their data using the Riley et al. [2]
database indicates that 17 OMPs were identified (Table 2).
Lopez-Campistrous et al. [28] carried out a large scale 2D gel
analysis of the E. coli proteome in cells fractionated into cytoplasm, inner membrane, periplasm and outer membrane by the
Yamato procedure [6]. They identified 60 proteins in the
isolated outer membrane fraction. Thirty-one proteins are
characterized in Swiss-Prot as outer membrane proteins (34
proteins using the Riley [2] annotation Table 2). Fig. 1 is a Venn
diagram, graphically depicting the degree of overlap and
uniquely identified proteins between the Molloy, Fountoulakis,
and Lopez-Campistrous databases. A large number of soluble
cytoplasmic proteins were found in the outer membrane
fraction. Fountoulakis [25] also found a large number of
cytoplasmic proteins associated with the membrane envelope.
This highlights the difficulty of using fractionation methodologies as proteins from the cytoplasm can adventitiously stick to
other fractions giving erroneous localization. Inner membrane
proteins can be found in the outer membrane fraction if there are
zones of adhesion between the membranes [29].
Lai et al. [30] extended the proteomic characterization of the
E. coli membrane to one of the first functional analysis. Using
2D PAGE, image analysis techniques and trypsin in gel
digestion with MALDI mass spectrometry they compared the
membrane proteome of minicells that are enriched in polar
proteins to rod cell membranes. One hundred seventy-three
spots were analyzed and 54 proteins identified. Thirty-six spots
were enriched in minicells and 15 spots in rod cells showing that
there is polar distribution of envelope proteins. Examination of
their results indicated that they identified 14 integral outer
membrane proteins, 6 lipoproteins and 4 associated cytoplasmic
membrane proteins. Only 1 integral membrane protein, YiaF,
was identified.
In a recent study Marini et al. [31] examined new candidate
outer membrane proteins that have been identified by bioinformatics prediction. The proteins were cloned and overexpressed,
and finally localized by cell fractionation to the outer membrane. They confirmed the outer membrane localization for five
proteins – YftM, YaiO, YfaZ, CsgF, and YliI – and also provide
preliminary data supporting an outer membrane location for a
sixth — YfaL.
Thus, unlike the predicted outer membrane proteome, experiments to date have only identified a limited number of proteins
in the outer membrane fraction.
4. The periplasmic proteome
4.1. Predicted periplasmic proteome
The periplasmic space is a gel-like layer composed of soluble
proteins located between the outer and inner membranes. It is
highly enriched in proteases and nucleases that would be
detrimental if they were located in the cytoplasm. The periplasm
contains many substrate binding proteins to capture nutrients
which are often present at very low concentrations. Proteins
localized in the periplasmic space can be identified by searching
the genome for leader sequences associated with Sec [32] or Tat
motifs [33] which address these proteins to their respective
translocons. Several algorithms and databases are available for
this analysis. PSORTdb predicts 141 periplasmic proteins [23].
Nielsen et al. [34] used an approach based on neural networks
trained on separate sets of prokaryotic sequences and identified
224 proteins.
The Project Cybercell (www.projectcybercell.ca) database
lists 200 periplasmic proteins. The recent consortium database
[2] lists 367 periplasmic proteins (Table 3) using a combination
of “cell localization” and “gene product description” and is the
most complete annotation of E. coli. This listing may be an
over-estimate as it includes some proteins that are associated
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
Table 3
Predicted and experimental periplasmic proteome
1703
Table 3 (continued)
Riley
Riley
Riley
Riley
Lopez-Cam
Gevaert
ackA
agp
alsB
amiA
amiB
amiC
ampC
ampH
ansB
aphA
appA
araF
argT
artI
artJ
aslA
asr
bax
bglH
bglJ
bglX
btuB
btuF
carB
ccmB
ccmE
ccmH
chbB
chiA
citE
coaE
cpdB
cpxP
creA
csgA
csgB
csgC
csgE
cueO
cusF
cybC
cysP
dacA
dacB
dacC
dacD
dcrB
ddpA
degP
degQ
degS
dmsA
dppA
dsbA
dsbC
dsbG
eco
ecpD
endA
erfK
eutP
fabF
fdoG
fecA
hdeB
hisJ
hlpA
hofQ
htrE
hybA
iap
imp
ivy
kdsC
ligB
livJ
livK
lolA
lsrB
malE
malM
malS
marB
mdh
mdoD
mdoG
mepA
metQ
metR
mglB
miaA
modA
mpl
mppA
mqo
mviM
napA
napB
napC
napD
napF
napH
nfo
nfrA
nikA
nrfA
nrfB
nrfC
ompA
ompG
ompT
oppA
osmY
paaH
pbl
pbpG
phnD
phnP
phoA
potD
potF
ppiA
prc
prkB
proX
pspE
pstS
pta
treA
trxB
tynA
ugpB
uidC
ushA
wcaM
xylF
yaaI
yacC
yacH
yadM
yadN
yaeT
yagW
yagX
yagY
yagZ
yahB
yahJ
yahO
yaiT
ybaE
ybaV
ybcL
ybcS
ybfC
ybgD
ybgF
ybgO
ybgP
ybgQ
ybgS
ycbB
ycbF
ycbQ
ycbR
ycbV
yccT
ycdB
ycdK
ycdO
ycdS
yceI
ycfR
ycgH
ycgJ
ycgK
ychP
yciM
ycjN
ydaS
ydbD
ydcA
ydcS
yddB
yddL
ydeI
ydeN
ydeR
ydfD
ydgD
ydgH
ydhO
yfcU
yfdX
yfeK
yfeW
yffQ
yfgI
yfhD
yfiR
yfjT
ygcG
ygcI
ygeQ
yggE
yggM
yggN
yghX
ygiL
ygiS
ygiW
ygjG
ygjJ
ygjK
yhbN
yhcA
yhcD
yhcE
yhcF
yhcN
yhdW
yheM
yhhA
yhjJ
yiaO
yiaT
yibG
yieL
yifB
yigE
yiiQ
yiiX
yijF
yjbG
yjcO
yjcS
yjfJ
yjfN
yjfY
yjhA
yjhT
yjjA
ykfB
ykfF
ykgI
ykgJ
yliB
yliI
ymcA
ymcB
ymgD
yncD
yncE
yncI
yncJ
ynfB
agp
ansB
araF
artI
bglX
cpdB
dppA
dsbA
dsbC
eco
fkpA
fliY
glnH
glpQ
hisJ
livJ
livK
lolA
malE
mdh
mdoG
mglB
modA
mppA
nikA
oppA
osmY
potD
potF
proX
ptr
pstS
rbsB
slt
sodC
sufI
surA
thrC
tolB
treA
ugpB
ushA
ybgF
ycdO
ydcS
ydeN
yehZ
yggN
ygiW
yhbN
yncE
ynjE
yrbC
ytfQ
ackA
alsB
ansB
aphA
araF
artI
bglX
btuB
cpxP
dacA
dacC
dcrB
degP
dppA
dsbA
dsbG
erfK
fabF
fdoG
fhuE
fimC
fliY
galF
glpQ
gltI
gltL
gltX
glyA
glyQ
glyS
gnd
gnsB
gntR
gor
gph
gpmB
greA
greB
groL
grpE
grxB
gshA
gshB
gsk
gst
guaA
guaB
guaD
gudD
gudX
gyrA
gyrB
hdeB
hisJ
ivy
lolA
malE
malS
mdh
mdoG
mglB
miaA
mqo
napA
(continued on next page)
Riley
Riley
Riley
Riley
fecB
fepB
fhuD
fhuE
fimC
fimF
fimI
fkpA
flgA
flgD
flgI
flhE
fliY
frdA
frwB
frwD
galF
gatB
ggt
glcG
glnH
glpQ
gltF
gltI
gntX
gpsA
gspD
hcaD
pth
ptr
puuE
rbsB
recG
rffD
rna
rseB
rsxG
sapA
sbp
sfmA
sfmC
sfmF
sfmH
slt
sodC
spy
ssuA
sufI
surA
tauA
tbpA
tolB
tolC
torA
torT
torZ
ydhS
ydiY
ydjG
yebF
yecG
yecT
yedS
yedX
yedY
yeeJ
yeeZ
yegJ
yehA
yehC
yehE
yehZ
yejA
yejO
yfaL
yfaP
yfaQ
yfaS
yfaT
yfcO
yfcQ
yfcR
yfcS
ynfD
ynfE
ynfF
ynhG
ynjB
ynjE
ynjH
yobA
yodA
yoeA
ypdH
ypeC
yphF
yqhG
yqiH
yqiI
yqjC
yraH
yraI
yraJ
yraK
yrbC
yrfA
ytfJ
ytfM
ytfQ
znuA
zraP
Lopez-Cam
Gevaert
oppA
osmY
potD
prc
pspE
pta
pth
ptrA
rbsB
rseB
slt
sufI
surA
tolB
tolC
torT
trxB
ugpB
ushA
yaeT
yahO
yciN
ydcS
ydeN
ydgH
yeeZ
yhhA
yliB
ynfF
ynjE
ytfQ
znuA
The theoretical periplasmic proteome is taken from the Riley annotation [2]. The
list includes those proteins listed as periplasmic by “cell localization” as well as
those proteins listed as periplasmic by “gene product description” shown in
bold. The underlined proteins are annotated as periplasmic, but it is this is not the
case for DmsA, FrdA, OmpA, OmpG, OmpT, YnfE and YnfF. A listing of the
periplasmic proteins determined by Lopez-Campistrous [28] using 2D PAGE
methodology and Gevaert [26] using LC-MS was undertaken using the
Supplementary Data in their publications with the Riley et al. annotation [2].
with the cytoplasmic membrane, but have Tat leaders (e.g.
