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P. aeruginosa type III and type VI secretion systems modulate early response gene expression in type II pneumocytes in vitro

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Background Lung airway epithelial cells are part of innate immunity and the frontline of defense against bacterial infections. During infection, airway epithelial cells secrete proinflammatory mediators that participate in the recruitment of immune cells. Virulence factors expressed by bacterial pathogens can alter epithelial cell gene expression and modulate this response. Pseudomonas aeruginosa, a Gram-negative opportunistic pathogen, expresses numerous virulence factors to facilitate establishment of infection and evade the host immune response. This study focused on identifying the role of two major P. aeruginosa virulence factors, type III (T3SS) and type VI (T6SS) secretion systems, on the early transcriptome response of airway epithelial cells in vitro. Results We performed RNA-seq analysis of the transcriptome response of type II pneumocytes during infection with P. aeruginosa in vitro. We observed that P. aeruginosa differentially upregulates immediate-early response genes and transcription factors that induce proinflammatory responses in type II pneumocytes. P. aeruginosa infection of type II pneumocytes was characterized by up-regulation of proinflammatory networks, including MAPK, TNF, and IL-17 signaling pathways. We also identified early response genes and proinflammatory signaling pathways whose expression change in response to infection with P. aeruginosa T3SS and T6SS mutants in type II pneumocytes. We determined that T3SS and T6SS modulate the expression of EGR1, FOS, and numerous genes that are involved in proinflammatory responses in epithelial cells during infection. T3SS and T6SS were associated with two distinct transcriptomic signatures related to the activation of transcription factors such as AP1, STAT1, and SP1, and the secretion of pro-inflammatory cytokines such as IL-6 and IL-8. Conclusions Taken together, transcriptomic analysis of epithelial cells indicates that the expression of immediate-early response genes quickly changes upon infection with P. aeruginosa and this response varies depending on bacterial viability and injectosomes. These data shed light on how P. aeruginosa modulates host epithelial transcriptome response during infection using T3SS and T6SS.
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
https://doi.org/10.1186/s12864‑022‑08554‑0
RESEARCH
P. aeruginosa type III andtype VI secretion
systems modulate early response gene
expression intype II pneumocytes invitro
Emel Sen‑Kilic1,2, Annalisa B. Huckaby1,2, F. Heath Damron1,2 and Mariette Barbier1,2*
Abstract
Background: Lung airway epithelial cells are part of innate immunity and the frontline of defense against bacterial
infections. During infection, airway epithelial cells secrete proinflammatory mediators that participate in the recruit‑
ment of immune cells. Virulence factors expressed by bacterial pathogens can alter epithelial cell gene expression
and modulate this response. Pseudomonas aeruginosa, a Gram‑negative opportunistic pathogen, expresses numerous
virulence factors to facilitate establishment of infection and evade the host immune response. This study focused on
identifying the role of two major P. aeruginosa virulence factors, type III (T3SS) and type VI (T6SS) secretion systems, on
the early transcriptome response of airway epithelial cells in vitro.
Results: We performed RNA‑seq analysis of the transcriptome response of type II pneumocytes during infection with
P. aeruginosa in vitro. We observed that P. aeruginosa differentially upregulates immediate‑early response genes and
transcription factors that induce proinflammatory responses in type II pneumocytes. P. aeruginosa infection of type II
pneumocytes was characterized by up‑regulation of proinflammatory networks, including MAPK, TNF, and IL‑17 sign‑
aling pathways. We also identified early response genes and proinflammatory signaling pathways whose expression
change in response to infection with P. aeruginosa T3SS and T6SS mutants in type II pneumocytes. We determined
that T3SS and T6SS modulate the expression of EGR1, FOS, and numerous genes that are involved in proinflammatory
responses in epithelial cells during infection. T3SS and T6SS were associated with two distinct transcriptomic signa‑
tures related to the activation of transcription factors such as AP1, STAT1, and SP1, and the secretion of pro‑inflamma‑
tory cytokines such as IL‑6 and IL‑8.
Conclusions: Taken together, transcriptomic analysis of epithelial cells indicates that the expression of immediate‑
early response genes quickly changes upon infection with P. aeruginosa and this response varies depending on bacte‑
rial viability and injectosomes. These data shed light on how P. aeruginosa modulates host epithelial transcriptome
response during infection using T3SS and T6SS.
Keywords: P. aeruginosa, Epithelial cells, Type III secretion system, Type VI secretion system, Early response genes,
Transcriptomics, RNAseq
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Background
P. aeruginosa is an opportunistic Gram-negative bac-
terium responsible for a wide array of infections in
humans. If acute infections caused by P. aeruginosa are
not treated, this bacterium can adapt to the lung envi-
ronment, form biofilms, and persist, resulting in chronic
infections that are difficult to eradicate [1]. Respiratory
Open Access
*Correspondence: mabarbier@hsc.wvu.edu
1 Department of Microbiology, Immunology, and Cell Biology, West Virginia
University School of Medicine, Morgantown, WV, USA
Full list of author information is available at the end of the article
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Page 2 of 16
Sen‑Kilicetal. BMC Genomics (2022) 23:345
infections caused by this organism can be life-threaten-
ing in immunocompromised individuals and cystic fibro-
sis (CF) patients [2]. In particular, P. aeruginosa causes
in CF patients both acute and chronic respiratory infec-
tions that negatively affect pulmonary function, morbid-
ity, and mortality [3, 4]. is problem is worsened by the
emergence of multidrug-resistant P. aeruginosa strains
responsible for causing hard-to-treat infections, raising
serious health concerns for susceptible individuals [5, 6].
Understanding the interactions of P. aeruginosa with its
host can be beneficial for the development of novel ther-
apeutics targeting bacterial virulence or host immune
response mechanisms of bacterial clearance.
Airway epithelial cells constitute the first line of defense
against pathogens during respiratory infections and form
a physical barrier [7]. ese cells participate in the innate
immune response through mechanical mucociliary clear-
ance and secretion of antimicrobial compounds in the
airways [8, 9]. During infection, bacteria interact with
airway epithelial cells, triggering a series of cellular host
signaling pathways. Some of these signaling pathways
lead to expression of immediate-early response genes
(IEGs), which are controlled by constitutively active and/
or post-translationally activated transcription factors
[10, 11]. As a result, IEGs expression is induced rapidly
after external stimulation [10]. Expression of IEGs shapes
the host response by regulating the secondary response
genes and downstream proinflammatory signaling cas-
cades [11]. Infection with P. aeruginosa upregulates
several IEGs such as JUN, KLF2, and ZFP36 in epithe-
lial cells [12, 13]. However, the molecular mechanisms
involved in the induction of each of these IEGs during
infection are unclear.
