Abstract
Sepsis is a life-threatening disease induced by a systemic inflammatory
response, which leads to organ dysfunction and mortality. In sepsis,
the host immune response is depressed and unable to cope with
infection; no drug is currently available to treat this. The lungs are
frequently the starting point for sepsis. This study aimed to identify
potential genes for diagnostics and therapeutic purposes in sepsis by a
comprehensive bioinformatics analysis. Our criteria are to unravel
sepsis-associated signature genes from gene expression datasets.
Differentially expressed genes (DEGs) were identified from samples of
sepsis patients using a meta-analysis and then further subjected to
functional enrichment and protein‒protein interaction (PPI) network
analysis for examining their potential functions. Finally, the
expression of the topmost upregulated genes (ARG1, IL1R2, ELANE, MMP9)
was quantified by reverse transcriptase-PCR (RT-PCR), and
myeloperoxidase (MPO) expression was confirmed by immunohistochemistry
(IHC) staining in the lungs of a well-established sepsis mouse model.
We found that all the four genes were upregulated in semiquantitative
RT-PCR studies; however, MMP9 showed a nonsignificant increase in
expression. MPO staining showed strong immunoreactivity in sepsis as
compared to the control. This study demonstrates the role of
significant and widespread immune activation (IL1R2, MMP9), along with
oxidative stress (ARG1) and the recruitment of neutrophils, in sepsis
(ELANE, MPO).
Keywords: sepsis, meta-analysis, DEG, PPI
1. Introduction
Sepsis is a clinically heterogeneous and biologically complex disease
characterized by the improper response of the immune system due to an
infection caused by bacteria, viruses, fungi, or parasites, leading to
organ dysfunction [[42]1]. Globally, 30 million people are affected by
this life-threatening disease each year, of which 6 million deaths have
been reported annually [[43]2,[44]3]. In recent times, sepsis incidence
rates have increased rapidly and the World Health Organization (WHO)
has declared it as a key healthcare priority for the coming decade.
Despite the best possible therapies such as antimicrobial therapy,
hemodynamic resuscitation, and supportive therapy (lung-protective
ventilation, use of sedatives, nutrition management) [[45]4], this
disease is evolving over time and becoming a critical issue for
clinicians and researchers. The major cause of sepsis is a lack of
knowledge about the pathophysiological and biochemical mechanisms
behind the perturbation of the host immune response. This reflects the
patient’s risk and often involves a delay in diagnosis. The
pathogenesis of sepsis on a genetic basis is underappreciated, but
death from this acute condition is more heritable than cancer [[46]5].
In the pathogenesis of sepsis, genetic factors play an important role;
however, the mRNAs associated with sepsis still need to be explored.
A high-throughput technology such as microarray is a tool of expression
analysis that provides information on the genetic contribution to
sepsis and other diseases [[47]6]. Datasets obtained through this have
been used to detect the differentially expressed genes (DEGs) in sepsis
and healthy individuals to explore its pathogenesis. For instance, the
microarray data obtained by Tang et al. [[48]7] were analyzed by Qiao
et al. [[49]8] to identify the DEGs associated with different pathways
in sepsis pathogenesis. Another study from Wang et al. [[50]9] analyzed
potential biomarkers of severe sepsis with multiple organ failure using
a microarray dataset and found that lung failure sepsis had the highest
number of DEGs. These collaborative studies established on
transcription profiling analysis can provide information to guide
future research. This high-throughput technology assesses various mRNA
levels of diverse genes simultaneously in a highly cost-effective
manner and helps us to understand and analyze global genomic patterns
of diverse diseases [[51]10]. Despite these advantages, the genes
distinguished in one study are often not distinguished in other studies
[[52]11]. To improve the reliability of results, integrating
information from multiple studies has been reported [[53]6]. The
meta-analysis approach merges information from different datasets that
share a molecular mechanism of disease. This helps to combat the
inconsistency in results which may have arisen due to differences in
the microarray platforms, sample source, and analysis techniques
[[54]11]. This approach thoroughly studies the available data in a
relatively inexpensive manner. Therefore, an accurate estimation of
gene expression differentials can be obtained, and the heterogeneity of
a comprehensive estimate can be accessed through it. This approach has
been utilized in various complex diseases to identify the key genes.
Our study criteria whirl around the establishment of crucial genes
associated with sepsis. Uncovering the genes responsible for the
disease is a prerequisite for the prompt identification and diagnosis
of a disease. We conducted our study to determine the role of signature
genes associated with sepsis, based on the [55]GSE13904 [[56]12] and
[57]GSE54514 [[58]13] datasets retrieved from the Gene Expression
Omnibus (GEO) database [[59]14]. DEGs were identified using a
meta-analysis approach. The functions of these potential DEGs were
analyzed using the Gene Ontology (GO) and pathway enrichment analysis
which was conducted using DAVID (the Database for Annotation,
Visualization and Integrated Discovery [[60]15]. Subsequently, a
protein‒protein interaction (PPI) network for functionally enriched
DEGs was constructed using BioGRID (the Biological General Repository
for Interaction Datasets) [[61]16] and visualized in Cytoscape
respectively. Finally, we utilized the reverse transcriptase-PCR and
immunohistochemistry techniques to validate the few topmost upregulated
genes.