DmsA, YnfE, YnfF), proteins that are associated with the outer
membrane (e.g. OmpA, OmpG, OmpT) and proteins that are
clearly on the cytoplasmic side of the membrane (e.g. FrdA).
Nonetheless, the consortium database provides the best estimate
of the number of periplasmic proteins.
4.2. Experimental periplasmic proteome
E. coli is a facultative anaerobe that can grow in diverse
environments by inducing the synthesis of appropriate transporters and enzymes. The periplasmic protein composition can
respond to changes in the physiologic or environmental state
and we can expect that only a subset of the proteins in the
predicted periplasmic proteome will be seen in any one condition. Most of the studies reported are carried out with cells
grown in Neidhardt's glucose minimal medium.
Several studies of the soluble protein proteome of E. coli
have been carried out [35–37]. These studies did not distinguish
1704
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
Fig. 2. Venn diagram noting the overlapping and uniquely identified proteins in
the experimentally identified periplasmic proteins by Lopez-Campistrous (L)
and Gevaert (G) data as taken from Table 3 and [26,28].
between cytoplasmic and periplasmic proteins or localization.
In the Gevaert analysis (see above) [26] of 800 E. coli proteins
they found 39 periplasmic proteins using MASCOT and 42
periplasmic proteins using PROCORR. We have re-analyzed
their supplementary data and find 96 periplasmic proteins in
their total database (Table 3). This assumes that their peptide
assignments were correct.
In the large-scale 2D PAGE study of Lopez-Campistrous et
al. [28] 107 different proteins were identified on the gel of the
periplasmic fraction isolated by cold osmotic shock of which at
least 54 were periplasmic proteins based on the Riley et al.
database [2]. Fig. 2 is a Venn diagram, graphically depicting the
degree of overlap and uniquely identified proteins between the
Gevaert, and Lopez-Campistrous databases.
Hervey et al. (ASM Meeting 2006 Abstract K-125) used
differential isotope labeling with 14N and 15N minimal medium
coupled with LC-MS-MS to identify periplasmic proteins
released by cold osmotic shock. They analyzed 667 proteins
and found 103 with a high periplasmic:whole cell mass ratio. Of
these 39 were annotated as periplasmic in Swissprot, an
additional 43 contained a signal sequence based on various
prediction algorithms and 21 of the 103 were membrane
proteins. Twenty-one proteins were cytoplasmic and presumably arose from cell lysis. Twelve known periplasmic proteins
had low periplasmic:whole cell mass ratios either because they
were associated with the membrane or had unusual turnover
properties.
5. The cytoplasmic membrane proteome
5.1. Predicted cytoplasmic membrane proteome
The cytoplasmic membrane (inner membrane or plasma
membrane) retains the cytoplasm and separates it from the
surrounding environment. This membrane also serves as a
selectively permeable barrier: it allows particular ions and
molecules to pass, either into or out of the cell, while
preventing the movement of others. The bacterial cytoplasmic
membrane is the location of a variety of crucial metabolic
processes, such as respiration and the synthesis of lipids and
cell wall constituents. Finally, the membrane contains special
receptor molecules that help bacteria detect and respond to
chemicals in their surroundings.
Based on protein diversity the cytoplasmic membrane is by
far the largest and most complex compartment of the envelope.
As for the other compartments, the protein composition of the
membrane will change with physiologic and environmental
conditions. It is useful to define all the open reading frames on
the genome that can be membrane-bound and several groups
have developed algorithms to search the genomic sequence for
potential integral membrane proteins. Defining what is a
membrane-associated protein has been the subject of infinite
debate [38]. The simple situation involves those proteins that
traverse the membrane as an α-helical bundle or contain a lipid
anchor. Far more difficult is defining peripheral or extrinsic
proteins that are associated with the membrane. There will be
proteins that migrate from cytoplasm to membrane due to posttranslational modifications or physiologic changes. An example
is proline dehydrogenase, PutA [39,40] that migrates to the
membrane upon reduction. Early studies by the Corbin et al.
[36] group attempted to define a database of known and predicted membrane proteins by combining data from the E. coli
entry point (http://coli.berkeley.edu/cgi-bin/ecoli/coli_entry.pl)
(156 proteins), proteins listed as membrane proteins in the
GenProtEC database (http://genprotec.mbl.edu) (634 proteins),
or proteins that contained at least two transmembrane (TM)
helices by the PHD algorithm [41] (821 proteins). This created
a database of 1017 proteins that occurred in at least one of the
databases. The GenProt database includes integral membrane
proteins as well as some extrinsic proteins that are part of
multi-subunit respiratory complexes, but it is not comprehensive. For example, FrdA and FrdB, extrinsic subunits of the
fumarate reductase complex [42] are not included, but DmsA
and DmsB, extrinsic subunits of the DMSO reductase complex
[43], are included. The extrinsic components of ATP Binding
Cassette (ABC) transporter class of membrane proteins [44]
are generally not included. Furthermore, some proteins, known
to be membrane-associated through hydrophobic interactions
such as Dld, D-lactate dehydrogenase [45] or GlpD, glycerol-3phosphate dehydrogenase [46] are defined as cytoplasmic in all
of the databases.
Further complicating the creation of a database, many of the
proteins identified in the Corbin et al. database [36] may not
be associated with the membrane. This is because single
transmembrane-spanning proteins can often be confused with
proteins that will be exported through the Sec [47] or Tat [48]
translocons to the periplasm. These proteins have an amino
terminal leader that can mimic a transmembranal helix [49].
Proteins with two or more helices are more readily identified.
Wallin and von Heijne [50] carried out a statistical analysis of
helix bundle proteins in many organisms including E. coli.
They found that 20–30% of all the open reading frames in an
organism code for α-helical membrane proteins. In E. coli the
value ranges from 24% (∼ 1028 proteins) using two “certain”
transmembrane helices for the analysis to 40% (∼ 1714
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
proteins) for proteins with two “putative” or “certain” transmembrane helices.
The Riley et al. [2] database identified 757 integral membrane proteins, 144 membrane anchored proteins, 12 inner
membrane lipoproteins and 11 membrane associated lipoproteins that could be on the inner membrane.
5.2. Respiratory complexes
While multi-spanning integral membrane proteins and lipoproteins can be identified by machine searching techniques, the
proteome also contains many extrinsic polypeptides that are
hydrophilic subunits of respiratory chain complexes. This
problem was noted above in consideration of the GenProt
database. The subunits of respiratory complexes should be considered part of the membrane proteome, as they will be found in
experimental proteomic analysis.
The structures of the succinate dehydrogenase (SdhCDAB)
complex [51], the fumarate reductase (FrdABCD) complex
[42,52], the formate dehydrogenase complex [53], and the
nitrate reductase complex [5] have all been determined by X-ray
crystallographic techniques and each is composed of hydrophilic subunits bound to hydrophobic, membrane-integral,
anchoring subunit(s). These hydrophilic subunits are exposed
to the cytoplasmic or periplasmic side of the membrane and are
clearly part of the membrane proteome. Unfortunately, they are
often annotated as cytoplasmic or periplasmic, depending on the
side of the membrane to which they are bound, as in the Riley et
al. database [2]. This analysis can still be open to error. For
example, FrdA which has been shown in many publications to
be on the cytoplasmic side of the membrane [42,52] is listed as
periplasmic [2]. Similar problems arise with subunits of ATP
Binding Cassette transporters.
A further complication of determining the membrane proteome relates to soluble cytoplasmic proteins that will interact
with membrane proteins through various types of functional
association or metabolons. These peripheral membrane proteins
will only be identified by experimental analysis of the proteome
or tagging and immunologic precipitation studies [54]. These
functional associations can be very important. For example, in
the red blood cell the soluble enzyme catalase is associated with
the Band III chloride/bicarbonate exchanger [55] to form a
metabolon. Current efforts often utilize a Na carbonate wash to
remove peripheral proteins [7]. Our view is that these associated
proteins must be considered part of the proteome and as methodologies improve it will be possible to analyze these peripheral
proteins in detail.
5.3. Experimental cytoplasmic membrane proteome
Until recently, analysis of the membrane proteome relied on
2D PAGE and this method identified primarily hydrophilic
polypeptides associated with the membrane. It failed to identify
many integral proteins. This is the result of difficulty in solubilizing these proteins in a detergent suitable for isoelectric
focusing in the first dimension and problems with in situ proteolysis of the proteins to obtain peptides for MS analysis.
