Next Article in Journal
Where to Draw Cerebrospinal Fluid from an External Ventricular Drain? Comparison of Cerebrospinal Fluid Parameters between Two Different Collection Sites
Previous Article in Journal
Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 13
Issue 23
10.3390/diagnostics13233542
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Escherichia coli Infection Sepsis: An Analysis of Specifically Expressed Genes and Clinical Indicators

by
Qingyi Shao
,
Danlei Chen
,
Simiao Chen
,
Xuanwen Ru
and
Qing Ye
*
The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children’s Regional Medical Center, Hangzhou 310052, China
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(23), 3542; https://doi.org/10.3390/diagnostics13233542
Submission received: 25 October 2023 / Revised: 22 November 2023 / Accepted: 23 November 2023 / Published: 27 November 2023
(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
Browse Figures
Versions Notes

Abstract

:
Since E. coli is the most prevalent sepsis bacterium, studying its pathogenic molecular pathways may help with its early diagnosis and individualized treatment. However, few studies have investigated the molecular characterization of E. coli infection only. We extracted E. coli infection-specific genes and indicators from published data and clinical laboratory results in this study. GSE65088 showed 277, 377, and 408 DEGs for E. coli and other bacteria, E. coli and healthy groups, and other bacteria and healthy groups, respectively. DEGs, the MEgreen module with the highest relevance in WGCNA, and the first three MCODE subnetworks were used to find E. coli infection-specific hub genes. HSPA1B and TNF were verified in GSE6269 with ROC-AUCs of 0.7038 and 0.7116, respectively. CIBERSORT showed increased B-cell naive and T-cell CD4 naive infiltration in E. coli infectious sepsis. Patients infected with E. coli were younger than those infected with other pathogens. Compared to the other bacterially infectious sepsis patients, the E. coli patients had low globulin, prealbumin, creatine kinase, and high bilirubin levels. The clinically significant difference indicator IL-2, in combination with hub genes, better differentiated the healthy and E. coli groups, with an ROC-AUC of 0.8793. The study suggested that HSPA1B and TNF may be E.-coli-infection-specific genes, which may help explain the molecular mechanism of infectious sepsis.

1. Introduction

Despite significant progress in the study of how pathogens cause disease and the development of treatments to fight infections, infections continue to be the main cause of death today [1,2]. Sepsis is a life-threatening organ dysfunction, mainly due to the dysregulated host response to infection, and it is an important global health problem [3,4].
Molecular patterns associated with pathogens can interact with particular pattern recognition receptors that are present in immune cells. Therefore, the blood immune cells of infected patients may have pathogen-specific transcriptional signatures [5]. In sepsis, the most prevalent bacteria that can cause serious clinical consequences are Staphylococcus aureus (S. aureus) and E. coli [6,7]. E. coli infection most commonly causes intestinal diseases, such as watery or bloody diarrhea, but few studies have solely positioned it in systemic infections such as sepsis [8,9]. The colonization and infection of extraintestinal locations via pathogenic E. coli are the leading causes of sepsis in hospitals and communities [10]. For instance, E. coli was the pathogen responsible for the majority of cases in newborns with preterm sepsis among ICU patients in Saudi Arabia. [11]. E. coli is also the most common cause of bacteremia in England, with an incidence rate of 50.7 cases/100,000 people [12]. E. coli sepsis is a systemic infectious disease caused by E. coli. Severe fever, chills, tachycardia, hypotension, and multiple-organ dysfunction syndrome are typical clinical symptoms. In addition, clotting problems and the inflammatory response that follows an infection can cause symptoms, including hemorrhage and rashes [13].
Although genome-wide investigations have revealed possible host genes for sepsis development, only a few studies have characterized the various pathophysiological mechanisms of infectious illnesses caused by Gram-positive and Gram-negative bacteria [14,15]. In addition, studies that have analyzed only E. coli infection characteristics are even scarcer. However, for patients with sepsis, initial diagnosis and appropriate medical care are essential to improve clinical outcomes [16]. Understanding the potential molecular mechanisms of E. coli infectious sepsis is critical for the identification of early diagnostic biomarkers, the development of individualized treatment strategies, and the search for effective drugs.
Some studies have analyzed the difference in innate immune activation in whole-blood-cell gene expression infected by bacteria (S. aureus or E. coli) and mock-treated blood. Thirty-eight biomarker genes, including IL6, SOCS3, and IRG1, were discovered in one study and were later related to sepsis in subsequent investigations [17]. In addition, another study analyzed the gene expression characteristics of the peripheral blood leukocytes of patients infected with different pathogens and identified 35 genes that could be used to differentiate different pathogens. These potential biomarkers were also used in the study of molecular mechanisms and therapeutic objectives [5].
In the present study, we also reviewed the data of a total of 109 septic patients with confirmed pathogens and divided them into an E. coli infection group, an other Gram-negative bacteria infection group, and a Gram-positive bacteria infection group. To investigate and contrast the clinical indicator traits of patients who were infected with E. coli, clinical indicator data from patients were gathered. At the same time, we used a variety of bioinformatics methods to analyze the signature genes of E. coli infection, and an outside dataset was used to confirm them. To provide physicians with novel ideas about diagnosis and treatment, we also assessed the enriched signaling pathways of these genes and their functions in immune cell infiltration.

