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SUPPLEMENTARY INFORMATION

Global landscape of cell envelope protein complexes in Escherichia coli

Mohan Babu1,15,16,17, Cedoljub Bundalovic-Torma2,3,15, Charles Calmettes3,4,15, Sadhna Phanse1,5,

Qingzhou Zhang1, Yue Jiang2, Zoran Minic1, Sunyoung Kim1, Jitender Mehla6, Alla Gagarinova8,

Irina Rodionova7, Ashwani Kumar9, Hongbo Guo5, Olga Kagan5, Oxana Pogoutse5, Hiroyuki

Aoki1, Viktor Deineko1, J. Harry Caufield6, Erik Holtzapple7, Zhongge Zhang7, Ake Vastermark7,

Yogee Pandya5, Christine Chieh-lin Lai3, Majida El Bakkouri3, Yogesh Hooda3, Megha Shah3, Dan

Burnside10, Mohsen Hooshyar10, James Vlasblom1, Sessandra V. Rajagopala11, Ashkan Golshani10,

Stefan Wuchty12, Jack Greenblatt5,13, Milton Saier7,16, Peter Uetz6,16, Trevor Moraes3,16,

John Parkinson2,3,13,16, and Andrew Emili5,13,14,16,17

17Correspondence should be addressed to A.E. ([email protected]) or M.B. ([email protected])

SUPPLEMENTARY NOTE 1

Detergent selection for CEP target purification

Our initial strategy was to identify a ‘best’ set of detergents for MP extraction. Consequently, we

systematically evaluated 14 different detergents (ionic, nonionic and zwitterionic) compatible

with MS to define a set of conditions and the maximum concentrations of detergent suitable for

the routine purification for large numbers of E. coli CEPs. These included, for example, dodecyl

maltoside (DDM) and octaethylene glycol monododecyl ether (C12E8), relatively gentle

detergents that likely preserve protein function and protein-protein interactions (PPI)1,2.

To evaluate the effectiveness of these detergents, we compared the total recovery of 11

SPA-tagged CEPs with and without detergent (Supplementary Fig. 1b). We then computed

detergent extraction efficiency by quantifying the relative band signal intensity, reported in terms

of percentile. Three detergents (DDM, C12E8, and Triton X-100) in particular were deemed most

effective and complementary. We note that the amounts of detergents used during extraction and

Nature Biotechnology: doi:10.1038/nbt.4024

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purification were chosen based on the critical micelle concentration (CMC) for each detergent,

and that the detergent concentration was never exceeded 1%. Most of the detergents tested are

widely used to isolate CEPs at around 1% concentration3-6, and the literature further supports 1%

non-ionic DDM as ideal for extraction of native CEPs4-6. In all subsequent purification and

washing steps, the buffers used contained 0.1% detergent, or around twice the CMC in all cases.

The table shown below, indicating the actual CMC (mM) of a given detergent compared to the

amount used for purification.

Detergent used

Abbreviation

Detergent CMC

in mM (or in %)

Detergent CMC in mM

used for CE extraction

(% detergent used)

n-dodecyl-β-D

maltopyranoside

DDM

0.17 (0.0087%)

19.5 (1%)

Octaethylene glycol

monododecyl ether

C12E8 0.09 (0.0048%) 18.8 (1%)

Triton X-100 TX-100 0.23(0.015%) 15.3 (1%)

3-[(3-cholamidopropyl)

dimethylammonio]-1-

propanesulfonate

CHAPS

8 (0.49%)

16.3 (1%)

n-Octyl-β-D-gluco

pyranoside

OG

18-20 (0.53%)

34.0 -37.7 (1%)

Lauryldimethylamine-

N-oxide

LDAO

1-2 (0.023%

43.5-87.0 (1%)

n-decyl-β-D-

maltopyranoside

DM

1.8 (0.087%)

20.7 (1%)

Digitonin Digitonin 0.75 (0.08%) 9.36 (1%)

Sodium cholate SC (or Sod.

Cholate)

9.5 (0.41%)

23.2 (1%)

Decanoyl-N-

hydroxyethylglucamide

HEGA-10

7.0 (0.26%)

26.9 (1%)

N,N-dimethyl-1-

tetradecanamine-N-oxide

TDAO

0.29 (0.0075%)

38.6 (1%)

n-nonyl-β-D-thiomaltoside NTM 6 (0.28%) 21.4 (1%)

3-[(3-Cholamidopropyl)

dimethylammonio]-2-

hydroxy-1-propane

sulfonate

CHAPSO

8 (0.50%)

16.0 (1%)

n-dodecyl-β-D-thiomalto

pyranoside

LTM

0.05 (0.0026%)

19.2 (1%)


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We then investigated how well three chosen (Triton, DDM, C12E8) detergents performed in

complete large-scale purifications of a broader set of 39 SPA-tagged E. coli CEPs with diverse

molecular weights, abundance or number of transmembrane helices. We were able to identify

both the bait and putative binding partners in about half the analyses, indicating that many

bacterial CEP complexes can be affinity-purified in the presence of at least one of these

detergents (data not shown). Based on the results from these preliminary proteomics screens, we

selected DDM, C12E8 and Triton X-100 for large-scale affinity purifications.

AP/MS methods

For purifications, the C-terminally SPA-tagged E. coli CE strains created with a kanamycin

selectable marker integrated by targeted homologous recombination in the E. coli chromosome

were grown to exponential (mid-log) phase in 1 L of Luria-Bertani (LB) media using shake batch

culture flasks. After mechanical lysis of harvested cells by sonication and pelleting of debris, the

lysates were ultra-centrifuged to isolate the membranes. The membrane fractions were washed

and stably-associated CEPs extracted with buffers containing various non-denaturing detergents,

with the remaining insoluble material was removed by a second round of ultra-centrifugation.

The solubilized CEP baits, and their stably associated proteins, were then purified successively

on anti-FLAG and calmodulin columns, as is usual for the SPA method7,8, except that all but the

first three column-washing steps included calmodulin wash buffer (30 mM Tris-HCl pH 7.9, 150

mM NaCl, 2 mM CaCl2, and 0.1-0.5 mM TCEP) in the presence of 0.1% detergent, followed by

the last two column-washing steps performed in the absence of detergent. Buffers used in each

step of the procedure are described in detail in our earlier publications7,8, including a video file

illustrating these procedures available via the Journal of Visualized Experiments8.


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The purified protein preparations extracted using a PerfectFOCUSTM kit (G-Biosciences)

to remove residual detergent were resuspended in 25μl of 8 M urea (1.5 M final concentration),

20 mM HEPES (pH 8) and then reduced with 5 mM Tris (2-carboxyethyl) phosphine for 45 min

at room temperature and alkylated with iodoacetamide (15 mM) for 60 min in the dark. Sequence

grade modified trypsin (0.3 mg; Promega) was added and samples were digested overnight with

gentle shaking at room temperature. The peptide mixtures were then acidified with formic acid

and desalted using C-18 TopTips (Glygen). The resulting peptides were loaded using an

autosampler onto a 75-μm inner diameter microcapillary fused silica column packed with ~10

cm of reverse phase resin (C18, 3 μm, 100 Å; Phenomenex) placed in-line with a Proxeon EASY

nLC 1000 (Proxeon) nano high-performance liquid chromatography and the Orbitrap Velos or

Elite mass spectrometer (Thermo Fisher Scientific). Bound peptides were eluted by electrospray

ionization using a 100 min water/acetonitrile gradient with a stable tip flow rate of ~0.30 µl min-

1. Precursor ions [350-2000 m/z] were subjected to data-dependent, collision-induced

dissociation while the mass spectrometer cycled through one full mass scan followed by 15

successive tandem mass scans of the intense precursor ions with dynamic exclusion enabled.

Prior to generating the high-quality interactions using the integrated log-likelihood score

threshold (Σ LLS; see below), we mapped the MS/MS spectra from each SPA-tagged bait

purification to reference E. coli protein sequences using both the SEQUEST (ver. 27 - rev.9)7,9,10

and MS-GF+11 algorithms. Notably, we opted to use the same proven methodology (e.g. search

engine) applied in our previous E. coli soluble interactome studies9,12, allowing for comparison

and integration of the data to address additional and unique research questions. Like Mascot and

X-Tandem, SEQUEST is still one of the most commonly used search engines, but we note that

we also employed a newer alternative, MS-GF+, for peptide identifications in a parallel


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analysis11. As in our previous study9, precursor mass tolerance was set to 3 Da (daughter mass

ion tolerance set to the default of 0), while enabling partial tryptic enzyme and single site missed

cleavages. The STATQUEST filtering algorithm13 was then applied to all putative SEQUEST

search results to assign statistical confidence. High confidence (>90% likelihood) spectral counts

from SEQUEST/STATQUEST were combined and averaged with the MS-GF+ search results. If

the spectral count was not detected by MS-GF+, the interaction was still considered as long as it

passed a 90% confidence threshold by SEQUEST/

STATQUEST. The default parameters used in SEQUEST/STATQUEST and MS-GF+ searches

are listed in the tables below:

SEQUEST (ver. 27 - rev.9) Parameters Comments

Database EcoCyc (ver.14.1) Target and decoy sequences

Precursor mass tolerance 3.0 Default SEQUEST parameter

Fragment ion tolerance 0 Default SEQUEST parameter

Missed cleavage 1 Default SEQUEST parameter

Fixed modification + 57 Cysteine carbamido-methylation Default SEQUEST parameter

Variable modification +16 methionine oxidation Default SEQUEST parameter

Peptide-Protein identification FDR 0.01 (~ 99%) Default SEQUEST parameter

Protein identifications (hits)

filtered

20 ppm Remove false positives

STATQUEST Parameters Comments

Assign confidence score p-value 0.01 (~ 99%) Confidence score ranges

between 99.6% to 50%

MS-GF+ (ver. 9949) Parameters Comments

Database EcoCyc (ver.14.1) Target and decoy sequences

ParentMassTolerance -t 20ppm Default MS-GF+ parameter

IsotopeErrorRange -ti

-1, 2 The combination of -t and -ti

determines the precursor

mass tolerance

Number of tolerable (tryptic)

termini –ntt

2 This parameter is used to

apply the enzyme cleavage

specificity rule when

searching the database

Fixed modification + 57 Cysteine carbamidomethylation Default MS-GF+ parameter

Variable modification +16 methionine oxidation Default MS-GF+ parameter

Q-value FDR 0.01 (~ 99%) Default MS-GF+ parameter


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To boost coverage, we employed several AP/MS strategies. From inception of this study,

we purified bait CEPs using a two-step (anti-FLAG and calmodulin bead capture) affinity

purification protocol. For failed baits, on a case-by-case basis, we attempted a more efficient

one-step affinity purification using anti-FLAG M2 agarose beads or with dynabeads immobilized

antibody (Life Technologies), which occasionally worked better. The antibody-coupling to

dynabeads pulldown strategy has been shown to exhibit surface activated chemistry with

hydrophobic characteristics and with ultra-low background binding.

Briefly, after the cells were cultured and lysed using the procedure described in our SPA

method7, the supernatant was coupled with 30 ul dynabeads slurry [i.e. 200 ul beads and 3ul

FLAG antibody incubated for 10 min at room temperature, and beads washed twice with 200 ul

of purification buffer (30 mM Tris-Hcl pH 7.9, 150 mM NaCl, 0.02% Tween 20, 0.1-0.5 mM

TCEP)] and incubated for 30 min at 4 ºC following the manufacturer instructions with slight

modifications. The resulting supernatant was discarded, and the beads were washed three times

with 200 ul purification buffer. Proteins that bound to the beads were subsequently eluted with

50 ul elution buffer (10% ammonium hydroxide, 0.1-0.5 mM TCEP) after incubating at room

temperature for 15 min. The purified protein preparations were finally subjected to detergent

removal, trypsin digestion, and protein identification by MS as aforementioned above.

In total, we purified 590 bait CEPs (of 785 tagged) using only one method, while 195 were

purified using two methods (see Table below). In general, we found that both single- (anti-

FLAG) and dual-step (anti-FLAG + calmodulin) purifications recovered >85% of the bait CEPs

from MS, while the dynabeads purifications were slightly less effective (~60% bait recovery). As

noted in the table below, we were able to detect 582 baits by MS using various purification

methods. However, for generating the cePPI network, we took all 785 CEPs into consideration


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for analysis because even though certain baits were not detected by MS, we were nevertheless

able to identify PPI pairs in some cases that were either reported in the literature or had known

functional associations (e.g. encoded in same operon).

Purification

methods

Number of CEPs

purified as baits

Number of CEPs

recovered*

Successful

purification (%)

Two-step (FLAG-Calmodulin-

binding peptides)

277

247

89.16

One-step (FLAG) 80 71 88.75

Dynabeads antibody coupling 233 138 59.22

Two-step and one-step 3 3 100.00

Two-step and dynabeads

strategy

180 114 63.33

One-step and dynabeads strategy 12 9 75.00

All three methods (Two-step,

one-step, and dynabeads)

0 0 0

Total 785 582

* Numbers indicate bait CEP detection by MS.

Scoring procedures to identify reliable PPIs and define CEP complexes

As with any other high-throughput AP/MS study, detection of promiscuous non-specific

interactors is an inherent concern. We mitigated this in three ways. First, proteins identified in

mock AP/MS analyses (standard anti-FLAG and calmodulin affinity workflow) of an untagged

(WT) negative control strain (without a tagged bait) were removed from further consideration.

Second, proteins detected routinely with 80% or more of bait were deemed contaminants

(frequent flyers). Third, the PPI data from each bait CEP was filtered using a probability score of

90% or greater as the majority of known interactions curated in EcoCyc passed this threshold

(Supplementary Fig. 3a).

As for the number of biological repeats and variance between repeats (i.e. 466 CEPs

purified in 3 detergents, 39 in 2 detergents, and 280 only using one detergent; Supplementary

Fig. 2a), we evaluated the results obtained for 290 SPA-tagged CEP preparations in different

non-ionic detergents, with at least 2 biological replicates per detergent, for a total of 1,751


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AP samples analyzed by precision Orbitrap MS. We computed the variance and average Pearson

correlation of proteins co-purifying (spectral counts) between each replicate per detergent.

The filtered interactions were then subjected to HyperGeometric Spectral Counts score

(HGSCore)14 and Comparative Proteomic Analysis Software Suite (CompPASS or S-score)15

scoring algorithms to define high-quality associations. The HGS incorporates the spectral counts

into Hart’s hypergeometric distribution error model to compute the probability of an interaction

being observed at random16. The HGSCore algorithm assumes a matrix model of interactions,

inferring potential prey-prey links from the bait-prey data captured in the experiments. First, the

normalized spectral abundance factor (NSAF) of the prey protein for each AP experiment is

calculated as follows:

NSAF = SPCk / (Lk . Σ (SPC/L))

For a prey protein, k, its NSAF is determined from the number of spectral counts (SPC)

associated with protein k divided by its length (L), divided by the sum of SPC/L for all N

proteins in the experiment17.

𝑁𝑆𝐴𝐹𝑘 = (𝑆𝑃𝐶/𝐿)𝑘

∑ (𝑆𝑃𝐶/𝐿)𝑖𝑁𝑖=1

NSAF values are then normalized (by dividing all scores by the lowest NSAF score) and

converted to a TN score. For any two putatively interacting proteins, i and j, we take the smaller

of the two TN scores as a measure of the frequency of co-occurrence and use this value to

calculate the corresponding hypergeometric probability of interaction between the two proteins

using the following formula:

𝑃 (∑ min(𝑇𝑁) > 𝑘|𝑛, 𝑚, 𝑁) = ∑ 𝑃ℎ𝑦𝑔𝑒𝑜(𝑥|𝑛, 𝑚, 𝑁)

min(𝑛,𝑚)

𝑥=𝑘


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𝑃ℎ𝑦𝑔𝑒𝑜(𝑥|𝑛, 𝑚, 𝑁) = (𝑛

𝑥) (𝑁−𝑛𝑚−𝑥)

(𝑁𝑚)

Where

k = ∑ min (TN) for experiments with TN;i > 0 and TN;j > 0

n = ∑ min (TN) for experiments with TN;i > 0

m = ∑ min (TN) for experiments with TN;j > 0

N = ∑ min (TN) for experiments

The final HGSCore among all potential prey-prey interactions are calculated as follows:

𝐻𝐺𝑆𝐶𝑜𝑟𝑒𝑖.𝑗 = − log (𝑃ℎ𝑦𝑔𝑒𝑜;𝑖.𝑗)

In the case of CompPASS, the S-score assumes a spoke model of interactions, where each

column is indicated by a prey and each row to a bait. If a bait-prey interaction is observed over

multiple experiments, their average SPC is used to calculate the S-score using the following

formula:

𝑆𝑖,𝑗 = √(𝑘

∑ 𝑓𝑖,𝑗𝑖=𝑘𝑖=1

) 𝑥𝑖,𝑗; 𝑓𝑖,𝑗 = {1; 𝑥𝑖,𝑗>0

Where k is the total number of baits in the dataset, xi,j is the average SPC for bait i with prey j, fi,j

is the frequency that prey j interacts with the set of baits.

After computing the HGSCore and S-score, we employed a log likelihood scoring scheme

(LLS) to integrate the HGSCore and S-score into a single combined score. The LLS represents

the likelihood that the interaction is genuine and is calculated as previously described18.

LLS = In 𝑃 (𝐿|𝐸)/ ~ 𝑃 (𝐿|𝐸)

𝑃 (𝐿)/ ~ 𝑃 (𝐿)

Where P(L|E) represent the frequency of interactions (L) in dataset (E), which contains true

positive (TP) interactions from the literature curated EcoCyc complexes (i.e. 1,249 PPIs in total;

Supplementary Table 2). ~P(L|E) represents the frequency of L in a set of true negative (TN)

interactions (defined as those that occur between two proteins that belong to different

complexes). P(L)/~P(L) represents the prior odds ratio of TPs and TNs.