1705
In the Gevaert analysis [26] of 800 E. coli proteins they
found 56 IMPs using MASCOT and 68 IMPs using PROCORR
(13% of the proteins in their database (69/525). In the largescale analysis of the E. coli proteome carried out by LopezCampistrous et al. [28], 479 spots on the cytoplasmic membrane
gel could be identified by MALDI-TOF MS. This corresponded
to 164 unique proteins. When these proteins were characterized
as to subcellular location using the Riley annotation, we find 94
cytoplasmic, 16 periplasmic, 12 outer membrane, 2 outer membrane lipoproteins, 14 inner membrane, 16 membrane associated, 6 membrane lipoprotein and 3 unknown. Although only 30
proteins (18%) were identified as inner membrane or membrane-associated, twenty-five of the cytoplasmic proteins are
extrinsic components of respiratory complexes, F0F1 ATPase or
ABC transporters and six are membrane lipoproteins (19%).
Thus, 37% of the proteins identified were clearly associated with
the cytoplasmic membrane. Nonetheless, this study showed that
relatively few of the α-helical membrane proteins were
identified.
Corbin et al. [36] used SDS solubilization and an LC MS/MS
approach to analyze the membrane protein profile as part of a
global analysis of protein expression in E. coli. They reported a
“long list” of 1147 proteins with at least one peptide used for
identification. Of these, 287 were classified as membrane
proteins using their database of 1017 membrane proteins.
Yan et al. [56] used fluorescence difference 2D gel electrophoresis (2D-DIGE) with proteins differentially labeled with
Cy3 (control) and Cy5 (benzoic acid treatment of cells to reduce
the proton motive force) dyes. They analyzed the gels by
DeCyder software and studied 197 spots by tryptic digestion
and MALDI-TOF and QTOF mass spectrometry to identify the
proteins. Although they indicate that a number of membrane
proteins were identified, examination of their data indicates that
these are outer membrane or peripheral proteins.
In a recent study Ji et al. [57] compared N-terminal dimethylation after lysine guanidation (2MEGA labeling) in concert
with 2-dimensional LC MS/MS to analyze the membrane
proteome and reduce the number of false positive proteins. Six
hundred forty proteins were identified in the membrane fraction,
which included 258 membrane and membrane-associated
proteins. The labeling method resulted in 153 integral proteins
being identified compared to only 77 proteins in the unlabeled
sample.
Molloy et al. [58] used organic solvent extraction with
chloroform:methanol to try to enrich for integral membrane
proteins prior to 2D gel electrophoresis. This did not result in
the identification of any additional integral proteins. Zhang et al.
[59] used a combination of SDS and methanol assisted protein
solubilization with LC MS/MS to provide a further characterization of the membrane proteome. They identified 431
different proteins of which 217 (50%) were membrane intrinsic
(168 with two or more transmembrane α-helices) and an additional 55 were lipoproteins or components of membrane-bound
complexes. Additionally, 29 outer membrane proteins were
identified in this analysis.
Daley et al. [49], Granseth et al. [60] have taken a different
approach to study the E. coli proteome. Although about 1000
1706
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
proteins have at least two transmembrane helices, they studied
737 inner membrane proteins that traverse the membrane at least
twice and are greater than 100 amino acids in length. Each open
reading frame was tagged at the C-terminus with alkaline
phosphatase and green fluorescent protein. Over six hundred
membrane proteins could be expressed and their topology
determined. A tagging approach has great benefit for determining
the subcellular localization of each protein and in many cases will
provide functional information. For example, it has been possible
to localize cell division proteins within the bacterial cell [61],
However, this approach has limitations as the chimeric proteins
are normally expressed from an inducible promoter. In order to
compare different metabolic, environmental or genetic variations
a detailed profile of proteins identifiable by MS techniques and/
or 2-dimensional PAGE will be required.
6. Comparison with microarray
Both Corbin et al. [36] and Zhang et al. [59] were able to
compare their protein profile with Affymetrix microarray data
of the transcriptome obtained under identical growth conditions.
In both studies there was a strong correlation between mRNA
expression level and protein identification indicating that the
most highly expressed proteins were found. Additional purification and LC techniques will be required to identify low
abundance proteins.
7. Methodology for membrane proteome analysis
Proteome analysis generally involves several steps including
cell lysis, protein extraction, protein separation, protein
digestion, mass spectrometric analysis of peptides, and data
processing for peptide and protein identification. Each step is
important and should be optimized to generate a comprehensive
profile of the E. coli membrane proteome. While there are many
sample handling protocols and analytical techniques available
to detect hydrophilic and readily soluble proteins, analysis of
hydrophobic membrane proteins is still a challenging task.
Fortunately, in the past several years, a number of research
groups have been actively involved in developing new and
improved analytical tools to characterize the membrane
proteome of cells, tissues, and other samples. Good progress
has been made for membrane proteome analysis. Characterization of the membrane proteome of a relatively simple
microorganism, E. coli, is one of the excellent ways of judging
the performance of a newly developed technique. Although
techniques described below may not directly involve the
analysis of the E. coli membrane proteome, they should be
applicable to E. coli samples with or without any modifications.
There are mainly two major analytical platforms commonly
used for analyzing membrane proteomes. One uses gel electrophoresis for proteome display followed by in-gel digestion of
protein spots of interest and mass spectrometric analysis of the
resultant peptides for peptide and protein identification (i.e.,
gel-based method) [7,21,38,62–65]. Another platform involves
the digestion of a proteome with little or no separation at the
protein level followed by liquid chromatography (LC) separa-
tion of the complex peptide mixture and mass spectrometric
sequencing of individual peptides for identification with electrospray ionization (ESI) [66] or MALDI [67] (i.e., solutionbased method). As discussed below, each platform has its pros
and cons and they often provide complementary information on
a proteome. While technical advances in both platforms have
been made for membrane proteome analysis, the solution-based
method has gained popularity in the past few years due to its
impressive proteome coverage and rapid quantification power.
7.1. Gel-based proteome analysis platform
There are several ways of implementing the gel-based
membrane proteome analysis platform [38]. Isoelectric focusing
(IEF) of proteins can be carried out using a solution-based IEF
system [68–71] or an immobilized IEF gel strip to provide the
first dimension protein separation. Polyacrylamide gel electrophoresis (PAGE) can then be employed to separate the proteins
further according to their molecular sizes. The combination of
IEF and PAGE (i.e. 2D-PAGE) provides an efficient means of
separating proteins from a complex proteome sample. Because
of the high resolving power, particularly for separating proteins
with different conformers or different degrees of modifications,
2D-PAGE is an excellent tool to unravel subtle changes in the
proteome. For example, it is possible to monitor the change of
posttranslational modifications of proteins from cells of different states that is difficult or impossible to detect using the
solution-based platform [56].
Applying 2D-PAGE to separate very hydrophobic proteins
such as integral membrane proteins is challenging. Solubilization of these proteins requires the use of strong surfactants, such
as SDS which are not compatible with IEF. Several zwitterionic
detergents have been shown to improve the performance of 2DPAGE separation of integral membrane proteins [38,72,73].
However, hydrophobic proteins may precipitate at their isoelectric points during the IEF separation process, resulting in
sample loss and difficulty for second dimension separation. 2DPAGE analysis of the yeast, tobacco or Arabidopsis proteome
revealed that many integral transmembrane proteins could not be
identified [64]. To overcome the problem of IEF, Zahedi et al.
reported an interesting technique based on the use of cationic
detergent benzyldimethyl-n-hexadecylammonium chloride for
the first dimension protein separation followed by anionic
detergent SDS-PAGE separation ([74]). While separation of
several integral membrane proteins was demonstrated, it remains
to be seen whether this technique offers sufficient separation
power for a complicated membrane proteome. It appears that
1D-SDS-PAGE is at present a better choice for handling very
hydrophobic membrane proteins, albeit with reduced resolving
power compared to 2D-PAGE [75].
One of the major advantages of the gel-based method for
proteome analysis is that it can provide quantitative information
on proteins directly, offering a convenient means of comparing
proteomes of different samples. If protein isoforms and modified proteins are resolved in the gel, they can be profiled and
relative distributions of these proteins can potentially be correlated with a certain phenotype of the samples to study their
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
functions. To generate quantitative information, gels are often
stained with Coomassie blue or silver. Fluorescence dyes are
being increasingly used for protein display. Differential display
and analysis of relative abundance changes of two proteomes is
possible by labeling individual proteomes with different dyes
and mixing of the labeled proteins followed by gel separation
and fluorescence detection of labeled proteins with different
wavelengths in a gel [56]. These staining methods are compatible with mass spectrometric analysis, providing high quality
reagents are used (i.e., using mass spectrometry compatible
reagents available from leading gel electrophoresis suppliers).
Radiolabeling or Western blotting of proteins of interest can be
more sensitive than silver or fluorescence staining methods
for detecting proteins in a gel, but the sensitivity of mass
spectrometric techniques may not be sufficient to identify these
proteins.
To identify the proteins detected in a gel by mass spectrometric techniques, all gel spots (e.g., in qualitative proteome
profiling) or a selected few spots of interest (e.g. only the
proteins showing significant changes in relative quantification
of proteomes) are excised and subjected to in-gel digestion with
an enzyme, such as trypsin, or a chemical reagent, such as
cyanogen bromide (CNBr). Extracting or blotting proteins from
a gel band to a solution for digestion is less efficient than in-gel
digestion where proteins are degraded into peptides which can
be more readily extracted into a solution than the intact proteins.