P. aeruginosa uses numerous strategies to establish
infection and persist in the host. One of the primary
virulence factors of P. aeruginosa is the type III secretion
system (T3SS) that injects effector proteins directly into
the cytoplasm [14]. In addition to T3SS, P. aeruginosa
can also use a type VI secretion system (T6SS) to inject
effector proteins inside eukaryotic cells [15]. is study
aims to characterize the initial early host transcriptomic
response produced by human airway epithelial cells to P.
aeruginosa, and to elucidate the role of T3SS and T6SS in
this process. To achieve this goal, transcriptomic analy-
sis of type II pulmonary epithelial cells stimulated with
live, heat-inactivated P. aeruginosa, and T3SS and T6SS
mutants was performed. We showed that type II pneu-
mocyte response to infection was characterized by up-
regulation of pro-inflammatory genes, including genes
whose products are involved in MAPK, TNF, and IL-17
signaling pathways. Upon stimulation with P. aeruginosa,
most of the differentially upregulated transcription fac-
tors were immediate-early response genes known to be
involved in proinflammatory responses. We identified
that T3SS and T6SS affect EGR1 and FOS genes expres-
sion in type II pneumocytes during infection. In addition,
infections with T3SS and T6SS mutants were associated
with two distinct host response profiles and transcription
factor activation patterns including AP1, STAT1 and SP1,
and lower levels of IL-6 and IL-8 secretion. Overall, this
study is beneficial to understand the early activated tran-
scriptomic signatures during P. aeruginosa infection and
the role of injectosomes on the host response.
Results
P. aeruginosa triggers changes inexpression ofgenes
whose products are involved inproinammatory pathways
inlung epithelial cells
To gain insights into the response of lung epithelial cells
to P. aeruginosa at the early stage of infection, transcrip-
tomic responses of human type II pneumocytes (A549
cells) were analyzed after 1h infection with P. aeruginosa
PA14. e length of incubation was selected to identify
early changes in gene expression [16, 17] without induc-
ing cell death (Figure S1). e gene expression analy-
sis was performed and genes with a p-value 0.05, fold
change > 2, and average FPKM difference > 7 were fur-
ther analyzed. P. aeruginosa infection led to changes in
expression of 143 genes in epithelial cells compared to
non-infected mock control. Among these genes, 73%
(105 genes) were upregulated, and 27% were down-reg-
ulated (38 genes) compared to the non-infected mock
control (Fig.1A). e top 10 differentially upregulated
and down-regulated protein-encoding genes are shown
in Table1.
To understand the biological processes associated with
the products of the genes differentially regulated dur-
ing P. aeruginosa infection, GO term and KEGG path-
way analysis was performed (Fig. 1B-C). In addition, a
protein–protein interaction network with differentially
expressed protein-encoding genes during P. aeruginosa
infection was generated using STRING V11.0 [18] to
determine what molecular networks are activated upon
P. aeruginosa infection (Fig. 1C). We identified that
molecular networks and pathways associated with pro-
inflammatory responses and external stimuli are differ-
entially regulated in epithelial cells upon infection with
P. aeruginosa (Fig.1B-C). In particular, protein-encoding
genes associated with MAPK, TNF, and IL-17 signaling
pathways were upregulated in response to P. aeruginosa
in airway epithelial cells.
Among the genes significantly upregulated in response
to P. aeruginosa infection, 17.5% of them encoded tran-
scription factors and proteins previously identified as
IEGs (Fig.2A, Table2). e top 2 IEGs upregulated in
epithelial cells after infection with P. aeruginosa were
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
the genes encoding for the transcription factors EGR1
and c-Fos. e expression of these two genes was vali-
dated by qPCR (Figure S2). EGR1 and c-Fos are known
to be involved in response to extracellular stimuli and
play a role in regulating expression of genes with diverse
functions including regulating inflammation, cell prolif-
eration, and signal transduction [1921]. In general, the
products of IEGs play a major role in the activation of
downstream signaling pathways essential for stimulating
an immune response [11].
e expression of IEGs is controlled by transcrip-
tional regulators already present in the cell ready for
signal-mediated activation such as phosphorylation.
As a result, the induction of IEG expression happens
rapidly upon stimulation and does not require de novo
protein synthesis [16]. To identify the upstream regula-
tors potentially involved in regulating gene expression
in response to P. aeruginosa, Ingenuity Pathway Analy-
sis (IPA) was performed [32]. e IPA upstream regula-
tor analysis predicts the transcriptional regulators that
are activated/inhibited based on the differential expres-
sion of downstream genes. One hundred eighty-four
upstream transcriptional regulators were predicted to be
activated or repressed in P. aeruginosa infected epithelial
cells. Among these genes, six upstream regulators were
also IEGs differentially regulated in response to P. aerugi-
nosa infection (Fig.2B). e IPA upstream regulator and
Cytoscape network analyses predicted that transcription
factors encoded by EGR1 and FOS were activated, acted
as upstream regulators, and were important hub proteins
in the protein interaction network (Fig.2B, Fig.1C). In
addition, the gene encoding IL-6 had the highest positive
activation z-score and IL-6 had the most interactions in
the protein interaction network (Fig.2B, Fig.1C). Inter-
estingly, the gene encoding ZFP36 was predicted to be
inhibited even though it was measured as upregulated
in epithelial cells after infection with P. aeruginosa, high-
lighting the importance of combining in silico prediction
analyses with in vitro transcriptomic studies (Fig. 2B).
Overall, these data show that during early type II pneu-
mocyte infection with P. aeruginosa, IEGs are one of the
main classes of genes upregulated, and products of these
IEGs are predicted to participate in the regulation of gene
expression.
P. aeruginosa T3SS andT6SS trigger distinct changes
ingene expression intype II pneumocytes
P. aeruginosa manipulates host responses by directly
injecting its effectors into epithelial cells using T3SS and
T6SS [14, 15]. We hypothesized that the changes in the
transcriptomic response to P. aeruginosa are in part due
to the action of T3SS and T6SS effector proteins. To test
this hypothesis, lung epithelial cells were infected for
1h with P. aeruginosa mutants with defective T3SS and
T6SS. P. aeruginosa PA14::pscC transposon mutant [33]
was used to study the effect of T3SS. is mutant has a
Table 1 Top 10 differentially up‑ and down‑regulated protein‑encoding genes in epithelial cells post‑P. aeruginosa infection
Gene Symbol Ocial full name Fold change P value
EGR1 Early growth response 1 28.41 2.17 × 10–24
FOS Fos proto‑oncogene, AP‑1 transcription factor subunit 17.47 9.93 × 10–17
FOSB FosB proto‑oncogene, AP‑1 transcription factor subunit 7.39 1.05 × 10–5
SCG2 Secretogranin II 6.17 2.14 × 10–4
SLC6A15 Solute carrier family 6 member 15 4.59 1.01 × 10–4
NR4A2 Nuclear receptor subfamily 4 group A member 2 4.39 5.95 × 10–7
CCL20 C–C motif chemokine ligand 20 4.15 1.07 × 10–4
CRABP2 Cellular retinoic acid binding protein 2 4.10 8.12 × 10–7
ATF3 Activating transcription factor 3 3.51 1.02 × 10–5
IL6 Interleukin 6 3.50 3.65 × 10–3
IGFBP3 Insulin like growth factor binding protein 3 ‑2.27 4.71 × 10–6
VCAN Versican ‑2.30 4.91 × 10–8
ANXA8 Annexin A8 ‑2.32 3.62 × 10–4
FICD FIC domain protein adenylyltransferase ‑2.34 1.00 × 10–2
CRELD2 Cysteine rich with EGF like domains 2 ‑2.35 6.92 × 10–3
TM4SF4 Transmembrane 4 L six family member 4 ‑2.41 1.21 × 10–4
ASS1 Argininosuccinate synthase 1 ‑2.48 8.65 × 10–3
FRMD3 FERM domain containing 3 ‑2.65 3.74 × 10–4
TC2N Tandem C2 domains, nuclear ‑3.32 3.52 × 10–5
FAM25C Family with sequence similarity 25 member C ‑4.36 2.63 × 10–5
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
transposon insertion in the pscC gene, an outer-mem-
brane secretion component of P. aeruginosa T3SS [34].
