Access to this full-text is provided by American Society for Microbiology.
Content available from mSystems®
This content is subject to copyright. Terms and conditions apply.
| Open Peer Review | Systems Biology | Research Article
CemR atypical response regulator impacts energy conversion
inCampylobacteria
Mateusz Noszka,1 Agnieszka Strzałka,2 Jakub Muraszko,1 Dirk Hofreuter,3 Miriam Abele,4 Christina Ludwig,4 Kerstin Stingl,5 Anna
Zawilak-Pawlik1
AUTHOR AFFILIATIONS See aliation list on p. 22.
ABSTRACT Campylobacter jejuni and Arcobacter butzleri are microaerobic food-borne
human gastrointestinal pathogens that mainly cause diarrheal disease. These related
species of the Campylobacteria class face variable atmospheric environments during
infection and transmission, ranging from nearly anaerobic to aerobic conditions.
Consequently, their lifestyles require that both pathogens need to adjust their metab
olism and respiration to the changing oxygen concentrations of the colonization sites.
Our transcriptomic and proteomic studies revealed that C. jejuni and A. butzleri, lacking a
Campylobacteria-specic regulatory protein, C. jejuni Cj1608, or a homolog, A. butzleri
Abu0127, are unable to reprogram tricarboxylic acid cycle or respiration pathways,
respectively, to produce ATP eciently and, in consequence, adjust growth to chang
ing oxygen supply. We propose that these Campylobacteria energy and metabolism
regulators (CemRs) are long-sought transcription factors controlling the metabolic shift
related to oxygen availability, essential for these bacteria’s survival and adaptation to the
niches they inhabit. Besides their signicant universal role in Campylobacteria, CemRs,
as pleiotropic regulators, control the transcription of many genes, often specic to the
species, under microaerophilic conditions and in response to oxidative stress.
IMPORTANCE C. jejuni and A. butzleri are closely related pathogens that infect the
human gastrointestinal tract. In order to infect humans successfully, they need to change
their metabolism as nutrient and respiratory conditions change. A regulator called CemR
has been identied, which helps them adapt their metabolism to changing conditions,
particularly oxygen availability in the gastrointestinal tract so that they can produce
enough energy for survival and spread. Without CemR, these bacteria, as well as a related
species, Helicobacter pylori, produce less energy, grow more slowly, or, in the case of
C. jejuni, do not grow at all. Furthermore, CemR is a global regulator that controls the
synthesis of many genes in each species, potentially allowing them to adapt to their
ecological niches as well as establish infection. Therefore, the identication of CemR
opens new possibilities for studying the pathogenicity of C. jejuni and A. butzleri.
KEYWORDS Campylobacter jejuni, Arcobacter butzleri, carbon metabolism, respiration,
transcription factors, proteomics, transcriptomics, oxidative stress, Helicobacter pylori,
signal transduction
Campylobacter jejuni and Arcobacter butzleri are Gram-negative, microaerobic bacteria
that belong to the Campylobacteria class (1). C. jejuni is a commensal bacterium of
the gastrointestinal tracts of wildlife and domestic animals. However, in humans, C. jejuni is
the leading cause of food-borne bacterial diarrheal disease (2). Also, it causes autoimmune
neurological diseases such as Guillian-Barré and Miller-Fisher syndromes (3). A. butzleri is
found in many ecological niches, such as environmental water and animals. Still, in humans,
A. butzleri causes diarrhea, enteritis, and bacteremia (2). Due to the wide distribution of the
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 1
Editor Jack A. Gilbert, University of California San
Diego, La Jolla, California, USA
Address correspondence to Anna Zawilak-Pawlik,
anna.pawlik@hirszfeld.pl.
The authors declare no conict of interest.
See the funding table on p. 22.
Received 9 June 2024
Accepted 12 June 2024
Published 9 July 2024
Copyright © 2024 Noszka et al. This is an open-
access article distributed under the terms of the
Creative Commons Attribution 4.0 International
license.
two species and relatively high resistance to environmental conditions (4) and antibiotics (5,
6), both species pose a severe threat to human health.
To adapt to dierent ecological niches and decide whether conditions are optimal
for reproduction and transmission, C. jejuni and A. butzleri encode numerous regulatory
proteins. According to the MiST database (7), C. jejuni NCTC 11168 [1.64 Mbp; 1,643 coding
sequences (8)] and A. butzleri RM4018 [2.34 Mbp; 2,259 coding sequences (9)] encode 56
and 218 signal transduction proteins, respectively. While A. butzleri regulatory systems
have been hardly studied (10), the C. jejuni signal transduction systems have been partially
deciphered (11–15). Particular interest was directed to studying processes related to the C.
jejuni colonization and focused on factors controlling the expression of genes involved in
such processes as host adaptation and niche detection (BumSR and DccSR), oxidative stress
(e.g., CosR and PerR), metabolism, and respiration [e.g., BumSR, RacSR, CosR, CsrA, LysR
(Cj1000), and CprSR] (11, 13–15). Notably, aerobic or microaerophilic respiration, in which
oxygen is used as a terminal electron acceptor, is far more ecient in supplying energy
than anaerobic respiration or fermentation (16). On the other hand, oxygen-dependent
respiration is a source of reactive oxygen species (ROS) (17). C. jejuni requires oxygen for
growth but, unlike facultative aerobic A. butzleri, is sensitive to aerobic conditions faced
during transmission. In addition, C. jejuni and A. butzleri may encounter conditions decient
in oxygen in the intestine or during intracellular persistence (18). Thus, C. jejuni and A.
butzleri must adjust their metabolism and respiration as oxygen concentrations change at
colonization sites to get sucient energy while maintaining redox homeostasis. However,
no orthologs of energy or redox metabolism regulators such as gammaproteobacterial
ArcA, FNR, or NarP are found in C. jejuni (14, 19–21).
The roles of many C. jejuni and most A. butzleri regulators have still not been explored.
One such regulator is the C. jejuni NCTC 11168 Cj1608 orphan response regulator,
which is homologous to uncharacterized A. butzleri RM4018 Abu0127. These regulators
are homologous to Helicobacter pylori 26695 HP1021 and are conserved across most
Campylobacteria class species, constituting most of the Campylobacterota phylum.
The H. pylori HP1021 response regulator is one of 28 signal transduction proteins
encoded by H. pylori 26695 (7, 22). HP1021 interacts with the origin of chromosome
replication region (oriC) in vitro, probably participating in the control of the initiation
of H. pylori chromosome replication (23). HP1021 acts as a redox switch protein, i.e.,
senses redox imbalance and transmits the signal and triggers the cells’ response (24).
The HP1021 regulon, initially determined in H. pylori 26695 by microarray analyses (25),
has been updated in H. pylori N6 using a multi-omics approach (26). HP1021 inuences
the transcription of almost 30% of all H. pylori N6 genes of dierent cellular categories;
the transcription of most of these genes is related to response to oxidative stress.
HP1021 directly controls typical ROS response pathways and less canonical ones, such as
central carbohydrate metabolism. The level of ATP and the growth rate of the knock-
out H. pylori ΔHP1021 are lower than in the wild-type strain, which is possibly due to
reduced transcription of many tricarboxylic acid cycle (TCA) genes and/or increased ATP
consumption in catabolic processes in ΔHP1021 compared to the wild-type strain. Thus,
HP1021, among many cellular processes, probably controls H. pylori metabolic uxes to
maintain the balance between anabolic and catabolic reactions, possibly for ecient
oxidative stress response (26).
In this work, we focused on the two hardly characterized regulatory proteins of
the microaerobic C. jejuni NCTC 11168 and the facultatively aerobic A. butzleri RM4018,
Cj1608, and Abu0127, respectively. To get insight into the function of these proteins,
we constructed mutants lacking these regulators and looked at transcriptomic and
proteomic changes under optimal growth and oxidative stress conditions. Our results
indicate that Cj1608 and Abu0127, which we named Campylobacteria energy and
metabolism regulators (CemRs), support energy conservation in bacterial cells by
controlling metabolic and respiration pathways in response to oxygen availability.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 2
RESULTS
Inuence of the Cj1608 regulator on C. jejuni gene and protein expression
Cj1608 has been hardly characterized thus far. It is only known that it interacts in vitro
with the C. jejuni oriC region and the promoter of the lctP lactate transporter, probably
participating in the control of the initiation of the C. jejuni chromosome replication and
lactate metabolism (27, 28). Therefore, to elucidate the role of the Cj1608 regulator
in controlling C. jejuni gene expression and oxidative stress response, we performed
transcriptome analysis [RNA sequencing (RNA-seq)] of the C. jejuni NCTC 11168 wild-type
(CJ) and deletion mutant (ΔCj1608) strains under microaerobic growth (CJ, ΔCj1608) and
during paraquat-induced oxidative stress (CJS, ΔCj1608S) (Fig. S1A).
A comparison of C. jejuni ΔCj1608 and CJ transcriptomes revealed 380 dierentially
transcribed genes (Fig. 1A and B; Fig. S2A; S1 Data). The paraquat stress aected the
transcription of genes in wild type and ΔCj1608 strains (232 and 123 genes, respectively)
(Fig. 1B; Fig. S2B and C). The transcription of 44 genes was similarly induced or repressed
in CJS and ΔCj1608S cells (Fig. S2D, red dots). Thus, these genes responded to oxidative
stress, but other or additional factors than Cj1608 controlled them. Using ClusterProler
(29), we performed a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment
analysis to identify processes aected most by the lack of Cj1608 or under stress. In
the ΔCj1608 strain, three KEGG groups were activated (nitrogen metabolism, ribosomes,
and aminoacyl-tRNA-biosynthesis). One KEGG group was suppressed, namely, the TCA
cycle (Fig. 1C). In the CJ strain under stress conditions, six KEGG groups were suppressed
(e.g., oxidative phosphorylation), while four KEGG groups were activated (e.g., carbon
metabolism).
Since post-transcriptional regulation is common in bacteria, including species closely
related to C. jejuni (30, 31), a proteomics approach [liquid chromatography coupled with
tandem mass spectrometry (LC-MS/MS)] was applied to detect proteins whose levels
changed between analyzed strains or conditions. A total of 1,170 proteins were detected
(S1 Data); however, 173 proteins encoded by genes whose transcription changed in
ΔCj1608 were not detected by MS (S1 Data). The comparison between proteomes
of ΔCj1608 and CJ strains under microaerobic conditions revealed dierent levels of
156 proteins (Fig. S4A; red, yellow, and green dots). The transcriptomic and proteomic
data correlated strongly (Fig. 1D, red dots; Fig. S5A). However, the dierences between
transcription and translation patterns were observed for many genes, suggesting a
post-transcriptional regulation of C. jejuni gene expression (Fig. 1D; blue, yellow, and
green dots). Under oxidative stress, the expression of only four genes changed, namely,
the expression of ferrochelatase HemH (Cj0503c) was downregulated in the CJS cells;
at the same time, the levels of three proteins were upregulated [the catalase KatA
(Cj1385), the atypical hemin-binding protein Cj1386, mediating the heme tracking to
KatA (32), and the periplasmic protein Cj0200c] (Fig. S4B and C). The small number
of proteins whose expression was altered under oxidative stress conrmed the known
phenomenon of ceasing protein synthesis by bacteria under oxidative stress (26, 33,
34). The ClusterProler KEGG enrichment analysis revealed that genes of two KEGG
groups, nitrogen metabolism and ribosome, were activated in the ΔCj1608 strain in
comparison to CJ, while the genes of three KEGG groups were suppressed (TCA cycle,
ABC transporters, and carbon metabolism) (Fig. 1E). It should be noted that the nitrogen
metabolism group includes proteins such as GlnA and GltBD, which are involved in
glutamate metabolism and TCA cycle (35), whose levels increased more than 10-fold,
and Nap and Nrf complexes, constituting an electron transport chain (ETC) system (see
below).
To conclude, the transcriptomic and proteomic data revealed that Cj1608 impacts the
transcription of 585 genes and the expression of 156 proteins in C. jejuni NCTC 11168,
with the TCA and nitrogen metabolism KEGG groups most aected.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 3
FIG 1 RNA-seq and LC-MS/MS analyses of C. jejuni gene expression controlled by Cj1608. (A) The comparison of gene transcription in the CJ, CJS, and Cj1608
knock-out mutant (ΔCj1608) cells revealed by RNA-seq. Genes whose transcription signicantly changed (|log2FC| ≥ 1, FDR < 0.05) are depicted by colored dots
(see the legend in the gure). The genes named on the graph correspond to the citric acid cycle and electron transport chain. (B) The Venn diagram presents the
number of dierentially transcribed genes in the analyzed strains and conditions. (C) ClusterProler protein enrichment plot showing activation or suppression
of KEGG groups in the analyzed strains. (D) The correlation between gene expression in ΔCj1608 and CJ cells at the transcription and proteome levels. Red
dots represent homodirectional genes up-regulated or down-regulated at the transcriptome and proteome levels. Yellow dots represent opposite changes.