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In order to generate the LLS score, we used the aforementioned scoring procedures to our

interaction dataset, which resulted in 1,348,793 PPI pairs with HGSCore > 0 and 140,805 pairs

with S score > 0. For each pairwise interaction within a score set, we calculated the LLS

independently, and used a weighted sum to produce a final score:

S = ∑𝐿𝐿𝑆𝑖

𝐷(𝑖−1)

n

i=1

Where LLSi represents the LLS of data set i, D is a free parameter representing the relative

degree of dependency between various datasets, and n is the number of interactions after

resampling. Here, we tested a series of D value from 0 to ∞ and found D = 1 gave the best

performance for both coverage and accuracy. Based on the area-under-the ROC (receiver

operating characteristic) curve analysis and a set of 1,249 cePPIs for E. coli proteins compiled

from EcoCyc (Supplementary Table 2), we chose a Σ LLS cut-off ≥ 5.27, resulting in 12,801

high-confidence associations (Supplementary Table 2). Since a quarter (28%, 351) of all

literature curated PPIs from the EcoCyc training set is involved in flagellar process, we repeated

the area-under-the ROC performance by eliminating these associations, and found that the ROC

performance was comparable with the inclusive training set, resulting in a score threshold of

Σ LLS ≥ 5.26 (data not shown) compared to 5.27 selected for study.

As an independent test of reliability, during our initial scoring optimization, we tested the

two algorithms (HGSCore, CompPASS) to see how well they perform in capturing known

EcoCyc complexes. By recapitulating the reference cePPIs according to area-under-the ROC

analysis, we were able to define stringent score thresholds for each (6.58 for CompPASS, 5.13

for HGSCore). Consistent with the notion that these scoring metrics work differently in capturing

interactions19, at the selected cut-off, we found cePPIs of certain literature curated complexes,

such as the β-barrel assembly machine (BAM; see table inset below), were preferentially


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detected or penalized by either algorithm, despite the fact that these PPIs were clearly evident in

our raw data. Hence, by integrating the output from CompPASS and HGSCore into a single

probabilistic log-likelihood score (LLS), we maximized coverage and reliability.

Bait

protein

Prey

protein

CompPASS score

(cut-off at 6.58)

HGScore

(cut-off at 5.13) ∑ LLS score

(cut-off at 5.27)

BamA BamB

BamD

5.15E-14

9.60E-30

10.4

13.5

8.71

8.90

BamB BamD 1.93E-31 5.64 7.87

BamD BamE 7.20E-39 5.51 7.81

Notably, the purpose of using two scoring algorithms is to: (1) capture as many

biologically relevant complexes as possible; (2) reliably predict HC or MC PPIs; and (3)

maximize coverage and accuracy in benchmarking against an established reference set of curated

cePPIs derived from EcoCyc. It is worth noting that the majority (3,841) of the “prey-prey”

associations involve one or more CEPs in our reported network, but only 3% (131) were deemed

HC, while the remaining (3,710) were assigned to the MC category. Whereas only a few (<1%,

or 104) cytoplasmic (prey-prey) protein pairs had no connection to CEPs, we gained meaningful

functional insights by confirming their physiological relevance as detailed in three mechanistic

follow-up studies.

First, we provided independent evidence that the cytoplasmic phosphocarrier protein, HPr

(an essential component of the bacterial phosphotransferase system) allosterically regulates

cytoplasmic phosphofructokinase (PfkB, but not PfkA), glucosamine 6-phosphate deaminase

(NagB), and adenylate kinase (Adk), suggesting that HPr serves as a global regulator of carbon

and energy metabolism and probably other physiological processes in enteric bacteria

(Rodionova et al., 201720).


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Second, we provide further evidence that the cytoplasmic nitrogen regulatory PII protein,

GlnB, and N-acetyl-glucosamine 6-phosphate epimerase, NanE, allosterically activate

glucosamine 6-phosphate deaminase (NagB) in E. coli (Rodionova et al., Journal of

Bacteriology; Manuscript Submitted).

Third, in the current study, we have shown by steady-state kinetics (Supplementary Fig.

7a) that the cytoplasmic protein, PykF is activated upon binding to non-phosphorylated HPr,

suggesting that, like PykA of Vibrio vulnificus21, HPr regulates PykF by increasing its affinity for

phosphoenolpyruvate.

In fact, we estimate that the rate of spurious PPIs in our network is likely less than 3% (as

derived from the estimated true/false positive ratio of 33/1 based on the benchmark precision vs.

the EcoCyc reference set reported in Supplementary Table 2). This represents a putative total

of approximately 371 spurious interactions, which is a level of precision comparable to that

reported in a previous AP/MS survey of membrane protein complexes in yeast (Babu et al.,

Nature 2012, 489, 585-9). The 12,801 PPIs were then clustered using core-attachment-based

clustering algorithm, as previously described22, to generate 540 clusters (420 multiprotein

complexes with at least one CEP; 120 with cytosolic proteins; Supplementary Table 4). While

all proteins in a predicted cluster are expected to be part of the same complex, the core-

attachment algorithm employed in our study predicts clusters with “cores” and then adds

“attachment” onto the cores to form biologically meaningful structures23. However, without

structural information (i.e. crystallographic data) or detailed experimental follow-up for each

subunit of a predicted complex, it is practically impossible (solely based on their degree of PPI

connectivity) to suggest whether the proteins form discrete sub-assemblies.


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Biochemical fractionation coupled to MS (BF/MS)

A global interaction mapping approach was conducted to validate the original 12,801 putative

PPIs based on the chromatographic separation of detergent solubilized macromolecules extracted

from E. coli using 0.020% Triton or 0.05% DDM (below the CMC for each detergent).

(i) Isolation and preparation of membrane (or CE) extracts

Unless otherwise stated, all steps were performed at 4oC. E. coli DY330 cells collected from 1 L

exponential (mid-log) phase culture was centrifuged at 10,000 x g for 15 min and washed with

25 ml of 50 mM Tris HCl (pH 7.5). The pellet was resuspended in 12 ml of 50 mM Tris HCl (pH

7.5) with Benzonase (Sigma) added to the buffer. The cells were disrupted with a sonicator for

15 to 20-s pulses with ~1 min waiting time between pulses. After lysing, cells were centrifuged

at 1,500 x g for 15 min at 4 oC. The membranes were purified by layering the lysate onto two

discontinuous sucrose gradients (6 ml of lysate/gradient), comprising 8 ml of 2.02 M sucrose in

50 mM Tris HCl (pH 7.5) overlaid with 10 ml of 0.44 M sucrose in 50 mM Tris HCl (pH 7.5).

The gradients are then centrifuged in a SW 60 Ti rotor at 60,000 rpm (255,000 x g) for a

total of 75 min in a Beckman L8-M Ultracentrifuge. The middle (membrane) layer is collected

after carefully removing the top layer with a pipette. The membrane is diluted with 4 volumes of

50 mM Tris HCl (pH 7.5) buffer and pelleted by centrifugation at 255,000 x g for 3 h. The

membrane pellets were resuspended in 1 ml of Tris HCl (pH 7.5) and forced through hypodermic

needles (23 gauge). The resulting membrane solution was frozen with liquid nitrogen and stored

at -80 oC until use. After thawing, the E. coli membrane was solubilized in 0.5 mL ice-cold

buffer [20 mM Tris-HCl (pH 7.5), 5% glycerol, 0.020 % Triton X-100 or 0.05% DDM,

phosphatase inhibitor cocktail 2 (Sigma), phosphatase inhibitor cocktail 3 (Sigma), and protease

inhibitors] and ground using mortar and pestle for 10 min. The suspension was centrifuged for


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10 min at 15, 000 x g, and protein concentration for the supernatant collected was determined

using Bradford assay.

(ii) Size exclusion chromatography (SEC)

The chromatographic fractionation of proteins was performed using an Agilent 1100 semi-

preparative high-performance liquid chromatography (HPLC) equipped with a binary pump

system (Agilent Technologies). Protein elution was monitored by absorption at 280 nm. A total

of 200 µg of membrane (or CE) extract was subjected to a 300 × 7.8 mm BioSep4000 Column

(Phenomenex), pre-calibrated with the following markers of known molecular mass: blue

dextran (2000 kDa), bovine serum albumin (67 kDa), ovalbumin (43 kDa), chymotrypsin

(25 kDa) and ribonuclease (13.7 kDa). Equilibration and elution was performed with 50 mM

Tris-HCl (pH 7.5), 50 mM NaCl, 0.015% Triton X-100 or 0.025% DDM, and

1% glycerol. About 0.1 mL of the protein fractions was collected at a flow rate of 0.5 mL min−1.

The extracts were fractionated into 84 biochemical fractions using SEC, fractionated

complexes were proteolytically digested, and the resulting peptides analyzed, in duplicate, by

precision tandem MS. Fragmentation spectra was collected on an Orbitrap mass spectrometer

from a total of 336 samples (i.e. 84 fractions x 2 detergents x 2 replicates) and were searched

against an E. coli target-decoy sequence database using the SEQUEST algorithm, and the

identifications filtered to a minimum of 90% probability matching score using STATQUEST7.

As in our previous studies of eukaryotic protein assemblies24,25, we determined the patterns of

co-eluting together across the 336 samples analyzed to derive stably-associated assemblies using

interaction likelihood metrics as previously reported24,25.