P. aeruginosa has three T6SS loci responsible for various
functions of this system. H1-T6SS is thought to be asso-
ciated with P. aeruginosa interactions with prokaryotic
cells, while H2-T6SS and H3-T6SS have been reported to
be involved in interactions with eukaryotic cells [15]. In
addition, H2-T6SS and H3-T6SS are known to function-
ally compensate for each other in animal models of P. aer-
uginosa infection [35]. erefore, the P. aeruginosa PA14
ΔHSI-II:: III mutant [35] was used to study the effect of
T6SS on epithelial cell response. is mutant harbors
deletion of the HSI-II and HSI-III gene loci of P. aerugi-
nosa, which are essential for forming the T6SS needle
and secretion of effector proteins [27]. As a control, heat-
killed inactivated P. aeruginosa was used. Heat-killed
P. aeruginosa can no longer actively use injectosomes
and secrete effector proteins directly into the epithelial
cells, however, heat-killed bacteria still contains surface
components capable of binding to eukaryotic cells and
triggering immune responses such as lipopolysaccharide
or flagellin. Differential gene expression of epithelial cells
in response to live, heat-killed, or P. aeruginosa mutants
with defective T3SS or T6SS were performed (Table S1)
and Cluster analysis showed gene expression patterns
common or specific for each group (Fig. 3A). A total
of 13 genes were differentially regulated in all groups
compared to the non-infected mock control (Fig. 3A).
Differentially regulated genes included the cytokines/
chemokines CXCL8, CXCL2, CXCL3, CCL20, and imme-
diate-early response genes EGR1, FOS, KLF6, NR4A1,
and IER2 (Table3). Differential gene expression patterns
of EGR1, FOS and CXCL8 genes were also confirmed
using qRT-PCR (Figure S2). While the expression levels
of these genes varied in each group, all were differentially
expressed in response to P. aeruginosa independently of
bacterial viability and presence of T3SS or T6SS. Inter-
estingly, most of the genes differentially regulated in each
Table 2 Immediate‑early response gene dataset
Gene
symbol Ocial full name Fold change P value Refs
EGR1 Early growth response 1 28.41 2.17 × 10–24 [16, 17]
FOS Fos proto‑oncogene, AP‑1 transcription factor subunit 17.47 9.93 × 10–17 [16, 17]
FOSB FosB proto‑oncogene, AP‑1 transcription factor subunit 7.39 1.05 × 10–5 [16, 17]
NR4A2 Nuclear receptor subfamily 4 group A member 2 4.39 5.95 × 10–7 [16]
ATF3 Activating transcription factor 3 3.51 1.02 × 10–5 [16, 22]
IL6 Interleukin 6 3.50 3.66 × 10–3 [16, 22]
NR4A1 Nuclear receptor subfamily 4 group A member 1 3.38 2.92 × 10–3 [16]
KLF6 Kruppel like factor 6 3.22 2.50 × 10–22 [22, 23]
GDF15 Growth differentiation factor 15 2.92 4.31 × 10–3 [22]
IER2 Immediate early response 2 2.68 9.76 × 10–14 [17, 22]
MAFF MAF bZIP transcription factor F 2.57 4.75 × 10–5 [22]
TRIB1 Tribbles pseudokinase 1 2.52 1.15 × 10–8 [17]
DUSP5 Dual specificity phosphatase 5 2.50 1.76 × 10–5 [16]
FOSL1 FOS like 1, AP‑1 transcription factor subunit 2.47 6.45 × 10–3 [22, 24]
PTGS2 Prostaglandin‑endoperoxide synthase 2 2.46 3.51 × 10–4 [22, 25, 26]
PHLDA1 Pleckstrin homology‑like domain family A member 1 2.44 6.94 × 10–10 [27, 28]
ZFP36 ZFP36 ring finger protein 2.32 7.91 × 10–10 [16]
DUSP6 Dual specificity phosphatase 6 2.32 8.04 × 10–4 [16]
EREG Epiregulin 2.30 5.69 × 10–5 [27, 29]
GEM GTP binding protein overexpressed in skeletal muscle 2.29 3.09 × 10–6 [16]
IER3 Immediate early response 3 2.26 1.00 × 10–9 [16, 17]
BDNF Brain derived neurotrophic factor 2.21 9.78 × 10–4 [22, 30]
NR4A3 Nuclear receptor subfamily 4 group A member 3 2.24 4.43 × 10–2 [16]
KLF4 Kruppel like factor 4 2.15 7.92 × 10–4 [23]
LDLR Low density lipoprotein receptor 2.07 1.11 × 10–4 [16, 22]
RHOB Ras homolog family member B 1.98 2.13 × 10–2 [31]
KLF2 Kruppel like factor 2 1.97 2.73 × 10–2 [22, 23]
HES1 Hes family bHLH transcription factor 1 1.82 6.19 × 10–4 [22]
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
condition were unique to that condition, suggesting that
infection with each strain or mutant is characterized by a
distinct transcriptional signature (Fig.3A).
To better characterize these unique transcriptomic sig-
natures, GO term analysis was performed on the genes
upregulated in response to each treatment. Only genes
associated with the cytokine-mediated signaling path-
way were enriched in heat-killed P. aeruginosa infected
epithelial cells compared to mock control (Fig.3B). e
biological GO terms shared in response to P. aeruginosa
parental strain, T3SS, and T6SS mutants included GO
terms associated with cell motility,positive regulation of
developmental process, response to endogenous stimu-
lus, and regulation of cell proliferation compared to non-
infected cells (Fig.3B). Interestingly, among the enriched
GO terms, the regulation of the apoptotic process was
only enriched in epithelial cells in response to P. aerugi-
nosa parental strain PA14 and the T3SS mutant, but not
with the heat-killed PA14 nor the T6SS mutant (Fig.3B).
is result suggests the potential effect of T6SS on the
cellular apoptosis process inside the host. T3SS of P. aer-
uginosa secretes exotoxins involved in the modulation of
cytoskeleton rearrangement [14]. Accordingly, GO terms
associated with cell localization were only enriched in
epithelial cells infected with the parental P. aeruginosa
strain and the T6SS mutant, but not with the heat-killed
PA14 or T3SS mutant (Fig.3B).
e transcriptomic response of type II pneumocytes
to P. aeruginosa was characterized by the up-regulation
of IEGs at 1h post-infection (Fig.2A). e expression
of these genes can be stimulated by a variety of internal
and external signals [11]. We therefore explored whether
injectosomes of P. aeruginosa have a specific role in the
upregulation of IEGs by comparing the total number of
differentially regulated IEGs in each group (Fig.4A). Epi-
thelial cells incubated with the PA14 parental strain P.
aeruginosa had the highest, while heat-killed P. aerugi-
nosa had the lowest number of differentially upregulated
IEGs (Fig.4A). Epithelial cells infected with P. aeruginosa
deficient of T3SS or T6SS still led to the differential regu-
lation of IEGs, although to a lower extend. Interestingly,
the expression patterns of several IEGs were unique to
T3SS and T6SS mutants compared to the PA14 paren-
tal strain (Fig.4B). In the absence of T3SS, especially the
induction of EGR1, and FOS expression was lower than
the parental strain in transcriptomic analysis (Fig. 4B,
Table 3). During infection with the T6SS mutant, the
expression of the FOS and EGR1 genes increased in
epithelial cells compared to infection with the parental
strain (Fig.4B, Fig.5A, Fig. 5B, Table3). ese results
suggest that T3SS is associated with downregulation of
the expression of IEGs at the center of the response elic-
ited by epithelial cells, such as EGR1 and FOS (Fig.1C).