Blue dots represent genes that changed only at the transcription level, while green dots represent genes that changed only at the proteome level. Numbers
of dierentially expressed genes in the indicated strains and conditions are given in parentheses. The proteins named on the graph correspond to the citric
acid cycle and electron transport chain. (E) ClusterProler protein enrichment plot showing activation or suppression of KEGG groups in the analyzed strains. (A
and D) Values outside the black dashed lines indicate a change in the expression of |log2FC| ≥ 1. Gray dots correspond to genes whose transcription was not
changed (|log2FC| < 1). CJ, C. jejuni wild type; CJS, stressed wild type; FC, fold change; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes;
LC-MS/MS, liquid chromatography coupled with tandem mass spectrometry.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 4
Inuence of Abu0127 on A. butzleri gene and protein expression
To elucidate the role of the Abu0127 regulator in controlling gene expression and
oxidative stress response of A. butzleri, we performed RNA-seq and LC-MS/MS analyses
similar to those for C. jejuni. We analyzed gene expression in A. butzleri RM4018 wild-type
(AB) and deletion mutant (ΔAbu0127) strains; paraquat was used to induce oxidative
stress (ABS, ΔAbu0127S) [Fig. S1B (36)].
Comparison of A. butzleri ΔAbu127 and AB transcriptomes revealed 779 dierentially
transcribed genes (Fig. 2A and B; Fig. S3A; S2 Data). The paraquat stress aected the
transcription of genes in the wild type and, to a much lesser extent, in the ΔCj1608
strain (290 and 14 genes, respectively) (Fig. 2B; Fig. S3B and C). Of these 14 dierently
transcribed genes in the ΔCj1608 strain, 12 were also induced in ABS cells (Fig. S3D, red
dots). Thus, these genes responded to oxidative stress but were controlled by factors
other than or additional to Abu0127. KEGG enrichment analysis revealed that in the
ΔAbu0127 strain, 2 KEGG groups were activated, ABC transporters and sulfur metabo
lism, while 13 groups were suppressed (e.g., oxidative phosphorylation and TCA cycle)
(Fig. 2C). In the AB strain under stress conditions, seven KEGG groups were suppressed
(e.g., oxidative phosphorylation and citrate cycle), while six groups were activated (e.g.,
sulfur metabolism).
As in our C. jejuni study, a proteomic approach was used to detect proteins whose
levels changed between the strains and conditions analyzed. A total of 1,586 A. butzleri
proteins were detected (S2 Data). However, proteomics did not detect 224 proteins
encoded by genes whose transcription changed in ΔAbu0127 (S2 Data). The compari
son between proteomes of ΔAbu0127 and AB strains under microaerobic conditions
revealed 124 dierentially expressed proteins (Fig. 2D; Fig. S4D, red, yellow, and green
dots). As in C. jejuni studies, the transcriptomics and proteomics data correlated (Fig. 2D,
red dots; Fig. S5B;); however, in cases of many genes, post-transcriptional modications
probably aected the nal protein levels in the A. butzleri cell (Fig. 2D; blue, yellow,
and green dots). Under paraquat stress, the level of only one protein changed; namely,
Abu0530, of unknown function, was produced at a higher level in ABS cells compared to
AB cells (Fig. S4E and F). Thus, A. butzleri, like C. jejuni, ceased protein translation upon
oxidative stress, which is also typical for other bacterial species (37). The ClusterProler
KEGG enrichment analysis revealed that genes of one KEGG group, sulfur metabolism,
were activated in the ΔAbu0127 strain compared to AB. In contrast, the genes of three
KEGG groups were suppressed (oxidative phosphorylation, taurine and hypotaurine
metabolism, and ribosome) (Fig. 2E).
To conclude, the results of transcriptomic and proteomic analyses indicated that
Abu0127 aects the transcription of 904 genes and the expression of 124 proteins in A.
butzleri RM4018, with the oxidative phosphorylation KEGG group most aected.
Cj1608 and Abu0127 are involved in the regulation of energy production and
conversion
The transcriptome and proteome analyses of both species revealed that the energy
production and conversion processes, such as the TCA cycle and oxidative phosphoryla
tion, are putatively controlled in C. jejuni by Cj1608 and in A. butlzeri by Abu0127 (S1
Data, S2 Data and S3 Data). In C. jejuni ΔCj1608, the protein levels of all but two TCA
enzymes, fumarate reductase FrdABC and fumarate hydratase FumC, decreased, with
the citrate synthase GltA protein level reduced by as much as 70-fold (Fig. 3A; Fig. S6;
the level of oxoglutarate-acceptor oxidoreductase Oor was reduced by 1.7- to 2.0-fold).
Many genes encoding nutrient importers and downstream processing enzymes were
downregulated in C. jejuni ΔCj1608 (e.g., amino acid ABC transporter permease Peb,
fumarate importer DcuAB, L-lactate permease LctP, and lactate utilization proteins Lut).
However, DctA succinate/aspartate importer and the enzymes glutamine synthetase
GlnA and glutamate synthase small subunit GltB involved in glutamine and glutamate
synthesis were upregulated (Fig. 3A). The levels of many proteins forming ETC complexes
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 5
were also downregulated, including subunits of NADH-quinone oxidoreductase Nuo
and cytochromes (Fig. 3B). In A. butzleri ΔAbu0127, the levels of GltA and isocitrate
FIG 2 RNA-seq and LC-MS/MS analyses of A. butzleri gene expression controlled by Abu0127. (A) The comparison of gene transcription in the AB, ABS, and
Abu0127 knock-out mutant (ΔAbu0127) cells revealed by RNA-seq. Genes whose transcription signicantly changed (|log2FC| ≥ 1; FDR < 0.05) are depicted by
colored dots (see the legend in the gure). The genes named on the graph correspond to the citric acid cycle and electron transport chain. (B) The Venn diagram
presents the number of dierentially transcribed genes in the analyzed strains and conditions. (C) ClusterProler protein enrichment plot showing activation or
suppression of KEGG groups in the analyzed strains. (D) The correlation between gene expression of ΔAbu0127 and AB cells at the transcription and proteome
levels. Red dots represent homodirectional genes up-regulated or down-regulated at the transcriptome and proteome levels. Yellow dots represent opposite
changes. Blue dots represent genes that changed only at the transcription level, while green dots represent genes that changed only at the proteome level.
Numbers of dierentially expressed genes in the indicated strains and conditions are given in parentheses. The proteins named on the graph correspond to
the citric acid cycle and electron transport chain. (E) ClusterProler protein enrichment plot showing activation or suppression of KEGG groups in the analyzed
strains. (A and D) Values outside the black dashed lines indicate a change in the expression of |log2FC| ≥ 1. Gray dots correspond to genes whose transcription
was not changed (|log2FC| < 1). AB, A. butzleri wild type; ABS, stressed wild type, FC, fold change; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes
and Genomes; LC-MS/MS, liquid chromatography coupled with tandem mass spectrometry.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 6
dehydrogenase Icd proteins were reduced (Fig. 3C; Fig. S7C). As in C. jejuni, the levels of
many proteins forming ETC complexes decreased, with Nuo complex subunits being the
most highly downregulated (Fig. 3D; Fig. S7A and B).
Moreover, a functional analysis annotation with eggNOG-mapper indicated the
Clusters of Orthologous Groups (COGs) of dierentially expressed genes and proteins.
In both species, the most variable groups belonged to two categories, namely, energy
production and conservation (C) and translation, ribosomal structure, and biogenesis
(J) (Fig. 4; the category of unknown genes was excluded from the graph, S1 Data and
S2 Data). Many genes and proteins suppressed in both species belong to the energy
production and conversion group.
These comprehensive data suggest that mutants of both species produce less energy
due to the inhibition of the TCA cycle and oxidative phosphorylation. Therefore, we
decided to investigate these processes in more detail and analyze how the lack of Cj1608
and Abu0127 proteins inuences energy production and conversion in C. jejuni and A.
butzleri, respectively.
Cj1608 controls gltA expression, ATP level, and growth in response to O2
supply
To analyze the inuence of Cj1608 on C. jejuni growth, we compared the growth of
wild-type CJ, knock-out ΔCj1608, and complementation (CCj1608) strains (Fig. 5A). It
should be noted that unless an accessory ETC electron donor and proton motive force
generator H2 was supplied in the microaerobic gas mixture (4% H2; see Materials and
Methods) [see also reference (38)], we could not obtain the ΔCj1608 mutant strain of
C. jejuni NCTC 11168. When C. jejuni was cultured microaerobically in the presence of
H2, the growth of C. jejuni ΔCj1608 was slower [i.e., the generation time (G) was higher]
than the CJ and CCj1608 strains (Fig. 5A). The ΔCj1608 culture entered the stationary
phase at optical density (OD) like that of CJ and CCj1608 strains (Fig. 5A). Under micro
aerobic conditions without H2, the CJ and CCj1608 strains grew similarly to conditions
with H2, albeit reaching lower OD600 at the stationary phase than with H2 (Fig. 5A). In
contrast, the ΔCj1608 strain almost did not grow without H2, conrming that its growth
strictly depended on hydrogen. The ATP analysis indicated that the relative energy
levels corresponded to the bacterial growth rates. The ATP level of the CJ strain under
microaerobic growth without H2 was assumed to be 100% (Fig. 5B). The ATP level in
CJ and CCj1608 strains was constant, regardless of the presence or absence of H2 (85%–
100%). Without H2, the ATP level in the ΔCj1608 strain dropped to 25% ± 4% of the ATP
level in the CJ strain. In the presence of H2, the level of ATP in ΔCj1608 cells increased
to 53% ± 8% compared to that of the CJ strain. These results indicated that the availa
bility of additional electrons and proton motive force derived from hydrogen (38), an
alternative to those produced by the TCA cycle and used in oxidative phosphorylation,
enabled ΔCj1608 cells to produce more energy and multiply more eciently.
Next, we analyzed whether Cj1608 directly aects the TCA cycle eciency, as
suggested by hydrogen boost analysis. We studied the expression of gltA since the
expression of this gene was severely downregulated in the ΔCj1608 strain (Fig. S8A and
B; S3 Data). GltA is the rst enzyme in the TCA cycle whose activity impacts the ow
of substrates through the TCA cycle and energy production via NADH/FADH/menaqui
none cofactors (Fig. 3A). It was previously shown that gltA transcription depends on
the O2 concentration, being lower at 1.88% O2 and higher at 7.5% O2, which helps C.
jejuni optimize energy production and expense during dierent oxygen availability as
the electron acceptor (39). Here, we conrmed the specic interaction of the Cj1608
protein with the gltA promoter region in vivo by chromatin immunoprecipitation (ChIP)
and in vitro by electrophoretic mobility shift assay (EMSA) (Fig. 5C and D); Cj1608 did
not interact with a control C. jejuni recA region. Next, we used reverse transcription
quantitative PCR (RT-qPCR) to analyze gltA transcription under oxidative stress triggered
by paraquat or under diverse O2 availability: reduced 1% O2, optimal microaerobic 5%
O2, and increased 10% O2 (see Materials and Methods). Transcription of gltA was lower
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 7
in ΔCj1608 than in CJ and CCj1608 strains under optimal conditions [fold change (FC)
of 0.14 ± 0.14] and unaected by paraquat stress, conrming the transcriptomic results
(Fig. S8C). Moreover, transcription of gltA was upregulated in the CJ and CCj1608 strains
at 5% O2 compared to 1% O2 (Fig. 5E). However, in the CJ and CCj1608 strains, the gltA
transcription was downregulated at 10% O2 compared to 5% O2 (Fig. 5E), suggesting that
the prolonged bacterial growth for 5 h at increased O2 concentration possibly caused
adaptation to higher oxygen level conditions or stress by reducing TCA cycle activity
and possible ROS production during increased aerobiosis. However, gltA transcription
was constant in ΔCj1608 at all O2 concentrations, indicating that cells could not adjust
gltA transcription to changing O2 conditions. Next, we analyzed C. jejuni growth and ATP
FIG 3 Schematic presentation of C. jejuni and A. butzleri energy conservation factors whose expression changed in ΔCj1608 and ΔAbu0127, respectively.