2. Materials and Methods
2.1. Sepsis-Associated Gene Expression Dataset Extraction
For microarray dataset selection, the GEO database was exhaustively
searched. The criterion for selection process was based on the PRISMA
(Preferred Reporting Items for Systematic Reviews and Meta-Analysis)
guidelines published in 2009 [[62]17]. Benchmarking for the selection
of gene expression datasets was as follows: Organism—Homo sapiens,
Study type—Expression profiling by array, and Attribute name—Tissue.
GEO datasets possessing accession numbers [63]GSE13904 and [64]GSE54514
were selected from the National Center for Biotechnology Information
(NCBI) GEO after a thorough search. For conducting the meta-analysis,
necessary information was extracted from these two datasets. Here we
considered both sepsis and septic shock samples as sepsis cases. Series
matrix expression files of both these datasets were extracted for
further analysis. Every probe in the expression file was allocated to
its respective HGNC (HUGO Gene Nomenclature Committee) [[65]18] gene
symbol(s). To achieve this, numerous databases and tools were used,
such as Synergizer [[66]19], bioDBnet:db2db conversion [[67]20],
gprofiler ID converter [[68]21], AbIDconvert [[69]22], and GEO2R.
Duplicate gene symbols mapping to multiple probe IDs were eliminated by
averaging their relative expression vaules [[70]23]. Sepsis-infected
CLP mice model datasets possessing accession numbers [71]GSE24357
[[72]24] and [73]GSE15379 [[74]25] were also extracted from GEO. The
CLP sample probe IDs were mapped to their respective genes using GEO2R.
2.2. Meta-Analysis and DEGs Screening
An unpaired t-test with Welch’s correction was applied for all genes
within each study [[75]26] to compare the gene expression vaules in
sepsis samples with those of control subjects. The t-test for unpaired
data and both for an equal and unequal variance can be computed as
follows:
[MATH:
t=X´1−X´2s12N1+s22<
mrow>N2, :MATH]
(1)
where
[MATH:
X´1
:MATH]
and
[MATH:
X´2
:MATH]
are the means,
[MATH:
s12 :MATH]
and
[MATH:
s22 :MATH]
are the variances, and
[MATH: N1
:MATH]
and
[MATH: N2
:MATH]
are the sizes of the two groups of the samples. A p-vaule was returned
for each gene during the t-test. R software v. 3.6.1 was used to
conduct this t-test on gene profiles obtained from the retrieved
datasets.
Then we conducted a meta-analysis by combining p-vaules according to
the Fisher’s combined probability test method [[76]27] in R using the
formula
[MATH:
X2k2=−2∑i=1<
/mn>kln(
pi), :MATH]
(2)
where p[i] is the p-vaule, k is the number of tests being combined and
2k is the degrees of freedom. The p − vaules were adjusted using the
approach of false discovery rate (FDR), as given in the
Benjamini‒Hochberg (BH) method [[77]28].
At this stage, we calculated the fold change (FC) vaule for each gene
to be used for filtering purposes. FC is a measure that describes how
much the expression level of a gene changes over two different samples
(conditions) or groups. The FC for linear data can be calculated as
follows:
[MATH:
FCi=
log2y´ix´i or
log2y´i−
log2x´i,<
/mrow> :MATH]
(3)
where
[MATH:
x´i
:MATH]
and
[MATH:
y´i
:MATH]
are the means of the gene expression profiles of the control group and
sepsis group, respectively. In this case, where the gene expression
data are already in log[2]-transformed form, FC can be computed as
follows:
[MATH:
FCi=
y´
ix´i <
mi>or y´i−x´<
/mo>i.
:MATH]
(4)
The two log-fold changes obtained for each gene were averaged to
produce a single log-FC. The DEGs between sepsis and normal healthy
individuals were selected based on a certain criterion, i.e., p-vaules
< 0.05 and FC > 2. To find the genes that are differentially expressed
in a specific group, we checked for superimposed DEGs in sepsis day1
samples and sepsis day3 samples. For identifying overlapping genes
between both groups, we used Venny 2.1.0.
2.3. Functional and Pathway Enrichment Analysis
After a meta-analysis, the biological implications of identified DEGs
is important to understand, so pathway and functional enrichment
analysis was performed. We carried out GO and pathway enrichment
analysis (Kyoto Encyclopedia of Genes and Genomes, KEGG), using DAVID
v6.8 (David.abcc.ncifcrf.gov) [[78]29], with a significance of p- vaule
< 0.05.
2.4. PPI Network Construction and Analysis
A PPI network was created using Cytoscape v3.7.2 [[79]30] to further
understand and predict the biological activity of the identified DEGs
based on GO and KEGG enrichment analysis. The DEGs’ encoding proteins
and their interacting partners were computed from the BioGRID database
[[80]31] for PPI network construction. This PPI network was
subsequently visualized in Cytoscape. A box- and -whisker plot is a
very informative tool that helps us to gain an insight into the
distribution of data. The box plot function in R was used to create the
box- and -whisker plot.
