Abstract

Background

   The host response to bacterial sepsis is reported to be nonspecific
   regardless of the causative pathogen. However, newer paradigms
   indicated that the host response of Gram-negative sepsis may be
   different from Gram-positive sepsis, and the difference has not been
   clearly clarified. The current study aimed to explore the difference by
   identifying the differential gene sets using the genome-wide technique.

Methods

   The training dataset [37]GSE6535 and the validation dataset
   [38]GSE13015 were used for bioinformatics analysis. The distinct gene
   sets of sepsis with different infections were screened using gene set
   variation analysis (GSVA) and gene set enrichment analysis (GSEA). The
   intersection gene sets based on the two algorithms were confirmed
   through Venn analysis. Finally, the common gene sets between
   [39]GSE6535 and [40]GSE13015 were determined by GSEA.

Results

   Two immunological gene sets in [41]GSE6535 were identified based on
   GSVA, which could be used to discriminate sepsis caused by
   Gram-positive, Gram-negative, or mixed infection. A total of 19 gene
   sets were obtained in [42]GSE6535 through Venn analysis based on GSVA
   and GSEA, which revealed the heterogeneity of Gram-negative and
   Gram-positive sepsis at the molecular level. The result was also
   verified by analysis of the validation set [43]GSE13015, and 40 common
   differential gene sets were identified between dataset [44]GSE13015 and
   dataset [45]GSE6535 by GSEA.

Conclusions

   The identified differential gene sets indicated that host response may
   differ dramatically depending on the inciting organism. The findings
   offer new insight to investigate the pathophysiology of bacterial
   sepsis.

   Keywords: sepsis, gene sets, Gram-positive, Gram-negative, microarray
   analysis

Introduction

   Sepsis is a potentially life-threatening condition caused mainly by
   bacterial infection, with high morbidity and mortality. It is now
   defined as infection accompanied by organ dysfunction resulting from
   dysregulated host responses ([46]Singer et al., 2016). The early phase
   of sepsis is characterized by systemic excessive inflammation followed
   by a prolonged period of sepsis-induced immunosuppression ([47]Delano
   and Ward, 2016). However, the pathophysiological mechanisms and host
   responses to sepsis have not been clearly elucidated, which hindered
   the development of new therapeutic approaches.

   Although organs damaged by Gram-positive sepsis are clinically no
   different from Gram-negative sepsis, there is increasing evidence that
   differences exist in the host response ([48]Li et al., 2017). The
   initiating factor of Gram-negative bacterial sepsis is endotoxin, while
   Gram-positive bacterial sepsis relies on the production of exotoxin
   ([49]Ramachandran, 2014). Gram-negative sepsis differs from
   Gram-positive sepsis in that the organisms often arise from enteric or
   genitourinary sources rather than skin, wounds, and catheter sites
   ([50]Martin, 2012). In addition, Gram-positive bacteria require a
   highly orchestrated host response, with intracellular killing by
   neutrophils and macrophages. This is different for Gram-negative
   pathogens, which may be readily killed in the extracellular space by
   antibody and complement ([51]Van Amersfoort et al., 2003). It is
   gradually realized that the major difference between Gram-positive and
   Gram-negative sepsis is the way in which they initiate disease. Thus,
   exploring the difference in host response between Gram-negative and
   Gram-positive sepsis becomes increasingly important.

   Microarray technology provides a powerful tool to examine genome-wide
   expression profiles. Although a great deal of information has become
   available for the molecular signature of sepsis ([52]Chinnaiyan et al.,
   2001; [53]Pop-Began et al., 2014; [54]Lu et al., 2018), few reports
   have compared the difference between Gram-negative and Gram-positive
   sepsis. After analysis of the gene expression profiling of circulating
   neutrophils, Tang et al. verified that there was no difference in the
   expression profile. Gram-positive and Gram-negative sepsis share a
   common host response at a transcriptome level ([55]Tang et al., 2008).
   However, the plasma IL-1β, IL-6, and IL-18 concentrations were
   significantly higher in Gram-positive sepsis patients even though the
   host inflammatory responses to Gram-negative and Gram-positive stimuli
   share some common response elements ([56]Feezor et al., 2003).