As expected, in-gel digestion of membrane proteins and subsequent sample preparation for mass spectrometric analysis are
much more difficult than in the case of analyzing hydrophilic
proteins. The standard trypsin digestion protocol can be applied
to membrane proteins where trypsin digestion can take place in
the gel and the resulting peptides effectively extracted for MS
analysis. However, the method would fail if digestion is inefficient due to lack of cleavage sites or lack of accessibility of
the protease to the cleavage sites, such as those within the
membrane-spanning segments of a transmembrane protein. The
method would also fail if the resulting peptides are too large or
too hydrophobic to be extracted or to be analyzed by MS and
MS/MS analysis.
New digestion and sample handling protocols are being
developed to tackle these problems. For example, van Montfort
et al. reported a protocol involving sequential in-gel trypsin and
CNBr digestion [76] in which larger fragments generated from
in-gel trypsin digestion were further digested by CNBr to yield
smaller peptides which could be more efficiently extracted for
MS analysis. Quach et al. have developed an improved in-gel
digestion protocol that involves the use of CNBr digestion,
followed by further trypsin digestion [77]. The chemical
cleaving reagent CNBr is expected to interact readily with a
membrane protein, compared to a protease which is bulky and
must retain its optimal conformer for activity. CNBr selectively
cleaves methionine, but due to the low number of methionine in
residues in proteins, CNBr cleavage produces a small number of
large peptide fragments with MW typically N 2000 Da that are
also difficult to extract from gel pieces with high efficiency. In
addition, these large peptides cannot be readily fragmented to
produce MS/MS spectra for protein identification. To generate a
1707
larger number of small peptides than can be obtained using
CNBr alone, trypsin can be used to digest further the CNBrcleaved fragments. The protocol has been applied for the
analysis of bacteriorhodopsin, nitrate reductase 1 gamma chain
(NarI E. coli), and a complex protein mixture extracted from the
endoplasmic reticulum membrane of mouse liver [77]. In these
instances it was demonstrated that the sensitivity of membrane
protein identification is in the low picomole regime that is
compatible with Coomassie staining of gel-spots.
Mass spectrometric analysis of the extracted peptides from gel
spots can be done on a variety of instruments. When 2D-PAGE is
used for protein separation, an individual spot may contain a
protein with relatively high abundance plus a few other minor
components. As long as one protein is a dominant species in a
spot, peptide mass mapping or fingerprinting (PMF) can be
applied to identify the protein. In PMF, the peptide extract is
analyzed directly by MALDI or ESI MS. Peptide masses determined can be searched against a proteome database comprised of
protein names, protein sequences and their predicted peptide
fragments after enzyme digestion with known specificity. In the
case of trypsin digestion, expected sequences and masses of
tryptic peptides from cleavage of terminal lysine or arginine
residues can be readily generated a priori for any given protein in
a proteome whose sequence is known. By comparing the number
and quality of peptide mass matching between the experimental
data and the predicted peptides in the database, one can often
identify a protein with high confidence. Several search engines
are available to perform PMF. For example, the web-based, free
search engine MASCOT can be used to perform quickly PMF and
to generate a report of protein candidates along with confidence
levels of the matches [78]. PMF works well if many peptides are
detected from a protein and the mass measurement accuracy for
determining the peptide mass is moderately high. It is a rapid
method and does not require the use of sophisticated mass
spectrometric instrumentation. However, if only one or two
peptides are detected from a gel spot or the gel spot contains more
than one dominant protein, PMF would fail to arrive at a unique
match. In this case, a more powerful technique, tandem mass
spectrometry or MS/MS, is required for protein identification.
There are a number of different types of tandem mass spectrometric instruments being used to produce MS/MS spectra. In
general, they start with the generation of a mass spectrum of the
peptides (MS mode), followed by selecting a peptide ion and
subjecting it to collision-induced dissociation using a collision
gas introduced to the mass spectrometer (MS/MS mode). The
intensities and mass-to-charge values (m/z's) of the fragment
ions are recorded to produce a MS/MS spectrum. Peptide ion
fragmentation takes place mainly from the breakage of the
amide bonds between amino acids. Although a MS/MS spectrum usually does not contain complete amino acid sequence
information for de novo sequencing of a peptide, it contains
partial information on peptide sequence. For protein identification, an un-interpreted MS/MS spectrum can be entered into a
database search engine where the fragment ion masses (some
search engine also considers intensities) are searched against the
predicted fragment ion spectra of individual peptides of protein
digests for possible matches.
1708
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
From the automated MS/MS database search, a report is
generated including matched peptide sequences from a given
MS/MS spectrum and their corresponding proteins along with
matching scores. A probability-based matching algorithm has
been introduced recently in several search engines to provide a
statistical evaluation of the fragment ion and sequence matches.
For example, in the MASCOT search engine, for each peptide
sequence matched to a MS/MS spectrum, a matching score as
well as a threshold score is listed [78]. The threshold score defines
the minimum score required to call a positive identification within
a confidence level (e.g., N95% certainty). For a peptide with a
matching score of slightly above the threshold (e.g., confidence
level of between 95% and 99%), manual comparison of the MS/
MS spectrum with the predict fragmentation pattern of the peptide
ion may be carried out to confirm or discard the peptide match
from the automated database search step.
MS/MS spectral acquisition can be carried out in conjunction
with liquid chromatography (LC) separation of the extracted
peptides from a gel spot. Either LC-ESI or LC-MALDI MS and
MS/MS or both may be used for analyzing a sample [67]. This
is particularly important for identifying proteins separated only
by one-dimensional gel electrophoresis. A gel band from 1DPAGE may contain many proteins. Thus, the rapid and simple
PMF method would fail to generate unique protein identification. The peptide sample from a 1D gel band is a complex
mixture. Direct MS/MS analysis of the mixture may result in the
detection of only a few readily detectable peptides (i.e. high
abundance and more ionizable peptides) due to an ion suppression effect, i.e., a few peptides dominating the spectrum at
the expense of other low abundance and less ionizable peptides.
Peptide separation by LC can significantly reduce the ion
suppression effect, resulting in the detection of a greater number
of peptides from a complex mixture [79].
To summarize the application of the gel-based platform for
E. coli membrane proteome analysis, it has been shown that,
if the membrane proteins are amenable to 2D-PAGE separation,
relative quantification of proteomes can be carried out at the
protein level with the possibility of examining proteins with
different conformers or modifications. Protein identification can
be carried out by in-gel digestion, peptide extraction and PMF
or MS/MS of the peptides. If only 1D-PAGE can be used to
separate the membrane proteins, due to its limited separation
power, individual gel bands would contain mixtures of proteins,
making quantitative analysis of individual proteins difficult.
Identification of proteins residing in a gel band can be done by
using in-gel digestion, peptide extraction and MS/MS or LC
MS/MS of the peptide mixture.
7.2. Solution-based proteome analysis platform
In the past several years, a solution-based or gel-free technical platform has been developed for membrane proteome
analysis [79]. In the solution-based method, the entire protein
mixture is first digested and the resulting peptides analyzed by
LC MS/MS. In applying this method to the membrane proteome, protein solubilization and digestion must proceed with
high efficiency. Due to the hydrophobic nature of membrane
proteins, dissolving all proteins in a solvent system that is
comparable with enzymatic or chemical protein digestion can
be challenging. An efficient means of degrading proteins into
peptides of suitable length for LC separation and MS analysis is
also vital to the success of the solution-based platform.
Among the reported protein digestion methods, trypsin digestion is still the most commonly used due to trypsin's relatively
high enzyme specificity. Trypsin generates peptides of near ideal
size (b 30 residues containing basic amino acids Arg and/or Lys)
for MS and MS/MS and digestion can be carried out as long as the
solvent system used to dissolve the membrane proteins does not
totally denature the trypsin. Ammonium bicarbonate buffer is
widely used to dissolve soluble proteins and may dissolve a
membrane protein containing extensive hydrophilic moieties. To
increase the solubility of membrane proteins, surfactants can be
added to a solution. For example, 0.5% SDS has been used by
Han et al. to solubilize a membrane-enriched microsomal fraction,
followed by trypsin digestion in dilute SDS solution [80].
However, surfactants may adversely affect the digestion process.
For example, 1% SDS, a strong ionic surfactant, can be used to
dissolve many membrane proteins, but trypsin will be denatured
at this high concentration of SDS, making its useless for digestion.
However, by diluting the membrane protein solution to about
0.1% SDS, protein digestion can proceed with reasonably good
efficiency [59]. A cleavable detergent, 3-[3-(1,1-bisalkyloxyethyl)pyridin-1-yl]propane-1-sulfonate (PPS) is compatible
with trypsin digestion and has been useful to dissolve membrane
proteins [81]. Wu et al. reported a method for comprehensive
membrane protein analysis using non-specific Proteinase K for
digestion and subsequent analysis by LC-ESI MS/MS [66].