(A) Regulation of the C. jejuni citric acid cycle, the selected associated catabolic pathways, and nutrient transporters. (B) Regulation of C. jejuni electron transport
chain enzymes. (C) Regulation of the A. butzleri tricarboxylic acid cycle selected associated catabolic pathways and nutrient transporters. (D) Regulation of A.
butlzeri electron transport chain enzymes. (A–D) The increase or decrease in protein abundance in the deletion mutant strains compared to wild-type strains
is shown based on the LC-MS/MS data (S3 Data). When MS did not detect the protein but the corresponding transcript level was increased or decreased,
the color code depicts RNA-seq results (denoted as •). For details concerning transcriptomic data or complex transcriptome/proteome interpretation (denoted
as *), see S1 to S3 Data. Cco, cbb3-type cytochrome c oxidase; Cio, cyanide-insensitive cytochrome bd-like quinol oxidase; Fdh, formate dehydrogenase; Frd,
fumarate reductase; G-6P, glucose 6-phosphate; Gdh, gluconate 2-dehydrogenase; Hyd, Hya, and Hua, hydrogenases; Lut, Dld, L-lactate utilization complex;
Mfr, methylmenaquinol:fumarate reductase (misannotated as succinate dehydrogenase Sdh); Mqo, malate:quinone oxidoreductase; Nap, nitrate reductase; Nrf,
nitrate reductase; Nuo, NADH-quinone oxidoreductase; Oor, 2-oxoglutarate:acceptor oxidoreductase; PEP, phosphoeneoopyruvate; Pet, ubiquinol-cytochrome C
reductase; POR, pyruvate:acceptor oxidoreductase; SOR, sulte:cytochrome c oxidoreductase; SOX, thiosulfate oxidation by SOX system; Tor, SN oxide reductase;
“?,” unknown enzyme. All genes and enzyme complexes can be found in S3 Data.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 8
production under various O2 supplies. Under 1% O2, the ΔCj1608 strain grew faster than
WT and CCj1608 strains, suggesting that the metabolic state of the ΔCj1608 mutant strain
was optimized for reduced O2 concentration (Fig. 5G). Under optimal O2 level, CJ and
CCj1608 strains grew faster than under 1% O2 and faster than the ΔCj1608 strain under
5% O2, indicating that metabolic pathways of CJ and CCj1608 strains were adjusted to
increased O2 availability, while the metabolism of ΔCj1608 was not. Under increased
O2 concentration, all three strains grew slower than under optimal conditions, with
the ΔCj1608 strain growing slower than the CJ and CCj1608 strains. The ATP analyses
indicated that the relative ATP levels corresponded to the bacterial growth rates. The
ATP level of the CJ strain under optimal microaerobic growth was assumed to be 100%.
Compared to that, the level of ATP in the CJ strain under reduced O2 concentration
dropped to 38% ± 4%; at increased O2 concentration, the level of ATP also decreased
to 51% ± 7% (Fig. 5F; see Discussion). Under optimal conditions in the ΔCj1608 mutant
FIG 4 Impact of Cj1608 and Abu0127 on gene regulation in C. jejuni and A. butzleri, respectively. (A) Circos plots presenting the correlation between COG
and dierentially expressed C. jejuni genes revealed by RNA-seq in the ΔCj1608 strain compared to the C. jejuni wild-type strain. (B) Circos plots presenting the
correlation between COG and dierentially expressed A. butzleri genes revealed by RNA-seq in the ΔAbu0127 strain compared to the A. butzleri wild-type strain.
(C) Circos plots presenting the correlation between COG and dierentially expressed C. jejuni proteins revealed by LC-MS/MS in the ΔCj1608 strain compared to
the C. jejuni wild-type strain. (D) Circos plots presenting the correlation between COG and dierentially expressed A. butzleri proteins revealed by LC-MS/MS in
the ΔAbu0127 strain compared to the A. butzleri wild-type strain. (A–D) Expression changes of |log2FC| ≥ 1 and FDR < 0.05 were considered signicant and were
included in the analyses. The category of unknown genes was excluded from the analysis. COG, Cluster of Orthologous Groups.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 9
strain, the ATP level reached approximately 70% ± 8% of that in the CJ strain and was
constant regardless of increased or decreased O2 supply. The ATP levels in the CCj1608
strain analyzed at dierent O2 levels resembled that of the CJ strain. Thus, the results
indicated that the ΔCj1608 mutant strain could not adjust pathways responsible for
energy conservation to changing O2 levels.
To summarize C. jejuni’s results, Cj1608 helps C. jejuni control pathways that are
important for energy conservation and are dependent on oxygen availability. As we have
FIG 5 Cj1608-dependent control of C. jejuni energy conservation pathways. (A) Growth curves and generation times of CJ, ΔCj1608, and CCj1608 strains
cultivated in the presence of H2 or without H2. (B), ATP production by CJ, ΔCj1608, and CCj1608 strains cultivated as in part A. (C) ChIP-qPCR analysis of pgltA
fragment immunoprecipitated from CJ and ΔCj1608 cultured under microaerobic conditions; the recA gene was used as a negative control which Cj1608 does
not bind. (D) EMSA analysis of Cj1608 binding to the pgltA-FAM promoter region in vitro; the recA-Cy5 fragment was used as a negative control. (E) RT-qPCR
analysis of gltA transcription in CJ, ΔCj1608, and CCj1608 cells cultured at dierent oxygen concentrations: (i) 1% O2, (ii) 5% O2, or (iii) 10% O2 (see Materials and
Methods). (F) Growth curves of CJ, ΔCj1608, and CCj1608 strains cultivated as in panel E. (G) ATP production by CJ, ΔCj1608, and CCj1608 strains cultivated as in
panel E. (A–C and E–G) Data presented as the mean values ± SD. Ordinary one-way ANOVA with Tukey’s multiple comparison test determined the P value. n = 3
biologically independent experiments. ANOVA, analysis of variance; ChIP-qPCR, chromatine immunoprecipitation quantitive PCR; CJ, C. jejuni wild type; CCj1608,
Cj1608 complementation mutant; ΔCj1608, Cj1608 knock-out mutant ; EMSA, Electrophoretic mobility shift assay; G, generation time; ND, non-determined;
RT-qPCR, Reverse transcription quantitative PCR.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 10
shown using the gltA gene as an example, Cj1608 directly controls gene expression in
response to changing O2 concentrations and oxidative stress.
Abu0127 controls nuo expression, ATP level, and growth in response to O2
supply
Next, we examined the impact of Abu0127 on A. butzleri’s growth. As for C. jejuni, the
presence or absence of H2 did not signicantly aect the growth of the wild-type AB
strain nor ATP level in AB cells under microaerobic conditions (Fig. 6A). In the presence
of H2, the ΔAbu0127 strain grew slower than the AB strain (Fig. 6A), and the ATP level
of ΔAbu0127 cells reached 77% ± 3% of that in the WT cells (Fig. 6B). However, the
ΔAbu0127 culture entered the stationary phase at OD600 comparable to that of the AB
strain. Under microaerobic conditions without H2, the growth and ATP level of ΔAbu0127
was further lowered; ΔAbu0127 ATP level reached 57% ± 3% of the AB cells, and cells
entered stationary phase at lower OD600 than the wild-type AB strain (Fig. 6A and B).
Despite many attempts, we could not construct an Abu0127 complementation mutant,
possibly due to the imperfect molecular biology tools dedicated to A. butzleri (10).
Nonetheless, the results indicated that the ΔAbu0127 strain produced less energy than
AB cells, and using H2, the ΔAbu0127 cells could produce more energy and multiply
more eciently (38). This suggests a similar pattern of bypass of TCA-dependent energy
production via oxygen-dependent oxidative phosphorylation to that observed in C.
jejuni.
Next, we analyzed whether Abu0127 directly aects the Nuo complex activity. We
studied the expression of nuoB since it is the second gene of the nuoA-N operon, whose
expression was severely downregulated in the ΔAbu0127 strain at the transcription and
translation levels (Fig. S9A and B; S3 Data). We conrmed the interaction of the Abu0127
protein with the nuoA-N promoter region in vivo by ChIP-qPCR and in vitro by EMSA (Fig.
6C and D), and we found that it was specic because Abu0127 did not interact with
control A. butzleri gyrA and recJ regions, respectively. Next, we used RT-qPCR to analyze
nuoB transcription under paraquat-induced oxidative stress and dierent O2 supply. It
should be noted that A. butzleri can grow under aerobic conditions (40); thus, aerobic
O2 concentration is less harmful to A. butzleri than to C. jejuni. The transcription of nuoB
changed across dierent O2 concentrations, being the lowest at 1% O2 (FC of 0.61 ± 0.06
compared to AB under 5% O2) and highest at 10% O2 (FC of 1.7 ± 0.27 compared to
AB under 5% O2) (Fig. 6E). Under optimal O2 conditions, nuoB transcription was lower in
the ΔAbu0127 strain than in the AB strain (FC of 0.3 ± 0.05 compared to AB under 5%
O2), and it was invariant across dierent O2 concentrations (Fig. 6H). Paraquat-induced
oxidative stress aected nuoB transcription neither in the wild-type AB nor ΔAbu0127
strain, conrming the transcriptomic results (Fig. S9C, S3 Data). Next, we analyzed A.
butzleri growth and ATP production under various O2 supplies. Under 1% O2, the AB
and ΔAbu0127 strains grew similarly. However, ΔAbu0127 reached a lower OD600 at the
stationary growth phase than the AB strain (Fig. 6F). Under optimal and increased O2
concentrations, the AB strain grew similarly at both concentrations, faster than under
1% O2, but reached a similar OD600 upon entry to a stationary growth phase as under
1% O2. The ΔAbu0127 strain grew similarly under 5% and 10% O2 but faster than under
1% O2. Nonetheless, ΔAbu0127 grew slower than AB under the same conditions, and
the culture nally reached a lower OD600 than AB (Fig. 6F). The relative ATP energy
levels corresponded to the bacterial growth rates. The ATP level of the AB strain under
microaerobic growth was assumed to be 100%. Compared to that, under 1% O2, the level
of ATP in the AB strain dropped to 44% ± 4%, while the level of ATP did not change in
the culture grown at 10% O2 (Fig. 6G). Under reduced O2 concentration, the level of ATP
in ΔAbu0127 was similar to that of the AB strain under the same conditions (35% ± 4%
compared to AB under microaerobic growth). However, under 5% O2, the ATP level of the
ΔAbu0127 mutant strain reached 76% ± 3% of that in the AB strain and did not increase
under 10% O2. Thus, the results indicated that the ΔAbu0127 mutant strain could not
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 11
eciently adjust pathways responsible for energy conservation to changing O2 levels, in
which it resembled C. jejuni ΔCj1608.
To summarize A. butzleri’s results, Abu0127 helps A. butzleri control pathways
important for energy conservation dependent on oxygen availability. As we have shown
using the nuoB gene as an example, Abu0127 directly controls gene expression in
response to changing O2 concentrations.
DISCUSSION
One of the challenges for a bacterial cell is to gain energy to grow and reproduce under
dierent environmental conditions. Available energy sources and electron acceptors
used for energy conversion are the two major factors dening the activity of metabolic
pathways. The regulatory proteins redirect metabolism and ETC pathways, prioritizing
electron donor or acceptor usage to maximize the energy gain (e.g., aerobic/microaero
philic over anaerobic respiration and fermentation or nitrate respiration over fumarate
respiration). The ne-tuning of metabolism and respiration to particular conditions is
usually hierarchical and orchestrated by multiple factors (41, 42), which often control
the same genes. For example, in the model, facultative anaerobe Escherichia coli, but
also other species of Gammaproteobacteria, three global transcriptional regulators, FNR,
ArcA, and NarL/NarP, mainly control energy conservation processes dependent on the
availability of electron acceptors (21, 43). In the energy reprogramming network, under
low oxygen availability, FNR provides the rst level of regulation, switching metabolism
FIG 6 Abu0127-dependent control of A. butzleri energy conservation pathways. (A) Growth curves and generation times of AB and ΔAbu0127 strains cultivated
in the presence of H2 or without H2. (B) ATP production by AB and ΔAbu0127 strains cultivated as in panel A. (C) ChIP-qPCR fold enrichment of DNA fragment
in pnuoA-N by ChIP-qPCR in AB and ΔAbu0127 cultured under microaerobic conditions. The gyrA gene was used as a negative control not bound by Abu0127.
(D) EMSA analysis of Abu0127 binding to the pnuoA-FAM promoter region in vitro; the recJ-Cy5 fragment was used as a negative control. (E) RT-qPCR analysis
of nuoB transcription in AB and ΔAbu0127 strains cultured at dierent oxygen concentrations: (i) 1% O2, (ii) 5% O2, or (iii) 10% O2 (see Materials and Methods).
(F) Growth curves of AB and ΔAbu0127 strains cultivated as in panel E. (G) ATP production by AB and ΔAbu0127 strains cultivated as in panel E. (A–C, E–G) Data
presented as the mean values ± SD. Ordinary one-way ANOVA with Tukey’s multiple comparison test determined the P value. n = 3 biologically independent
experiments. AB, A. butzleri wild type; ANOVA, analysis of variance; ΔAbu0127, Abu0127 knock-out mutant; ChIP, chromatin immunoprecipitation quantitative
PCR; EMSA, electrophoretic mobility shift assay; G, generation time; RT-qPCR, reverse transcription quantitative PCR.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 12
from aerobic to anaerobic and controlling the expression of other transcriptional factors
(44). ArcA, a response regulator of the ArcAB two-component system (45), possibly
controls the switch between anaerobic respiration and fermentation, as well as the
expression of a set of secondary regulators (46). NarL/NarP switches on nitrate and
nitrite respiration when these electron acceptors are available while repressing genes
for less eective anaerobic respiration (47). Studies have shown that the intensive
cross-talk between regulatory proteins, often controlling the same genes hierarchically
and controlling genes beyond energy conservation pathways, is essential for ecient
bacterial responses or adaptation to diverse conditions (21, 48–50).
Until recently, no regulators redirecting energy conversion pathways in response to
oxygen as an electron acceptor have been found in Campylobacterota (14, 51). It was
experimentally shown that C. jejuni reprograms its metabolism upon changes to oxygen
availability; nonetheless, the regulator remained unknown (39, 52). It was surprising,
given that microaerobic species of this phylum must respond to changing oxygen levels,
including harmful oxygen deciency and excess.