T3SS andT6SS trigger activation ofdierent transcription
factors duringP. aeruginosa infection
e expression of IEGs is controlled by transcriptional
regulators, which are activated by cell-intrinsic and
extrinsic signals upon infection [11]. A transcription fac-
tor activation assay was therefore performed to under-
stand the effect of P. aeruginosa injectosomes on the
activation of transcription factors. To do that, activation
of transcription factors predicted to be upstream regula-
tors of EGR1 and FOS was measured in nuclear extracts
of epithelial cells infected with PA14, the T3SS and T6SS
mutants (Table4, Fig.5C). AP1 transcription factor was
activated in the parental strain, and the activation level
of this transcription factor was lower in T3SS and T6SS
mutants (Fig.5C). On the other hand, STAT3 was only
activated during infection with the parental P. aeruginosa
strain. Interestingly, STAT1 and SP1 activation increased
in the T6SS mutant compared to the parental strain and
the T3SS mutant (Fig. 5C). While the EGR1 encoding
gene was the most differentially upregulated gene in epi-
thelial cells upon infection with P. aeruginosa, the acti-
vation of the EGR transcription factor was not observed
at the protein level, highlighting the importance of per-
forming this type of analysis at both the mRNA and the
protein level (Fig.5C). To take these observations a step
further, we also quantified the production of IL-6 and
IL-8, two cytokines whose expression is controlled by
EGR1 and FOS. Since no cytokines were detectable in
culture supernatants of infected cells 1h post-infection
(data not shown), we extended the duration of infec-
tion to 6h by washing the cells to remove non-adherent
Table 3 Common DEG’s in response to parental strain, heat‑
killed, T3SS, or T6SS mutant P. aeruginosa. Fold‑change of
common DEG’s compared to non‑infected mock control
epithelial cells are shown
Gene symbol PA14 HK T3SS T6SS
EGR1 28.41 9.08 16.64 31.27
FOS 17.47 7.23 8.93 36.46
NR4A1 3.38 2.36 2.42 3.16
KLF6 3.22 2.05 2.43 2.73
IER2 2.68 2.05 2.03 2.78
U1_8 83.98 41.99 60.44 57.08
RNVU1-18 14.23 3.36 8.22 13.02
CCL20 4.15 2.64 2.92 3.86
CRABP2 4.1 2.11 4.61 2.09
CXCL3 3.2 3.49 2.04 3.17
PRDM1 3.1 2.38 3.23 2.77
CXCL2 3.01 3.12 2.83 3.99
CXCL8 2.57 2.26 2.06 3.24
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Page 6 of 16
Sen‑Kilicetal. BMC Genomics (2022) 23:345
bacteria and replacing the medium with fresh medium
containing antibiotic to prevent bacterial overgrowth and
cell dealth. Six hours post-infection, we detected a signif-
icant increase in IL-6 and IL-8 secretion in cells infected
with PA14 (Fig.5D and E). We did not detect a signifi-
cant increase in the secretion of either cytokines in cells
infected with the T3SS and T6SS mutants, suggesting
that both of these virulence systems play a role in trigger-
ing IL-6 and IL-8 production during infection. Overall,
these data indicate that T3SS and T6SS affect the activa-
tion levels of transcription factors that are important for
induction and control of IEG expression during infection
with P. aeruginosa in epithelial cells, and that this activa-
tion results in changes in the secretion of proinflamma-
tory mediators. A model with an example of the AP-1
signaling cascade and its downstream effects is shown in
Fig.5F.
Discussion
Airway epithelial cells are the first barrier against patho-
gens and one of the first responders during respiratory
infections. Previous studies provided substantial infor-
mation on epithelial cell responses during P. aeruginosa
infection using RNA-sequencing and microarray tech-
nologies [12, 13, 36]. Here, we performed RNA-sequenc-
ing to study the response of airway epithelial cells during
the early stages of infection with P. aeruginosa PA14,
T3SS, and T6SS mutants to understand the role of the
pathogen’s injectosomes in this process. is study iden-
tified various signaling pathways differentially controlled
by T3SS and T6SS during airway epithelial cell infection
invitro and provides additional insights into the role of
these two important virulence factors.
During lung infection, the initial response of airway
epithelial cells to bacterial pathogens is critical to drive
the recruitment of innate immune cells to the site of
infection. As the infection progresses, the transcriptional
landscape becomes more complex as various popula-
tions of immune cells are recruited and P. aeruginosa
causes damage, inducing among other things, cell death.
To gain insights into the initial changes in gene expres-
sion that occur upon infection with P. aeruginosa in
A549 type II pneumocytes, we selected a short incuba-
tion timeframe (1h). RNA-sequencing analysis identified
143 genes differentially expressed in A549 cells based on
p-value 0.05, fold change > 2, and average FPKM dif-
ference > 7 criteria one hour post-infection with P. aer-
uginosa. Previously, high-density DNA microarray was
used to identify differentially regulated genes in A549
cells [12]. As a part of that study, P. aeruginosa PAK strain
and cells were incubated with bacteria for 3h and only 24
genes were found to be differentially regulated. Another
similar microarray study using P. aeruginosa PA103 to
infect 9HTEo- cells for 3h reported differential regulation
of 46 genes [36]. More recently, Balloy etal. performed a
transcriptomic study using human airway epithelial cells
from bronchial biopsies infected with P. aeruginosa PAK
strain using RNA-sequencing at different time points of
infection [13]. Some of the differentially regulated genes
and pathways identified in these studies coincide with
the findings of this current study, such as TNF signaling,
CCL20, ZFP36 (TTP), IL6 or FOS, however there were
also some discrepancies. Differences in bacterial strains
or epithelia cell lines, timing of the experiment, multi-
plicity of infection, sensitivity of the techniques used,
and stringency of differential expression analysis likely
account for some of the differences observed between
these studies.