2.5. Animal Model
In total, six C57BL/6 mice (six weeks old, 20–25 g) were obtained from
the Animal House Facility of Defence Research Development Organization
(DRDO)‒Institute of Nuclear Medicine and Allied Science (INMAS), New
Delhi. The study protocol was approved by the Institutional Animal
Ethics Committee (IAEC) of DRDO-INMAS (INM/IAEC/2018/25/ext). Animals
were caged under stable conditions (temperature: 21 ± 2,12 h light/dark
cycle and humidity: 50–60). Animals had access to food and water ad
libitum. Experiments were done with the utmost care and according to
the guidelines.
2.6. Experimental Protocol
Animals were divided into two groups: the Cecal Ligation and Puncture
(CLP) group and a sham group (n = 3/group). CLP was performed according
to the protocol followed by Das et al. [[81]32]. For CLP group animals,
the lower areas of the abdomen were shaved and disinfected, and an
incision was made. After dissection, the cecum was ligated below the
ileocecal valve, followed by through and through puncture using a
26-gauge needle. The cecum was then placed back in peritoneal cavity
and the peritoneum was closed using absorbable suture 4.0 Chromic
(Ethicon, New Jersey, NJ, USA lot no-B7002). The skin was closed using
non-absorbable 4.0 silk suture (Ethicon, New Jersey, NJ, USA lot
no-B7006) and then betadine was applied around the surgery area. Sham
group animals underwent the same procedure except for the puncture and
ligation. After surgery, animals were returned to their cages and
provided with food and water ad libitum. After 24 h of treatment, the
animals were sacrificed, and lung tissues were harvested and stored at
−80 °C until RNA extraction and formalin-fixed for immunohistochemistry
analysis.
2.7. Semiquantitative RT-PCR
Extraction of RNA was done from lung tissue using TRIZOL (Ambion,
Carlsbad, CA, USA) in accordance with the manufacturer’s protocol. cDNA
was synthesized using Bio-Rad’s (Hercules, CA, USA) iScript cDNA
synthesis kit and amplified for Arginase 1 (ARG1), Interleukin 1
Receptor Type 2 (IL1R2), Matrix Metallopeptidase 9 (MMP9), and
Elastase, Neutrophil Expressed (ELANE) using a PCR green master mix
(Promega, Madison, WI, USA). Actin was used as an endogenous control
gene for data normalization. The PCR thermocycling conditions for 35
cycles were as follows: Initial denaturation for 5 min at 95 °C, cycle
with denaturation for 45 s at 95 °C, annealing for 1 min at (48.5
°C—ARG1), (63 °C—IL1R2), (54 °C—MMP9 and ELANE). Primer extension for 1
min at 72 °C and final extension for 5 min at 72 °C PCR amplicons were
run on a 1% agarose gel electrophoresis containing ethidium bromide (1
mg/mL). A Gel Doc EZ system (Bio-Rad) was used for gel picture
visualization and the intensity of bands was quantified using ImageJ
(Bethesda, Maryland, MD, USA) software. The results are relative to
endogenous control actin expression. The sequences of the primer used
were: IL1R2: GGTGCGGACAATGTTCATCTTG and GGGAACTGCTGGAGATCTCGGAGTG:
Product size—239 bp, ARG1: CAGAAGAATGGAAGAG
TCAG and CAGATATGCAGGGAGTCACC: Product size—250 bp, Actin: fwd:
CTGTCCCTGTATGCCTCTG Rev: ATGTCACGCACGATTTCC: Product size—220 bp. MMP9:
fwd: CGTCGTGATCCCCACTTACT Rev: AACACACAGGGTTTGCCTTC: Product size—405
bp, ELANE: fwd: GGCTTTGACCCATCACACAACT: Rev: CGGCACATGTTAGTCACCAC.
2.8. Immunohistochemistry for MPO
Lung tissues were formalin-fixed and embedded in paraffin.
Five-micrometer sections were cut and dewaxed with xylene, hydrated,
and the antigen retrieved in a citrate buffer (pH: 6.00, 98 °C) for 20
min. Endogenous peroxidase activity was blocked by 3% H[2]O[2] for 10
min. Subsequently, the sections were incubated with 5% bovine serum
albumin for 30 min. MPO heavy chain goat polyclonal (Santa Cruz, CA,
USA) antibody was added and incubated overnight at 4 °C in a humid
chamber. Afterwards, the sections were washed and incubated with
biotin-labeled rabbit anti-goat secondary antibody. The sections were
washed again and then incubated with an avidin‒peroxidase complex
(ImmunoCruz ABC kit, Santa Cruz). Slides were stained with 3, 3′
Diamobenzidine (DAB, ChemCruz) to prompt the MPO to be visualized and
then counterstained with hematoxylin to dye the cell nucleus.
Dehydration with alcohol series was done and then sections were placed
in xylene for differentiation. Finally, the sections were mounted using
a DPX mount and visualized under a microscope, and image quantification
was done using ImageJ software (Bethesda, Maryland, MD, USA).