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(iii) Generation of cePPIs from BF/MS using machine learning approaches

To objectively score the co-fractionation profiles, we used the protein spectral counts

consistently detected in both replicates of each of the two detergent solubilized extracts to

compute interaction likelihood metrics as previously reported24,25. These include: (1) Pearson

correlation coefficient (PCC), with added Poisson noise to reduce the influence of low-count

proteins; (2) weighted cross-correlation (WCC); and (3) co-apex score, which we then compared

to a reference set of curated cePPIs derived from EcoCyc (Supplementary Fig. 4a and

Supplementary Table 2). A threshold cut-off was subsequently computed for each score based

on the capture of annotated PPIs (area-under-curve ROC analysis; Supplementary Fig. 4c). This

resulted in 128,919 unique PPI pairs (Supplementary Fig. 4a), of which, we validated 1,678

candidate protein pairs with either a co-apex score of 1 or a PCC or WCC measure above the

threshold that had likewise also been detected by AP/MS originally (Supplementary Table 2).

Selection of paralogous CEPs

Proteins that have undergone paralogous expansion in the cePPI network were identified using

the integration of complementary orthology prediction databases, OMA (Orthologous Matrix)26

and EggNOG27. Briefly, predicted orthology groups were downloaded from the current releases

of OMA (ver. 17) and EggNOG (ver. 4) and orthology group memberships for interaction

networks of CEPs were retrieved. Paralogous genes that are duplicated and diverged following a

speciation event were identified based on membership in both the same EggNOG and OMA

orthology groups. Given that EggNOG provides orthologous group predictions at different

taxonomic resolutions, the degree of overlap was compared between OMA and three EggNOG

(bactNOG, proNOG and gproNOG) orthology groups. The proNOG had the greatest degree of

overlap with OMA ortholog group and hence was used for subsequent analysis.


16

Paralog PPI overlap

Physical interaction subnetworks for each identified paralog was extracted from the finalized CE

network and used to construct a set of paralog PPI profiles. The proportion of physical

interactions shared between paralogous CE protein pairs was calculated using the Jaccard Index

of their respective PPI profiles, where, for proteins A and B, the Jaccard Index of their physical

interaction profiles, JPPI-AB, is calculated by:

JPPI-AB = OverlapPPI-AB / (OverlapPPI-AB + UniquePPI-A + UniquePPI-B)

Individual paralog PPI subnetworks extracted from the CEP network were imported and

visualized in Cytoscape (ver. 3.2.0).

External sources used in this study

Data type Short description Pathways and complexes EcoCyc pathways and complexes

(http://bioinformatics.ai.sri.com/ecocyc/dist/flatfiles-52983746/)

Experimental/Protein

information Protein expression (PaxDB database28)

Experimental PPI network derived from AP/MS datasets9,29

Experimental Yeast two-hybrid (Y2H) dataset12

Experimental Phenotypic dataset30

Subcellular localization

EcoCyc, Díaz-Mejía et al (2009)2, EchoLocation31, GO, UniProt,

and STEPdb32,

Signal peptide prediction SignalP (http://www.cbs.dtu.dk/services/SignalP/)

TMH prediction for IMPs Phobius33

ß-barrel prediction for OMPs BOCTOPUS234

Genetic interaction network CE35, genome-integrity36, and genome-wide screens37

PPI network PPIs from IntAct database38

Protein information GO annotations

Orthology information InParanoid39, OMA26 and EggNOG27

Protein information UniProt

Validation strategy to test PPIs

We randomly selected 103 PPIs above the Σ LLS scores (≥ 5.27), involving CEPs for validation

using the orthogonal bacterial (B2H) and yeast (Y2H) two-hybrid assays. Of those tested, we

were able to confirm 44 interactions by B2H and/or Y2H screens, resulting in the confirmation


17

of 43% (44 of 103 tested) of the physical associations. Assays were conducted using the

following procedures:

(i) B2H assay

(a) Gateway cloning

The recombination reactions were performed according to the manufacturer’s guidelines

(Invitrogen). The entry clones for all protein (baits and prey) pairs tested were obtained from the

E. coli ORFeome clones assembled into the pDONR221 vector system. The ORFeome library

was constructed and sequence-verified essentially as previously described40. The attL-flanked

ORFs were then recombined into the attR-flanked bacterial two-hybrid (BACTH)-DEST

plasmids (pST25-DEST and pUT18C-DEST) using the LR reaction to generate an attB-flanked

ORFs within the BACTH vectors. The LR recombination reaction was performed according to

the Gateway recombination manual to generate B2H expression clones (pST25-DEST, pSTM25-

DEST, pUT18C-DEST, and pUTM18-DEST). All extra-cytoplasmic and IMPs were screened in

pST25-DEST and pUT18C-DEST, whereas the OM or MR proteins were transferred into

pSTM25-DEST or pUTM18-DEST B2H modified expression vectors containing an extra

transmembrane segment fused downstream to T25 or T18 cyclase domains41.

(b) BACTH screening

The B2H expression clones for both bait and prey were co-transformed into an adenylate cyclase

(cya) deficient E. coli strain (BTH101). The BTH101 competent cells was prepared using the

standard protocol. The co-transformed cells were plated on LB plates containing 100 ug/ml

ampicillin and 100 ug/ml spectinomycin. Plates were incubated at 30°C for 48 h. The co-

transformants were selected and screened onto two different indicator plates (i.e. LB-X-Gal-

IPTG and McConkey/Maltose medium) at 30°C for 24 to 36 h. The PPIs were quantified using


18

the ß-galactosidase assay, with the same batch of cells used in all screening experiments. The ß-

galactosidase activity was measured from ~3 biological replicate experiments, and the miller

units is represented as mean log10 after subtracting the mean value from the negative control.

The quantification results of each PPI pair from replicate experiments, along with

Student’s t-test p-values, are shown in Supplementary Table 3. Most notably, we only

considered PPI pairs with a p-value ≤ 0.05 and a 2 to 5 fold difference in miller units compared

to negative control as a reliably confirmed interaction. In some cases, higher standard deviations

were observed for certain PPI pairs and this is likely due to the instability of the plasmid

construct used, consistent with studies reported previously for analyzing cePPIs by B2H42,43.

(ii) Y2H assay

Y2H screens were performed as previously described44. Briefly, the baits (in pGBGT7g) and

prey (in pGADT7g) arrays for test interaction sets were created. To identify self-activation, the

baits were mated with yeast carrying empty prey vectors (pGADT7g). In parallel, we also

conducted our bait vs prey Y2H screens. Here, each bait (DBD-X) was mated with specific prey

(AD-Y) on YPDA for 36 to 48 h at 30°C, followed by the selection of diploid cells onto selective

-Leu-Trp agar plates for 2 to 3 days. The diploids were then screened for interacting proteins on

selective medium (-Leu -Trp -His) at 30°C for another 6 to 7 days. As reported in our previous

yeast two-hybrid studies12,45,46, we used the minimal 3-AT concentration strategy by screening

all the baits for auto-activation by titrating the 3-AT concentration on which the baits do not

auto-activate. To suppress non-specific background or self-activation from baits, -Leu -Trp -His

plates containing 3-AT were used for screening. The plates were monitored each day, and

positive colonies were evaluated with respect to the background growth, and only positives with

high signal-to-noise ratio were used.


19

Steady-state kinetic analysis

(i) Protein purification

Recombinant proteins containing an N-terminal His6 tag were overexpressed in E. coli and

purified using Ni2+-chelating chromatography. The E. coli overexpression strains, PykF and PtsH

(Hpr) from the ASKA collection47 were grown in 50 ml Luria-Bertani (LB) medium, induced by

0.6 mM isopropyl-β-d-thiogalactopyranoside (IPTG), and harvested after 4 h of shaking. Rapid

purification of the recombinant proteins on a Ni-nitrilotriacetic acid (NTA) agarose minicolumn

was performed as described previously48.

Briefly, cells were harvested and resuspended in 20 mM HEPES buffer (pH 7.0),

containing 100 mM NaCl, 2 mM β-mercaptoethanol, and 0.03% Tween 20 with 2 mM

phenylmethyl sulfonyl fluoride. Cells were lysed by incubation with lysozyme (1 mg/ml) for 30

min, followed by a freeze-thaw cycle and sonication. After centrifugation, Tris-HCl (pH 8.0)

buffer was added to the supernatant to a final concentration of 50 mM. The supernatant was then

loaded onto a Ni-NTA agarose minicolumn (0.2 ml) from Qiagen Inc. (Valencia, CA). After

bound proteins were washed with At-buffer containing 50 mM Tris-HCl buffer (pH 8.0), 0.5 M

NaCl, 5 mM Imidazole and 0.3% Tween 20, they were eluted with 0.3 ml of the same buffer

supplemented with 250 mM imidazole. Protein size, expression level, distribution between

soluble and insoluble forms, and the extent of purification were monitored by SDS-PAGE.

Since except HPr, PykF was obtained with high yield (>1 mg) and purity (80 to 90%), we

modified the protocol for Hpr. The E. coli overexpression strain for HPr protein purification was

as follows: Cells were grown in 2L LB medium at 37 oC, induced by 0.6 mM IPTG at 24 oC and

harvested after 12 hrs of shaking. After harvesting, the cells were resuspended in the same buffer

as described above, and after lysis by sonication followed by centrifugation, the insoluble


20

fraction was resuspended in At-buffer containing 7M urea and 25 mM imidazole. Inclusion

bodies were dissolved, and after sonication and centrifugation, the protein was purified on an

NTA agarose column. Then the bound proteins were washed with 7M urea At-buffer, and HPr

was refolded by washing the column with At-buffer before eluting with the same buffer

containing 300 mM imidazole on an FPLC system. The buffer was changed to At-buffer by

dialysis. Protein concentration was measured using the Bradford assay.