In this and previous works (24, 26), we revealed that under microaerobic condi
tions, three homologous atypical response regulators, HP1021, Cj1608, and Abu0127,
which we name Campylobacteria energy and metabolism regulators (CemR), control
the expression of many genes involved in energy conversion, including TCA and ETC
pathways [Fig. 3 and 4; Fig. S10; Supplementary Data 3 in reference (26)]. Indeed, when
cemR genes were deleted, the most remarkable gene and protein expression changes
occurred in energy production and conversion COG, reaching between 20% and 40%
of all the downregulated genes or proteins (Fig. 4; Fig. S10; the category of unknown
proteins was excluded from the study). Moreover, we showed that in strains lacking
CemR, the transcriptional control was lost over the genes whose transcription was
dependent on the oxygen level, e.g., C. jejuni TCA gltA, A. butzleri ETC, nuoA-N operon
complex, H. pylori, TCA pyruvate:ferredoxin oxidoreductase PFOR, and ETC cytochrome
c oxidase ccoN-Q [Fig. 5E and 6E and reference (26)]. Consequently, under microaerobic
conditions in CemR deletion mutants of all three species, the ATP level and the growth
rates were lower than in wild-type strains [Fig. 5F, G and 6F G, , and (26)]. The presence of
CemR allowed C. jejuni and A. butlzeri to optimize energy conservation under microaero
bic compared to reduced oxygen concentration, increasing the ATP level and bacterial
growth rate (Fig. 5F, G and 6F G, ). The lack of CemR specically impaired pathways
connected with oxygen utilization as an electron acceptor because in the presence of
hydrogen as an alternative electron donor and proton motive force generator, C. jejuni
and A. butzleri gained additional energy, allowing for faster growth (Fig. 5A, B and 6A B, ).
All these data indicate that CemR responds to oxygen levels and redirects metabolism
toward optimal energy conservation. It is important to point out that data obtained
from each single species would not have allowed us to conclude on the general role
of CemR as an energy conservation regulator because, in each species, slightly dierent
pathways were primarily controlled (e.g., TCA in C. jejuni, Nuo ETC in A. butzleri, and
glucose uptake and pentose phosphate pathway in H. pylori). Nonetheless, the role of
CemR in controlling other pathways or processes, often in a species-specic manner, still
awaits detailed characterization.
An interesting question arises: how many energy conservation regulators are
encoded in addition to CemR in each Campylobacteria species, and how do they
all integrate into the regulatory circuits of a given species? H. pylori, C. jejuni, and
A. butzleri dier in lifestyles, which shape the complexity of regulatory circuits, as
recently illustrated using Campylobacterota as a representative phylum (20). H. pylori
can be classied as a specialist bacterium (53), strictly human associated, using a
limited repertoire of electron donors (hydrogen, pyruvate, 2-oxo-glutarate, malate, and
succinate), and only oxygen or fumarate as electron acceptors during respiration (20).
Oxygen, being a primary electron acceptor in H. pylori, is also toxic for H. pylori at
higher than microaerobic concentrations (54). As a consequence of speciation and
host adaptation, H. pylori lost many regulatory proteins. Nonetheless, the studies on
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 13
regulatory circuits in H. pylori indicated that HP1043 (55, 56), controlled by unknown
stimuli, acid-responsive ArsRS (57), and metal-dependent regulators NikR and Fur (20,
58), aects H. pylori energy conversion to various extents in response to dierent stimuli.
C. jejuni is a host-associated organism that infects various animals and humans and can
survive long periods under aerobic conditions (59). In contrast to H. pylori, C. jejuni has
a branched electron transport chain with multiple enzymes that utilize more molecules
as electron donors and acceptors than H. pylori (20). C. jejuni genome size is similar to
H. pylori; however, the number of C. jejuni genes encoding regulatory proteins is higher
and reects the broader spectrum of ecological niches inhabited by this bacterium.
Consequently, the number of regulatory proteins controlling energy conversion is higher
than that in H. pylori, with RacRS, LysR (Cj1000), CsrA, and CprRS playing the most
signicant roles [for details, see reviews (11, 13–15)]. Depending on growth conditions,
particularly the availability of electron donors and acceptors, LysR, RacRS, and CprRS
regulate the expression of fumarate respiration genes, which is an excellent example
of a cross-talk between regulators controlling the same pathways via transcriptional
control over the same genes (e.g., aspA, dcuA, mfr, or frd) (51, 60, 61). RNA-binding CsrA,
a homolog of E. coli carbon starvation regulator, is an example of post-transcriptional
energy conservation regulation in C. jejuni, including control of expression of TCA and
ETC genes (62). A. butzleri can lead anaerobic, microaerophilic, and aerobic lifestyles as
an environmental or animal- and human-associated bacterium. As a generalist, it utilizes
many electron donors and acceptors using an electron transport chain similar to those
used by the free-living marine Campylobacterota (20). A. butzleri’s genome encodes many
signal transduction proteins (see Introduction), including extracytoplasmic sigma factors
(ECF) to adapt to dierent conditions. However, hardly anything is known about the
regulation of A. butzleri energy conversion except that a few species of ECFσ impact
electron and carbon metabolism by aecting the transcription of genes from carbon
metabolism pathways and electron acceptor complexes (10). However, one can expect
the highest complexity and speciation of A. butzleri’s regulatory circuits, including energy
conservation, of all three species.
CemR regulators are highly conserved Campylobacteria regulators. However, the
molecular mechanism of signal perception by CemR is still enigmatic and cannot be
predicted by analogy. Transcriptional regulatory proteins may sense respiration status,
the concentrations of electron donors or acceptors, or respiration byproducts (63).
We have shown that cysteine residues of HpCemR become oxidized under O2-trig
gered oxidative stress in vivo and in vitro, which changes the protein’s DNA-binding
activity possibly by structural changes of DNA-protein complex rather than on-o
mechanism (24, 26). Recent studies on CjCemR interactions with the promoter region
of lctP suggested a similar mechanism of CjCemR activity control and lctP transcrip
tion regulation (27). AbCemR protein also contains cysteine residues, one of which
is conserved in all three species (C27 in HpCemR and CjCemR, C31 in AbCemR). The
mechanism of activity regulation by the redox state of the cysteine residues resembles
that of ArcA-ArcB two-component system regulation. However, in that TCS, the cysteine
residues of ArcB sensor kinase are oxidated, triggering autophosphorylation of ArcB,
while ArcA response regulator is activated by phosphorylation. CemR proteins are
orphan atypical response regulators in which the same molecule receives and executes
the signal. Thus, despite some biochemical similarity to ArcAB system activation, the
exact molecular mechanism of CemR activity control has not yet been discovered.
In summary, CemR, the global regulatory protein, is a part of the cell response to a
metabolic redox imbalance either caused by environmental conditions such as oxygen
availability or ROS or triggered intracellularly due to metabolic changes and increased
ROS production. However, the exact pathway of signal transduction by CemR and the
levels or hierarchy of regulation are still unknown. Further comprehensive, multi-omic
studies are needed to reveal the complex circuits of regulation involving CemR and other
regulatory proteins in Campylobacteria energy conservation.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 14
MATERIALS AND METHODS
Materials and culture conditions
The strains, plasmids, and proteins used in this work are listed in the Table S1. The
primers used in this study are listed in Table S2. C. jejuni and A. butzleri plate cultures
were grown on Columbia blood agar base medium supplemented with 5% debrinated
sheep blood (CBA-B). The liquid cultures were prepared in brain heart infusion broth
(BHI) (Oxoid) and incubated with 140-rpm orbital shaking. All C. jejuni and A. butzleri
cultures were supplemented with antibiotic mixes [C. jejuni: vancomycin (5 µg/mL),
polymyxin B (2.5 U/mL), trimethoprim (5 µg/mL), and amphotericin B (4 µg/mL) (64); A.
butzleri: cefoperazone (8 µg/mL), amphotericin (10 µg/mL), and teicoplanin (4 µg/mL)
(65)]. If necessary for selecting mutants, appropriate antibiotics were used with the
following nal concentrations: (i) kanamycin 20 µg/mL (C. jejuni and A. butzleri) and (ii)
chloramphenicol 8 µg/mL (C. jejuni). Routinely, C. jejuni and A. butzleri were cultivated
at 42°C or 30°C, respectively, under optimal microaerobic conditions (C. jejuni: 5% O2,
8% CO2, 4% H2, and 83% N2; A. butzleri: 6% O2, 9% CO2, and 85% N2) generated by
the jar evacuation-replacement method using Anaerobic Gas System PetriSphere. The
gas mixtures were modied by lowering or increasing O2 and H2 concentrations for ATP
assays and growth curve analyses. Briey, bacteria were cultured under microaerobic
conditions optimal for each species to the late logarithmic growth phase (OD600 ~0.8),
diluted to an OD600 of ~0.05 and incubated under the desired atmosphere as long as
required, up to OD600 = 0.2–0.6 for ATP and RT-qPCR or until late stationary phase in
growth analyses. The gas mixture with or without H2 was composed of optimal gas
mixtures for C. jejuni and A. butzleri, respectively. Low oxygen gas mixture was composed
of 1% O2, 10% CO2, 5% H2, and 84% N2, while high oxygen gas mixture was composed
of 10% O2, 5% CO2, 3% H2, and 82% N2. Escherichia coli DH5α and BL21 were used for
cloning and recombinant protein synthesis. If necessary for selecting E. coli, appropriate
antibiotics were used with he following nal concentrations: (i) kanamycin 50 µg/mL and
(ii) ampicillin 100µg/mL.
Construction of C. jejuni mutant strains
C. jejuni mutant strains were constructed using a homologous recombination and natural
transformation approach (66). C. jejuni NCTC 11168 was grown in 12-mL BHI to an OD600
= 0.2. Next, 150 µL of the culture was centrifuged and resuspended in fresh 150 µL of
BHI. One hundred fty nanograms (1 µg/mL) of EcoRI methylated plasmid was added to
the culture and cultivated at 42°C in microaerobic (5% O2, 8% CO2, 4% H2, and 83% N2)
conditions with shaking (140 rpm) for 4 h. Next, 100 µL was spread on CBA-B plates with
an appropriate antibiotic and incubated for 3 days at 42°C under microaerobic (5% O2,
8% CO2, 4% H2, and 83% N2) conditions.
C. jejuni NCTC 11168 ΔCj1608
The C. jejuni Cj1608 deletion construct (pCR2.1/ΔCj1608) was prepared as follows (Fig.
S11A). The upstream and downstream regions of Cj1608 were amplied by PCR using
the P1-P2 and P5-P6 primer pairs, respectively, and a C. jejuni NCTC 11168 genomic
DNA as a template. The aphA-3 cassette was amplied using the P3-P4 primer pair;
pTZ57R/TΔHP1021 (23) was used as a template. The resulting fragments were puried
on an agarose gel. Subsequently, the fragments were combined into one DNA amplicon
with an overlap extension PCR reaction using the P1-P6 primers. The generated amplicon
was puried on an agarose gel and cloned to the pCR2.1-TOPO plasmid (Thermo
Fisher Scientic) according to the manufacturer’s protocol. The DNA fragment cloned
in pCR2.1-TOPO was sequenced. Subsequently, C. jejuni NCTC 11168 was transformed
with the pCR2.1/ΔCj1608 plasmid, and the transformants were selected by plating on
CBA-B plates supplemented with kanamycin. The addition of hydrogen was necessary to
obtain the kanamycin-resistant colonies. The allelic exchange was veried by PCR using
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 15
the P7-P8 primer pair. The lack of Cj1608 in C. jejuni NCTC 11168 ΔCj1608 was conrmed
by RNA-seq (Fig. S11B), LC-MS/MS (S1 Data), and Western blot (Fig. S11C and D).
C. jejuni NCTC 11168 CCj1608
The C. jejuni Cj1608 complementation construct (pUC18/COMCj1608) was prepared as
follows (Fig. S11A). The regions upstream and downstream of the Cj1608 gene were
amplied by PCR using the P1-P6 primer pair and C. jejuni Cj1608 genomic DNA as
a template. The resulting fragment was puried on an agarose gel. Subsequently, the
generated amplicon and the SmaI digested vector pUC18 were ligated according to
the method described by Gibson et al. (67) to give pUC18/COMCj1608. E. coli DH5α
competent cells were transformed with the construct by heat shock. The insert cloned
into pUC18 was sequenced. The pUC18/COMCj1608 was used to complement the lack
of Cj1608 in C. jejuni NCTC 11168 ΔCj1608 by an attempt similar to Multiplex Genome
Editing by Natural Transformation (MuGENT) (68). For the selection, a pSB3021 suicide
plasmid was used. The plasmid harbors a cat cassette anked by arms of homology,
enabling cat cassette integration into the hsdM gene. Subsequently, the obtained
pUC18/COMCj1608 (1 µg/mL) and pSB3021 (0.25 µg/mL) plasmids were used in the
natural transformation of C. jejuni NCTC 11168 ΔCj1608. The transformants were selected
by plating on CBA-B plates supplemented with chloramphenicol. The bacteria were
cultured without hydrogen to increase the selection process. The allelic exchange was
veried by PCR using the P7-P9 primers. The presence of Cj1608 in C. jejuni NCTC 11168
CCj1608was conrmed by RNA-seq (Fig. S11B), LC-MS/MS (S1 Data), and Western blot (Fig.