2.9. Statistical Analysis
Data are represented as mean ± SEM. Results were analyzed by an
unpaired t-test. Statistical significance was obtained when p-vaules
were less than 0.05 and 0.0001 using GraphPad Prism 6 software (La
Jolla, CA, USA).
3. Results
3.1. Sepsis-Associated Microarray Dataset Selection
The GEO datasets possessing accession numbers [82]GSE13904 and
[83]GSE54514 contain a total of 275 human samples, of which 134 were
case samples of day1, 87 of day3, and 54 control samples. The
significant dossiers were extracted from exclusive studies such as
accession number of GEO, sample source, number of cases and controls,
and gene expression, platform, and profile ([84]Table 1). To detect
DEGs, normalized expression data were obtained from GEO and an analysis
was done based on two criteria, FC and p-vaule ([85]Figure 1). A
comparison was done between sepsis and control for individual genes and
the p-vaules obtained were averaged. The two log-fold changes were also
averaged to produce a single log FC per gene. The [86]GSE24357 dataset
based on the Illumina MouseWG-6 v2.0 Expression Beadchip platform had
12 total samples, of which we considered sham/saline (four samples) as
controls and CLP/saline (four samples) as infected ones. [87]GSE15379,
based on the Affymetrix Mouse Genome 430 2.0 Array platform, had 12
total samples, of which we considered lung sham wild-type saline (three
samples) as controls and lung CLP wildtype (three samples) as infected
ones, respectively.
Table 1.
This table represents the characteristics of discrete studies procured
from the Gene Expression Omnibus (GEO) for meta-analysis.
GEO Accession Number Disease Sample Sample Source Platform
Day1 Day3
[88]GSE13904 Sepsis n = 99 n = 59 Blood Affymetrix Human Genome U 133
Plus 2.0 Array
[89]GSE54514 Sepsis n = 35 n = 38 Blood IlluminaHumanHT-12 V3.0
Expression BeadChip
[90]Open in a new tab
Figure 1.
[91]Figure 1
[92]Open in a new tab
Proposed methodology workflow. Analysis of genes was performed by
comparing sepsis with control. From these two datasets, the p-vaules
were calculated and then combined to compute a single p-vaule per gene,
adjusted for multiple testing (FDR). One hundred and forty-six genes
with significant p-vaule < 0.05 and FC > 2 were considered as
differentially expressed (upregulated) in sepsis. The 146 upregulated
genes were subjected to enrichment analysis. A protein‒protein
interaction (PPI) network of the identified differentially expressed
genes (DEGs) based on the pathway and Gene Ontology (GO) term
enrichment analysis was constructed. Thereafter, RT-PCR and
immunohistochemistry (IHC) validation studies were performed.
3.2. Meta-Analysis of Sepsis Datasets and DEGs Screening
In both human datasets, 146 genes altogether (81 DEGs in Sepsis day1
samples and 65 DEGs in Sepsis day3 samples) were identified as DEGs.
DEGs were identified following more than 2.0-fold enrichment (FC,
biological significance) over random expectation (p-vaule < 0.05
statistical significance). Using the same criteria for
screening—BH-corrected p-vaule < 0.05 and FC > 2—we discovered that all
146 DEGs were consistently upregulated and there was no gene that was
downregulated at this level of significance. However, the day1 sepsis
group had highly upregulated genes as compared to the day3 sepsis
group. The top 20 upregulated genes in both groups are listed in
[93]Table 2. Genes were stacked according to FC, superseded by
corresponding p-vaule adjustment using the Benjamini‒Hochberg
procedure, positioning the FDR. A subset of the top 25 DEGs of both
groups was visualized with heatmaps using R and are shown in [94]Figure
2A,B, respectively. Nineteen genes were included exclusively in the
“Sepsis day1 group,” three genes were included exclusively in the
“Sepsis day3 group,” and 62 genes were common to the “Sepsis day1
group” and “Sepsis day3” groups ([95]Figure 3). From the Venny results,
we found that there were 19 DEGs (BCL2A1, C1QB, CEACAM1, CST7, DACH1,
DHRS9, FCAR, FGF13, FKBP5, GADD45A, IFI27, IL18RAP, KIF1B, NLRC4,
PCOLCE2, PSTPIP2, S100P, SERPINB2, SERPING1) that were upregulated in
sepsis samples on day1, but their expression levels became normal at
day3; likewise, there were three genes (CTSG, PI3, VSIG4) that were
only upregulated in sepsis samples on day3. Also, a total of 48 DEGs
(forty six upregulated and two downregulated) between controls and
sepsis-infected CLP samples were filtered out based on the criteria,
i.e., p-vaules < 0.05 and FC > 2. The lists of sepsis-associated DEGs
in human samples and CLP model samples are shown in [96]Supplementary
Tables S1 and S2, respectively.
Table 2.
Top 20 upregulated DEGs in sepsis. The genes were ranked based on the
fold change vaule.