(ii) Enzyme assays

Activity of the purified recombinant E. coli PykF was routinely assayed in a cuvette at 37 °C

using the standard enzymatic coupling assays as described previously49. To determine the effect

of HPr on PykF activity, 1 µM phosphorylated (HPr-P) and non-phosphorylated (HPr) forms of

HPr was added to the assay mixture. HPr was phosphorylated in the assay mixture containing

100mM Tris (pH 8), 2mM DTT, 8 mM PEP (phosphoenol-pyruvate), 10 mM MgCl2, and 10 ng

enzyme I (EI) and incubated for 40 min at 30oC. Then 1 µM of P-HPr protein, 10 ng of EI, and 8

mM PEP were added to the assay mixture. The observed rates (calculated using an NADH

extinction coefficient of 6.22 mM−1 cm−1) were compared to those for the two sets of control

samples: one control without the tested enzyme and another without adenosine monophosphate

(AMP) or PEP. The Km and Vmax values were determined by the GraphPad Prism software.

Pyruvate kinase activity was tested similarly using a coupled assay with lactate dehydrogenase

(LDH). Pyruvate kinase (25 ng) was added to 100 μl of a reaction mixture containing 200 mM

Tris-HCl (pH 7.5), 10 mM MgSO4, 1.5 mM ADP, 0-8 mM PEP, 0.3 mM NADH, 0.2M KCl, 100

µM ZnSO4, 15mM phosphate, and 1.2 U of LDH.


21

SUPPLEMENTARY NOTE 2

Evidences supporting cell envelope protein (CEP) interactions captured in the network

(i) Periplasmic solute binding receptors of ABC transport systems interacting with each other

ABC transport systems usually consist of two and five subunits: two cytoplasmically localized

ATPases, which can be present as a homodimer or a heterodimer; two integral membrane

subunits, which similarly may be present as a homodimer or a heterodimer; and, for uptake

systems, but not for efflux systems, a periplasmic solute binding receptor is usually, but not

always, present50-52. The ATPase energizes the transport process, while the receptor feeds the

substrate into the integral membrane channel complex. Binding of the substrate-occupied

receptor transmits a signal to the ATPase, which increases its ATPase activity. Usually, the

periplasmic receptor is synthesized in much larger amounts than the membrane constituent(s) or

the ATPase(s). However, in several characterized systems, the receptor only gains high affinity

for the integral membrane constituents when the substrate is bound to it.

With this background, we examined the interactions observed for the periplasmic solute

binding proteins of the various ABC transport systems in E. coli (Supplementary Table 3). The

first two entries in Supplementary Table 3 involve the interactions of different binding

receptors that function in the uptake of sulfur-containing compounds, for example, the

interaction of CysP with the alkanesulfonate receptor, SsuA (Σ LLS score 12.9). While this

system is involved in the uptake of sulfur-containing compounds, the transport systems to which

they belong are constituents of different families within the ABC superfamily. The second

interaction involving CysP was with the sulphate binding protein, Sbp (Σ LLS score 11.8).

Most notably, the ABC sulphate/thiosulphate uptake porter includes the transporter

CysTWAP complex, where CysP is the periplasmic thiosulphate binding receptor53. A second


22

binding receptor, Sbp, specific for sulphate, can function with this transporter. We also found

that CysP binds with the two subunits, SsuD and SsuE (Supplementary Table 2), of the

alkanesulfonate monooxygenase (Σ LLS scores 13.5, respectively). It is known that the two

protein constituents of this enzyme system interact to form the heterodimer54, but an interaction

between the monooxygenase subunits and the periplasmic binding protein had not been

documented. Since CysP normally resides in the periplasm (PE) while SsuDE resides in the

cytoplasm (CY), it is difficult to understand how these proteins might interact. It is possible that

CysP is not restricted to the PE and is initially synthesized in the CY before being exported via

the general secretory pathway. In some instances, some of the secreted proteins remain in the

CY, and this could be true for CysP, explaining the observed interaction.

A similar situation was observed for the two arginine binding proteins, ArtI and ArtJ,

which function with ArtPQM transport system. In contrast, we also observed receptors binding

to two different transport systems within the same ABC superfamily. For example, like methyl-

galactosidase (MglB) and D-ribose transporter (RbsB) subunit association, the interaction was

observed between two distinct amino acid systems: FliY (TcyJ), the cysteine/diaminopimelate

receptor, and the glutamine receptor, GlnH, with a score of 3.7 (below the chosen threshold).

Lastly, the ZnuA zinc receptor interacted with the AraF L-arabinose receptor with a score

of 13.9 (Supplementary Fig. 7d). As well, the ZnuA zinc receptor proved to interact with the

periplasmic ZinT (YodA) protein with a score of 6.6. ZinT and ZnuA are believed to feed into

the same ABC transport system, ZnuBC. Recent studies indicate that these two proteins act in

series rather than in parallel, and that both are required for maximal rates of Zn2+ uptake55-57.

Since small amounts of ZinT are found extracellularly, although most is periplasmic, it has been


23

suggested that ZinT serves a scavenging function, feeding zinc into the ZnuA periplasmic

binding receptor58. This is the first report to suggest that these two proteins interact.

(ii) Physiologically important CE protein-protein interactions (cePPIs) in E. coli

Many of the potential metabolons identified in this study are described in Supplementary Table

3. Considering the phosphotransferase system (PTS) first, we see that the three constituents of

the N, N’-diacetylchitobiose-specific enzyme II complex, IIA (ChbA), IIB (ChbB) and IIC

(ChbC), interact, and the IIC constituent is also associated with the NAD (P)-binding phospho-β-

glucosidase (ChbF). This observation suggests that the disaccharide transport, phosphorylation

and hydrolysis appear to be catalyzed by a single physical complex (a metabolon) involving all

of the sugar-specific constituents of this system59.

Another potential metabolon involves the D-alanine-D-alanine dipeptide transport system,

in which the periplasmic receptor, DdpA, interacts with the peptidase, YegQ, of the U32

peptidase super family60. The iron enterobactin transport protein FepG61 (TCDB search; TC#

3.A.1.14.2) appears to interact with the enterobactin biosynthetic enzyme, EntA62. The alkane

sulfonate ABC transporter, SsuA, interact with both subunits of the heterodimeric alkane

sulfonate monooxygenase, SsuDE63. Moreover, as aforesaid, the periplasmic CysP sulphate

receptor (TC# 3.A.1.6.1) was linked with both subunits of the SsuDE monooxygenase. Perhaps

all of these proteins comprise a metabolon jointly with other proteins in sulphur metabolism.

Next, the allantoinase, AllB64,65, was found to interact with the YbbW putative allantoin

transport protein (TC# 3.A.39.3.8) with a score of 6.5 (Supplementary Table 3), implicating

these proteins comprise a metabolon in mediating both the transport and degradation of allantoin.

We also observed two subunits of heme lyase, CcmF and CcmH, forming a tight binding

complex with a score of 13. Similarly, the BaeS sensor kinase, which phosphorylates the


24

response regulator, BaeR66, to induce synthesis of the MdtBC exporter, also physically coupled

with the multidrug resistance efflux pump, MdtBC67, with a score of 14. While it is known that

the BaeS/BaeR sensor kinase/response regulator pair controls expression of the Mdt exporter, a

physical interaction between these proteins and the exporter had not been reported before. The

interaction we observed between BaeS and MdtBC suggests that MdtABC regulate the kinase

activity of BaeS, and therefore the expression of its target transport systems.

We also noted an interaction of the FliI ATPase of the flagellar system68 with the DNA-

binding transcriptional regulator, NikR (Σ LLS score 6.8), as well as between the two inner

membrane proteins (IMPs) involved in cell division and septum formation, FtsL and FtsQ, as

part of the cell division complex69. The latter interaction had been reported previously70. While

MdtBC71 and MdtEF72 multidrug resistance (MDR) pumps function independently of each other,

we found MdtB interacting with MdtC, and MdtBCF with membrane fusion protein (MFP),

YbhG, and nucleoside transporter, NupC, as well as between member of the major facilitator

superfamily (MFS) MdtH and hypothetical protein, YceI.

The physical association we found between glucuronide transporter UidB in the inner

membrane (IM)73 and putative glucuronide porin UidC in the outer membrane (OM) suggest that

these proteins interact to allow transport across both membranes of the E. coli envelope in a

single energy-coupling step. No such example of interaction between an OM porin and an IM

uptake transporter has been documented, although many examples of equivalent interactions for

export systems have been reported. However, these efflux systems use an adaptor, the MFP, to

link the OM porin to the IM transporter. If this glucuronide uptake system proves to be a

transenvelope transport channel, this would be a novel finding.