S10C and D).
Construction of A. butzleri mutant strain
A. butzleri RM4018 mutant strain was constructed using a homologous recombination
and electroporation approach. A. butzleri was grown in 25-mL BHI to an OD600 = 0.4.
Next, the culture was transferred on ice and left for 10 min, then centrifuged (4,700 g,
10 min, 4°C) and washed twice with 20 mL of ice-cold glycerine water (15% glycerol and
7% sucrose, ltrated). Finally, the culture was suspended in 250 µL of ice-cold glycerine
water. For electroporation, 50 µL of electrocompetent cells was mixed with 5 µg of
appropriate plasmid. Electroporation was performed in 0.1-cm electroporation cuvettes
(The Cell Projects) using a Gene Pulser II Electroporator (Bio-Rad) using the following
parameters: 12.5 kV/cm, 200 Ω, and 25 µF. Cells were regenerated by adding 1 mL of
BHI medium to the cuvette, then by transferring the cells to a ask with 2-mL BHI and
cultivated with shaking (140 rpm) for 4 h at 30°C in microaerobic conditions. Next, the
culture was centrifuged (4,700 g, 10 min, 22°C), resuspended in 100 µL of BHI, spread
on CBA-B plates with appropriate antibiotic, and incubated for 5 days at 30°C under
microaerobic conditions.
A. butzleri RM4018 ΔAbu0127
The A. butzleri Abu0127 deletion construct (pUC18/ΔAbu0127) was prepared as
follows (Fig. S12A) (69). The upstream and downstream regions of Abu0127 were
amplied by PCR using the P10-P11 and P14-P15 primer pairs, respectively, and an
A. butzleri RM4018 genomic DNA as a template. The aphA-3 cassette was amplied
using the P12-P13 primer pair; pTZ57R/TΔHP1021 (23) was used as a template. The
resulting fragments were puried on an agarose gel. Subsequently, the PCR-ampli
ed fragments and the SmaI digested vector pUC18 were ligated according to the
method of Gibson et al. (67). Subsequently, A. butzleri RM4018 was transformed
via electroporation with the pUC18/ΔAbu0127 plasmid, and the transformants were
selected by plating on CBA-B plates supplemented with kanamycin. The allelic
exchange was veried by PCR using the P16-P17 primer pair. The lack of Abu0127 in
A. butzleri RM4018 ΔAbu0127 was conrmed by RNA-seq (Fig. S12B), LC-MS/MS (S2
Data), and Western blot (Fig. S12C and D).
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 16
Disk diusion assay
Bacteria were cultured in BHI to OD600 = ~0.5 to 0.7 and then diluted to OD600 =
0.1 in BHI. Each culture was spread by a cotton swab on CBA-B plates. Sterile, glass
ber 6-mm disks were placed on plates, and 5 µL of tested solutions were dropped on
disks: 2% H2O2 (POCH, 885193111), 2% paraquat dichloride (Acros Organics, 227320010),
5% hydroxyurea (Merck, H8627-1G), 3% sodium nitroprusside (Merck, S0501), 2.5%
menadion (Merck, A13593), and 15% sodium hypochlorite (Merck, 1056142500). Sodium
hypochlorite solutions for assays were made fresh; the solutions’ pH was adjusted by
adding HCl to pH 7 and kept in phosphate-buered saline (PBS) buer prior to experi
ments (70). Menadion solution was prepared in dimethyl sulfoxide (DMSO). The diameter
of the inhibition zone around the disks was determined after 3 days of incubation
under microaerobic conditions. The experiment was performed using three biological
replicates.
RNA isolation
Bacterial cultures (12-mL BHI) of C. jejuni and A. butzleri strains were grown under
microaerobic conditions to OD600 of 0.5–0.6. Immediately after opening the jar, 2 mL
of the non-stressed culture was added to 2 mL of the RNAprotect Bacteria Reagent
(Qiagen), vortexed, and incubated for 5 min at room temperature. In parallel, the cultures
were treated with 1-mM paraquat (nal concentration) for 25 min (C. jejuni 42°C, A.
butzleri 30°C, 140-rpm orbital shaking). After oxidative stress, samples were collected
similarly to non-stressed cells. After 5-min incubation with RNAprotect Bacteria Reagent,
bacteria were collected by centrifugation (4,700 × g, 10 min, room temperature). RNA
was isolated by GeneJET RNA Purication Kit (Thermo Fisher Scientic, K0731) according
to the manufacturer’s protocol and treated with RNase-free DNase I (Thermo Fisher
Scientic). Next, purication by the GeneJET RNA Purication Kit was performed to
remove DNase I. A NanoDrop Lite spectrophotometer, agarose gel electrophoresis, and
Agilent 4200 TapeStation System were used to determine the RNA quality and quantity.
RNA was isolated immediately after bacteria collection, stored at −80°C for up to 1 month
and used for RNA sequencing. RNA was isolated from three independent cultures.
For analyses of gene transcription dependent on oxygen supply, bacterial cells were
collected from cultures under the logarithmic phase of growth (OD600 ~0.2 to 0.5), grown
under the desired atmosphere (see Materials and Culture Conditions), and RNA was
isolated as described above.
RNA-seq
Preparation and sequencing of the prokaryotic directional mRNA library were performed
at the Novogene Bioinformatics Technology Co. Ltd. (Cambridge, UK). Briey, the
ribosomal RNA was removed from the total RNA, followed by ethanol precipitation.
After fragmentation, the rst strand of cDNA was synthesized using random hexamer
primers. During the second-strand cDNA synthesis, deoxyuridine triphosphates were
replaced with deoxythymidine triphosphates in the reaction buer. The directional
library was ready after end repair, A-tailing, adapter ligation, size selection, USER enzyme
digestion, amplication, and purication. The library was checked with Qubit, RT-qPCR
for quantication, and a bioanalyzer for size distribution detection. The libraries were
sequenced with the NovaSeq 6000 (Illumina), and 150-bp reads were produced.
RNA-seq analysis
The 150-bp paired reads were mapped to the C. jejuni NCTC 11168 (NC_002163.1) or
A. butzleri RM4018 (NC_009850.1) genome depending on the species analyzed using
Bowtie2 software with local setting (version 2.3.5.1) (71, 72) and processed using
samtools (version 1.10) (73), achieving more than 106 mapped reads on average.
Dierential analysis was performed using R packages Rsubread (version 2.10) and edgeR
(version 3.38) (74, 75), following a protocol described in reference (76). Genes rarely
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 17
transcribed were removed from the analysis (less than 10 mapped reads per library). The
obtained count data were normalized using the edgeR package, and a quasi-likelihood
negative binomial was tted. Dierential expression was tested using the glmTtreat
function with a 1.45-fold change (FC) threshold. Only genes with a false discovery rate
(FDR) less than 0.05 and |log2FC| of ≥ 1 were considered dierentially expressed. Data
visualization with volcano plots was done using the EnhancedVolcano and tidyHeat
map R packages (versions 1.14 and 1.8.1) (77). The reproducibility of C. jejuni and A.
butzleri biological replicates was visualized by principal component analysis (PCA) of the
normalized RNA-seq CPM data (Fig. S13A and B).
Proteomic sample preparation
Bacterial cultures of wild-type C. jejuni NCTC 11168 and A. butzleri RM4018 strains and
their isogenic mutant strains, ΔCj1608 or ΔAbu0127, respectively, were grown in BHI
under microaerobic conditions to an OD600 of 0.5–0.7 and split into two subcultures of
10 mL each. The rst subculture was harvested, washed, and lysed immediately after
opening the jar; the second subculture (only WT strains) was harvested, washed, and
lysed after incubation with 1-mM paraquat (C. jejuni: 42°C, A. butzleri: 30°C, with orbital
shaking at 140 rpm) for 30 and 60 min. The bacterial proteomes were prepared as
described previously by Abele et al. (78). Briey, cells were harvested by centrifugation at
10,000 × g for 2 min; media were removed; and cells were washed once with 20 mL of 1
× PBS. The cell pellets were suspended and lysed in 100 µL of 100% triuoroacetic acid
(Roth) (79) for 5 min at 55°C. Next, 900 µL of neutralization buer (2-M Tris) was added
and vortexed. Protein concentration was measured using Bradford assay (Bio-Rad). Fifty
micrograms of protein per sample was reduced [9-mM tris(2-carboxyethyl)phosphine]
and carbamidomethylated (40 mM chloroacetamide) for 5 min at 95°C. The proteins
were digested by adding trypsin (proteomics grade, Roche) at a 1:50 enzyme:protein
ratio (wt/wt) and incubation at 37°C overnight. Digests were acidied by the addition
of 3% (vol/vol) formic acid (FA) and desalted using self-packed StageTips (ve disks per
microcolumn, ø 1.5 mm, C18 material; 3M Empore). The peptide eluates were dried
to completeness and stored at −80°C. Before the LC-MS/MS measurement, all samples
were freshly resuspended in 12-µL 0.1% FA in high-performance liquid chromatogra
phy (HPLC)-grade water, and around 25 µg of total peptide amount was injected into
the mass spectrometer per measurement. Each experiment was performed using four
biological replicates.
Proteomic data acquisition and data analysis
Peptides were analyzed on a Vanquish Neo liquid chromatography system (microow
conguration) coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher
Scientic). Around 25 µg of peptides was applied onto an Acclaim PepMap 100 C18
column (2-µm particle size, 1-mm ID × 150 mm, 100-Å pore size; Thermo Fisher Scientic)
and separated using a two-step gradient. In the rst step, a 50-min linear gradient
ranging from 3% to 24% solvent B (0.1% FA, 3% DMSO in acetonitrile) in solvent A
(0.1% FA and 3% DMSO in HPLC-grade water) at a ow rate of 50 µL/min was applied.
In the second step, solvent B was further increased from 24% to 31% over a 10-min
linear gradient. The mass spectrometer was operated in data-dependent acquisition
and positive ionization modes. MS1 full scans (360–1,300 m/z) were acquired with a
resolution of 60,000, a normalized automatic gain control (AGC) target value of 100%,
and a maximum injection time of 50 ms. Peptide precursor selection for fragmentation
was carried out using a xed cycle time of 1.2 s. Only precursors with charge states from
2 to 6 were selected, and dynamic exclusion of 30 s was enabled. Peptide fragmentation
was performed using higher-energy collision-induced dissociation and a normalized
collision energy of 28%. The precursor isolation window width of the quadrupole was
set to 1.1 m/z. MS2 spectra were acquired with a resolution of 15,000, a xed rst mass
of 100 m/z, a normalized AGC target value of 100%, and a maximum injection time of
40 ms.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 18
Peptide identication and quantication were performed using MaxQuant (version
1.6.3.4) with its built-in search engine Andromeda (80, 81). MS2 spectra were searched
against the C. jejuni or A. butzleri proteome database derived from C. jejuni NCTC 11168
(NC_002163.1) or A. butzleri RM4018 (NC_009850.1), respectively. Trypsin/P was specied
as the proteolytic enzyme. The precursor tolerance was set to 4.5 ppm, and fragment
ion tolerance was set to 20 ppm. Results were adjusted to a 1% FDR on peptide
spectrum match level and protein level employing a target-decoy approach using
reversed protein sequences. The minimal peptide length was dened as seven amino
acids; carbamidomethylated cysteine was set as xed modication and oxidation of
methionine and N-terminal protein acetylation as variable modications. The match-
between-run function was disabled. Protein abundances were calculated using the
label-free quantication (LFQ) algorithm from MaxQuant (82). Protein intensity values
were logarithm transformed (base 2), and a Student t-test was used to identify proteins
dierentially expressed between conditions. The resulting P values were adjusted by the
Benjamini-Hochberg algorithm (83) to control the FDR. Since low abundant proteins are
more likely to result in missing values, we lled in missing values with a constant of half
the lowest detected LFQ intensity per protein. However, if the imputed value was higher
than the 20% quantile of all LFQ intensities in that sample, we used the 20% quantile
as the imputed value. Only proteins with |log2FC| of ≥ 1 were considered dierentially
expressed (84).
The reproducibility of C. jejuni and A. butzleri biological replicates was visualized by
PCA of the normalized LC-MS/MS CPM data (Fig. S13C and D).
Omics data analysis
The KEGG (85) gene/protein set enrichment analysis of the dierentially expressed genes
and proteins was performed and visualized based on the clusterProler (29) (version
4.8.2) package in R software with a P value of < 0.05. Genome-wide functional anno
tation was carried out with the eggNOG-mapper (version 5.0) (86) according to the
COG database (87) with an e value of 0.001 and a minimum hit bit score of 60. Visual
representations of RNA expression levels and LC-MS/MS expressed proteins in dierent
COGs were performed with the Circos Table viewer (http://mkweb.bcgsc.ca/tableviewer/,
accessed on 5 November 2023). For the analyses, only genes and proteins with a |log2FC|
of ≥ 1and an FDR of < 0.05 were considered as signicantly changed.
ChIP
Bacterial cultures (70- mL BHI) of C. jejuni NTCT 11168 and A. butzleri RM4018 wild-types
and ΔCj1608 and Abu0127 mutants were grown to an OD600 of 0.5–0.7. The culture
was cross-linked with 1% formaldehyde for 5 min immediately after opening the jar.