Sepsis Day1 Sepsis Day3
Gene BH-p-Vaule Fold Change Gene BH-p-Vaule Fold Change
MMP8 3.03 × 10^−12 90.83269343 MMP8 1.25 × 10^−6 53.11011783
OLFM4 7.66 × 10^−7 54.94062655 OLFM4 0.000177074 33.23986192
CD177 1.73 × 10^−15 27.69570432 CD177 2.68 × 10^−7 24.4262987
CEACAM8 2.01 × 10^−6 17.21435595 CEACAM8 1.54 × 10^−6 18.71820844
LTF 3.79 × 10^−11 14.76500984 LTF 1.35 × 10^−7 14.33448059
LCN2 5.64 × 10^−9 12.69709454 MMP9 4.75 × 10^−10 13.37223858
OLAH 3.36 × 10^−9 12.50259397 OLAH 0.000755255 11.87923585
MMP9 6.75 × 10^−17 11.6185382 LCN2 8.36 × 10^−7 10.78809213
ANXA3 1.29 × 10^−21 10.82040432 DEFA4 3.74 × 10^−8 10.37992756
IL1R2 2.97 × 10^−19 10.5634416 IL1R2 2.86 × 10^−8 8.994229615
RETN 4.97 × 10^−12 10.42790653 ANXA3 5.44 × 10^−10 8.817467871
HP 8.36 × 10^−13 10.0624041 DEFA1 2.46 × 10^−12 7.365054135
GPR48 4.23 × 10^−15 8.581561273 RETN 9.41 × 10^−7 6.628552774
ANKRD22 2.59 × 10^−14 7.460137951 HP 9.96 × 10^−7 6.35862726
CLEC5A 2.76 × 10^−13 6.669614066 MS4A4A 4.37 × 10^−5 6.16777804
DEFA4 0.000155626 6.278035491 ELANE 9.42 × 10^−5 5.65341365
MS4A4A 6.31 × 10^−12 5.989831378 VNN1 3.55 × 10^−8 5.537816869
VNN1 3.55 × 10^−18 5.855759515 GPR84 1.64 × 10^−6 5.069264307
TCN1 3.20 × 10^−10 5.533269805 TCN1 2.72 × 10^−8 4.837923713
[97]Open in a new tab
Figure 2.
[98]Figure 2
[99]Open in a new tab
Heatmap for the top 25 upregulated DEGs. Clustering of the top 25
significant DEGs was performed and shown as a heatmap plot in (A) the
Sepsis day1 group and (B) the Sepsis day3 group. A hierarchical
clustering algorithm uses an average linkage method and Pearson’s
correlation coefficient. Green and red in the plot represent lower and
higher expression vaules, respectively.
Figure 3.
[100]Figure 3
[101]Open in a new tab
Overlapping DEGs in Sepsis day1 group and Sepsis day3 group. The Venn
diagram shows the intersections between the Sepsis day1 group (purple
circle) and the Sepsis day3 group (yellow circle). Nineteen genes were
included exclusively in “Sepsis day1”, three genes were included
exclusively in “Sepsis day3”, and 62 genes were in both groups.
3.3. Pathway and Functional Enrichment Analysis
DEGs identified through this meta-analysis approach were classified
according to GO hierarchy into functional categories {Cellular
Compartment (CC), Biological Process (BP), and Molecular Function (MF)}
with a threshold significance of p-vaule < 0.05. The most significant
sepsis day1 DEGs were enriched in the following descending GO terms:
‘innate immune response’ (GO:0045087), ‘immune response’ (GO:0006955)
and ‘defense response to bacterium’ (GO:0042742). ‘Serine-type
endopeptidase activity’ (GO:0004252) and ‘extracellular space’
(GO:0005615) were highly enriched GO terms under the MF and CC
categories, respectively. The significantly enriched KEGG pathways of
sepsis day1 group DEGs were (in descending order): ‘Transcriptional
misregulation in cancer’ (hsa05202), ‘Staphylococcus aureus infection’
(hsa05150) and ‘Legionellosis’ (hsa05134) ([102]Table 3). On the other
hand, the DEGs in the sepsis day3 group were highly enriched for the
following GO terms (most significant) under the BP such as ‘innate
immune response’ (GO:0045087), ‘defense response to fungus’
(GO:0050832), and ‘defense response to bacterium’ (GO:0042742). The
most convincing GO terms under the MF and CC categories were
‘serine-type endopeptidase activity’ (GO:0004252) and ‘extracellular
exosome’ (GO:0070062). The significantly enriched KEGG pathways of the
sepsis day3 group DEGs were (in descending order) were:
‘Transcriptional misregulation in cancer (has05202), and ‘Amoebiasis’
(hsa05146) ([103]Table 4). From the above analysis, we found that
sepsis is closely related to biological processes associated with the
immune response. Pathway enrichment analysis of these two groups
revealed two common pathways: Transcriptional misregulation in cancer
and Amoebiasis. Both these pathways comprised six common functionally
enriched DEGs.
Table 3.
Sepsis day1 group DEGs’ functional enrichment analysis, representing
top GO terms and pathways. Ranking of enriched terms was based on the
p-vaules.