   The different mechanisms of sepsis caused by Gram−positive and
   Gram−negative bacteria were also illustrated previously
   ([57]Giamarellos-Bourboulis et al., 2011; [58]Mahabeleshwar et al.,
   2012; [59]Kager et al., 2013). It was also reported that NADH:
   ubiquinone oxidoreductase subunit B2 (NDUFB2), NADH: ubiquinone
   oxidoreductase subunit B8 (NDUFB8), and ubiquinol−cytochrome c
   reductase hinge protein (UQCRH) may be associated with Gram−negative
   bacterial sepsis, while large tumor suppressor kinase 2 (LATS2) may
   contribute to the progression of Gram−positive bacterial sepsis ([60]Li
   et al., 2017). Since sepsis was an overwhelming inflammatory response,
   it is really difficulty to distinguish the difference at the molecular
   level just with several differentially expressed genes. To further
   elucidate the effect of sepsis on host response, we undertook gene sets
   comparison analysis based on gene set variation analysis (GSVA) and
   gene set enrichment analysis (GSEA) in this study. By screening
   differentially expressed gene sets, we want to provide a novel approach
   to gain important biological insights into the host response of sepsis.

Methods

Microarray Data

   The training dataset [61]GSE6535 ([62]Tang et al., 2008) and validation
   dataset [63]GSE13015 ([64]Pankla et al., 2009) were obtained from the
   Gene Expression Omnibus database ([65]www.ncbi.nlm.nih.gov/geo). The
   original study was approved by the ethics committee of each
   institution, and written informed consent was provided by the patients
   or their families. There were totally 72 critically ill patients in
   [66]GSE6535, 17 of whom were served as control. Based on the results of
   clinical features and microbiological culture, 18 patients were
   diagnosed as Gram−positive sepsis, 25 were confirmed as Gram-negative
   sepsis, while 12 were identified as mixed sepsis. The type of infection
   for mixed sepsis was pneumonia (four cases), intra-abdominal infection
   (six cases), urinary tract infection (one case), and meningitis (one
   case). There were nine cases of pneumonia, one case of intra-abdominal
   infection, and eight cases of other infections for Gram-positive
   sepsis, while five cases of pneumonia, one case of intra-abdominal
   infection, eight cases of urinary tract infection, four cases of
   meningitis, and seven cases of other infections for Gram-negative
   sepsis. The neutrophil RNA was isolated within 24 h of admission and
   microarray experiments were then performed. Whole blood of 63 patients
   with sepsis was used to generate genome-wide transcriptional profiles
   in [67]GSE13015. All patients were diagnosed as sepsis based on blood
   culture, including 43 patients with Gram-negative bacteria (mainly
   Burkholderia pseudomallei), 3 patients with fungi, and 17 patients with
   Gram-positive sepsis. Owing to the biased data of Gram-negative sepsis,
   we only randomly selected four cases of B. pseudomallei for further
   analysis. The analyzed microbiology data in this study were also
   summarized ([68] Table 1 ).

Table 1.

   Microbiology data analyzed in this study.
   [69]GSE6535 [70]GSE13015
   Gram-positive (18) Gram-negative (25) Mixed (12) Gram-positive (17)
   Gram-negative (15)
   Streptococcus (8) Escherichia (11) Mixed anaerobes (6)
   Coagulase-negative staphylococcus (6) Escherichia (6)
   Staphylococcus (5) Pseudomonas (4) Escherichia (4) Corynebacterium spp.
   (3) B. pseudomallei (4)
   MRSA (3) Neisseria (3) MRSA (4) S. aureus (2) K. pneumoniae (1)
   Enterococcus (1) Klebsiella (1) Enterococcus (4) Streptococcus
   non-group A or B (1) A. baumannii (1)
   Listeria (1) Citrobacter (1) Klebsiella (4) Staphylococcus aureus (1)
   Salmonella serotype B (1)
   Enterobacter (1) Pseudomonas (3) Enterococcus spp. (1) Salmonella spp.
   (1)
   Proteus (1) Streptococcus (3) S. pneumoniae (1) A. hydrophila (1)
   Bacteroides (1) Stenotrophomonas (2) Enterococcus spp. (1)
   Haemophilus (1) Nocardia (1) E. faecium (1)
   Serratia (1) Haemophilus (1)
   Staphylococcus (1)
   [71]Open in a new tab