25

Additionally, many poorly characterized proteins, some of unknown function (YxxX

proteins; Supplementary Table 3), prove to interact with each other. Many of these are likely to

have physiological ramifications. For example, YbhF (ABC transporter) and YbhG (a MFP)

interact with a score of 11.6. YegT, a putative MFS nucleoside transporter (TC# 2.A.1.10.4),

interacts with the sensor kinase, YehU with a score of 6.5. YehU phosphorylates YehT, the

corresponding response regulator. YehU/YehT control synthesis of transporters such as the

homolog of the oxalate::formate antiporter (TC# 2.A.1.11.3) and the peptide uptake porter YjiY

of the CstA family (TC# 2.A.114)74. This uptake system may play a role in adaptation to

stationary phase conditions.

The fimbrial adhesion protein, YehA was found to interact with the periplasmic chaperone,

YehC, with a score of 13.7. Two subunits, YejB and YejE, both in the IM, interacted with a

score of 14. These two proteins are believed to be constituents of an ABC-type oligopeptide

uptake permease. YicJ, a putative sugar transporter, was physically coupled with YicI, an alpha

glucosidase, with a score of 7.6. YnfF and YnfG, two putative subunits of an oxidoreductase,

were also found to be associated with a score of 14.9. Finally, the putative OM protein (OMP),

YtfM, as expected75, interact with a hypothetical protein, YtfN, with a score of 14.

CadC of E. coli is an acid-sensing DNA-binding transcriptional activator that interacted

with the lysine uptake transporter LysP with a score of 14. Together, these two proteins induce

lysine-dependent adaptation to acidic stress conditions76. In vivo analyses revealed that, in the

absence of either stimulus, the two proteins form a stable association, which is modulated by

lysine and low pH76,77. In addition to its transmembrane helix, the periplasmic domain of CadC

participated in the interaction. It was concluded that CadC was inhibited by LysP via

intramembrane and periplasmic contacts under non-inducing conditions. Upon induction, lysine-


26

dependent conformational changes in LysP transduce the lysine signal via a conformational

coupling to CadC without resolving the interaction completely76. This recent study76 confirms

and extends the results of our interactome analysis.

Moreover, it has been proposed that CadC is a pleiotropic regulator relevant to several

systems, including but not limited to the lysine::cadaverine antiporter, CadB, which functions

with CadA, the cytoplasmic enzyme that converts lysine to cadaverine, promoting pH

homeostasis78. While the relationship of CadAB to CadC has not been investigated, we predict

that binding of CadC to LysP represents a key regulatory step76.

(iii) Membrane transport metabolon

(a) PTS system

All E. coli K-12 proteins that belong to the PTS are known through sequence homology

searches, although the functions of several of these have not been defined79. We examined all

proteins of the PTS system for potential interactions (Supplementary Table 3). The most

surprising result was that several of the integral membrane transport proteins of the PTS (IIC

constituents) showed interactions with other IIC constituents, suggesting that the integral

membrane PTS transporter/enzymes might form a large complex.

While published evidence suggests that the soluble energy coupling proteins of the PTS

(Enzyme I, HPr, and sugar-specific IIA proteins) associate with PTS permeases80, no evidence

had been available prior to this study to suggest that the different transport complexes of the PTS

might interact. For instance, several PTS enzyme IIs81 form a complex (Supplementary Table

3). These include fructose permease (FruA) interaction, as expected82, with its cognate energy

coupling protein, FruB, and with the following enzyme IIC constituents: (1) NagE, specific for

N-acetylglucosamine, (2) GatC, for galactitol, (3) TreB, for trehalose and (4) MngA, for


27

mannosyl glycerate. Besides, FruB also interacted with the N-acetylglucosamine permease

(NagE)80. Establishment of such a complex would be both novel and important, as presence of

multiple PTS permeases in a single complex could facilitate energy mediated by the energy-

coupling proteins of the system, Enzyme I and HPr, as well as explain the non-uniform

distribution of PTS proteins within the cytoplasmic membrane.

(b) HPr interactions

In some bacteria, such as firmicutes, HPr (or PtsH), the small PTS energy coupling protein, plays

a dominant role in the regulation of carbon metabolism83. However, in E. coli and other enteric

bacteria, interactions of HPr with other constituents for purposes of regulation are not well

established. Interactions of HPr with other cellular proteins that we report in our study

(Supplementary Table 3) makes physiological sense. In fact, the results suggest that HPr

interacts with a number of cellular constituents involved in carbon metabolism and protein

synthesis, and possibly other bioprocesses.

E. coli HPr is known to interact with, energize and be involved in the regulation of PTS

enzymes II complexes84. It also interacts with the E. coli central regulatory protein, the IIAGlc

(Crr) protein85, and the dihydroxyacetone kinase (DhaK)86,87. The interaction of HPr with IIAGlc

and DhaK are confirmed in our study. Other metabolic enzymes that appear to interact with HPr

include the critical glycolytic enzymes, 6-phosphofructokinase II (PfkB) and pyruvate kinase I

(PykF), glucosamine 6-phosphate deaminase (NagB) and 3-deoxy-D-manno-octulosonate-8-

phosphate synthase (KdsA). HPr also interacted with adenylate kinase (Adk) that interconverts

ADP and ATP88, as well as the iron storage and detoxification protein, bacterioferritin (Bfr).

The interactome data presented in Supplementary Table 3 also suggests that HPr plays a

significant role in ribosome-dependent protein biosynthesis89. Interactions relevant to this


28

process include those with protein chain elongation factor (EF-Ts) (the Tsf protein) which plays

a critical role in the rate of translation90, and the predicted ribosome-associated σ54 modulation

protein (Hpf)91. Moreover, HPr found to interact with the 16S rRNA processing proteins, RimP

(YhbC) and RimM92. RimP is important for maturation of the 30S ribosomal subunit93. It

associates with this ribosomal subunit, but not with the 50S or 70S ribosomal complexes, and it

is essential for the formation of RNA pseudoknot94. RimM is another 16S rRNA processing

protein that interacted with HPr with comparable affinity. As well, we found HPr to be

associated with the ribosomal recycling factor, Frr95.

Several other proteins with HPr were also linked (Supplementary Table 3). For example,

YgiW is known in some bacteria (such as Aggregatibacter) to be a periplasmic stress protein,

which regulates biofilm formation96, while in Pseudomonas species, its ortholog is involved in

protection against heavy metals, oxidative stress, acid stress and reactive oxygen species97.

While HPr is predominantly present in the CY, and YgiW is believed to be periplasmic98, this

difference in location does not preclude the possibility of interaction since YgiW is made in the

CY prior to export to the PE. Moreover there are reports99,100 indicating that predominantly

cytoplasmic proteins can exist in the PE, and periplasmic proteins can exist in the CY.

Interactions within ABC transporters

In this section, we will discuss the data observed within the 12 individual ABC transport systems

(Supplementary Fig. 7b, c). The results comply with expectations since in many of these

systems, interactions between the ATPases, transmembrane subunits, and receptors have been

documented in previous studies51. The first system to be analyzed is the arginine transport

system, Art. As shown in Supplementary Fig. 7b, this system consists of a homodimeric

ATPase, ArtP, a heterodimeric channel forming membrane complex, ArtMQ, and one of two


29

periplasmic amino acid binding receptors, I and J101-103. Our interactome data indicate that P

interacts with both M and Q, and that I interacts with J.

The next three systems portrayed in Supplementary Fig. 7b, c are specific for peptides104.

The dipeptide transport (Dpp) system (Supplementary Fig. 7c) showed extensive interactions

where both ATPases, DppD and DppF, interact with both membrane constituents, DppB and

DppC. All four of these interactions gave good Σ LLS scores, between 7 and 14. An interaction

between the membrane subunit, DppB, and the PE receptor, DppA, was also noted with a score

of 12. Interactions were also observed for the Gsi peptide transport system105, where the GsiA

glutathione ATPase interacted with both membrane constituents, GsiC and GsiD, which also

linked with each other. GsiC was also associated with the PE receptor, GsiB. Likewise,

examination of the Sap peptide uptake system106 revealed interactions between dissimilar

ATPase subunits SapDF, and integral membrane constituents SapB, SapC as well as SapBCF.

Additionally, the iron uptake system, FhuBCD107 (Supplementary Fig. 7b), showed

strong interactions between the homodimeric ATPase (FhuC2), and the homodimeric channel

forming complex (FhuB2). A much stronger interaction was measured between FhuB and the

periplasmic receptor, FhuD, while weaker with FhuC and FhuD. Likewise, for the glutamine

uptake system, the homodimeric ATPase, GlnQ, was found to interact with the homodimeric

membrane constituent, GlnP. In this case, no interaction with the binding receptor was

detected108.

The next system we examined appeared to be specific for polyamines (Supplementary

Fig. 7b). In the PotABCD system109, interactions of PotA with both PotB and C were observed,

and an interaction between the two integral membrane constituents, PotB and PotC, was also

observed with a much higher score. The Ydc putative polyamine uptake system showed only


30

interactions between the homodimeric YdcT ATPase and the heterodimeric channel forming

constituents, YdcU and YdcV.