The cross-linking reactions were stopped by treatment with 125 mM glycine for 10 min
at room temperature. The cultures were centrifuged at 4,700 × g for 10 min at 4°C
and washed twice with 25 mL of ice-cold 1 × PBS, followed by the same centrifugation
step. Samples were resuspended in 1.1- mL immunoprecipitation (IP) buer (150-mM
NaCl, 50-mM Tris-HCl, pH 7.5, 5-mM EDTA, 0.5% vol/vol NP-40, and 1.0% vol/vol Triton
X-100) and sonicated [Ultraschallprozessor UP200s (0.6%/50% power, 30-s on, 0-s o,
ice bucket)] to reach a 100- to 500-bp DNA fragment size. Next, the samples were
centrifuged at 12,000 × g for 10 min at 4°C. One hundred microliters of the superna
tant was used for input preparation. Nine hundred microliters of the supernatant was
incubated with 30 µL of Sepharose Protein A (Rockland, PA50-00-0002) (pre-equilibrated
in IP buer) for 1 h at 4°C on a rotation wheel. The samples were centrifuged at 1,000 ×
g for 2 min at 4°C. The supernatants were incubated with 100-µL antibody-Sepharose A
complex (see below) and incubated at 4°C for 24 h on a rotation wheel. Next, the samples
were centrifuged at 1,000 × g for 2 min at 4°C, and the supernatant was discarded. The
beads were washed four times with IP-wash buer (50-mM Tris-HCl, pH 7.5, 150-mM
NaCl, 0.5% NP-40, 0.1% SDS), twice with Tris-EDTA (TE) buer (10-mM Tris-HCl, pH 8.0;
0.1-mM EDTA), resuspended in 180 µL of TE buer, and treated with 20-µg/mL RNase A at
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 19
37°C for 30 min. Next, cross-links were reversed by adding sodium dodecyl sulfate (SDS)
at a nal concentration of 0.5% and proteinase K at a nal concentration of 20 µg/mL,
followed by incubation for 16 h at 37°C. The beads were removed by centrifugation
at 1,000 × g for 2 min at 4°C, and the DNAs from the supernatants were isolated with
ChIP DNA Clean & Concentrator (Zymo Research). The quality of DNA was validated
by electrophoresis in 2% agarose gel, and the concentration was determined with
QuantiFluor dsDNA System (Promega). The ChIP-DNA was isolated from three independ
ent bacteria cultures.
The C. jejuni antibody-Sepharose A complex was prepared by adding 40 µg of
desalted rabbit polyclonal anti-Cj1608 antibody to 100 µL of the Sepharose Protein A
pre-equilibrated in IP buer. A. butzleri antibody-sepharose A complex was prepared by
adding 120 µg of desalted rabbit polyclonal anti-Abu0127 antibody to 100 µL of the
Sepharose Protein A pre-equilibrated in IP buer. The binding reaction was performed
on a rotation wheel for 24 h at 4°C. Next, the complex was washed ve times with an IP
buer. The antibodies used in ChIP were raised in rabbits and validated with Western blot
(Fig. S11D and D).
Quantitative polymerase chain reaction
RT-qPCR quantied the mRNA levels of the selected genes. The reverse transcription was
conducted using 500 ng of RNA in a 20-µL volume reaction mixture of iScript cDNA
Synthesis Kit (Bio-Rad). Diluted cDNA (1:10, 2.5 µL) was added to 7.5 µL of Sensi-FAST
SYBR No-ROX (Bioline) and 400 nM of forward and reverse primers in a 15-µL nal
volume. The RT-qPCR program was 95°C for 3 min, followed by 40 three-step amplica-
tion cycles consisting of 5 s at 95°C, 10 s at 58°C, and 20 s at 72°C. The following
primer pairs were used: P18-P19, gltA for C. jejuni RT-qPCR, and P23-P24, nuoB for A.
butzleri RT-qPCR (Table S2). The relative quantity of mRNA for each gene of C. jejuni was
determined by referring to the mRNA levels of recA (P25-P26 primer pair). The relative
quantity of mRNA for each gene of A. butzleri was determined by referring to the mRNA
levels of gyrA (P27-P28 primer pair). The RT-qPCR was performed for three independent
bacterial cultures.
The protein-DNA interactions in the cell in vivo of the selected DNA regions were
quantied by ChIP-qPCR. Diluted immunoprecipitation output (1:0, 2.5 µL) was added to
7.5 µL of Sensi-FAST SYBR No-ROX (Bioline) and 400 nM of forward and reverse primers
in a 15-µL nal volume. The ChIP-qPCR was performed using the following program:
95°C for 3 min, followed by 40 three-step amplication cycles consisting of 10 s at 95°C,
10 s at 59°C, and 20 s at 72°C. The following primer pairs were used: P43-P44, pgltA (C.
jejuni), and P45-P46, pnuoA (A. butzleri) (Table S2). The recA (P25-P26) and gyrA (P27-P28)
genes were used as a negative control for C. jejuni and A. butzleri, respectively (Table S2).
No-antibody control was used for ChIP-qPCR normalization, and the fold enrichment was
calculated. The ChIP-qPCR was performed for three independent bacterial cultures.
The RT-qPCR and ChIP-qPCR were performed using the CFX96 Touch Real-Time PCR
Detection Systems, and data were analyzed with CFX Maestro (Bio-Rad) software.
Construction of plasmids expressing recombinant wild-type Cj1608 and
Abu0127 proteins
The Cj1608 (Cj1509 in C. jejuni 81–116) gene (888 bp) was amplied with primer pair
P29-P30 using C. jejuni 81–116 genomic DNA as a template. The Abu0127 gene (891 bp)
was amplied with primer pair P31-P32 using A. butzleri RM4018 genomic DNA as a
template. The PCR products were digested with BamHI/SalI and cloned into BamHI/SalI
sites of pET28Strep (24) to generate pETStrepCj1509 and pETStrepAbu0127, respectively
(Table S1).
Protein expression and purication
The recombinant Strep-tagged Cj1608 (Cj1509 of C. jejuni 81–116) and Abu0127 proteins
were puried according to the Strep-Tactin manufacturer’s protocol (IBA Lifesciences).
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 20
Briey, E. coli BL21 cells (1 L) carrying either the pET28/StrepCj1509 or pET28/Stre
pAbu0127 vectors were grown at 37°C. At an OD600 of 0.8, protein synthesis was induced
with 0.05-mM IPTG for 3 h at 30°C for Cj1608 and overnight at 18°C for Abu0127.
The cultures were harvested by centrifugation (10 min; 5,000 × g; at 4°C). The cells
were suspended in 20 mL of ice-cold buer W (100-mM Tris-HCl, pH 8.0; 300-mM NaCl;
and 1-mM EDTA) supplemented with cOmplete, EDTA-free protease inhibitor Cocktail
(Roche), disrupted by sonication, and centrifuged (30 min; 31,000 × g; at 4°C). The
supernatant was applied onto a Strep-Tactin Superow high-capacity Sepharose column
(1-mL bed volume, IBA). The column was washed with buer W until Bradford tests
yielded a negative result, and then washed with 5 mL of buer W without EDTA. The
elution was carried out with approximately 6 × 0.8 mL of buer E (100-mM Tris-HCl, pH
8.0; 300-mM NaCl; and 5-mM desthiobiotin). Protein purity was analyzed by SDS-PAGE
electrophoresis using GelDoc XR+ and ImageLab software (Bio-Rad). The fractions were
stored at −20°C in buer E diluted with glycerol to a nal concentration of 50%.
EMSA
PCR amplied DNA probes in two steps. DNA fragments were amplied in the rst
step using unlabeled primer pairs P33-P34 (pgltA) and P35-P36 (recJ) with a C. jejuni
NCTC 11168 genomic DNA template, and P37-P38 (pnuo) and P39-P40 (recA) with an A.
butzleri RM4018 genomic DNA template (Table S1). The forward primers were designed
with overhangs complementary to P41 (FAM labeled) for pnuo and pgltA and P42 (Cy5
labeled) for recJ and recA. The unlabeled fragment was puried and used as a template
in the second round of PCR using the appropriate uorophore-labeled primer and the
appropriate reverse primer used in the rst step. Both uorophore-labeled DNAs (each
5 nM) were incubated with the Strep-tagged protein at 30°C (Abu0127) or 37°C (Cj1608)
for 20 min in Tris buer (50-mM Tris-HCl, pH 8.0; 100-mM NaCl; and 0.2% Triton X-100).
The complexes were separated by electrophoresis on a 4% polyacrylamide gel in 0.5 ×
Tris-Borate-EDTA (TBE) (1× TBE: 89-mM Tris, 89-mM borate, and 2-mM EDTA) at 10 V/cm
in the cold room (approximately 10°C). The gels were analyzed using Typhoon 9500 FLA
Imager and ImageQuant software.
ATP assay
The ATP level was measured with the BacTiter-Glo Assay. C. jejuni and A. butzleri cultures
were grown in the BHI medium to the logarithmic growth phase (OD600 of 0.5–0.7). Cells
were diluted to an OD600 = 0.1 with fresh medium. Next, 50 µL of bacteria was mixed
with 50 µL of BacTiter-Glo (Promega) and incubated at room temperature for 5 min.
The luminescence was measured with a CLARIOstar plate reader on opaque-walled
multi-well plates (SPL Life Sciences, 2–200203). Each experiment was performed using
three biological replicates.
Statistics and reproducibility
Statistical analysis was performed using GraphPad Prism (version 8.4.2) and R (version
4.3.1) statistical software. All in vivo experiments were repeated at least three times,
and data were presented as mean ± SD. The statistical signicance between the two
conditions was calculated by paired two-tailed Student t-test. The statistical signicance
between multiple groups was calculated by one-way analysis of variance (ANOVA) with
Tukey’s post hoc test. A P value of < 0.05 was considered statistically signicant. The
proteome and transcriptome correlations were determined with the Pearson correlation
coecient. The EMSA and Western blot experiments were repeated twice with similar
results.
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 21
ACKNOWLEDGMENTS
We thank Kinga Surmacz for helping with disk diusion assays, Maria Cieślak for helping
with pUC18/ΔAbu0127 plasmid preparation, and Verena Breitner and Tayma Midari for
technical assistance at BayBioMS.
This work has been supported by the OPUS 17 (project number
2019/33/B/NZ6/01648, funded by the National Science Centre, Poland, to A.Z.-P.) and
by the EPIC-XS (project number 823839, funded by the Horizon 2020 Program of the
European Union, to C.L.).
The open-access publication of this article was funded by the OPUS 17
(2019/33/B/NZ6/01648, National Science Centre, Poland) and statutory funds from the
Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of
Sciences.
AUTHOR AFFILIATIONS
1Department of Microbiology, Hirszfeld Institute of Immunology and Experimental
Therapy, Polish Academy of Sciences, Wrocław, Poland
2Department of Molecular Microbiology, Faculty of Biotechnology, University of Wrocław,
Wrocław, Poland
3Department of Biological Safety, Unit of Product Hygiene and Disinfection Strategies,
German Federal Institute for Risk Assessment, Berlin, Germany
4Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of
Munich (TUM), Freising, Germany
5Department of Biological Safety, National Reference Laboratory for Campylobacter,
German Federal Institute for Risk Assessment, Berlin, Germany
AUTHOR ORCIDs
Mateusz Noszka http://orcid.org/0000-0002-6694-1394
Agnieszka Strzałka http://orcid.org/0000-0002-7092-0609
Jakub Muraszko http://orcid.org/0000-0001-7232-0298
Dirk Hofreuter http://orcid.org/0000-0002-2589-9206
Miriam Abele http://orcid.org/0000-0003-0084-2999
Christina Ludwig http://orcid.org/0000-0002-6131-7322
Kerstin Stingl http://orcid.org/0000-0002-8338-717X
Anna Zawilak-Pawlik http://orcid.org/0000-0003-1824-1550
FUNDING
Funder Grant(s) Author(s)
Narodowe Centrum Nauki (NCN,
National Science Centre, Poland) OPUS 17
2019/33/B/NZ6/01648 Anna Zawilak-Paw
lik
EC | Horizon 2020 Framework
Programme (H2020) EPIC-XS Project Number
823839 Christina Ludwig
AUTHOR CONTRIBUTIONS
Mateusz Noszka, Conceptualization, Data curation, Formal analysis, Funding acquis
ition, Investigation, Methodology, Validation, Visualization, Writing – original draft,
Writing – review and editing, Software, Supervision | Agnieszka Strzałka, Formal
analysis, Methodology, Software, Writing – review and editing, Supervision, Investiga
tion, Visualization, Validation | Jakub Muraszko, Investigation, Writing – review and
editing | Dirk Hofreuter, Validation, Writing – review and editing, Formal analysis |
Miriam Abele, Data curation, Methodology, Writing – review and editing, Validation
| Christina Ludwig, Funding acquisition, Methodology, Writing – review and editing |
Kerstin Stingl, Conceptualization, Methodology, Writing – review and editing, Formal
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 22
analysis, Project administration, Validation | Anna Zawilak-Pawlik, Conceptualization,
Formal analysis, Funding acquisition, Project administration, Supervision, Validation,
Visualization, Writing – original draft, Writing – review and editing, Data curation
DATA AVAILABILITY
The Campylobacter jejuni RNA-seq FASTQ and processed data generated in this study
have been deposited in the ArrayExpress database (EMBL-EBI) under accession code
E-MTAB-13650. The Arcobacter butzleri RNA-seq FASTQ and processed data generated
in this study have been deposited in the ArrayExpress database (EMBL-EBI) under
accession code E-MTAB-13649. The raw proteomics data, MaxQuant search results, and
the used protein sequence database generated in this study have been deposited in
the ProteomeXchange Consortium via the PRIDE partner repository (88) under accession
code PXD048711. Campylobacter jejuni NCTC 11168 reference genome is deposited in
the National Center for Biotechnology Information under accession code NC_002163.1.