Gene Set Variation Analysis

   GSVA was applied to assess individual samples using a non-parametric
   approach in dataset [72]GSE6535. Probe IDs were first converted into
   their corresponding gene symbols. GSVA package in R platform (4.0.3)
   was used to calculate the enrichment score of the pathways in each
   sample, while p <0.05 was considered statistically significant. The
   results were then visualized in a heatmap, generated by the
   ComplexHeatmap package in R. The reference gene sets were the Hallmark
   gene sets, C2 gene sets, and C7 gene sets owing to their close
   relationship to sepsis. Subsequently, the common gene sets between
   Gram−positive and Gram−negative samples, Gram−positive and mixed
   samples, and Gram−negative and mixed samples were identified with the
   Venn Diagram in R.

Protein–Protein Interaction Network Analysis

   Protein–protein interaction (PPI) network was analyzed with the online
   database Search Tool for the Retrieval of Interacting Genes (STRING
   11.0, [73]https://string-db.org). The distinct gene-sets-encoded
   proteins were employed to build the PPI network with the default
   threshold value (a combined score ≥0.4). Then, the PPI network was
   constructed by means of Cytoscape software (version 3.8.0), and the
   plug-in of Molecular Complex Detection (MCODE) and cytoHubba were
   applied for further analysis. The criteria for selection was that MCODE
   scores >5.

Gene Set Enrichment Analysis

   GSEA is a computational method for assessing whether a set of genes
   defined by a priori show statistical significance between two
   biological states. It was used to explore the differential gene sets
   between Gram-negative and Gram-positive sepsis in dataset [74]GSE6535
   and [75]GSE13015. The annotated gene sets related to sepsis, “C2,
   curated gene sets”, “C7, gene immunologic signature gene sets”, and
   “Hallmark gene sets”, downloaded from the Molecular Signature Database
   (MSigDB), were considered as the reference gene sets. The number of
   permutations was 1,000, and other parameters were set to default. A
   significant difference at p-value <0.05 was defined as the cutoff
   criteria after 1,000-time permutations.

GO and KEGG Enrichment Analysis

   Gene Ontology (GO) and Kyoto encyclopedia of Genes and Genomes (KEGG)
   were used to elucidate the potential gene functional annotation and
   pathway enrichment. Both GO and KEGG analyses were performed by R
   package “cluster Profiler”, and adjusted p-value <0.05 were regarded as
   statistically significant. GO analysis was comprised of biological
   process (BP), cellular component (CC), and molecular function (MF) and
   described the facilities of genes in three distinct biological aspects.
   Enrichment maps visualizing the results were drawn by R Software and
   Bioconductor ([76]http://bioconductor.org/).

Results

Identify the Distinct Gene Sets Based on GSVA

   The flowchart of this study is illustrated in [77]Figure 1 . All
   patients in [78]GSE6535 were grouped according to the infection status
   and analyzed by GSVA. The variation in the activity for gene sets was
   estimated, and the matrix containing enrichment scores was depicted in
   a heatmap ([79] Figure 2 ). Next, the enrichment score (ES) of gene
   sets between Gram-positive sepsis patients and Gram-negative sepsis
   patients was compared. A total of 373 differential gene sets were
   confirmed. The heatmap showed that the ES patterns may distinguish
   Gram-positive sepsis patients from Gram-negative sepsis patients easily
   ([80] Figure 3A ). In addition, we also screened 640 differential gene
   sets between Gram-negative sepsis patients and mixed infection patients
   and 682 differential gene sets between Gram-positive sepsis patients
   and mixed infection patients, which were also displayed in the heatmap
   ([81] Figures 3B, C ). After intersection analysis, two distinct
   immunologic gene sets, namely,
   “GSE13522_CTRL_VS_T_CRUZI_Y_STRAIN_INF_SKIN_129_MOUSE_UP” and
   “GSE23308_WT_VS_MINERALCORTICOID_REC_KO_MACROPHAGE_CORTICOSTERONE_TREAT
   ED_DN” were identified ([82] Figure 3D ). The detailed expression of
   each infected patient was also described in the heatmap, in which
   Gram-positive sepsis patients exhibit the relatively highest expression
   in gene set
   “GSE23308_WT_VS_MINERALCORTICOID_REC_KO_MACROPHAGE_CORTICOSTERONE_TREAT
   ED_DN” and the lowest expression in gene set
   “GSE13522_CTRL_VS_T_CRUZI_Y_STRAIN_INF_SKIN_129_MOUSE_UP” ([83]
   Figure 3E ). The complete gene list of the two gene sets is also shown
   ([84] Supplementary Table S1 ).