Both organic acids (e.g. trifluoroacetic acid (TFA)) and organic
solvents (e.g. methanol) have been reported to be effective in
dissolving membrane proteins. Washburn et al. developed a
surfactant-free method that used 90% formic acid to solubilize
proteins in the presence of CNBr, with further enzymatic
digestion of the CNBr-cleaved protein fragments by LysC and
trypsin [82]. Recent studies suggest that trypsin is functional for
digestion with a methanol concentration of up to about 65% [83–
87]. Thus, a high concentration of methanol can be used to
solubilize membrane proteins, followed by trypsin digestion and
this technique has been reported to be useful for membrane
proteome analysis [85–87]. The use of an organic solvent, such as
60% methanol, to solubilize membrane proteins has one major
advantage compared to the use of a solvent system containing a
strong surfactant, such as SDS. The organic solvent can be readily
removed after protein digestion, while a strong surfactant, such as
SDS, is difficult to remove.
The advantage of SDS over organic solvents is that it can
dissolve a wider range of proteins, including misfolded and
precipitated proteins. As SDS can degrade the performance of
reversed-phase separations and MS analyses of peptides, the
remaining SDS in the digested peptide sample must be removed
by using an ion-exchange column. In a solution-based method,
where two-dimensional (2D) peptide separation is used, the first
dimension of separation is generally based on ion-exchange
chromatography. Thus, SDS removal from the digested peptide
sample is integrated into the first-dimensional peptide separation.
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
Zhang et al., compared the ability of 60% methanol and 1%
SDS to dissolve the inner membrane fraction of an E. coli K12
cell lysate [59]. By using trypsin digestion and 2D-LC ESI MS/
MS they found that 358 proteins (1417 unique peptides) and 299
proteins (892 peptides) were identified from the methanolsolubilized protein mixture and the SDS-solubilized sample,
respectively. The methanol method detected more hydrophobic
peptides, resulting in a greater number of proteins identified, than
the SDS method. 159 out of 358 proteins (44%) detected by SDS
solubilization and 120 out of 299 proteins (40%) detected by
methanol solubilization were integral membrane proteins. Of a
total of 190 integral membrane proteins 70 were identified
exclusively in the methanol-solubilized sample, 89 were
identified by both methods, and only 31 proteins were exclusively
identified by the SDS method. In addition, it was determined that
the protein solubilization potential of SDS or methanol was not
the crucial parameter determining overall performance of each
method but rather the compatibility of these two reagents with
protein digestion, downstream peptide separation and ESI-MS
analysis was critical for their analytical performance. The better
compatibility of the methanol method with 2D-LC-MS/MS
resulted in a higher number of total peptide and protein identification, higher reproducibility and pronounced bias of the
method towards hydrophobic peptide and integral membrane
proteins. Using the combined datasets obtained from the two
methods, it was shown that there was no bias in the experimental
proteome compared to the predicted membrane proteome [59].
To achieve maximum digestion efficiency, Ji et al. applied
two consecutive digestion steps for the analysis of the E. coli
membrane proteome [57] in which membrane proteins were
first dissolved in 60% methanol and digested with trypsin for
5 h. In a second step un-dissolved protein was pelleted and resuspended in 0.05% SDS with trypsin and digested overnight.
Both digests were pooled and subjected to LC-ESI MS/MS
analysis.
When enzymes or chemical reagents are used to degrade
membrane proteins, an important requisite for these protein
degradation methods to work is that the proteins to be degraded
must be dissolved in a suitable solution. Unfortunately, during
the protein sample workup, many proteins may become highly
de-natured and are not readily soluble in any solvent including
that containing strong surfactant. As a consequence, these proteins are not detected by the solution-based proteome analysis
method. Zhong et al. have recently described a method that does
not require the use of a solvent to solubilize proteins by using
microwave-assisted acid hydrolysis (MAAH) [88] in 25%
aqueous TFA to degrade proteins into peptides for MS characterization [89]. Compared to enzymatic digestion, MAAH is
fast and detergent-free. It involves a simple sample handling
process and there are no background peptides, such as those
from protease autolysis in enzyme digestion, introduced in
MAAH. MAAH is particularly useful in dealing with membrane
proteome samples of cultured cells and tissue samples. The
application of this method, in combination with LC-MALDI MS
and MS/MS, was illustrated in the analysis of membrane
proteins isolated from a human breast cancer cell line [89] and a
heart tissue sample [Mulu et al., submitted].
1709
Considering the complexity of the membrane proteome, it
appears that one method of solubilization and digestion cannot
be universally applied to handle all proteins. A combination
of several complementary methods may result in greater
proteome coverage. More recently, Wang et al. reported a
sequential protein solubilization and digestion protocol for
zebrafish liver proteome analysis [90]. In their work, it was
found that, after dissolving the protein pellet from the liver
tissue extracts in a basic buffer and subjecting it to trypsin
digestion, a non-soluble residue remained in the vials. The
residue was subjected to additional levels of digestion, first
by methanol-assisted trypsin digestion, followed by SDSassisted trypsin digestion and finally by the MAAH method.
The peptide mixtures were pooled and subjected to strong
cation exchange chromatographic separation, followed by a
reversed-phase column and LC-ESI MS/MS analysis. Proteome analysis using the combined solubilization/digestion
methods led to the identification of 1204 unique proteins.
Among the 1204 proteins identified, 224 (19%) were found
in all three samples, while 113 (9%), 420 (35%), and 214
(18%) proteins or related protein groups were uniquely
observed in buffer/methanol digest, SDS digest, and MAAH
digest, respectively.
In the solution-based method, relative quantification of
proteomes of different samples is commonly done using stable
isotopic labeling of proteins or peptides [80]. One sample is
labeled with a light isotope and another one with a heavy isotope.
Isotope incorporation can be achieved at the protein level during
cell growth using either an isotope-enriched minimal medium or
conventional medium plus isotope-labeled amino acids [91,92].
This is followed by enzyme or chemical digestion of labeled
proteins to generate isotope-tagged peptides. Note that this
labeling strategy works only for cells that can grow in the special
culture medium and cannot be readily used for other samples,
such as tissues or body fluids. An alternative strategy is to use
chemical derivatization to attach an isotope tag to the digested
peptides of a proteome. In both strategies, the isotope labeled
peptides from a mixture of light- and heavy-isotope-labeled
samples produce pairs of peaks in a mass spectrum when the
mixture is analyzed using the MS scan mode. The relative
intensities of a pair of peaks can be used to determine the
abundance change of the peptide and its corresponding protein.
MS/MS spectra of the peptide pair can be generated for peptide
and protein identification.
There are many isotope labeling reactions reported for relative proteome quantification. Among them, the isotope-coded
affinity tag (ICAT) approach pioneered by Aebersold and
coworkers [80,93–95] has been extensively used. The main
advantage of this method is that it enriches peptides containing
the rare amino acid cysteine, thereby significantly reducing the
complexity of the peptide mixture and increasing the dynamic
range of MS analysis. On the other hand, the use of the ICAT
reagents fails for quantification of cysteine-free proteins. In
addition, the ICAT reagents are structurally complex and thus
the cost of the reagents is high. As alternatives to ICAT, other
chemical labeling protocols of peptides after protein digestion
have been developed and recently reviewed [96].
1710
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
Simple labeling chemistry and inexpensive reagents are
important in applying the isotope labeling approach for relative
quantification of proteomes. As an example, H218O can be used
in protease digestion to introduce the 18O tag through the hydrolysis reaction to label all proteolytic peptides uniformly at the
C-terminus [97–99]. Another example is differential dimethyl
labeling of N-termini and α-amino groups of lysine residues of
tryptic peptides with d(0)- or d(2)-formaldehyde [100–102].
Dimethyl labeling combined with LC-MALDI MS and MS/MS
has been applied to determine the proteins differentially expressed
between an E-cadherin-deficient human carcinoma cell line
(SCC9) and its transfectants expressing E-cadherin (SCC9-E)
[103]. A total of 5480 peptide pairs were examined and 320 of
them showed relative intensity changes of greater than 2-fold
which led to the identification of 49 differentially expressed
proteins. More recently, Ji et al. reported a modified N-terminal
dimethyl labeling strategy, in which the N-termini of tryptic
peptides are differentially labeled with either d(0),12C-formaldehyde or d(2),13C-formaldehyde after lysine residues in peptides
are blocked by guanidination [57]. Guanidination is known to be
effective for selective labeling of lysine residues in peptides [104].
It has been demonstrated that N-terminal dimethylation (2ME)
after lysine guanidination (GA) or 2MEGA provides uniform 6Da differential isotope tags on peptides that facilitates protein
identification and quantification [57].
In summary, with the development of new protocols for solubilizing and digesting membrane proteins as well as improved
isotope labeling chemistry, the solution-based method will play an
increasingly important role in membrane proteomics. It is
anticipated that this method, combined with multi-dimensional
LC separation techniques, will allow us to examine the E. coli
membrane proteome with unprecedented coverage and accuracy.
In this review we have taken the protein identifications as
published by the investigators. In many studies confidence
limits are not presented and the number of false positives is not
listed. In the Gevaert study [26], they indicate that of the 689
proteins identified using the PROCORR algorithm approximately 42 false positives are obtained at the 98% confidence
limit. This rises to 69 false positives at 95% confidence. The
issues of variation in mass spectrometry technology and identification algorithms need to be addressed so that readers can
have confidence in the data presented. As proteomics moves to
rely on increased quantitative analysis this information will
become even more important.