Here, we observed that infection of epithelial cells
with P. aeruginosa leads to the induction of genes asso-
ciated with MAPK, IL-17, and TNF signaling pathways
(Fig.1C). e induction of the MAPK pathway and pro-
inflammatory cytokines such as TNFα, IL-6 and IL-17
was previously observed during P. aeruginosa infection
[3640]. In addition, P. aeruginosa infection of type II
pneumocytes was characterized by the upregulation
of the expression of the proinflammatory cytokines/
chemokines CXCL8, CXCL2, CXCL3, and CCL20
encoding genes (Table3). e induction of these proin-
flammatory cytokines/chemokines can be mediated by
immunogenic components of P. aeruginosa, such as LPS
and flagella, that induce TLR4 and TLR5 signaling in epi-
thelial cells, respectively. It was previously demonstrated
that P. aeruginosa flagellin alone could stimulate CXCL8,
CXCL2, and CCL20 production by human airway epi-
thelial cells [41, 42]. In addition, P. aeruginosa LPS can
Table 4 Predicted upstream regulators of EGR1 and/or FOS
genes in epithelial cells infected with P. aeruginosa. Selected
upstream regulators were identified based on genes that were
differentially regulated in epithelial cells after one hour infection
with P. aeruginosa. Activation z‑score shows predicted activation
states of upstream regulators. The further the activation z‑score
from zero, the more likely that observed directionality of the
target genes are consistent with the upstream regulator
Upstream regulator Activation z-score P value
FOS 2.33 6.33 × 10–13
JUN 2.555 2.73 × 10–15
STAT3 3.553 1.71 × 10–14
STAT1 2.207 6.38 × 10–7
SP1 2.530 3.79 × 10–23
CREB 2.953 1.25 × 10–11
HIF1A 2.521 4.63 × 10–13
EGR1 3.093 3.98 × 10–11
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
induce CXCL3 and CCL20 gene expression in human
leukocytes and cause induction of CXCL2 in mouse
corneas [43, 44]. Among the differentially expressed
cytokines, the expression of the IL-6 encoding gene in
epithelial cells was lower in the absence of T3SS relative
to the parental P. aeruginosa strain (Fig.4B). is result
was confirmed at the protein level as well (Fig.5D). IL-6
was identified as one of the hub proteins and as upstream
regulator of differentially expressed genes during P. aer-
uginosa infection (Fig.1C, 2B). During invivo P. aerugi-
nosa infection, the Il6 gene was shown to be one of the
highest upregulated genes in mice lungs [45]. While mul-
tiple bacterial components of P. aeruginosa contribute to
the induction of IL-6 responses in epithelial cells [42, 46],
Fig. 1 Characterization of epithelial cells transcriptomic response to P. aeruginosa at one‑hour post‑infection. A The number of differentially
regulated genes in P. aeruginosa PA14 infected epithelial cells compared to non‑infected control. Only genes with a p‑value 0.05, fold change > 2,
and average FPKM difference > 7 were included in the analysis. B GO term analysis of “biological processes” enriched in P. aeruginosa PA14 infected
A549 cells compared to non‑infected control. (p 0.05). C Protein‑protein interaction network of differentially regulated genes in A549 cells during
P. aeruginosa infection compared to non‑infected control. The node size positively correlates with the degree of connectivity. The color of the nodes
correlates with the fold change of the corresponding genes. Genes associated with KEGG “MAPK signaling pathway”, “IL‑17 signaling pathway” and
“TNF signaling pathway” are circled in blue, green and red, respectively. Only nodes with connection are shown
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
it was previously reported that the T3SS and its effector
toxins alone can induce IL-6 secretion in human epi-
thelial cells [47]. Overall, the data obtained in this study
supports prior observations in the field on the induction
of proinflammatory cytokines, and in particular, IL-6 in
response to P. aeruginosa.
In this study, upon one-hour post-infection with P. aer-
uginosa, 17.5% of upregulated genes were IEGs. Some of
these IEGs were also predicted to have a role as upstream
regulators of the differentially expressed genes (Fig.2B).
Mitogens, growth factors or cell stressors are often
involved in triggering initial induction of IEGs [11, 16,
48]. While bacterial components such as LPS are known
to stimulate IEGs expression [49], the role of P. aer-
uginosa T3SS and T6SS in this process is still unknown.
Infection with T3SS and T6SS mutants led to differences
in the induction of IEGs, suggesting that each system
induces a unique transcriptomic signature. ese signa-
tures were associated with differences in the activation of
transcription factors such as AP1, STAT1, and SP1.
IEGs are associated with the activation of proinflam-
matory response genes and pathways [50]. Among IEGs,
EGR1 was the most differentially regulated IEG upon
infection with P. aeruginosa (Fig. 2A, Table 1). Eleva-
tion in the expression of the EGR1 gene was previously
reported in pulmonary diseases [51, 52] and bacterial
infections, including P. aeruginosa [5355]. An increase
in EGR1 expression is linked to the induction of various
inflammatory mediators such as IL-6, IL-8, IL-1β, and
TNF-α [53, 5660]. Recently, the role of EGR1 in host
defense against P. aeruginosa was shown using the Egr-
1-deficient mouse model [54]. Egr-1-deficiency was asso-
ciated with bacterial clearance and reduced mortality,
which might be linked to reduced systemic inflammation
[54]. It was also previously shown that Egr-1 induction
in epithelial cells depends on bacterial viability and con-
tact with the cells [53]. In line with this study, we showed
that EGR1 gene expression was decreased in response
to heat-killed P. aeruginosa compared to live bacte-
ria (Fig.4, Table3). Furthermore, the expression of the
EGR1 gene was lower in the T3SS mutant strain of P. aer-
uginosa compared to the parental strain (Fig.4, Table3).
ese results suggest that EGR1 gene expression during
the early phase of infection with P. aeruginosa depends
on bacterial viability and the T3SS. Similar observa-
tions have been made in other Gram-negative bacteria in
which T3SS was shown to play a role in the induction of
EGR1 expression in epithelial cells [6163].
e second most differentially upregulated IEG upon
P. aeruginosa infection was the FOS gene (Fig. 2A,
Table 1). C-Fos is part of the AP-1 transcription fac-
tor complex associated with inflammatory pathways
crucial for initial host-response against pathogens [64,
65]. e FOS gene was shown to be upregulated upon
P. aeruginosa infection [36, 55]. In line with these stud-
ies, upon infection with P. aeruginosa, the FOS gene
Fig. 2 Immediate‑early response genes in epithelial cells are upregulated upon P. aeruginosa infection. A Volcano plot of differentially expressed
genes. Only genes with a p‑value 0.05, fold change > 2, and average FPKM difference > 7 were analyzed as differentially regulated genes.
Upregulated genes in response to PA14 compared to non‑infected control are shown in red and down‑regulated genes are shown in blue.
Immediate‑early response genes are outlined in black. Black proportion on the pie charts shows the percentage of immediate‑early response genes
in total differentially up or downregulated genes B Differentially regulated immediate‑early response genes in P. aeruginosa PA14 infected epithelial
cells with predicted upstream regulator function. Activation z‑scores were calculated using IPA [24]. Only upstream transcriptional regulators with
z‑score > 2.0 or z‑score < ‑2.0, and p‑value < 0.01 are shown
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
was upregulated compared to the non-infected mock-
control (Table 1, Fig. 1). Bacterial viability and the
absence of the T3SS in P. aeruginosa negatively affected
the expression of the FOS gene (Table 3, Fig. 5A). In
addition, the activation of the AP-1 transcription fac-
tor was lower in epithelial cells infected with the P.
aeruginosa T3SS mutant (Fig. 5C). ese results are
in line with evidence that the T3SS effector ExoU is
essential in the upregulation of FOS expression in epi-
thelial cells [36]. Both EGR1 and FOS gene expression
increased in the T6SS mutant compared to parental
P. aeruginosa infected epithelial cells. ese results
might be in part due to the effect of the regulation of
the upstream activators of EGR1 and FOS or the effect
of differential regulation of other bacterial components
in mutant strains. In the future, mechanistic studies
Fig. 3 Comparison of transcriptomic and functional response in response to T3SS and T6SS P. aeruginosa mutants. A Venn diagram of differentially
expressed epithelial genes. Only genes with a p‑value 0.05, fold change > 2, and average FPKM difference > 7 were analyzed as differentially
regulated genes. B Comparison of GO term analysis of “biological processes” of epithelial cells positively enriched in response to P. aeruginosa PA14
(PA14), heat‑killed P. aeruginosa PA14 (HK), P. aeruginosa PA14::pscC (T3SS), and P. aeruginosa PA14 ΔHSI‑II : : III ( T6SS). Only GO terms with p 0.05 and
number of genes associated with GO term > 5 is shown. The node color indicates the significance of functionally enriched GO terms in each group.