Figure 1.

   [85]Figure 1
   [86]Open in a new tab

   Analysis workflow of this study.

Figure 2.

   [87]Figure 2
   [88]Open in a new tab

   Heatmap of enrichment score of (A) Hallmark gene sets, (B) C2 gene
   sets, and (C) C7 gene sets in patients with Gram−positive sepsis,
   Gram-negative sepsis, mixed sepsis, and normal control. The rows in the
   heatmap indicate the expression values of each gene set, and the
   columns indicate the 72 samples examined in dataset [89]GSE6535.

Figure 3.

   [90]Figure 3
   [91]Open in a new tab

   Heatmap of differential gene sets between (A) Gram-positive and
   Gram-negative sepsis, (B) mixed sepsis versus Gram-negative sepsis, and
   (C) mixed sepsis versus Gram-positive sepsis. Venn diagram of (D)
   differential gene sets across various infection types and (E) the
   identified two distinct gene sets.

PPI Network Construction, Module Analysis, and Hub Genes Identification

   Next, the PPI network of the two distinct gene sets (335 genes) was
   constructed from STRING. Based on the information of the public
   database, a total of 242 nodes and 479 protein pairs were obtained,
   while the isolated genes without interaction were removed. To further
   investigate the hub genes, the plug-in app “cytoHubba” was used to
   parse the network, and the top 5 hub genes were identified according to
   the “Degree” algorithm ([92] Figure 4A ), including SRC (degree = 33),
   IL1B (degree = 20), CD40 (degree = 20), TLR6 (degree = 16), and CCL2
   (degree = 16). After that, the module analysis was performed by MCODE,
   and three modules were screened. Module 1 was the most significant
   module, located in the center of the entire PPI network, including 8
   genes and 24 edges ([93] Figure 4B ). Modules 2 and 3 had 11 nodes
   ([94] Figure 4C ) and 6 nodes ([95] Figure 4D ), respectively,
   containing several hub genes such as IL1B, TLR6, and CCL2 ([96]
   Figure 4D ).

Figure 4.

   [97]Figure 4
   [98]Open in a new tab

   Protein–protein interaction network of the two distinct gene sets,
   namely, (A) the top 5 hub genes and (B–D) the top 3 clusters.

Screening Differential Gene Sets With GSEA and GSVA

   To further elucidate the different pathway involved in Gram-positive
   and Gram-negative sepsis, GSEA was performed between the two groups in
   [99]GSE6535. It evaluates the microarray data by performing unbiased
   global searches for genes that are coordinately regulated in the three
   predefined gene sets. The results showed a significant difference in
   enrichment. The analysis of the Hallmark gene sets revealed that there
   were four significantly enriched gene sets, namely,
   HALLMARK_APICAL_JUNCTION, HALLMARK_NOTCH_SIGNALING,
   HALLMARK_KRAS_SIGNALING_DN, and HALLMARK_INTERFERON_ALPHA_RESPONS. The
   enrichment of C2 indicated that there were 226 differential gene sets,
   while the enrichment of C7 showed 199 differential gene sets. The
   representative plots of each gene sets with the lowest p-value are
   shown in [100]Figure 5A . After that, the intersection gene sets based
   on the two algorithms, GSVA and GSEA, were finally confirmed through
   Venn analysis ([101] Figure 5B ). A total of 19 gene sets were obtained
   ([102] Table 2 ), most of which are related to immunity.

Figure 5.

   Figure 5
   [103]Open in a new tab

   Gene set enrichment analysis for dataset [104]GSE6535. (A)
   Representative images of annotated gene sets with p value. (B) Venn
   diagram of the common differential gene sets between Gram-negative and
   Gram-positive sepsis.

Table 2.