8. Future perspectives
Membrane proteomic studies are rapidly progressing with
ever more different proteins identified with increasing levels of
confidence. Future studies will utilize newly devised quantitative methods to monitor the membrane proteome and how it
changes as the physiologic or environmental conditions change
[105,106]. The turnover of membrane proteins will be
investigated providing the ability to correlate mRNA turnover
with protein turnover. It will also be possible to monitor posttranslational modifications of membrane proteins. More
complex will be the identification of metabolons where
historically “soluble” cytoplasmic proteins will be shown to
be part of functional complexes on the membrane. New
nanotechniques along with rapid proteomic approaches will
allow the characterization of bacterial infections without the
need to wait for cultures to grow in the clinical laboratory.
Proteomics is already being used to identify increased
expression of proteins in the clinical situation and the formation
of biofilms [107]. The techniques pioneered with the model
organism E. coli will be applied to the plasma membranes of
eukaryotic cells and the membranes of organelles.
Acknowledgements
Research in the authors' laboratories is supported by the
Canadian Institutes of Health Research and the Natural Sciences
and Engineering Research Council of Canada. JHW is a Canada
Research Chair in Membrane Biochemistry, LL is a Canada
Research Chair in Analytical Chemistry. JHW would like to
thank the Alberta Heritage Foundation for Medical Research for
support during the writing of this review and Dr. Fraser
Armstrong and the Laboratory of Inorganic Chemistry at
Oxford University. We thank Philip Winter for preparation of
the Venn diagrams.
References
[1] C. Holden, Cell biology. Alliance launched to model E. coli, Science
297 (2002) 1459–1460.
[2] M. Riley, T. Abe, M.B. Arnaud, M.K. Berlyn, F.R. Blattner, R.R.
Chaudhuri, J.D. Glasner, T. Horiuchi, I.M. Keseler, T. Kosuge, H. Mori,
N.T. Perna, G. Plunkett III, K.E. Rudd, M.H. Serres, G.H. Thomas, N.R.
Thomson, D. Wishart, B.L. Wanner, Escherichia coli K-12: a cooperatively developed annotation snapshot-2005, Nucleic Acids Res. 34
(2006) 1–9.
[3] P. Owen, H.R. Kaback, Antigenic architecture of membrane vesicles from
Escherichia coli, Biochemistry 18 (1979) 1422–1426.
[4] P. Dickie, J.H. Weiner, Purification and characterization of membranebound fumarate reductase from anaerobically grown Escherichia coli,
Can. J. Biochem. 57 (1979) 813–821.
[5] M.G. Bertero, R.A. Rothery, M. Palak, C. Hou, D. Lim, F. Blasco, J.H.
Weiner, N.C. Strynadka, Insights into the respiratory electron transfer
pathway from the structure of nitrate reductase A, Nat. Struct. Biol. 10
(2003) 681–687.
[6] I. Yamato, M. Futai, Y. Anraku, Y. Nonomura, Cytoplasmic membrane
vesicles of Escherichia coli. II. Orientation of the vesicles studied by
localization of enzymes, J. Biochem. (Tokyo) 83 (1978) 117–128.
[7] M.P. Molloy, N.D. Phadke, J.R. Maddock, P.C. Andrews, Twodimensional electrophoresis and peptide mass fingerprinting of bacterial
outer membrane proteins, Electrophoresis 22 (2001) 1686–1696.
[8] H.C. Neu, L.A. Heppel, The release of enzymes from Escherichia coli by
osmotic shock and during the formation of spheroplasts, J. Biol. Chem.
240 (1965) 3685–3692.
[9] G.F. Ames, K. Nikaido, Two-dimensional gel electrophoresis of
membrane proteins, Biochemistry 15 (1976) 616–623.
[10] F.R. Blattner, G. Plunkett III, C.A. Bloch, N.T. Perna, V. Burland, M.
Riley, J. Collado-Vides, J.D. Glasner, C.K. Rode, G.F. Mayhew, J. Gregor,
N.W. Davis, H.A. Kirkpatrick, M.A. Goeden, D.J. Rose, B. Mau, Y.
Shao, The complete genome sequence of Escherichia coli K-12, Science
277 (1997) 1453–1474.
[11] V. Braun, Covalent lipoprotein from the outer membrane of Escherichia
coli, Biochim. Biophys. Acta 415 (1975) 335–377.
[12] W. Vollmer, J.V. Holtje, Morphogenesis of Escherichia coli, Curr. Opin.
Microbiol. 4 (2001) 625–633.
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
[13] J. Kyte, R.F. Doolittle, A simple method for displaying the hydropathic
character of a protein, J. Mol. Biol. 157 (1982) 105–132.
[14] C.P. Chen, A. Kernytsky, B. Rost, Transmembrane helix predictions
revisited, Protein Sci. 11 (2002) 2774–2791.
[15] E.L. Sonnhammer, G. von Heijne, A. Krogh, A hidden Markov model for
predicting transmembrane helices in protein sequences, Proc. Int. Conf.
Intell. Syst. Mol. Biol. 6 (1998) 175–182.
[16] A. Krogh, B. Larsson, G. von Heijne, E.L. Sonnhammer, Predicting
transmembrane protein topology with a hidden Markov model:
application to complete genomes, J. Mol. Biol. 305 (2001) 567–580.
[17] F.S. Berven, K. Flikka, H.B. Jensen, I. Eidhammer, BOMP: a program to
predict integral beta-barrel outer membrane proteins encoded within genomes
of Gram-negative bacteria, Nucleic Acids Res. 32 (2004) W394–W399.
[18] A. Pautsch, G.E. Schulz, High-resolution structure of the OmpA membrane domain, J. Mol. Biol. 298 (2000) 273–282.
[19] S.K. Buchanan, B.S. Smith, L. Venkatramani, D. Xia, L. Esser, M. Palnitkar,
R. Chakraborty, D. van der Helm, J. Deisenhofer, Crystal structure of the
outer membrane active transporter FepA from Escherichia coli, Nat. Struct.
Biol. 6 (1999) 56–63.
[20] H.C. Wu, Biosynthesis of lipoproteins, American Society for Microbiology, 1996.
[21] M.P. Molloy, B.R. Herbert, M.B. Slade, T. Rabilloud, A.S. Nouwens, K.L.
Williams, A.A. Gooley, Proteomic analysis of the Escherichia coli outer
membrane, Eur. J. Biochem. 267 (2000) 2871–2881.
[22] A.G. Garrow, A. Agnew, D.R. Westhead, TMB-Hunt: an amino acid
composition based method to screen proteomes for beta-barrel transmembrane proteins, BMC Bioinformatics 6 (2005) 56.
[23] S. Rey, M. Acab, J.L. Gardy, M.R. Laird, K. deFays, C. Lambert, F.S.
Brinkman, PSORTdb: a protein subcellular localization database for
bacteria, Nucleic Acids Res. 33 (2005) D164–D168.
[24] M. Chevallet, V. Santoni, A. Poinas, D. Rouquie, A. Fuchs, S. Kieffer, M.
Rossignol, J. Lunardi, J. Garin, T. Rabilloud, New zwitterionic detergents
improve the analysis of membrane proteins by two-dimensional
electrophoresis, Electrophoresis 19 (1998) 1901–1909.
[25] M. Fountoulakis, R. Gasser, Proteomic analysis of the cell envelope
fraction of Escherichia coli, Amino Acids 24 (2003) 19–41.
[26] K. Gevaert, J. Van Damme, M. Goethals, G.R. Thomas, B. Hoorelbeke,
H. Demol, L. Martens, M. Puype, A. Staes, J. Vandekerckhove,
Chromatographic isolation of methionine-containing peptides for gelfree proteome analysis: identification of more than 800 Escherichia coli
proteins, Mol. Cell. Proteomics 1 (2002) 896–903.
[27] M. Hirosawa, M. Hoshida, M. Ishikawa, T. Toya, MASCOT: multiple
alignment system for protein sequences based on three-way dynamic
programming, Comput. Appl. Biosci. 9 (1993) 161–167.
[28] A. Lopez-Campistrous, P. Semchuk, L. Burke, T. Palmer-Stone, S.J. Brokx,
G. Broderick, D. Bottorff, S. Bolch, J.H. Weiner, M.J. Ellison, Localization,
annotation, and comparison of the Escherichia coli K-12 proteome under
two states of growth, Mol. Cell. Proteomics 4 (2005) 1205–1209.
[29] M.E. Bayer, Zones of membrane adhesion in the cryofixed envelope of
Escherichia coli, J. Struct. Biol. 107 (1991) 268–280.
[30] E.M. Lai, U. Nair, N.D. Phadke, J.R. Maddock, Proteomic screening and
identification of differentially distributed membrane proteins in Escherichia coli, Mol. Microbiol. 52 (2004) 1029–1044.
[31] P. Marani, S. Wagner, L. Baars, P. Genevaux, J.W. de Gier, I. Nilsson, R.
Casadio, G.G. von Heijne, New Escherichia coli outer membrane
proteins identified through prediction and experimental verification,
Protein Sci. 15 (2006) 884–889 (2002 #74).
[32] J.D. Bendtsen, H. Nielsen, G. von Heijne, S. Brunak, Improved prediction
of signal peptides: SignalP 3.0, J. Mol. Biol. 340 (2004) 783–795.