GO ID GO Term No. of Genes p-Vaule
Biological Process
GO:0045087 innate immune response 17 8.47 × 10^−11
GO:0006955 immune response 12 3.26 × 10^−6
GO:0042742 defense response to bacterium 8 4.49 × 10^−6
Molecular Functions
GO:0004252 serine − type endopeptidase activity 8 1.26 × 10^−4
GO:0008201 heparin binding 5 0.005346
GO:0004869 cysteine − type endopeptidase inhibitor activity 3 0.009713
Cellular Component
GO:0005615 extracellular space 27 2.49 × 10^−11
GO:0070062 extracellular exosome 38 2.57 × 10^−11
GO:0005576 extracellular region 26 6.39 × 10^−9
KEGG ID KEGG Pathway No. of Genes p-Vaule
hsa05202 Transcriptional misregulation in cancer 6 9.47 × 10^−4
hsa05150 Staphylococcus aureus infection 4 0.001906
hsa05134 Legionellosis 3 0.025598
hsa05321 Inflammatory bowel disease (IBD) 3 0.035048
hsa04610 Complement and coagulation cascades 3 0.040208
hsa05146 Amoebiasis 3 0.085888
[104]Open in a new tab
Table 4.
Sepsis day3 group DEGs’ functional enrichment analysis, representing
top GO terms and pathways. Ranking of enriched terms was based on the
p-vaules.
GO ID GO Term No. of Genes p-Vaule
Biological Process
GO:0045087 Innate immune response 14 3.65 × 10^−9
GO:0050832 Defense response to fungus 5 2.60 × 10^−6
GO:0042742 Defense response to bacterium 7 1.40 × 10^−5
Molecular Functions
GO:0004252 Serine − type endopeptidase activity 8 2.29 × 10^−5
GO:0008201 Heparin binding 5 0.002077
GO:0008233 Peptidase activity 4 0.003483
Cellular Component
GO:0070062 Extracellular exosome 35 8.32 × 10^−13
GO:0005615 Extracellular space 25 3.64 × 10^−12
GO:0005576 Extracellular region 21 1.83 × 10^−7
KEGG ID KEGG Pathway No. of Genes p-Vaule
hsa05202 Transcriptional misregulation in cancer 5 0.001750917
hsa05146 Amoebiasis 3 0.044114518
hsa05322 Systemic lupus erythematosus 3 0.066980623
hsa00052 Galactose metabolism 2 0.091415842
hsa00051 Fructose and mannose metabolism 2 0.097217685
[105]Open in a new tab
3.4. PPI Network Analysis
To understand the biological meaning of the six upregulated DEGs (ARG1,
IL1R2, FCGR1A, MMP9, ELANE, MPO) identified by the KEGG pathway under
the transcriptional misregulation in the cancer pathway and amoebiasis
at the protein level, we constructed a PPI network for these six
DEGs-encoding proteins with interactions that included 143 nodes and
142 edges, as shown in [106]Figure 4. The topological properties of the
PPI network are shown in [107]Table 5. Boxplots comparing the gene
expression levels of these six upregulated DEGs are shown in
[108]Figure 5. DEGs obtained after the meta-analysis from CLP mice
model samples had one gene, i.e., IL1R2, in common among the six highly
upregulated genes reported in our study.
Figure 4.
[109]Figure 4
[110]Open in a new tab
Protein-protein interaction (PPI) network analysis of significantly
enriched sepsis-associated DEGs. The network of PPI interaction was
constructed from the six identified DEGs. In total, 143 proteins were
involved in this network. The six DEGs are in yellow and their
interacting partners are purple.
Table 5.
Topological properties/centrality measures of the PPI network.
Gene Node Degree Betweenness Closeness
ARG1 43 0.78 0.49
IL1R2 40 0.68 0.44
FCGR1A 21 0.63 0.34
MMP9 20 0.61 0.33
ELANE 10 0.65 0.38
MPO 08 0.12 0.27
[111]Open in a new tab
Figure 5.
[112]Figure 5
[113]Open in a new tab
Box- and −whisker plot of six highly upregulated DEGs based on the
functional enrichment and PPI analysis for comparing their expression
levels. Green corresponds to day1 sepsis, blue to day3 sepsis, and red
to control subjects. Genes are shown at the bottom. For individual
genes, the vaules of gene expression in log intensities have been
normalized to the median of the control group expression. A box plot
displays the five-number summary of a set of data: the minimum, first
quartile, median, third quartile, and maximum. Endpoints of the axis
are labeled by the minimum and maximum vaules. The first and third
quartile marks one end and the other end of the box, respectively. The
median can be between the first and third quartiles.
3.5. Semiquantitative RT-PCR Validation and Immunohistochemistry
The results were validated in a well-established CLP sepsis animal
model. CLP-induced sepsis and sham lung tissues were used to validate
the results of the meta-analysis. As per the results of the
meta-analysis, the topmost upregulated genes in sepsis, such as ILIR2,
ARG1, ELANE, and MMP9, were validated via semiquantitative RT-PCR as
shown in [114]Figure 6A‒D, respectively. Also, the MPO expression was
assessed by immunohistochemical techniques, as shown in [115]Figure
7A,B.
Figure 6.