   The common differential gene sets between Gram-negative and
   Gram-positive sepsis based on GSVA and GSEA for dataset [105]GSE6535.
   Gene sets Collections
   MANNE_COVID19_NONICU_VS_HEALTHY_DONOR_PLATELETS_UP C2
   GSE19825_NAIVE_VS_IL2RALOW_DAY3_EFF_CD8_TCELL_UP C7
   GSE4142_PLASMA_CELL_VS_MEMORY_BCELL_DN C7
   GSE21546_UNSTIM_VS_ANTI_CD3_STIM_SAP1A_KO_AND_ELK1_KO_DP_THYMOCYTES_UP
   C7
   GSE45365_CD8A_DC_VS_CD11B_DC_IFNAR_KO_UP C7
   GSE1432_CTRL_VS_IFNG_24H_MICROGLIA_DN C7
   MIKKELSEN_MEF_LCP_WITH_H3K4ME3 C2
   GSE34006_WT_VS_A2AR_KO_TREG_DN C7
   GSE40273_EOS_KO_VS_WT_TREG_DN C7
   GSE21927_SPLENIC_C26GM_TUMOROUS_VS_BONE_MARROW_MONOCYTES_UP C7
   REACTOME_RHO_GTPASES_ACTIVATE_WASPS_AND_WAVES C2
   GSE41176_UNSTIM_VS_ANTI_IGM_STIM_TAK1_KO_BCELL_6H_UP C7
   HUPER_BREAST_BASAL_VS_LUMINAL_UP C2
   GSE17721_CTRL_VS_LPS_1H_BMDC_UP C7
   GSE21360_NAIVE_VS_QUATERNARY_MEMORY_CD8_TCELL_DN C7
   GSE37533_PPARG1_FOXP3_VS_FOXP3_TRANSDUCED_CD4_TCELL_PIOGLITAZONE_TREATE
   D_UP C7
   GRAESSMANN_RESPONSE_TO_MC_AND_SERUM_DEPRIVATION_UP C2
   GSE37534_UNTREATED_VS_PIOGLITAZONE_TREATED_CD4_TCELL_PPARG1_AND_FOXP3_T
   RASDUCED_DN C7
   GSE21546_WT_VS_SAP1A_KO_DP_THYMOCYTES_UP C7
   [106]Open in a new tab

GO and KEGG Enrichment Analysis

   To gain more biological insight into the screened gene sets, GO
   annotation and KEGG pathway enrichment analysis were conducted with the
   19 gene sets. The top 10 enriched GO terms and KEGG pathways were
   identified and presented in [107]Figure 6 . GO analysis showed that the
   most enriched MF terms were actin binding, cadherin binding, cytokine
   receptor binding, and protein–macromolecule adaptor activity ([108]
   Figure 6A ). For GO CC analysis, the top 5 significantly enriched terms
   were cell–substrate junction, focal adhesion, collagen-containing
   extracellular matrix, cell leading edge, and membrane region ([109]
   Figure 6B ). In the BP, the genes were mainly enriched in response to
   virus, defense response to virus, response to interferon-gamma,
   cellular response to interferon-gamma, and nuclear factor kappa B
   (NF-κB) signaling ([110] Figure 6C ). KEGG pathway analysis
   demonstrated that genes were mainly enriched in mitogen-activated
   protein kinase (MAPK) signaling pathway, pathogenic Escherichia coli
   infection, Salmonella infection, Epstein–Barr virus infection, and
   Influenza A ([111] Figure 6D ).

Figure 6.

   [112]Figure 6
   [113]Open in a new tab

   Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway of the genes involved in the intersection gene sets. (A)
   Molecular function, (B) cellular component, and (C) biological process
   for GO analysis. (D) The top 10 of KEGG pathway enrichment.

Differential Gene Sets Verification With [114]GSE13015

   The differential gene sets between Gram-positive and Gram-negative
   sepsis were further verified with dataset [115]GSE13015. According to
   GSEA, there were 9 significantly enriched gene sets in the Hallmark
   gene sets, 750 gene sets in C2 collections, and 819 gene sets in C7
   collections. The further analysis showed that there were 40 common
   differential gene sets based on GSEA between dataset [116]GSE13015 and
   dataset [117]GSE6535 ([118] Table 3 ). In addition, the results also
   verified our conclusion in [119]GSE6535, two additional intersection
   gene sets were confirmed after Venn analysis with the 19 gene sets,
   REACTOME_RHO_GTPASES_ACTIVATE_WASPS_AND_WAVES and MIKKELSEN_MEF_LCP_WIT
   H_H3K4ME3.

Table 3.