[33] J.D. Bendtsen, H. Nielsen, D. Widdick, T. Palmer, S. Brunak, Prediction
of twin-arginine signal peptides, BMC Bioinformatics 6 (2005) 167.
[34] H. Nielsen, J. Engelbrecht, S. Brunak, G. von Heijne, Identification of
prokaryotic and eukaryotic signal peptides and prediction of their
cleavage sites, Protein Eng. 10 (1997) 1–6.
[35] A.J. Link, K. Robison, G.M. Church, Comparing the predicted and
observed properties of proteins encoded in the genome of Escherichia
coli K-12, Electrophoresis 18 (1997) 1259–1313.
[36] R.W. Corbin, O. Paliy, F. Yang, J. Shabanowitz, M. Platt, C.E. Lyons Jr., K.
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
1711
Root, J. McAuliffe, M.I. Jordan, S. Kustu, E. Soupene, D.F. Hunt, Toward a
protein profile of Escherichia coli: comparison to its transcription profile,
Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 9232–9237.
R.A. VanBogelen, K.Z. Abshire, B. Moldover, E.R. Olson, F.C.
Neidhardt, Escherichia coli proteome analysis using the gene-protein
database, Electrophoresis 18 (1997) 1243–1251.
V. Santoni, M. Molloy, T. Rabilloud, Membrane proteins and proteomics:
un amour impossible? Electrophoresis F 21 (2000) 1054–1070.
J.M. Wood, Membrane association of proline dehydrogenase in Escherichia coli is redox dependent, Proc. Natl. Acad. Sci. U. S. A. 84 (1987)
373–377.
E.D. Brown, J.M. Wood, Conformational change and membrane
association of the PutA protein are coincident with reduction of its
FAD cofactor by proline, J. Biol. Chem. 268 (1993) 8972–8979.
B. Futcher, G.I. Latter, P. Monardo, C.S. McLaughlin, J.I. Garrels, A
sampling of the yeast proteome, Mol. Cell. Biol. 19 (1999) 7357–7368.
B.D. Lemire, J.J. Robinson, R.D. Bradley, D.G. Scraba, J.H. Weiner,
Structure of fumarate reductase on the cytoplasmic membrane of
Escherichia coli, J. Bacteriol. 155 (1983) 391–397.
P.T. Bilous, S.T. Cole, W.F. Anderson, J.H. Weiner, Nucleotide sequence
of the dmsABC operon encoding the anaerobic dimethylsulphoxide
reductase of Escherichia coli, Mol. Microbiol. 2 (1988) 785–795.
K.P. Locher, Structure and mechanism of ABC transporters, Curr. Opin.
Struck. Biol. 14 (2004) 426–431.
E.A. Pratt, J.A. Jones, P.F. Cottam, S.R. Dowd, C. Ho, A biochemical
study of the reconstitution of D-lactate dehydrogenase-deficient membrane vesicles using fluorine-labeled components, Biochim. Biophys.
Acta 729 (1983) 167–175.
A. Schryvers, E. Lohmeier, J.H. Weiner, Chemical and functional
properties of the native and reconstituted forms of the membrane-bound,
aerobic glycerol-3-phosphate dehydrogenase of Escherichia coli, J. Biol.
Chem. 253 (1978) 783–788.
A.J. Driessen, P. Fekkes, J.P. van der Wolk, The Sec system, Curr. Opin.
Microbiol. 1 (1998) 216–222.
B.C. Berks, T. Palmer, F. Sargent, The Tat protein translocation pathway
and its role in microbial physiology, Adv. Microb. Physiol. 47 (2003)
187–254.
D.O. Daley, M. Rapp, E. Granseth, K. Melen, D. Drew, G. von Heijne,
Global topology analysis of the Escherichia coli inner membrane
proteome, Science 308 (2005) 1321–1323.
E. Wallin, G. von Heijne, Genome-wide analysis of integral membrane
proteins from eubacterial, archaean, and eukaryotic organisms, Protein
Sci. 7 (1998) 1029–1038.
V. Yankovskaya, R. Horsefield, S. Tornroth, C. Luna-Chavez, H.
Miyoshi, C. Leger, B. Byrne, G. Cecchini, S. Iwata, Architecture of
succinate dehydrogenase and reactive oxygen species generation, Science
299 (2003) 700–704.
T.M. Iverson, C. Luna-Chavez, L.R. Croal, G. Cecchini, D.C. Rees,
Crystallographic studies of the Escherichia coli quinol-fumarate reductase
with inhibitors bound to the quinol-binding site, J. Biol. Chem. 277 (2002)
16124–16130.
M. Jormakka, S. Tornroth, J. Abramson, B. Byrne, S. Iwata, Purification
and crystallization of the respiratory complex formate dehydrogenase-N
from Escherichia coli, Acta Crystallogr., D Biol. Crystallogr. 58 (2002)
160–162.
G. Butland, J.M. Peregrin-Alvarez, J. Li, W. Yang, X. Yang, V. Canadien, A.
Starostine, D. Richards, B. Beattie, N. Krogan, M. Davey, J. Parkinson, J.
Greenblatt, A. Emili, Interaction network containing conserved and essential protein complexes in Escherichia coli, Nature 433 (2005) 531–537.
B.V. Alvarez, G.L. Vilas, J.R. Casey, Metabolon disruption: a mechanism
that regulates bicarbonate transport, EMBO J. 24 (2005) 2499–2511.
J.X. Yan, A.T. Devenish, R. Wait, T. Stone, S. Lewis, S. Fowler,
Fluorescence two-dimensional difference gel electrophoresis and mass
spectrometry based proteomic analysis of Escherichia coli, Proteomics 2
(2002) 1682–1698.
C. Ji, A. Lo, S. Marcus, L. Li, Effect of 2MEGA labeling on membrane
proteome analysis using LC-ESI QTOF MS, J. Proteome Res. 5 (2006)
2567–2576.
1712
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
[58] M.P. Molloy, B.R. Herbert, K.L. Williams, A.A. Gooley, Extraction of
Escherichia coli proteins with organic solvents prior to two-dimensional
electrophoresis, Electrophoresis 20 (1999) 701–704.
[59] N. Zhang, R. Chen, N. Young, D. Wishart, P. Winter, J.H. Weiner, L. Li,
Comparison of SDS- and methanol-assisted protein solubilization and
digestion methods for Escherichia coli membrane proteome analysis by
2-D LC-MS/MS, Proteomics 7 (2007) 484–493.
[60] E. Granseth, D.O. Daley, M. Rapp, K. Melen, G. von Heijne,
Experimentally constrained topology models for 51,208 bacterial inner
membrane proteins, J. Mol. Biol. 352 (2005) 489–494.
[61] C.A. Hale, P.A. de Boer, Recruitment of ZipA to the septal ring of
Escherichia coli is dependent on FtsZ and independent of FtsA,
J. Bacteriol. 181 (1999) 167–176.
[62] B. Herbert, Advances in protein solubilization for 2-dimensional
electrophoresis, Electrophoresis 20 (1999) 660–663.
[63] M.P. Molloy, Two-dimensional electrophoresis of membrane proteins
using immobilized pH gradients, Anal. Biochem. 280 (2000) 1–10.
[64] V. Santoni, S. Kieffer, D. Desclaux, F. Masson, T. Rabilloud, Membrane proteomics: use of additive main effects with multiplicative
interaction model to classify plasma membrane proteins according to
their solubility and electrophoretic properties, Electrophoresis 21
(2000) 3329–3344.
[65] V. Santoni, P. Doumas, D. Rouquie, M. Mansion, T. Rabilloud, M.
Rossignol, Large scale characterization of plant plasma membrane
proteins, Biochimie 81 (1999) 655–661.
[66] C.C. Wu, M.J. MacCoss, K.E. Howell, J.R. Yates, A method for the
comprehensive proteomic analysis of membrane proteins, Nat. Biotechnol. 21 (2003) 532–538.
[67] N. Zhang, N. Li, L. Li, Liquid chromatography MALDI MS/MS for
membrane proteome analysis, J. Proteome Res. 3 (2004) 719–727.
[68] T.Q. Shang, J.M. Ginter, M.V. Johnston, B.S. Larsen, C.N. McEwen,
Carrier ampholyte-free solution isoelectric focusing as a prefractionation
method for the proteomic analysis of complex protein mixtures,
Electrophoresis 24 (2003) 2359–2368.
[69] G. Weber, M. Islinger, P. Weber, C. Eckerskorn, A. Voelkl, Efficient
separation and analysis of peroxisomal membrane proteins using freeflow isoelectric focusing, Electrophoresis 25 (2004) 1735–1747.
[70] T. McDonald, S. Sheng, B. Stanley, D. Chen, Y. Ko, R.N. Cole, P.
Pedersen, J.E. Van Eyk, Expanding the subproteome of the inner mitochondria using protein separation technologies. One- and two-dimensional liquid chromatography and two-dimensional gel electrophoresis,
Mol. Cell. Proteomics 5 (2006) 2392–2411.
[71] X. Zuo, K.-B. Lee, D.W. Speicher, Fractionation of complex proteomes
by microscale solution isoelectrofocusing using ZOOM IEF fractionators
to improve protein profiling, Proteomics Protocols Handbook, 2005,
pp. 97–117.