[116]Figure 6
[117]Open in a new tab
Validation of mRNA expression of selected genes in the lung tissue of
an animal sepsis model. Mice were CLP operated for 24 h and then
sacrificed; lung tissues were collected for analysis. The figure shows
the semi-quantitative mRNA expression and densitometry of (A) ARG1, (B)
IL1R2, (C) ELANE, and (D) MMP9 in the lung tissue of the sham and CLP
groups. A minimum of three animals were used for each group of animals.
Data are presented as mean±SEM, p < 0.05. Note: + means treated and −
means non-treated.
Figure 7.
[118]Figure 7
[119]Open in a new tab
The expression of Myeloperoxidase in neutrophil granulocyte within lung
alveoli was assessed by the immunohistochemical technique in the sham
and CLP groups. (A)This represents strong immunoreactivity in the
well-established CLP sepsis animal model as compared to the sham group;
(B) the significant increase in neutrophil numbers in sepsis as
compared to the control. Data are represented as mean ± SEM;
experiments were performed in triplicate, with statistical significance
at p-vaule < 0.0001.
4. Discussion
Sepsis is a substantial cause of morbidity and thus an emerging health
concern in pediatrics, geriatric care, and ICUs. A comprehensive study
of sepsis’s pathophysiological mechanism will lead us to discover
therapies that can elevate the chances of survival. The fundamental
component of sepsis pathogenesis is inflammation, which is associated
with bacterial infection and dysfunction of the immune system. The lung
is one of the organs most often affected in sepsis, mainly because lung
infection/pneumonia is often the initial point of the septic process
and almost all infections are associated with a systemic inflammatory
response (SIRS) in which the lung is the first affected organ
[[120]33]. The quest for DEGs has accelerated in recent decades and
this differential expression exerts a widespread impact.
Dataset [121]GSE13904 summarizes the genomic expression profile of
critically ill children of sepsis, systemic inflammatory response
syndrome, and septic shock; and dataset [122]GSE54514 comprises a
transcriptomic analysis of the whole blood of survivors and non
survivors of sepsis. In our study, the DEGs of sepsis were identified
using a meta-analysis approach and then the topmost functionally
significant genes were used for validation in CLP mouse model studies
using semi-quantitative RT-PCR and immunohistochemistry. There were 146
differentially upregulated genes obtained from the above-mentioned
datasets. The DEGs obtained were remarkably enriched in GO terms under
biological processes, the innate immune response, and bacterial and
fungal infection. From both the datasets sepsis day1 and day3 samples
were compared with normal healthy individuals, and p-vaules and fold
change were determined for each gene. Genes with p-vaule < 0.05 and FC
> 2were selected as DEGs. The identified DEGs were further subjected to
functional enrichment analysis for understanding their biological
implications. GO functional enrichment and KEGG pathway enrichment
analysis were carried out using DAVID. DEGs in the sepsis day1 group
were enriched in pathways of misregulation in cancer and Staphylococcus
aureus infection, and sepsis day3 group DEGs were enriched in pathway
transcription misregulation in cancer and amoebiasis. The DEGs (IL1R2,
ARG1, FCGR1A, MMP9, ELANE, and MPO) that were common on day1 and day3
of sepsis, with at least 2.0-fold upregulation, were selected for
constructing and visualizing the PPI network using Cytoscape. The
proteins encoded by these six identified DEGs and their interactions
with other proteins were computed from the BioGRID. A total of 143
nodes and 142 edges were identified in the PPI network. The highest
degree genes obtained through PPI network analysis were IL1R2 and ARG1
(>30), whereas ELANE, MMP9, FCGR1A, and MPO were < 30. Using a
meta-analysis and network-based approach on samples of sepsis and
normal healthy controls, we identified the key genes for inflammation
in sepsis with increased expression of IL1R2 and ARG1, as indicated by
semiquantitative-PCR studies ([123]Figure 6A,B). We also found a higher
expression of ELANE and MMP9 in sepsis. In our expression studies, the
results obtained for ELANE were significant at p-vaule < 0.05, but MMP9
showed a nonsignificant increase in expression ([124]Figure 6C,D). MPO
was also overexpressed in sepsis animal tissue samples, as observed by
IHC ([125]Figure 7A,B).
Our study reported that the gene ARG1 is the topmost upregulated gene
in septic patients compared to normal healthy controls, as determined
by a PPI network analysis. ARG1 is a protein-encoding enzyme whose
catalytic activity is to hydrolyze arginine to ornithine and urea.
Arginase metabolism is a critical regulator of innate and immune
responses. A deregulated immune response is one of the major
characteristics of sepsis, and ARG1 metabolism is a regulator of it.
The overexpression of ARG1, as observed in our results, may play a role
in tissue repair. Increased plasma ARG1 activity depletes the
concentrations of L-arginine, the substrate for NO synthesis, leading
to vascular dysfunction during severe sepsis and suppressed NO-mediated
microbicidal effects [[126]34]. Increased ARG1 activity may also be a
bacterial survival strategy to escape the NO-dependent host
antimicrobial immune response [[127]35]. This may also be associated
with the M2 macrophage phenotype in sepsis, which is reportedly
associated with the wound healing process and tissue repair [[128]36].
The second-most upregulated gene in septic patients was IL1R2.