In the case of the leucine ABC transporter system, LIV-locus is comprised of two

periplasmic proteins, LivJ and LivK, IM permeases LivH and LivM, and cytoplasmic ATPases

LivG and LivF. While individual LIV ABC transporter components are responsible for the

uptake of branched amino acids, especially leucine, evidences110-112 suggest that the periplasmic

proteins of transport systems are able to use non-cognate permeases and ATPases to transport the

amino acids into the cytoplasm. Consistent with this proposed notion112, our interaction network

identified that ATPases (LivGF) interact with each other and with the PE protein LivK,

suggesting the requirement of these proteins in the transport of branched-chain amino acids into

the bacterial cell. Similar to the LIV system, interaction was observed between GltL ATPase and

GltJ, an integral membrane constituent113 of the aspartate/glutamate (GltIJKL) uptake system.

The last two systems depicted in Supplementary Fig. 7b are efflux systems, the first of

which (YbhFGRS) has not been characterized (TC# 3.A.1.105.15), but the second, LolCDE, is a

lipoprotein export system114 (TC# 3.A.1.125.1). There is no periplasmic receptor for either of

these systems, but interestingly, the Ybh exporter includes a MFP, YbhG. In this case,

interactions of the homodimeric YbhF ATPase, with the two dissimilar integral membrane

constituents, YbhR and YbhS, were documented as the interaction between these two membrane

components. Notably, both integral membrane components of this system interacted with YbhG

MFP. A similar case was observed for LolCDE lipoprotein export, where all three components of

this system were shown to interact, the two dissimilar membrane constituents interacting with a

much higher score than was observed for the interactions between the ATPase and the channel-

forming subunits.


31

In fact, a quick examination of Supplementary Fig. 7b showed that only two of the 12

systems depicted have heterodimeric ATPases, only one system, the Art arginine transporter, has

two sequence-similar receptors, and only two of the systems illustrated (Fhu and Gln) have

homodimeric membrane constituents. Interestingly, only the Dpp system provided evidence for

interactions between the cytoplasmic heterodimeric ATPase subunits (DppDF; Supplementary

Fig. 7c) and the periplasmic receptor, DppA. This result could be of physiological significance,

either because the interactions occur in the CY before export of the peptide binding receptor, or a

portion of the ATPase is likely to transiently cross the cell membrane115.

Sec protein interaction with cytosolic ClpAPX factors

The IM Sec translocon (SecDEY) was found to interact with the cytosolic proteins (ClpAPX)

involved in degradation/folding. Regarding the validity of Sec interactions with cytosolic

proteins, we note that: (i) interactions between Sec and Clp systems are supported with multiple

lines of evidences and hence are categorized as high confidence (Supplementary Table 2); (ii)

cytosolic molecular chaperones dynamically regulate folding (preventing aggregation in the case

of Sec and promoting folding in the case of Tat) and, in some instances, guide the substrate from

the ribosome to the translocon116; (iii) ClpAPX helps with protein folding, particularly with the

cytoplasmic domains of membrane-inserted proteins; and (iv) bacterial protein homeostasis is

tightly coupled with the protein translocation machinery117, which could aid in degrading

misfolded CE protein precursors.

Bam-Tam interplay advances existing understanding of the system

Gram-negative bacteria require dedicated machinery for the assembly and trafficking of proteins

that are required for bacterial pathogenesis. At least 7 specialized secretion systems have been

developed (Types I-VI and the chaperone-usher pathway) that facilitate navigation and secretion


32

of proteins across the bacterial CE. While most of these pathways assemble large protein

complexes, the type V secretion system - which encompasses the two-partner secretion system

and autotransporter pathway - uses a relatively simple mechanism to deliver autotransporter

proteins to the cell surface of the OM. The autotransporter superfamily is tightly linked to

virulence as the proteins it translocates play key roles in many pathogenic functions such as cell

adherence, biofilm formation, immune system evasion, and cytotoxin secretion118.

An archetypal premature autotransporter from the Type Va secretion pathway119 consists of

a signal peptide followed by the functional N-terminal passenger domain (NPD), and finally a C-

terminal -barrel domain that anchors the protein in the OM120. While the passenger domains

vary dramatically in sequence and size (20 to 400 kDa), they almost all contain a -helical

structure secreted through the C-terminal 12 stranded pore-forming -barrel (30 kDa) embedded

in the OM. After translocation, the passenger domains and the membrane embedded -domain

remain connected together within the PE by an -helical segment spanning the lumen of the -

barrel domain121. In this manner many passenger domains remain anchored to the cell surface.

However, some autotransporters release their passenger domains into the extracellular milieu

through a variety of mechanisms, including exogenous proteases or autocatalytic cleavage -

leaving the -helical segment occluding the pore118.

The translocation of the autotransporter’s passenger domain across the OM to the cell

surface occurs in a C- to N-terminal vectorial secretion, likely initiated by the formation of a

hairpin structure originating at the C-terminus of the passenger domain122. This transient hairpin

has been suggested to remain within the lumen of the -barrel domain while a labile strand of the

hairpin elongates through the pore until the passenger domain is completely translocated123.


33

However, the hairpin model is controversial given the steric constraints within the narrow pore

formed by the 12 stranded -barrel domain121.

A second model suggests the BamA - the central component of the Bam complex -

contributes to the secretion of the passenger domain124. Recent structural reports of BamA

suggest an alternative mechanism where BamA sequentially incorporates -strands from the

nascent OMP into its own 16-stranded barrel via a lateral opening mechanism125-127. This -

augmentation model of OMP biogenesis implies the formation of a hybrid BamA-OMP complex

with a larger pore, which is compatible with the hairpin secretion model given the enlarged

lumen and could also facilitate the secretion of a partially folded passenger domain. In either

case, the energy required to secrete the passenger domain remains to be discovered. It has been

proposed that the progressive folding of the passenger domain at the cell surface drives the

translocation across the OM to prevent any retrograde slip into the PE128; however the secretion

may require additional factors to increase the efficiency of the translocation step that has been

observed for engineered passengers containing intrinsically disordered domains129,130.

Recently the membrane spanning translocation and assembly module (Tam) composed of

TamA and TamB has been implicated in autotransporter secretion within specific bacterial

lineages75,131, however their precise contributions in the type V secretion system assembly

pathway remains unknown. TamA is a 60 kDa OMP of the Omp85 family (BamA, FhaC

homologs125,132), consisting of an N-terminal periplasmic domain subdivided into 3 polypeptide

transport associated (POTRA) repeats preceding a 16-stranded C-terminal -barrel domain that

is embedded in the OM. The TamA structure from E. coli revealed a similar fold to the BamA

OMP-insertase133 (Supplementary Fig. 8a), and has been suggested to function as the conduit

required to translocate autotransporters across the OM.


34

TamB is found in the same operon as TamA and encodes a 140 kDa IMP with no

characterized homolog. TamB consists of an amino-terminal transmembrane helix, a large

interdomain with no assigned gene ontology (GO) annotation, and a C-terminal DUF490

domain. Protease shaving assays and bioinformatic analyses of the transmembrane topology

indicate that TamB is localized within the periplasmic space75 and that the TamA and TamB

form a hetero-oligomeric complex crucial for the delivery of the autotransporter passenger

domain to the cell surface75.

To investigate the molecular mechanism employed by the Tam components, we

characterized the relative effect of each of the tamA and tamB deletion mutants using various

autotransporter proteins (Ag43, AidA, TibA and YadA) as part of the translocation model. These

selected autotransporters belong to phylogenetically diverse subfamilies134 including the

monomeric autotransporter group with both the PL1- (Ag43, AidA) and PL2- (TibA) subgroups

(type Va secretion system), as well as the trimeric autotransporter group (YadA; type Vc

secretion system). Our findings demonstrate that TamB is the only component of the Tam

complex essential in the secretion of autotransporters, as the tamB knockout prevents an

agglutination signature phenotype of the autotransporters (Fig. 4c-e). This gene deletion leads to

the delivery of non-functional autotransporters onto the cell surface, underlying a potential role

of TamB as a chaperone factor involved in the maturation process of the passenger domain.

Additionally, systematic proteomic and genetic approaches for both TamA and TamB

provided an important mechanistic insight on deciphering their molecular action in OM

biogenesis. Taken together, our findings report for the first time that autotransporter OMPs, such

as Ag43, AidA, TibA and YadA from the type Va (monomeric autotransporters) and Vc

(trimeric autotransporters) secretion systems, do not spontaneously self-assemble at the cell


35

surface but instead require chaperone assistance to nucleate their folding in the PE prior to

delivery to the bacterial cell surface through a cooperative mechanism involving the Bam and

Tam machineries.

(i) TamA-TamB functionally associated with Bam

Physical association of TamA and TamB with Bam components as revealed through our affinity

purification and mass spectrometry (AP/MS) experiments led us to examine epistatic

relationships between the members of these component machineries. To confirm this, we

generated double mutants by conjugating the E. coli tamA or tamB query donor mutant strain

(marked with chloramphenicol) against the non-essential or hypomorphic alleles of the essential

bam recipient mutants (marked with kanamycin) using our established synthetic genetic array

procedure135. The colony growth and relative fitness of the resulting double mutants surviving

dual drug selection was then compared to their corresponding single mutants.

As with our proteomics experiments, we observed a strong synthetic sick lethal (SSL)

phenotype between tamA or tamB with bamA, and not to other bam components (Fig. 4b). This

SSL phenotype, confirmed here through manual conjugation screens between tamA or tamB with

bamA in the standard nutrient rich growth condition, was not tested in the large-scale envelope

biogenesis epistatic screens35 and has not been previously reported. Conversely, we were unable

to observe the SSL growth defect noted before35 between tamB and bamC or bamD. This could

be due to subtle differences in growth conditions, duration of incubation, nutrient availability on

the plates at the time of screening, or the limitations in sensitivity within the scoring and

thresholding procedures used, which aim to highlight genetic interactions (GIs) that stand out in

contrast to the distribution of scores across all tests.