[72] M. Aivaliotis, W. Haase, M. Karas, G. Tsiotis, Proteomic analysis of
chlorosome-depleted membranes of the green sulfur bacterium Chlorobium tepidum, Proteomics 6 (2006) 217–232.
[73] R. Henningsen, B.L. Gale, K.M. Straub, D.C. DeNagel, Application of
zwitterionic detergents to the solubilization of integral membrane proteins
for two-dimensional gel electrophoresis and mass spectrometry, Proteomics 2 (2002) 1479–1488.
[74] R.-P. Zahedi, C. Meisinger, A. Sickmann, Proteomics 5 (2005) 3581–3588.
[75] R.J. Simpson, L.M. Connolly, J.S. Eddes, J.J. Pereira, R.L. Moritz, G.E.
Reid, Electrophoresis 21 (2000) 1707–1732.
[76] B.A. van Montfort, M.K. Doeven, B. Canas, L.M. Veenhoff, B. Poolman,
G.T. Robillard, Combined in-gel tryptic digestion and CNBr cleavage for
the generation of peptide maps of an integral membrane protein with
MALDI-TOF mass spectrometry, Biochim. Biophys. Acta, Bioenerg.
1555 (2002) 111–115.
[77] T.T.T. Quach, N. Li, D.P. Richards, J. Zheng, B.O. Keller, L. Li,
Development and applications of in-gel CNBr/tryptic digestion combined
with mass spectrometry for the analysis of membrane proteins, J. Proteome
Res. 2 (2003) 543–552.
[78] D.N. Perkins, D.J.C. Pappin, D.M. Creasy, J.S. Cottrell, Probabilitybased protein identification by searching sequence databases using mass
spectrometry data, Electrophoresis 20 (1999) 3551–3567.
[79] C.C. Wu, J.R. Yates, The application of mass spectrometry to membrane
proteomics, Nat. Biotechnol. 21 (2003) 262–267.
[80] D.K. Han, J. Eng, H. Zhou, R. Aebersold, Quantitative profiling of
differentiation-induced microsomal proteins using isotope-coded affinity
tags and mass spectrometry, Nat. Biotechnol. 19 (2001) 946–951.
[81] J.L. Norris, N.A. Porter, R.M. Caprioli, Mass spectrometry of intracellular and membrane proteins using cleavable detergents, Anal. Chem.
75 (2003) 6642–6647.
[82] M.P. Washburn, D. Wolters, J.R. Yates III, Large-scale analysis of the
yeast proteome by multidimensional protein identification technology,
Nat. Biotechnol. 19 (2001) 242–247.
[83] L.M. Simon, M. Kotorman, G. Garab, I. Laczko, Structure and activity of
a-chymotrypsin and trypsin in aqueous organic media, Biochem.
Biophys. Res. Commun. 280 (2001) 1367–1371.
[84] W.K. Russell, Z.Y. Park, D.H. Russell, Proteolysis in mixed organicaqueous solvent systems: applications for peptide mass mapping using
mass spectrometry, Anal. Chem. 73 (2001) 2682–2685.
[85] J. Blonder, M.L. Hale, D.A. Lucas, C.F. Schaefer, L.-R. Yu, T.P. Conrads,
H.J. Issaq, B.G. Stiles, T.D. Veenstra, Proteomic analysis of detergentresistant membrane rafts, Electrophoresis 25 (2004) 1307–1318.
[86] J. Blonder, M.B. Goshe, R.J. Moore, L. Pasa-Tolic, C.D. Masselon, M.S.
Lipton, R.D. Smith, Enrichment of integral membrane proteins for
proteomic analysis using liquid chromatography-tandem mass spectrometry, J. Proteome Res. 1 (2002) 351–360.
[87] J. Blonder, P. Conrads Thomas, L.-R. Yu, A. Terunuma, M. Janini
George, J. Issaq Haleem, C. Vogel Jonathan, D. Veenstra Timothy,
A detergent- and cyanogen bromide-free method for integral membrane proteomics: application to Halobacterium purple membranes
and the human epidermal membrane proteome, Proteomics 4 (2004)
31–45.
[88] H. Zhong, Y. Zhang, Z. Wen, L. Li, Protein sequencing by mass analysis of
polypeptide ladders after controlled protein hydrolysis, Nat. Biotechnol.
22 (2004) 1291–1296.
[89] H. Zhong, S.L. Marcus, L. Li, Microwave-assisted acid hydrolysis of
proteins combined with liquid chromatography MALDI MS/MS for
protein identification, J. Am. Soc. Mass Spectrom. 16 (2005) 471–481.
[90] N. Wang, L. MacKenzie, A.G. De Souza, H. Zhong, G. Goss, L. Li,
Proteome profile of cytosolic component of zebrafish liver generated by
LC-ESI MS/MS combined with trypsin digestion and microwave-assisted
acid hydrolysis, J. Proteome Res. 6 (2007) 263–272.
[91] X. Chen, L. Sun, Y. Yu, Y. Xue, P. Yang, Amino acid-coded tagging
approaches in quantitative proteomics, Expert Rev. Proteomics 4 (2007)
25–37.
[92] M. Mann, Functional and quantitative proteomics using SILAC, Nat.
Rev., Mol. Cell Biol. 7 (2006) 952–958.
[93] T.J. Griffin, D.K.M. Han, S.P. Gygi, B. Rist, H. Lee, R. Aebersold, K.C.
Parker, Toward a high-throughput approach to quantitative proteomic
analysis: expression-dependent protein identification by mass spectrometry, J. Am. Soc. Mass Spectrom. 12 (2001) 1238–1246.
[94] S.P. Gygi, B. Rist, T.J. Griffin, J. Eng, R. Aebersold, Proteome analysis of
low-abundance proteins using multidimensional chromatography and
isotope-coded affinity tags, J. Proteome Res. 1 (2002) 47–54.
[95] H. Zhou, A. Ranish Jeffrey, D. Watts Julian, R. Aebersold, Quantitative
proteome analysis by solid-phase isotope tagging and mass spectrometry,
Nat. Biotechnol. 20 (2002) 512–515.
[96] A. Leitner, W. Linder, Chemistry meets proteomics: the use of chemical
tagging reactions for MS-based proteomics, Proteomics 6 (2006)
5418–5434.
[97] W.-J. Qian, M.E. Monroe, T. Liu, J.M. Jacobs, G.A. Anderson, Y. Shen, R.J.
Moore, D.J. Anderson, R. Zhang, S.E. Calvano, S.F. Lowry, W. Xiao, L.L.
Moldawer, R.W. Davis, R.G. Tompkins, D.G. Camp, R.D. Smith, Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and
time tag approach, Mol. Cell. Proteomics 4 (2005) 700–709.
[98] X. Yao, A. Freas, J. Ramirez, P.A. Demirev, C. Fenselau, Proteolytic 18O
labeling for comparative proteomics: model studies with two serotypes of
adenovirus. [Erratum to document cited in CA135:089410], Anal. Chem.
76 (2004) 2675.
J.H. Weiner, L. Li / Biochimica et Biophysica Acta 1778 (2008) 1698–1713
[99] K.L. Johnson, D.C. Muddiman, A method for calculating 16O/18O
peptide ion ratios for the relative quantification of proteomes, J. Am. Soc.
Mass Spectrom. 15 (2004) 437–445.
[100] J.-L. Hsu, S.-Y. Huang, N.-H. Chow, S.-H. Chen, Stable-isotope
dimethyl labeling for quantitative proteomics, Anal. Chem. 75 (2003)
6843–6852.
[101] C. Ji, L. Li, Quantitative proteome analysis using differential stable
isotopic labeling and microbore LC-MALDI MS and MS/MS, J. Proteome
Res. 4 (2005) 734–742.
[102] J.E. Melanson, S.L. Avery, D.M. Pinto, High-coverage quantitative
proteomics using amine-specific isotopic labeling, Proteomics 6 (2006)
4466–4474.
[103] C. Ji, L. Li, M. Gebre, M. Pasdar, L. Li, Identification and quantification of
differentially expressed proteins in E-cadherin deficient SCC9 cells and
[104]
[105]
[106]
[107]
1713
SCC9 transfectants expressing E-cadherin by dimethyl isotope labeling, LCMALDI MS and MS/MS. [Erratum to document cited in CA143:169101],
J. Proteome Res. 4 (2005) 1872.
J.R. Kimmel, Guanidination of proteins, Methods Enzymol. 11 (1967)
584–589.
A. Gilchrist, C.E. Au, J. Hiding, A.W. Bell, J. Fernandez-Rodriguez, S.
Lesimple, H. Nagaya, L. Roy, S.J. Gosline, M. Hallett, J. Paiement, R.E.
Kearney, T. Nilsson, J.J. Bergeron, Quantitative proteomics analysis of
the secretory pathway, Cell 127 (2006) 1265–1281.
J.J. Bergeron, M. Hallett, Peptides you can count, Nat. Biotechnol.
25 (2007) 61–62.
R. Orme, C.W. Douglas, S. Rimmer, M. Webb, Proteomic analysis of
Escherichia coli biofilms reveals the overexpression of the outer
membrane protein OmpA, Proteomics 6 (2006) 4269–4277.