The node size correlates with number of genes associated with functionally enriched GO terms
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
with reporter and KO strains can likely elucidate the
role of the T3SS and T6SS on EGR1 and FOS regula-
tion. Additional insights could also be obtained by
measuring the bacterial transcriptome response during
infection. To maintain high epithelial cell viability dur-
ing infection, this study was performed with a relatively
Fig. 4 Differential expression of IEG’s during contact with T3SS and T6SS defective P. aeruginosa strains. A Total number of differentially regulated
immediate‑early response genes Only genes with a p‑value 0.05, fold change > 2, and average FPKM difference > 7 were analyzed as differentially
regulated genes. B Heatmap of differentially regulated early response genes in epithelial cells incubated with P. aeruginosa PA14 (PA14), heat‑killed P.
aeruginosa PA14 (HK), P. aeruginosa PA14::pscC (T3SS), and P. aeruginosa PA14 ΔHSI‑II : : III (T6SS) compared to non‑infected mock control
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
low multiplicity of infection and the cells were washed
prior to being saved in RNA-protect. erefore, we did
not recover enough bacterial RNA and generate suf-
ficient reads to be able to characterize the transcrip-
tomic response of P. aeruginosa and its injectosome
mutants during infection. Modification of the experi-
mental setup to separate bacterial from eukaryotic cells
or mRNA would likely enable this type of dual RNA
sequencing approach for this invitro setting [45, 66].
Overall, this study sheds light on the early host
responses differentially regulated in epithelial cells dur-
ing infection with P. aeruginosa and the distinct role
of T3SS and T6SS in this response. Transcriptomics,
together with transcription factor activation assays and
Fig. 5 Activation of transcription factors and cytokine response in epithelial cells stimulated with T3SS, T6SS or parental strain of P. aeruginosa. RPKM
values of A FOS and B EGR1 genes. C Transcription factor activation of nuclear extracts of epithelial cells incubated with P. aeruginosa PA14 (PA14),
P. aeruginosa PA14::pscC (T3SS), and P. aeruginosa PA14 ΔHSI‑II : : III ( T6SS) and mock control. The values were represented as relative light units (RLU)
normalized to ER (Estrogen Receptor). Cytokines D IL‑6 and E IL‑8 were quantified from supernatants of epithelial cells at 6 hrs post‑infection. Kruskal
Wallis test with Dunn’s multiple comparisons was performed for statistical analysis (**, p 0.01) F Graphical illustration of epithelial cell response to
P. aeruginosa infection and the effect of injectosomes
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
cytokine measurements highlighted the role of T3SS and
T6SS in up-regulating the secretion of proinflammatory
mediators such as IL-6 and IL-8 in response to P. aerugi-
nosa infection (Fig.5F). Future studies need to examine
the expression of genes as well as proteins and their acti-
vation state overtime to better understand the epithelial
cell response during P. aeruginosa infection both invitro
and invivo. In addition, future work is required to deter-
mine the potential of therapeutic interventions targeting
early response genes, such as EGR1 and FOS, or T3SS of
P. aeruginosa to alleviate the disease burden of patients
infected with P. aeruginosa.
Conclusions
By comparing the transcriptional profiles of epithelial
cells during infection with wild-type, and T3SS, T6SS
deficient P. aeruginosa mutants, we show that immedi-
ate-early response genes are differentially regulated in
epithelial cells during the first hour of infection with P.
aeruginosa. is study illustrates that T3SS and T6SS
deficient mutants lead to two distinct expression profiles
in epithelial cells compared to the wild-type strain, and
identified key regulators regulated by T3SS and T6SS.
Methods
Bacterial strains
P. aeruginosa PA14 [25], PA14::pscC (PA14 transposon
mutant ID: 29,579) [33] and PA14 ΔHSI-II:: III strains
were used in this study [35]. All P. aeruginosa strains were
grown in 3ml of Miller’s lysogeny broth (LB) overnight at
37°C. Overnight cultures were diluted 1:100 in fresh LB
and grown until the cultures reached OD600 = 0.3.
Epithelial cell culture
Human alveolar basal epithelial adenocarcinoma A549
cells were obtained from the American Type Culture
Collection (ATCC). e cells were cultured using F-12K
medium supplemented with 10% v/v fetal bovine serum
(FBS) (Corning, 10–025-CV), 50 IU/mL penicillin, and
50μg/mL streptomycin (Corning, 30001Cl) at 37°C in
5% CO2.
Infection ofepithelial cells
Epithelial cells were seeded in T-75 cell culture flasks
(9 × 106 cells/flask) (Greiner Bio-one. 658,175) or 6 well
plates (1 × 106 cells/flask) (Greiner Bio-one, 657,165)
a day before the experiment. P. aeruginosa PA14,
PA14::pscC or PA14 ΔHSI-II:: III strains were grown as
previously described. e bacterial culture was diluted
to a multiplicity of infection of 10 using F-12K medium
supplemented with 10% v/v FBS. e infection dose
was validated by serially diluting and plating the dose
on Lysogeny Agar (LA) plates. For heat-killed control,
P. aeruginosa PA14 grown as described above and was
heat-inactivated for 1h at 60°C prior to get in contact
with epithelial cells. Epithelial cells were washed three
times, with 10 ml of phosphate buffered saline (PBS)
(Corning, 21–040-CV). e bacterial suspension was
added to epithelial cells and centrifuged at 167.8 × g
for 5min to promote bacterial attachment. Flasks were
then incubated for 1h at 37°C in 5% CO2. For the non-
infected mock control, epithelial cells were treated as
described above and incubated with F-12K with 10%
v/v FBS. Experiments were performed in independent
triplicates for each condition, and the same batch of
serum was used throughout the study to avoid batch-
to-batch variability. Epithelial cell viability assays were
performed using alamarBlue (ermo Fisher, DAL1025)
at 1h post-infection. e epithelial cells were washed
with1 ml of 1 × PBS three times. A549 cells were lysed
using 0.5% v/v Triton X-100 (Sigma-Aldrich, T8787)
in 1X PBS as a negative control. e cells were incu-
bated with F-12K medium with 10% v/v FBS (Corning,
10–025-CV) and 10% v/v alamarBlue (ermo Fisher,
DAL1025) for 5h. e fluorescence signal was detected
by using SpectraMax i3 (Molecular Devices LLC).