Arcobacter butzleri RM4018 reference genome is deposited in the National Center for
Biotechnology Information under accession code NC_009850.1. All of the code and
data used to generate the gure presented here are deposited in GitHub via https://
github.com/NoszkaM/LBMM.git.
ETHICS APPROVAL
The antibodies used in chromatin immunoprecipitation and western blot were raised in
rabbits under the approval of the First Local Committee for Experiments with the Use of
Laboratory Animals, Wroclaw, Poland (consent number 053/2020/P2).
ADDITIONAL FILES
The following material is available online.
Supplemental Material
Data S1 (mSystems00784-24-s0001.xlsx). Full list of genes and proteins of the Cj1608
regulon.
Data S2 (mSystems00784-24-s0002.xlsx). Full list of genes and proteins of the Abu0127
regulon.
Data S3 (mSystems00784-24-s0003.xlsx). Genes and proteins of selected processes or
pathways in C. jejuni, A. butzleri, and H. pylori.
Supplemental material (mSystems00784-24-s0004.pdf). Figures S1 to S13, Tables S1
and S2, and descriptions of Data S1 to S3.
Open Peer Review
PEER REVIEW HISTORY (review-history.pdf). An accounting of the reviewer comments
and feedback.
REFERENCES
1. Waite DW, Chuvochina MS, Hugenholtz P. 2019. Road map of the
phylum Campylobacterota, p 1–11. In Bergey’s manual of systematics of
archaea and bacteria
2. Chie D, Fanelli F, Fusco V. 2020. Arcobacter butzleri: up-to-date
taxonomy, ecology, and pathogenicity of an emerging pathogen. Compr
Rev Food Sci Food Saf 19:2071–2109. https://doi.org/10.1111/1541-
4337.12577
3. Burnham PM, Hendrixson DR. 2018. Campylobacter jejuni: collective
components promoting a successful enteric lifestyle. Nat Rev Microbiol
16:551–565. https://doi.org/10.1038/s41579-018-0037-9
4. Shange N, Gouws P, Homan LC. 2019. Campylobacter and Arcobacter
species in food-producing animals: prevalence at primary production
and during slaughter. World J Microbiol Biotechnol 35:146.
https://doi.org/10.1007/s11274-019-2722-x
5. Tacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL,
Pulcini C, Kahlmeter G, Kluytmans J, Carmeli Y, et al. 2018. Discovery,
research, and development of new antibiotics: the WHO priority list of
antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis 18:318–
327. https://doi.org/10.1016/S1473-3099(17)30753-3
6. Sciortino S, Arculeo P, Alio V, Cardamone C, Nicastro L, Arculeo M,
Alduina R, Costa A. 2021. Occurrence and antimicrobial resistance of
Arcobacter spp. recovered from aquatic environments. Antibiotics (Basel)
10:288. https://doi.org/10.3390/antibiotics10030288
7. Gumerov VM, Ulrich LE, Zhulin IB. 2024. MiST 4.0: a new release of the
microbial signal transduction database, now with a metagenomic
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 23
component. Nucleic Acids Res 52:D647–D653. https://doi.org/10.1093/
nar/gkad847
8. Gundogdu O, Bentley SD, Holden MT, Parkhill J, Dorrell N, Wren BW.
2007. Re-annotation and re-analysis of the Campylobacter jejuni
NCTC11168 genome sequence. BMC Genomics 8:1–8. https://doi.org/10.
1186/1471-2164-8-162
9. Miller WG, Parker CT, Rubeneld M, Mendz GL, Wösten M, Ussery DW,
Stolz JF, Binnewies TT, Hallin PF, Wang G, Malek JA, Rogosin A, Stanker
LH, Mandrell RE. 2007. The complete genome sequence and analysis of
the epsilonproteobacterium Arcobacter butzleri. PLoS One 2:e1358.
https://doi.org/10.1371/journal.pone.0001358
10. Martinez-Malaxetxebarria I, Muts R, van Dijk L, Parker CT, Miller WG,
Huynh S, Gaastra W, van Putten JPM, Fernandez-Astorga A, Wösten
MMSM. 2012. Regulation of energy metabolism by the extracytoplasmic
function (ECF) σ factors of Arcobacter butzleri. PLoS One 7:e44796. https:/
/doi.org/10.1371/journal.pone.0044796
11. Gahamanyi N, Song DG, Mboera LEG, Matee MI, Mutangana D,
Amachawadi RG, Komba EVG, Pan CH. 2022. Insights into the virulence
of Campylobacter jejuni associated with two-component signal
transduction systems and single regulators. Microbiol Res 13:188–200.
https://doi.org/10.3390/microbiolres13020016
12. Chandrashekhar K, Kassem II, Rajashekara G. 2017. Campylobacter jejuni
transducer like proteins: chemotaxis and beyond. Gut Microbes 8:323–
334. https://doi.org/10.1080/19490976.2017.1279380
13. Casado J, Lanas Á, González A. 2022. Two-component regulatory
systems in Helicobacter pylori and Campylobacter jejuni: attractive targets
for novel antibacterial drugs. Front Cell Infect Microbiol 12:977944.
https://doi.org/10.3389/fcimb.2022.977944
14. Taylor AJ, Kelly DJ. 2019. The function, biogenesis and regulation of the
electron transport chains in Campylobacter jejuni: new insights into the
bioenergetics of a major food-borne pathogen. Adv Microb Physiol
74:239–329. https://doi.org/10.1016/bs.ampbs.2019.02.003
15. Flint A, Stintzi A, Saraiva LM. 2016. Oxidative and nitrosative stress
defences of Helicobacter and Campylobacter species that counteract
mammalian immunity. FEMS Microbiol Rev 40:938–960. https://doi.org/
10.1093/femsre/fuw025
16. Shan Y, Lai Y, Yan A. 2012. Metabolic reprogramming under microaero
bic and anaerobic conditions in bacteria. Subcell Biochem 64:159–179.
https://doi.org/10.1007/978-94-007-5055-5_8
17. Imlay JA. 2019. Where in the world do bacteria experience oxidative
stress? Environ Microbiol 21:521–530. https://doi.org/10.1111/1462-
2920.14445
18. Espey MG. 2013. Role of oxygen gradients in shaping redox relationships
between the human intestine and its microbiota. Free Radic Biol Med
55:130–140. https://doi.org/10.1016/j.freeradbiomed.2012.10.554
19. van der Stel A-X, van de Lest CHA, Huynh S, Parker CT, van Putten JPM,
Wösten MMSM. 2018. Catabolite repression in Campylobacter jejuni
correlates with intracellular succinate levels. Environ Microbiol 20:1374–
1388. https://doi.org/10.1111/1462-2920.14042
20. van der Stel A-X, Wösten MMSM. 2019. Regulation of respiratory
pathways in Campylobacterota: a review. Front Microbiol 10:1719. https:/
/doi.org/10.3389/fmicb.2019.01719
21. Ravcheev DA, Gerasimova AV, Mironov AA, Gelfand MS. 2007.
Comparative genomic analysis of regulation of anaerobic respiration in
ten genomes from three families of gamma-proteobacteria (Enterobac
teriaceae, Pasteurellaceae, Vibrionaceae). BMC Genomics 8:1–17. https://
doi.org/10.1186/1471-2164-8-54
22. Tomb JF, White O, Kerlavage AR, Clayton RA, Sutton GG, Fleischmann
RD, Ketchum KA, Klenk HP, Gill S, Dougherty BA, et al. 1997. The
complete genome sequence of the gastric pathogen Helicobacter pylori.
Nature 388:539–547. https://doi.org/10.1038/41483
23. Donczew R, Makowski Ł, Jaworski P, Bezulska M, Nowaczyk M,
Zakrzewska-Czerwińska J, Zawilak-Pawlik A. 2015. The atypical response
regulator HP1021 controls formation of the Helicobacter pylori
replication initiation complex. Mol Microbiol 95:297–312. https://doi.
org/10.1111/mmi.12866
24. Szczepanowski P, Noszka M, Żyła-Uklejewicz D, Pikuła F, Nowaczyk-
Cieszewska M, Krężel A, Stingl K, Zawilak-Pawlik A. 2021. HP1021 is a
redox switch protein identied in Helicobacter pylori. Nucleic Acids Res
49:6863–6879. https://doi.org/10.1093/nar/gkab440
25. Pock M, Bathon M, Schär J, Müller S, Mollenkopf H, Meyer TF, Beier D.
2007. The orphan response regulator HP1021 of Helicobacter pylori
regulates transcription of a gene cluster presumably involved in acetone
metabolism. J Bacteriol 189:2339–2349. https://doi.org/10.1128/JB.
01827-06
26. Noszka M, Strzałka A, Muraszko J, Kolenda R, Meng C , Ludwig C, Stingl K,
Zawilak-Pawlik A. 2023. Proling of the Helicobacter pylori redox switch
HP1021 regulon using a multi-omics approach. Nat Commun 14:6715.
https://doi.org/10.1038/s41467-023-42364-6
27. Sinha R, LeVeque RM, Callahan SM, Chatterjee S, Stopnisek N, Kuipel M,
Johnson JG, DiRita VJ. 2024. Gut metabolite L-lactate supports
Campylobacter jejuni population expansion during acute infection. Proc
Natl Acad Sci U S A 121:e2316540120. https://doi.org/10.1073/pnas.
2316540120
28. Jaworski P, Donczew R, Mielke T, Weigel C, Stingl K, Zawilak-Pawlik A.
2018. Structure and function of the Campylobacter Jejuni Chromosome
replication origin. Front Microbiol 9:1533. https://doi.org/10.3389/fmicb.
2018.01533
29. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L,
Fu X, Liu S, Bo X, Yu G. 2021. clusterProler 4.0: a universal enrichment
tool for interpreting omics data. Innovation 2:100141. https://doi.org/10.
1016/j.xinn.2021.100141
30. Li S, Lam J, Souliotis L, Alam MT, Constantinidou C. 2022. Posttranscrip
tional regulation in response to dierent environmental stresses in
Campylobacter jejuni. Microbiol Spectr 10:e0020322. https://doi.org/10.
1128/spectrum.00203-22
31. Sharma CM, Homann S, Darfeuille F, Reignier J, Findeiss S, Sittka A,
Chabas S, Reiche K, Hackermüller J, Reinhardt R, Stadler PF, Vogel J.
2010. The primary transcriptome of the major human pathogen
Helicobacter pylori. Nature 464:250–255. https://doi.org/10.1038/
nature08756
32. Flint A, Stintzi A. 2015. Cj1386, an atypical hemin-binding protein,
mediates hemin tracking to KatA in Campylobacter jejuni. J Bacteriol
197:1002–1011. https://doi.org/10.1128/JB.02346-14
33. Fasnacht M, Polacek N. 2021. Oxidative stress in bacteria and the central
dogma of molecular biology. Front Mol Biosci 8:671037. https://doi.org/
10.3389/fmolb.2021.671037
34. Zhu M, Dai X. 2020. Bacterial stress defense: the crucial role of ribosome
speed. Cell Mol Life Sci 77:853–858. https://doi.org/10.1007/s00018-019-
03304-0
35. Stahl M, Butcher J, Stintzi A. 2012. Nutrient acquisition and metabolism
by Campylobacter jejuni. Front Cell Infect Microbiol 2:5. https://doi.org/
10.3389/fcimb.2012.00005
36. Surmacz K. 2022. Analysis of Aliarcobacter butzleri RM4018 sensitivity to
oxidative stress. Wroclaw University of Science and Technology.
37. Zhu M, Dai X. 2019. Maintenance of translational elongation rate
underlies the survival of Escherichia coli during oxidative stress. Nucleic
Acids Res 47:7592–7604. https://doi.org/10.1093/nar/gkz467
38. Benoit SL, Maier RJ, Sawers RG, Greening C. 2020. Molecular hydrogen
metabolism: a widespread trait of pathogenic bacteria and protists.
Microbiol Mol Biol Rev 84:e00092-19. https://doi.org/10.1128/MMBR.
00092-19
39. Guccione EJ, Kendall JJ, Hitchcock A, Garg N, White MA, Mulholland F,
Poole RK, Kelly DJ. 2017. Transcriptome and proteome dynamics in
chemostat culture reveal how Campylobacter jejuni modulates
metabolism, stress responses and virulence factors upon changes in
oxygen availability. Environ Microbiol 19:4326–4348. https://doi.org/10.