   The common differential gene sets between [120]GSE6535 and
   [121]GSE13015 based on gene set enrichment analysis.
   Gene sets Collections Gene sets Collections
   HALLMARK_APICAL_JUNCTION H GSE18791_UNSTIM_VS_NEWCATSLE_VIRUS_DC_2H_DN
   C7
   SCHAEFFER_PROSTATE_DEVELOPMENT_12HR_UP C2
   GSE17721_CTRL_VS_LPS_6H_BMDC_DN C7
   SIG_INSULIN_RECEPTOR_PATHWAY_IN_CARDIAC_MYOCYTES C2
   GSE17721_CTRL_VS_GARDIQUIMOD_2H_BMDC_DN C7
   BERENJENO_ROCK_SIGNALING_NOT_VIA_RHOA_DN C2
   GSE20500_CTRL_VS_RETINOIC_ACID_TREATED_CD4_TCELL_DN C7
   WP_CELL_MIGRATION_AND_INVASION_THROUGH_P75NTR C2
   GSE35685_CD34POS_CD38NEG_VS_CD34POS_CD10NEG_CD62LPOS_BONE_MARROW_DN C7
   KEGG_AXON_GUIDANCE C2
   GSE15930_NAIVE_VS_24H_IN_VITRO_STIM_INFAB_CD8_TCELL_UP C7
   TIEN_INTESTINE_PROBIOTICS_2HR_UP C2
   GSE7460_WT_VS_FOXP3_HET_ACT_TCONV_UP C7
   WP_G_PROTEIN_SIGNALING_PATHWAYS C2
   GSE15930_NAIVE_VS_24H_IN_VITRO_STIM_CD8_TCELL_UP C7
   REACTOME_RHO_GTPASES_ACTIVATE_WASPS_AND_WAVES C2
   GSE17721_POLYIC_VS_GARDIQUIMOD_8H_BMDC_UP C7
   LEONARD_HYPOXIA C2 GSE6269_FLU_VS_E_COLI_INF_PBMC_UP C7
   MIKKELSEN_MCV6_LCP_WITH_H3K4ME3 C2
   GSE9037_CTRL_VS_LPS_1H_STIM_IRAK4_KO_BMDM_DN C7
   WP_TOLLLIKE_RECEPTOR_SIGNALING_RELATED_TO_MYD88 C2
   GSE21670_STAT3_KO_VS_WT_CD4_TCELL_UP C7
   MIKKELSEN_MEF_LCP_WITH_H3K4ME3 C2
   GSE7831_UNSTIM_VS_INFLUENZA_STIM_PDC_4H_UP C7
   WP_FIBRIN_COMPLEMENT_RECEPTOR_3_SIGNALING_PATHWAY C2
   GSE46242_TH1_VS_ANERGIC_TH1_CD4_TCELL_UP C7
   REACTOME_MUSCLE_CONTRACTION C2
   GSE24634_IL4_VS_CTRL_TREATED_NAIVE_CD4_TCELL_DAY5_UP C7
   WP_EICOSANOID_METABOLISM_VIA_LIPO_OXYGENASES_LOX C2
   GSE21360_NAIVE_VS_SECONDARY_MEMORY_CD8_TCELL_DN C7
   REACTOME_CARDIAC_CONDUCTION C2
   GSE1460_INTRATHYMIC_T_PROGENITOR_VS_DP_THYMOCYTE_DN C7
   BIDUS_METASTASIS_DN C2 GSE360_DC_VS_MAC_B_MALAYI_HIGH_DOSE_DN C7
   WP_TLR4_SIGNALING_AND_TOLERANCE C2
   GSE37534_UNTREATED_VS_GW1929_TREATED_CD4_TCELL_PPARG1_AND_FOXP3_TRASDUC
   ED_UP C7
   MEBARKI_HCC_PROGENITOR_FZD8CRD_DN C2
   GSE22935_UNSTIM_VS_24H_MBOVIS_BCG_STIM_MYD88_KO_MACROPHAGE_DN C7
   [122]Open in a new tab

Discussion

   In the present study, the host response to different invading pathogens
   was assessed using gene expression patterns. The results from the
   training dataset revealed that the expression profiling of neutrophils
   could reliably distinguish the molecular difference. Exploring the
   potential difference in sepsis is essential to further understand the
   mechanism. GSVA provides increased power to detect subtle pathway
   activity changes in an unsupervised manner ([123]Hanzelmann et al.,
   2013). After GSVA enrichment and intersection analysis, two distinct
   immunological gene sets were confirmed, which can be used to
   discriminate the different types of sepsis. It also indicated that the
   host immune system is activated even in the early stage of sepsis,
   rather than at the classic anti-inflammatory phase ([124]Tang et al.,
   2010).