RNA preparation
Epithelial cells were incubated with P. aeruginosa
parental strain, heat-killed, T3SS mutant, T6SS mutant
or medium alone for one-hour. Then these cells were
washed with 10ml of PBS (Corning, 21–040-CV) three
times. Cells were scraped off using a cell scraper in
1ml of PBS and centrifuged at 16,200 × g for 1min.
The pellets were resuspended in 0.5 ml of RNApro-
tect cell reagent (Qiagen, 76,526) and stored at -80°C.
Qiagen RNeasy Mini Kit (Qiagen, 74,104) was used
for RNA extraction. The samples were thawed and
centrifuged at 9,600 × g for 3 min. The pelleted cells
were resuspended in 400µl of 1mg/ml of TE lysozyme
(1mg lysozyme in 1ml of 10mM/1mM Tris–EDTA
buffer) and incubated for 10min at room temperature.
The RNA was extracted using the RNeasy mini spin
column based on the manufacturer’s instructions. Any
remaining DNA was degraded using the RNase-free
DNase set (Qiagen, 79,254) according to the manu-
facturer’s instructions. The DNase was removed from
the samples using the RNeasy mini spin column (Qia-
gen, 74,104) and purified RNA was eluted in 50µl of
RNAse-free water (Qiagen, 74,104). The samples were
confirmed to be DNA-free by quantitative PCR (qPCR)
as described below. The amount of RNA in samples
was measured using a Spectramax i3x (Molecular
Devices), and the integrity of the samples was char-
acterized using an Agilent 2100 Bioanalyzer. The
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
RNA integrity number of the samples used for RNA-
sequencing was greater than or equal to 8.
Library construction, RNA sequencing, andanalysis
RNA samples were depleted of rRNA using the human
RiboMinus kit (ermo Fisher Scientific). e samples
were converted into Illumina sequencing libraries with
the ScriptSeq v2 RNA-Seq Library Preparation Kit (Epi-
centre, Illumina). e libraries passed standard qual-
ity control PCR and were sequenced using an Illumina
HiSeq1500 at Marshall University (2 × 75 bp reads).
ree biological replicates were sequenced in each group.
Resultant reads were trimmed and aligned
onto the human UCSC hg38 reference genome
(GCA_000001405.27) using CLC Genomics Workbench,
version 9.5.4 (Qiagen). After trimming, there were on
average 19,411,441 ± 1,447,196 reads on each sample.
Reads were mapped against the reference genome with
the following settings: mismatch cost = 2, insertion
cost = 3, deletion cost = 3, length fraction = 0.8, simi-
larity fraction = 0.8. e depth of coverage were 21,312
gene reads on average per sample. Fold changes in gene
expression, and statistical analyses were performed using
the extraction of differential gene expression (EDGE)
test as implemented in CLC Genomics, which is based
on the Exact Test [67, 68]. Differential gene expressions
of infected A549 cells were determined by comparison to
non-infected mock A549 cell controls (Table S1). P-val-
ues calculated by EDGE test without correction. Genes
with differences in gene expression with a p-value 0.05,
fold change > 2, and average FPKM difference > 7 were
used for analysis. Ribosomal proteins and RNA were
discarded from analysis. e immediate-early response
gene-set used in this manuscript was generated using
previous literature (Table2).
Functional analysis
Functional enrichment of differentially expressed genes
was performed using WebGestalt to determine nonre-
dundant biological processes [69]. Statistically enriched
Gene Ontology (GO) terms under “biological process”
with Bonferroni correction p-value 0.05 were iden-
tified. e redundancy of functional GO terms was
reduced using the affinity propagation method imple-
mented in WebGestalt. e upstream regulator analysis
of differentially regulated genes was performed by Inge-
nuity Pathway Analysis (IPA, QIAGEN Inc.) [32]. R sta-
tistical software v3.5.2 was used to visualize data using
volcano plots and enriched GO Term comparisons for
multiple groups [70]. Heatmaps were generated using
Heatmapper [71].
Network analysis
e protein–protein interaction network information
of differentially expressed genes during PA14 infection
was retrieved from STRING V11.0 [18]. e generated
protein interaction network was reconstructed using
Cytoscape software version 3.8 [72]. e protein nodes
with a high degree of connectivity were calculated by
Cytoscape network analysis. KEGG pathway enrichment
analysis was made using STRING V11.0 [18].
qRT-PCR analysis
Epithelial cells were seeded in 12 well plates (4 × 105
cells/flask) (Costar, 3513) a day before the experiment.
P. aeruginosa PA14, PA14::pscC or PA14 ΔHSI-II:: III
strains were grown as previously described. e bacte-
rial culture was diluted to a multiplicity of infection of 20
using F-12K medium supplemented with 10% v/v FBS.
e cells were infected and RNA was isolated after 1h of
infection from each sample as described above. Absence
of DNA was confirmed by performing qPCR on 20ng of
RNA using the following primer set: RPS13 F (CGA AAG
CAT CTT GAG AGG AACA) and RPS13 R (TCG AGC
CAA ACG GTG AAT C) [73]. cDNA synthesis was per-
formed using Moloney murine leukemia virus (MMLV)
reverse transcriptase (Promega, PR-M1705) accord-
ing to manufacturer’s instructions using 250ng of RNA
and gene-specific reverse primers for each target. qPCR
amplification was performed using SYBR Green PCR
master mix (Applied Biosystems, 4,309,155) according
to the manufacturer’s instructions. ree technical rep-
licates were run per gene target per sample on a StepO-
nePlus qPCR thermocycler (Applied Biosystems). Gene
expression was normalized to that of the RPS13 reference
gene using the 2ΔΔCT method [74]. e following primer
sequences were used in this study: EGR1 F (CCT GAC
ATC TCT CTG AAC AACG) EGR1 R (GGG AAA AGC
GGC CAG TAT AG) [55], FOS F (CCA ACC TGC TGA
AGG AGA AG), FOS R (AGA TCA AGG GAA GCC ACA
GA) [75], IL6 F (CCC ACC GGG AAC GAA AGA G). IL6
R (CAG GGA GAA GGC AAC TGG AC), CXCL8 F (CAC
TGC GCC AAC ACA GAA AT) CXCL8 R (AAG TTT CAC
TGG CAT CTT CACT), RPS13 F (CGA AAG CAT CTT
GAG AGG AACA) and RPS13 R (TCG AGC CAA ACG
GTG AAT C) [73].
Transcription factor activating proling array
e epithelial cells were infected with P. aeruginosa
mutants, as described above. At 1h post-infection, the
nuclear extracts were obtained using a nuclear extrac-
tion kit (Signosis, SK-0001) following the manufacturer’s
instructions. Triplicates for each condition were obtained
by completing three independent experiments. e
amount of protein in each extract was measured using
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Sen‑Kilicetal. BMC Genomics (2022) 23:345
both the NanoDrop Protein quantification instrument
(ermo Fisher Scientific) and using a bicinchoninic acid
assay (BCA) total protein kit (ermo Fisher Scientific,
23,225). Triplicates for each condition were combined
and the activation level of 48 different transcription fac-
tors in each condition was measured using the Transcrip-
tion Factor Activation Profiling Plate Array I (Signosis,
FA1001) according to the manufacturer’s instructions.