1111/1462-2920.13930
40. Kiehlbauch JA, Brenner DJ, Nicholson MA, Baker CN, Patton CM,
Steigerwalt AG, Wachsmuth IK. 1991. Campylobacter butzleri sp. nov.
isolated from humans and animals with diarrheal illness. J Clin Microbiol
29:376–385. https://doi.org/10.1128/jcm.29.2.376-385.1991
41. Ward B. 2015. Bacterial energy metabolism, p 201–233. In Molecular
medical microbiology
42. Perez JC, Groisman EA. 2009. Evolution of transcriptional regulatory
circuits in bacteria. Cell 138:233–244. https://doi.org/10.1016/j.cell.2009.
07.002
43. Federowicz S, Kim D, Ebrahim A, Lerman J, Nagarajan H, Cho B, Zengler
K, Palsson B. 2014. Determining the control circuitry of redox metabo
lism at the genome-scale. PLoS Genet 10:e1004264. https://doi.org/10.
1371/journal.pgen.1004264
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 24
44. Myers KS, Yan H, Ong IM, Chung D, Liang K, Tran F, Keleş S, Landick R,
Kiley PJ. 2013. Genome-scale analysis of Escherichia coli FNR reveals
complex features of transcription factor binding. PLoS Genet
9:e1003565. https://doi.org/10.1371/journal.pgen.1003565
45. Brown AN, Anderson MT, Bachman MA, Mobley HLT. 2022. The ArcAB
two-component system: function in metabolism, redox control, and
infection. Microbiol Mol Biol Rev 86:e0011021. https://doi.org/10.1128/
mmbr.00110-21
46. Park DM, Akhtar MS, Ansari AZ, Landick R, Kiley PJ. 2013. The bacterial
response regulator ArcA uses a diverse binding site architecture to
regulate carbon oxidation globally. PLoS Genet 9:e1003839. https://doi.
org/10.1371/journal.pgen.1003839
47. Stewart V. 1994. Dual interacting two-component regulatory systems
mediate nitrate- and nitrite-regulated gene expression in Escherichia
coli. Res Microbiol 145:450–454. https://doi.org/10.1016/0923-
2508(94)90093-0
48. Iyer MS, Pal A, Srinivasan S, Somvanshi PR, Venkatesh KV. 2021. Global
transcriptional regulators ne-tune the translational and metabolic
eciency for optimal growth of Escherichia coli. mSystems 6:e00001-21.
https://doi.org/10.1128/mSystems.00001-21
49. Overton TW, Griths L, Patel MD, Hobman JL, Penn CW, Cole JA,
Constantinidou C. 2006. Microarray analysis of gene regulation by
oxygen, nitrate, nitrite, FNR, NarL and NarP during anaerobic growth of
Escherichia coli: new insights into microbial physiology. Biochem Soc
Trans 34:104–107. https://doi.org/10.1042/BST0340104
50. Unden G, Bongaerts J. 1997. Alternative respiratory pathways of
Escherichia coli: energetics and transcriptional regulation in response to
electron acceptors. Biochim Biophys Acta 1320:217–234. https://doi.org/
10.1016/s0005-2728(97)00034-0
51. van der Stel A-X, van Mourik A, Heijmen-van Dijk L, Parker CT, Kelly DJ,
van de Lest CHA, van Putten JPM, Wösten MMSM. 2015. The Campylo
bacter jejuni RacRS system regulates fumarate utilization in a low oxygen
environment. Environ Microbiol 17:1049–1064. https://doi.org/10.1111/
1462-2920.12476
52. Liu X, Gao B, Novik V, Galán JE. 2012. Quantitative proteomics of
intracellular Campylobacter jejuni reveals metabolic reprogramming.
PLoS Pathog 8:e1002562. https://doi.org/10.1371/journal.ppat.1002562
53. Bell TH, Bell T. 2020. Many roads to bacterial generalism. FEMS Microbiol
Ecol 97:240. https://doi.org/10.1093/femsec/aa240
54. Bury-Moné S, Kaakoush NO, Asencio C, Mégraud F, Thibonnier M, De
Reuse H, Mendz GL. 2006. Is Helicobacter pylori a true microaerophile?
Helicobacter 11:296–303. https://doi.org/10.1111/j.1523-5378.2006.
00413.x
55. Pelliciari S, Pinatel E, Vannini A, Peano C, Puccio S, De Bellis G, Danielli A,
Scarlato V, Roncarati D. 2017. Insight into the essential role of the
Helicobacter pylori HP1043 orphan response regulator: genome-wide
identication and characterization of the DNA-binding sites. Sci Rep
7:41063. https://doi.org/10.1038/srep41063
56. Olekhnovich IN, Vitko S, Valliere M, Homan PS. 2014. Response to
metronidazole and oxidative stress is mediated through homeostatic
regulator HsrA (HP1043) in Helicobacter pylori. J Bacteriol 196:729–739.
https://doi.org/10.1128/JB.01047-13
57. Loh JT, Shum MV, Jossart SDR, Campbell AM, Sawhney N, McDonald WH,
Scholz MB, McClain MS, Forsyth MH, Cover TL. 2021. Delineation of the
pH-responsive regulon controlled by the Helicobacter pylori ArsRS two-
component system. Infect Immun 89:e00597-20. https://doi.org/10.
1128/IAI.00597-20
58. Vannini A, Pinatel E, Costantini PE, Pelliciari S, Roncarati D, Puccio S, De
Bellis G, Peano C, Danielli A. 2017. Comprehensive mapping of the
Helicobacter pylori NikR regulon provides new insights in bacterial nickel
responses. Sci Rep 7:45458. https://doi.org/10.1038/srep45458
59. Gölz G, Kittler S, Malakauskas M, Alter T. 2018. Survival of Campylobacter
in the food chain and the environment. Curr Clin Micro Rpt 5:126–134.
https://doi.org/10.1007/s40588-018-0092-z
60. Dufour V, Li J, Flint A, Rosenfeld E, Rivoal K, Georgeault S, Alazzam B,
Ermel G, Stintzi A, Bonnaure-Mallet M, Baysse C. 2013. Inactivation of the
LysR regulator Cj1000 of Campylobacter jejuni aects host colonization
and respiration. Microbiology (Reading) 159:1165–1178. https://doi.org/
10.1099/mic.0.062992-0
61. Svensson SL, Huynh S, Parker CT, Gaynor EC. 2015. The Campylobacter
jejuni CprRS two-component regulatory system regulates aspects of the
cell envelope. Mol Microbiol 96:189–209. https://doi.org/10.1111/mmi.
12927
62. Fields JA, Li J, Gulbronson CJ, Hendrixson DR, Thompson SA. 2016.
Campylobacter jejuni CSRA regulates metabolic and virulence associated
proteins and is necessary for mouse colonization. PLoS One
11:e0156932. https://doi.org/10.1371/journal.pone.0156932
63. Price EE, Román-Rodríguez F, Boyd JM. 2021. Bacterial approaches to
sensing and responding to respiration and respiration metabolites. Mol
Microbiol 116:1009–1021. https://doi.org/10.1111/mmi.14795
64. Contreras M, Thiberge JM, Mandrand-Berthelot MA, Labigne A. 2003.
Characterization of the roles of NikR, a nickel-responsive pleiotropic
autoregulator of Helicobacter pylori. Mol Microbiol 49:947–963. https://
doi.org/10.1046/j.1365-2958.2003.03621.x
65. Atabay HI, Corry JEL. 1998. Evaluation of a new Arcobacter enrichment
medium and comparison with two media developed for enrichment of
Campylobacter spp. Int J Food Microbiol 41:53–58. https://doi.org/10.
1016/s0168-1605(98)00034-8
66. Beauchamp JM, Leveque RM, Dawid S, DiRita VJ. 2017. Methylation-
dependent DNA discrimination in natural transformation of Campylo
bacter jejuni. Proc Natl Acad Sci U S A 114:E8053–E8061.
https://doi.org/10.1073/pnas.1703331114
67. Gibson DG, Young L, Chuang RY, Venter JC, Hutchison CA, Smith HO.
2009. Enzymatic assembly of DNA molecules up to several hundred
kilobases. Nat Methods 6:343–345. https://doi.org/10.1038/nmeth.1318
68. Dalia AB, McDonough E, Camilli A. 2014. Multiplex genome editing by
natural transformation. Proc Natl Acad Sci U S A 111:8937–8942. https://
doi.org/10.1073/pnas.1406478111
69. Cieślak M. 2021. Construction of the plasmid for the analysis of the
essentiality of the Abu0127 gene for the survival of Arcobacter butzleri
RM4018. Wroclaw University of Science and Technology.
70. Perkins A, Tudorica DA, Amieva MR, Remington SJ, Guillemin K. 2019.
Helicobacter pylori senses bleach (HOCl) as a chemoattractant using a
cytosolic chemoreceptor. PLoS Biol 17:e3000395. https://doi.org/10.
1371/journal.pbio.3000395
71. Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with
Bowtie 2. Nat Methods 9:357–359. https://doi.org/10.1038/nmeth.1923
72. Langmead B, Wilks C, Antonescu V, Charles R. 2019. Scaling read aligners
to hundreds of threads on general-purpose processors. Bioinformatics
35:421–432. https://doi.org/10.1093/bioinformatics/bty648
73. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G,
Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup.
2009. The sequence alignment/map format and SAMtools. Bioinformat
ics 25:2078–2079. https://doi.org/10.1093/bioinformatics/btp352
74. Liao Y, Smyth GK, Shi W. 2019. The R package Rsubread is easier, faster,
cheaper and better for alignment and quantication of RNA sequencing
reads. Nucleic Acids Res 47:e47. https://doi.org/10.1093/nar/gkz114
75. Robinson MD, McCarthy DJ, Smyth GK. 2010. edgeR: a bioconductor
package for dierential expression analysis of digital gene expression
data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformat
ics/btp616
76. Chen Y, Lun ATL, Smyth GK. 2016. From reads to genes to pathways:
dierential expression analysis of RNA-seq experiments using Rsubread
and the edgeR quasi-likelihood pipeline. F1000Res 5:1438. https://doi.
org/10.12688/f1000research.8987.1
77. Blighe K, Rana S, Lewis M. EnhancedVolcano: publication-ready volcano
plots with enhanced colouring and labeling. Available from: https://
bioconductor.org/packages/devel/bioc/vignettes/EnhancedVolcano/
inst/doc/EnhancedVolcano.html. Retrieved 21 Aug 2022.
78. Abele M, Doll E, Bayer FP, Meng C, Lomp N, Neuhaus K, Scherer S, Kuster
B, Ludwig C. 2023. Unied workow for the rapid and in-depth
characterization of bacterial proteomes. Mol Cell Proteomics 22:100612.
https://doi.org/10.1016/j.mcpro.2023.100612
79. Doellinger J, Schneider A, Hoeller M, Lasch P. 2020. Sample preparation
by easy extraction and digestion (SPEED) - a universal, rapid, and
detergent-free protocol for proteomics based on acid extraction. Mol
Cell Proteomics 19:209–222. https://doi.org/10.1074/mcp.TIR119.001616
80. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. 2011.
Andromeda: a peptide search engine integrated into the maxquant
environment. J Proteome Res 10:1794–1805. https://doi.org/10.1021/
pr101065j
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 25
81. Tyanova S, Temu T, Cox J. 2016. The MaxQuant computational platform
for mass spectrometry-based shotgun proteomics. Nat Protoc 11:2301–
2319. https://doi.org/10.1038/nprot.2016.136
82. Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. 2014. Accurate
proteome-wide label-free quantication by delayed normalization and
maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics
13:2513–2526. https://doi.org/10.1074/mcp.M113.031591
83. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a
practical and powerful approach to multiple testing. J R Stat Soc B
57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
84. GitHub - mengchen18/omicsViewer. Available from: https://github.com/
mengchen18/omicsViewer. Retrieved 23 Jan 2024.
85. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. 2017. KEGG:
new perspectives on genomes, pathways, diseases and drugs. Nucleic
Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092
86. Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK,
Cook H, Mende DR, Letunic I, Rattei T, Jensen LJ, von Mering C, Bork P.
2019. eggNOG 5.0: a hierarchical, functionally and phylogenetically
annotated orthology resource based on 5090 organisms and 2502
viruses. Nucleic Acids Res 47:D309–D314. https://doi.org/10.1093/nar/
gky1085
87. Galperin MY, Makarova KS, Wolf YI, Koonin EV. 2015. Expanded microbial
genome coverage and improved protein family annotation in the COG
database. Nucleic Acids Res 43:D261–D269. https://doi.org/10.1093/nar/
gku1223
88. Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S,
Kamatchinathan S, Kundu DJ, Prakash A, Frericks-Zipper A, Eisenacher M,
Walzer M, Wang S, Brazma A, Vizcaíno JA. 2022. The PRIDE database
resources in 2022: a hub for mass spectrometry-based proteomics
evidences. Nucleic Acids Res 50:D543–D552. https://doi.org/10.1093/
nar/gkab1038
Research Article mSystems
August 2024 Volume 9 Issue 8 10.1128/msystems.00784-24 26
Content uploaded by Mateusz Noszka
Author content
All content in this area was uploaded by Mateusz Noszka on Jul 10, 2024
Content may be subject to copyright.