36

As in any other large-scale epistatic screens135, it is therefore imperative to perform small-

scale validation experiments as previously described136, to verify the putative GIs generated from

the high-throughput eSGA screens. Nevertheless, one interpretation of our confirmed result (Fig.

4b) is that TamA and TamB jointly participates in autotransporter biogenesis in a pathway

overlapping with BamA mediated export, which constitutes a dominant secretion pore for Type

V autotransporters.

(ii) Contribution of Ag43 and TamB to existing knowledge and its connection to potential

interactions identified from AP/MS

Our proteomic analysis confirmed the TamA-TamB interaction, originally suggested by blue

native polyacrylamide gel electrophoresis analysis75 to form a heterooligomeric Tam complex

(spanning the inner and outer membrane) with a set of unidentified components. Consistent with

the latter, we detected physical association with proteins that function in OMP biogenesis

(BamABCD) and autotransporter processing (Ag43; Fig. 4a). These interactions are likely to be

physiologically relevant as the secretion mechanism of autotransporters relies on several

pathways that form the basis of OM protein trafficking and biogenesis. Autotransporters are

translated into the cytoplasm and translocated across the IM into the PE in a signal peptide and

Sec-dependent manner137. The Bam complex is thought to be responsible for the insertion of the

-barrel domain into the OM by acting as a generic foldase-insertase complex dedicated to OM

proteins138-141. However, the specific role or strict requirement of the Bam complex during

passenger secretion has not yet been elucidated.

The localization of TamB to the periplasmic side of the IM further suggests that TamB

primes the maturation of the passenger domain prior to its translocation across the OM. This

model is supported by both our proteomic and genetic studies, which reveal strong physical and


37

functional connectivity between the Tam and Bam machinery (Fig. 4a), suggesting a plausible

synergistic mechanism in the secretion and maturation of autotransporters. The interplay between

TamA and TamB with the ubiquitous Bam machinery likely facilitates or regulates the folding of

other dedicated protein clients as suggested by i) the BamA-TamA structural homology125,133;

(ii) our experimental data illustrating the multiple connections of the Tam complex with the

OMP biogenesis route; and (iii) the Bam-Tam epistatic genetic connectivity (synthetic lethality).

TamA has previously been reported to promote the secretion of the passenger domains

from the two autotransporter Ag43 and p1121 in E. coli and Citrobacter rodentium,

respectively75; however, consistent with other studies129,142 we were unable to reproduce the

Ag43 secretion defect in E. coli (Fig. 4c-g and Supplementary Fig. 8b-d). To validate our

initial observations, we characterized the secretion of three additional autotransporters (AidA,

TibA and YadA) that belong to different autotransporter subfamilies134, all of which provide a

robust indication of the innocuous effect of a tamA deletion on autotransporter secretion, while

supporting the key role of TamB for proper autotransporter maturation.

(iii) TamB deletion leads to the secretion of non-functional passenger domains

While the surface exposed and folded Ag43 NPD remains anchored to the OM via its C-terminal

-barrel domain, the mature fully translocated protein is normally clipped after the amino acid

D551 by an unidentified protease. This proteolysis results in the detachment of functional surface

domain (Ag43) only under thermal denaturation conditions (60 C)143. Hence, to exclude the

possibility of a minor segment of Ag43 NPD being detected at the cell surface due to a

hypothetical translocation defect in the tamB mutant, we confirmed the ability of the Ag43

passenger domain to fully secrete by exploiting the heat-shock release property of Ag43.


38

Flow cytometry performed on 60 C heated E. coli cells showed a complete disappearance

of the passenger domain from the cell surface of wild-type (WT) and tamB mutant strains (Fig.

4e, f). The extracellular supernatant obtained from the heat-shock of both WT and tamB mutant

E. coli cells was analyzed by SDS-PAGE and MS, identifying the presence of Ag43 fragment

in both supernatant fractions and thus confirming the secretion of the entire Ag43 passenger

domain outside the cells (Supplementary Fig. 8c, e). Finally, the molecular mass of the secreted

Ag43 functional domain from both WT and tamB mutant strains were identical

(Supplementary Fig. 8d), indicating that the agglutination defect in tamB cells is not caused by

any differential posttranslational modification that would influence Ag43 function.

To identify potential folding defects associated with the passenger domains translocated at

the cell surface of the tamB mutants, we performed a stability assay by treating Ag43-expressing

WT and tamB E. coli cells with proteinase-K while monitoring the degradation of Ag43 in a time

dependent manner (Fig. 4g). Our data represent the instability of the amino-terminal domain of

Ag43 translocated at the cell surface from the tamB strain, which is more susceptible to

proteinase-K digestion as compared to the passenger domain secreted through the OM of the WT

strains. Taken together, our data strongly suggest that the Tam complex is not involved in the

passenger domain translocation of autotransporters, but rather involved in the folding process of

the passenger domain to be translocated onto the cell surface.

(iv) Secretion model for the autotransporters from the type Va pathway

Our results demonstrate that the Tam complex is an auxiliary machinery linked to the Bam

complex (Fig. 4h), contributing to OM biogenesis. TamA is an Omp85 protein with high

structural similarity to BamA that forms a heterodimer with TamB. The TamA/B complex


39

protrudes 200Å from the inner-leaflet of the OM into the PE, with TamB harboring an elongated

shape previously suggested to form a ß-helical structure144.

First, the nascent autotransporter chain translocated into the PE in a Sec-dependant manner

binds with the Tam complex while it is targeted to the Bam machinery through its C-terminal β-

motif. Second, the 12-stranded membrane anchored domain from the autotransporter is

processed by the Bam complex to fold and insert the ß-barrel domain within the membrane. The

insertion of the membrane domain requires a lateral opening of BamA, thus BamA and the

autotransporter client form an hybrid pore composed of the insertion of the newly folded ß-

strands within the BamA barrel by ß-augmentation125,126. Third, the addition of newly folded ß-

strand within the hybrid pore enlarges the BamA exit pore126,127, allowing the translocation of the

passenger domain of the autotransporter. The TamB component then contributes to the proper

folding of the passenger domain while it translocates to the cell surface. Finally, the mature

autotransporter ultimately buds off from the BamA-client hybrid-pore to populate OM126.

Web portal details

We provided options to use our website (http://ecoli.med.utoronto.ca/membrane) as a resource to

download the entire set of interactions in excel table format, or to query individual protein

names, subcellular compartments or keywords. We also now allow users to access the interaction

data after specifying varying confidence filters:

1. High confidence (HC): interactions supported by multiple replicates, detergents, and

different experimental methods.

2. Medium confidence (MC): interactions supported by functional and/or physical

association evidence predicted by STRING145, GeneMANIA146, genomic context

methods9, or co-localization to the same compartment.


40

Putative interactions not supported by any of the above additional criteria were removed from the

analysis. Moreover, users are provided with information encompassing protein complex

composition, subcellular localization, abundance, and the presence (predicted) of transmembrane

helices, ß barrels, and/or signal peptides. The browser also allows searching for interactions in

batch mode (e.g. multiple CEPs at once), where the user can define the confidence (e.g. ΣLLS)

score cut-off and exclusion/inclusion of common interactors. The results can be readily

visualized in Cytoscape by clicking on the icon presented in the browse “complex” section.

Numbers presented in the edges between proteins refers to the ΣLLS score.

Next, we have implemented a high-level browsing function, where by clicking on either a

protein, interaction, or complex, one can quickly survey all relevant CEPs, interactions and

complexes found in this study. Users have the flexibility to rapidly identify: (1) interaction

partners of any given CEP bait and with what confidence (ΣLLS score), (2) in which predicted

complex, a given CEP and its interacting proteins are present, (3) if a given CEP was

successfully recovered as a bait, (4) the protein pairs with “bait-prey” and “prey-prey”

associations, and (5) the type of models (i.e. Matrix, Spoke or Socio-Affinity) assigned to each

predicted complex. Users can also access the supporting mass spectral evidence and probability

scores. Finally, we now include a “help” documentation page to assist navigation of the database,

and downloading of both the complete dataset and associated supplementary tables to raise the

reader’s awareness of how the data is presented and accessed via our website.

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Supplementary Tables

Supplementary Table 1 | Classification of the E. coli proteome and target cell envelope protein

selection for AP/MS screens


49

Supplementary Table 2 | Co-purifying protein pairs compiled from reference Ecoyc database

and from this study

Supplementary Table 3 | The physiological relevance and quality of PPIs by anecdotal

supporting evidence, drug sensitivity profiles, and two-hybrid screens

Supplementary Table 4 | Putative (or novel) CEP complexes and their subunits identified by the

core-attachment based clustering algorithm are indicated with their respective topology model

Supplementary Table 5 | Antibiotic susceptibility, evolutionary conservation, and paralogous

analyses on CEPs or complexes, and bacterial strains/plasmids used in this study


supplementary information global landscape of cell envelope protein ... - nature research ·...

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