   The functional interaction between proteins was also analyzed in the
   current study. Three densely connected regions and several hub genes
   were identified, which revealed important biological insights into the
   host response mediated by neutrophils. SRC belongs to the protein
   tyrosine kinases (PTKs) family and plays a critical role in initiating
   the numerous intracellular signaling pathway that affects cell
   migration, adhesion, phagocytosis, cell cycle, and cell survival
   ([125]Korade-Mirnics and Corey, 2000). It has been identified to be
   essential for the recruitment and activation of monocytes, macrophages,
   neutrophils, and other immune cells. It also plays a critical role in
   the regulation of vascular permeability and inflammatory responses in
   tissue cells ([126]Okutani et al., 2006). Toll-like receptors (TLRs)
   play an essential role in pathogen recognition and activation of innate
   immunity. TLR6 acts in a heterodimer form with TLR2, which mediates
   cell response to Gram-positive bacterial components. TLR2 regulates
   important neutrophil functions, including adhesion, generation of
   reactive oxygen species, release of chemokines, and activation of major
   proinflammatory signaling pathways, such as NF-κB pathway ([127]Andrews
   et al., 2013). IL1B is an important mediator of the inflammatory
   response and participates in a variety of cellular activities,
   including cell proliferation, differentiation, and apoptosis ([128]Liu
   and Sun, 2019). CD40 is a receptor in antigen-presenting cells of the
   immune system and is essential for mediating a broad variety of immune
   and inflammatory responses ([129]Michels et al., 2015). CCL2 is one of
   the key chemokines that regulate migration and infiltration of
   monocytes and macrophages ([130]Carson et al., 2017).

   Although the clinical manifestations of sepsis caused by Gram-negative
   and Gram-positive bacteria may appear similar, our study indicated that
   the host physiological response to these pathogens may behave
   differently due to the inciting organism. The findings were concordant
   with the results of Feezor et al., the host inflammatory responses to
   Gram-negative and Gram-positive stimuli not only share some common
   response elements but also exhibit distinct patterns of cytokine
   appearance and leukocyte gene expression ([131]Feezor et al., 2003). It
   was also confirmed by genome-wide gene expression analysis of a mouse
   sepsis model after infusion of either live Escherichia coli or
   Staphylococcus aureus ([132]Yu et al., 2004). The study of Li et al.
   also determined that there was no significant difference in the
   expression profile between Gram−positive and Gram−negative samples;
   however, several candidate genes may be biomarkers for distinguishing
   the different infections ([133]Li et al., 2017). Unlike these reports,
   the current study mainly focuses on the differences in pathways or gene
   sets rather than a single gene because no single molecule can
   recapitulate the complex changes that occur in sepsis.

   Gram-positive and Gram-negative bacteria activate different receptor
   pathways in the host, among which Toll-like receptors play a pivotal
   role ([134]Elson et al., 2007). TLR4 is regarded as the major
   lipopolysaccharide receptor for Gram-negative bacteria ([135]Branger
   et al., 2004), whereas cellular responses to components of
   Gram-positive bacteria are mainly mediated via TLR2
   ([136]Oliveira-Nascimento et al., 2012). Individual TLRs differentially
   recruit specific adaptor molecules, such as MyD88, TRIF, TIRAP/MAL, or
   TRAM, leading to the activation of NF-κB and MAP kinases pathways
   ([137]Kawasaki and Kawai, 2014). The results were also confirmed in our
   study after KEGG analysis; the genes were mainly enriched in MAPK
   signaling pathway. It was also reported that combined signaling of TLR2
   and CD137 augments antibacterial activities of neutrophils while that
   of TLR4-CD137 diminishes them ([138]Nguyen et al., 2013). Gram-negative
   and Gram-positive bacteria do not trigger monocyte activation through
   similar pathways. Lipopolysaccharide but not S. aureus Cowan used CD14
   internalization to induce cellular activation, resulting in p38 MAP
   kinase and ERK kinase activation pathways ([139]Takeuchi et al., 1999).
   Besides that, host-response pathway correlated metabolites could be
   used to distinguish between bacterial- and host-induced metabolic
   changes ([140]Hoerr et al., 2012).