Interleukin-1 receptor 2 (IL1R2) is responsible for reducing IL-1
bioavailability by capturing it. Therefore, it acts as an endogenous
inhibitor of pro-inflammatory interleukin-1 (IL1) signaling [[129]37].
IL-1 is one of the major pro-inflammatory cytokines that play a
critical role in obesity, cancer, heart conditions, and various immune
diseases. The activation of endogenous negative regulation of
inflammation or the response to anti-inflammatory or immunosuppressive
agents has been known to upregulate IL1R2 expression and soluble IL1R2
concentrations in biological fluids. Lang et al. [[130]38] reported
that IL1R2 serum concentration is also useful for differentiating
between Gram-positive and Gram-negative bacterial infection in sepsis.
Our study also identified MMP9, ELANE, and MPO, which were validated in
the CLP model. MPO expression was also found to be upregulated, as
observed by IHC. Matrix metalloproteins (MMPs) are zinc-dependent
endopeptidases that may play a pivotal role in severe sepsis. MMP9 or
gelatinase B, which amounts to slightly more than 0.1% of total bone
marrow protein, is thought to be pro-inflammatory [[131]39] and
critical to normal vascular development, remodeling, and functioning.
This is evidenced by their key functions in processes such as
angiogenesis, vasomotortone, and tumor invasion [[132]40]. The results
obtained were consistent with studies that suggest a protective role of
MMP9 in sepsis [[133]41]. It is known that basal levels of MMP9 are
highest in the bone marrow. Vandooren et al. [[134]42] reported that,
on the induction of endotoxemia, abrupt changes occurred in MMP9
protein levels, as evidenced by the approximately 90% decrease in
protein levels of multimeric MMP9 and proMMP9. This also coincided with
an increase in the (pro) MMP9 level in the lungs and liver. Similar
patterns were observed for the relative expression of the neutrophil
markers ELANE and MPO after the induction of endotoxemia, except in the
spleen. MMP9 is predominantly associated with neutrophils and late
stage maturing neutrophils, such as band cells and segmented cells,
present in the bone marrow [[135]43]. Myeloperoxidase (MPO) and
neutrophil elastase (ELANE) are the two key neutrophil markers in the
blood, liver, spleen, lungs, and bone marrow, as they are most
abundantly expressed by neutrophils. Attenuation of sepsis − induced
lung injury has been correlated with reduced levels of neutrophil
infiltration and chemokine expression by using MPO as a marker in
several reports [[136]44,[137]45]. As depicted in our results
([138]Figure 7), the increased expression of MPO correlates to a higher
percentage release of MPO by activated neutrophils for antibacterial
activities, which causes an increase in degranulated neutrophils
[[139]46]. During sepsis, free radical species and MPO production
exceed antioxidant defenses. This leads to increased oxidative stress,
which aggravates inflammation, resulting in direct mitochondrial
damage, which leads to major outcomes in sepsis − induced organ
dysfunction [[140]47].
However, many studies correlate increased MMP9 with increased mortality
by aggravating severe sepsis [[141]48]. In vivo studies suggest that
MMP9 inhibition or reduction is associated with improved outcomes and
increased survival rates in animals [[142]49]. The contradictory
results observed with MMP9 in severe sepsis may be due to differences
in the sample population, sampling time, MMP9 estimation techniques, or
clinical endpoints. Our study may point to the important role of MMP
inhibitors in therapeutic aspects of sepsis. However, MMP9 inhibition
has a limiting therapeutic window. Our study, which pinpointed ELANE
and MPO as important DEGs, may provide insights into targeting
neutrophils for the treatment of sepsis to prevent the collateral
damage to peripheral organs caused by sepsis.
The results of our study indicate that upregulated IL1R2 and ARG1 may
be further correlated with the key role of inflammasome in sepsis.
Inflammasomes are mediators of secretion of IL-1 (interleukin-1) family
cytokines (e.g., IL-1β and IL-18) and proteolytic processing. They also
cause the release of cell death-related DAMPs (damage − associated
molecular patterns), e.g., HMGB1 (high-mobility group box) and LDH
(lactate dehydrogenase). Pyroptosis, resulting from the excessive
activation of inflammasomes, has been implicated in sepsis [[143]50].
Tsalik et al. [[144]51] emphasized the importance of NLRP3-inflammasome
activation in sepsis survivors, supported by increased expression of
the genes downstream from inflammasome activation, including IL1R2. Few
studies show the role of various drugs that mediate their effects by
regulating macrophage polarization and NLRP3 inflammasome activation
[[145]52]. This further indicates that inflammasome assembly, IL-1 or
IL1R2, macrophage polarization, and the neutrophil recruitment process
could be viable drug targets for the treatment of sepsis.
5. Conclusions
In conclusion, this comprehensive meta-analysis study of gene
expression provides mechanistic insight into sepsis that was further
validated in the CLP model. The study demonstrates the role of
significant and widespread immune activation, with oxidative stress and
the recruitment of neutrophils in sepsis. Our analysis gives a better
understanding of the molecular mechanisms associated with sepsis, which
may help with choosing plausible targets for designing personalized
treatments.
Acknowledgments