Briefly, 10 µg nuclear extracts were incubated with
biotin-labeled probes encoding TF DNA-binding site
consensus sequences from 30 min at room tempera-
ture. e unbound probes were washed away using the
provided isolation column. e remaining probes were
eluted and hybridized to complementary sequences in
provided 96-well hybridization plate at 42°C overnight.
e plates were washed, blocked, and the luminescence
signal measured on Synergy HTX Multi-Mode Micro-
plate Reader (BioTek). e values were normalized to (ER
Estrogen Receptor).
Cytokine analysis
For cytokine analysis, epithelial cells were grown in 12
well plates (4 × 105 cells/flask) as previously described.
e bacterial culture was diluted to a multiplicity of
infection of 20:1 using F-12 K medium supplemented
with 10% v/v FBS. e cells were infected for 1 h as
described above. After 1h incubation, cells were washed
3 times with 1X PBS and the medium was replaced by
F12K with 10% v/v FBS and gentamicin 300 μg/mL.
Cells were incubated an additional 6 h, then superna-
tant was collected and preserved for cytokine analysis at
-80°C. IL-6 and IL-8 quantification was performed using
the Human Luminex Discovery Assay (R&D systems,
LXSAHM-20) according to manufacturer’s instructions.
e samples were run on a Magpix (Luminex) instru-
ment to detect cytokine levels. Cytokine concentration
values below the limit of detection were arbitrary setup
to 0 for statistical analysis and graphical representation.
Statistical analysis
Statistical analysis of transcriptomic data was performed
using differential gene expression (EDGE) test as imple-
mented in CLC Genomics, which is based on the Exact
Test [67, 68]. e statistical packages integrated in GO
term [69], IPA [32], and STRING V11.0 [18] analyses
were used for in silico functional and network data anal-
yses. Data comparisons for more than two groups were
performed using ordinary one-way analysis of variance
(ANOVA) followed by Tukey’s multiple comparisons test.
Dunnet’s multiple comparisons test was used when mul-
tiple groups were compared to control for normally dis-
tributed data. Kruskal–Wallis test with Dunn’s multiple
comparison test was used for nonparametric data. All
statistical analysis were done by using GraphPad Prism
9.2.0 (San Diego, California USA).
Abbreviations
P. aeruginosa: Pseudomonas aeruginosa; T3SS: Type Three Secretion System;
T6SS: Type Six Secretion System; IEG: Immediate‑Early Response Gene; LPS:
Lipopolysaccharides; CF: Cystic Fibrosis.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12864‑ 022‑ 08554‑0.
Additional le1: Figure S1. Cell viability of epithelial cells one‑hour
post‑infection with P. aeruginosa. Cell viability of uninfected, dead
or P.aeruginosa PA14 infected A549 epithelial cells. Relative fluores‑
cence intensity was calculated by subtracting the background fluores‑
cence signal. Experiments were performed in independent triplicates for
each condition. Samples were compared to negative control group using
One‑way ANOVA followed by Dunnet’s multiple comparisons test for sta‑
tistical analysis. Error bars indicate standard deviation. The asterisks show
statistical significance: p 0.0001. FigureS2. qRT‑PCR of selected genes in
response to live and heat‑killed PA14, and live T3SS and T6SS P.aeruginosa
mutants. qRT‑PCR analysis of A EGR1, B FOS, C IL6, and D CXCL8 rela‑
tive fold change compared to RPS13 housekeeping gene. Analysis was
performed using three biological replicates with three technical replicates.
Ordinary One‑way ANOVA with Tukey’s multiple comparison tests was
performed for statistical analysis (*, p 0.05, **, p 0.01). TableS1.
Analyzed RNAseq data. Each tab in the file lists the number of reads,
RPKM, fold changes, p-values, annotations, and other relevant information
for each comparison performed in this study.
Additional le2.
Acknowledgements
The authors would like to thank Dr. Laurence Rahme for kindly providing PA14
ΔHSI‑II: III strain, Dr. Allison Wolf for assistance with cytokine assay, William
T. Witt for assistance with designing qPCR primers, and Ryan Percifield and
West Virginia University Bioinformatics core facility for RNA‑sequencing library
preparation.
Authors’ contribution
ESK and ABH performed experiments and collected data. ESK and MB
analyzed the data and composed the manuscript. FHD and ESK performed
bioinformatic analysis. All authors reviewed and edited the manuscript. The
author(s) read and approved the final manuscript.
Funding
RNA sequencing was performed by Marshall University Bioinformatics core
facility supported by the WV‑INBRE grant (P20GM103434), the COBRE ACCORD
grant (P20GM121299) and the West Virginia Clinical and Translational Science
Institute (WV‑CTSI) grant (U54GM104942). MB and FHD were supported by
laboratory start‑up funds from West Virginia University. This work was also sup‑
ported by the Cystic Fibrosis Foundation (BARBIE1610 and BARBIE18G0).
Availability of data and materials
The raw RNA sequencing read datasets generated in this study are available in
NCBI Read Archive (SRA) repository with SRA Bioproject number PRJNA791600
(https:// datav iew. ncbi. nlm. nih. gov/ object/ PRJNA 791600). Analyzed data are
provided in the supplementary material.
Declarations
Ethics approval and consent
Not applicable.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 15 of 16
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Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Author details
1 Department of Microbiology, Immunology, and Cell Biology, West Virginia
University School of Medicine, Morgantown, WV, USA. 2 Vaccine Development
Center, West Virginia University Health Sciences Center, Morgantown, WV, USA.
Received: 23 December 2021 Accepted: 11 April 2022
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Pseudomonas aeruginosa infection induces vigorous inflammatory mediators secreted by epithelial cells, which do not necessarily eradicate the pathogen. Nonetheless, it reduces lung function due to significant airway damage, most importantly in cystic fibrosis patients. Recently, we published that TP359, a proprietary cationic peptide had potent bactericidal effects against P. aeruginosa, which were mediated by down-regulating its outer membrane biogenesis genes. Herein, we hypothesized that TP359 bactericidal effects could also serve to regulate P. aeruginosa-induced lung inflammation. We explored this hypothesis by infecting human A549 lung cells with live P. aeruginosa non-isogenic, mucoid and non-mucoid strains and assessed the capacity of TP359 to regulate the levels of elicited TNFα, IL-6 and IL-8 inflammatory cytokines. In all instances, the mucoid strain elicited higher concentrations of cytokines in comparison to the non-mucoid strain, and TP359 dose-dependently down-regulated their respective levels, suggesting its regulation of lung inflammation. Surprisingly, P. aeruginosa flagellin, and not its lipopolysaccharide moiety, was the primary inducer of inflammatory cytokines in lung cells, which were similarly down-regulated by TP359. Blocking of TLR5, the putative flagellin receptor, completely abrogated the capacity of infected lung cells to secrete cytokines, underscoring that TP359 regulates inflammation via the TLR5-dependent signaling pathway. Downstream pathway-specific inhibition studies further revealed that the MAPK pathway, essentially p38 and JNK are necessary for induction of P. aeruginosa elicited inflammatory cytokines and their down-regulation by TP359. Collectively, our data provides evidence to support exploring the relevancy of TP359 as an anti-microbial and anti-inflammatory agent against P. aeruginosa for clinical applications.
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Method
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