   According to the sepsis guidelines, empiric antimicrobial therapy was
   recommended before obtaining blood cultures ([141]Dellinger et al.,
   2013). However, the increasing antibiotic resistance requires novel
   approaches for early identification of the causative microorganism
   ([142]Najeeb et al., 2012). After analyzing the plasma free circulating
   DNA from sepsis patients, Grumaz et al. developed an alternative
   diagnostic platform to identify infectious microorganisms in roughly 30
   h by next-generation sequencing ([143]Grumaz et al., 2016). Recently,
   the focus for accurate and rapid diagnosis has moved from single
   disease-specific markers to bioprofiles or biosignatures comprising a
   well-defined set of reliable molecular indicators using platforms such
   as proteomics ([144]Vincent et al., 2010) transcriptomics ([145]Zhang
   et al., 2010), genomics ([146]Parida and Kaufmann, 2010), and
   metabolomics ([147]Claus et al., 2010). In this current study, besides
   the 19 gene sets identified in [148]GSE6535 based on GSVA and GSEA, we
   also identified 40 gene sets based on GSEA in the two datasets, of
   which 20 gene sets were immunological signature gene sets. Based on our
   results, the differential gene sets between Gram-negative and
   Gram-positive sepsis could be further explored for diagnosis purpose
   with the immunoassay.

   The data used in the training dataset were obtained from neutrophils
   collected within 24 h. We chose neutrophils instead of other leukocytes
   because neutrophils are crucial components of an early host’s innate
   immune response ([149]Kovach and Standiford, 2012). Experimental
   conditions were similar for all patients to minimize the difference
   between individual patients. Nonetheless, there are some limitations.
   The findings were based on a microarray dataset from a single
   institution with small sample size. Although similar results were
   obtained in the validation dataset, a large sample from multiple
   centers is needed to further verify our results. On the other hand,
   gene expression profiles are known to change rapidly in the early
   stages of sepsis ([150]Maslove and Wong, 2014). Thus, the timing of
   microarray analysis should also be considered to consolidate our
   results. In addition, specimens from different sources may affect the
   expression characteristics of the genome. In the validation dataset
   [151]GSE13015, whole blood contains a mixed population of leukocytes,
   the proportion of which varies depending on the stage of sepsis and
   between individuals. However, the common gene sets in the two datasets
   also indicated the molecular difference between Gram-negative and
   Gram-positive sepsis.

   In summary, our results highlight the heterogeneity of Gram-negative
   and Gram-positive sepsis at the molecular level. The screened
   differential gene set indicated that host response may differ
   dramatically depending on the inciting organism. The findings offer new
   insight to investigate the initiating mechanisms of sepsis and provide
   a potential method to identify the causative organism at the onset of
   sepsis.

Data Availability Statement

   The datasets presented in this study can be found in online
   repositories. The names of the repository/repositories and accession
   number(s) can be found in the article.

Ethics Statement

   The studies involving human participants were reviewed and approved by
   the ethics committee of each institution, and written informed consent
   was provided by the patients or their families. The
   patients/participants provided their written informed consent to
   participate in this study. Written informed consent was obtained from
   the individual(s) for the publication of any potentially identifiable
   images or data included in this article.

Author Contributions

   JG and BH conceived and developed the study and obtained funding for
   the study. QW and XL wrote the manuscript and prepared the figures. JG,
   XG, and ZX conducted the biostatistical analysis. YZ contributed to the
   data collection. All authors contributed to the article and approved
   the submitted version.

Funding

   This study was supported by the grant from the Science and Technology
   Program of Guangzhou, China (201903010039 and 202102010199), Basic and
   Clinical Cooperative Research Promotion Program of Anhui Medical
   University (2019xkjT029), Clinical Medicine Discipline Construction
   Project of Anhui Medical University(2020lcxk032), and Fundamental
   Research Funds for the Central Universities (20ykpy21).

Conflict of Interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s Note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [152]https://www.frontiersin.org/articles/10.3389/fcimb.2022.801232/ful
   l#supplementary-material
   [153]Click here for additional data file.^ (24.5KB, docx)

References