2. Materials and Methods

2.1. Clinical Data: Study Cohort and Information Collection

Criteria for inclusion and exclusion: (1) Patients with positive blood cultures and with a clear pathogen of E. coli or other bacteria were included; (2) they met the diagnostic criteria of sepsis 3.0 [18]. (3) Patients with tumors or hematological diseases, (4) patients with autoimmune diseases, and (5) patients with chronic diseases requiring immunomodulatory therapy were excluded. The age, sex, and basic diagnostic information of the included population were collected. In addition, data on common clinical laboratory indicators, such as biochemistry, routine blood tests, C-reactive protein (CRP), procalcitonin (PCT), and cytokines, were obtained.
This retrospective cohort study was conducted at the Children’s Hospital affiliated with Zhejiang University (2023-IRB-0107-P-01). The hospital’s Medical Ethics Committee gave its approval. The 1975 Declaration of Helsinki and the ethical guidelines of the responsible committee were followed when conducting this investigation. All subjects or their legal guardians gave their written, fully informed consent.

2.2. Data Source

Two datasets for this study, GSE65088 and GSE6269, were obtained via a Gene Expression Omnibus (GEO) download. GSE65088 was used as a training set, in which 10 cases of E. coli were cocultured with peripheral blood cells, 20 cases of other bacteria were cocultured with peripheral blood cells, and there were 15 cases in the peripheral blood model control group. As a validation set, GSE6269 includes 29 cases of E. coli sepsis, 44 cases of other bacterial sepsis, and 6 cases of healthy controls. The flow chart details all data analysis processes (Figure 1).

2.3. DEG Identification and Functional Enrichment Analysis

The LIMMA software package of R software (version 4.1.0) was used to evaluate the differentially expressed genes (DEGs) between the E. coli infection group and the control group, the E. coli infection group and the other bacterial groups, and the other bacterial infection and the control group. The criteria were |fold change (FC)| > 1 and p < 0.05. The clusterProfiler package in R was used for functional enrichment analyses of DEGs based on gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The biological functions and signaling pathways involved in E. coli infection should be further studied.

2.4. PPI Network and MCODE Analysis

The protein–protein interaction (PPI) network of DEGs infected with E. coli was predicted via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subsequently, a molecular interaction network was created and visualized using the Cytoscape (version 3.8.0) program. Target proteins are shown as nodes in the PPI network, while edges indicate potential or observed interactions between proteins. In addition, to cluster the PPI–protein interaction network to form multiple subnetworks, the molecular complex detection (MCDOE) plug-in was utilized, and the potential hub genes in the subnetworks were analyzed [19].

2.5. Weighted Gene Coexpression Network Analysis (WGCNA)

Based on the scale-free topology criterion, the coexpression network in the GSE65088 cohort was constructed using weighted gene coexpression network analysis (WGCNA). Employing the block module function, a scale-free network was developed, and gene coexpression modules were located through module partition analyses. Following the application of the dynamic tree-cutting technique, each module was defined via gene branching, and for the purpose of visualization, various colors were allocated to each module. Applying the dynamic tree-cutting method, we identified the coexpressed gene modules as having an acceptable module size of 50. Then, we assessed the relationship between the gene modules and E. coli infection using gene significance (GS) and module membership (MM), ultimately identifying the key modules [20,21].

2.6. Infiltration of Immune Cells in E. coli Sepsis

CIBERSORT (http://www.cibersort.stanford.edu/ (accessed on 11 July 2023)) is a technique that performs deconvolution on a gene expression matrix of 22 human immune cell subgroups utilizing linear regression based on the support vector regression principle. We used this method to assess immune cell infiltration in E. coli infectious disease [22,23]. The CIBERSORT program succeeded in deducing the corresponding proportions of 22 infiltrating immune cells, and the entirety of all predicted immune cell subtype scores in every sample was the same as 1. Additionally, a Spearman correlation study was conducted, comparing the levels of gene expression and the cellular composition of the immune system [24,25].

2.7. Statistical Analysis

Statistical analysis was carried out with the assistance of GraphPad Prism 8.0, SPSS 26.0, and R Program 4.0. An independent Student’s t-test was carried out to determine the statistical significance of variables with a normally distributed distribution for the contrast of two separate sets of continuous data. The Mann–Whitney U-test was applied to assess the distinction among nonnormally distributed data. The statistically significant effect of two separate sets of categorical variables was analyzed using either the chi-square test or Fisher’s exact test, depending on which method was preferred. The two-tailed distribution was implemented for all statistical p-values, and a value of p less than 0.05 was regarded as statistically significant.

3. Results

3.1. DEG Identification and Functional Enrichment

The flow chart details all data analysis processes (Figure 1). The GSE65088 and GSE6269 datasets related to E. coli infection were downloaded from the GEO database. Overall, 10 peripheral blood samples infected with E. coli, 15 mock blood samples, and 20 peripheral blood samples infected with other bacteria were included in GSE65088. Through screening with the LIMMA software package in R software, 377 (mock vs. E. coli), 409 (mock vs. others), and 277 (E. coli vs. others) differentially expressed genes were identified, with 9 genes being common DEGs (Figure 2A). Furthermore, functional enrichment analysis using GO and pathway analysis using KEGG were carried out across the DEGs from E. coli vs. the others, suggesting that signal transduction, plasma membrane, cytosol, cytoplasm, the integral component of the membrane, protein binding, and glycosphingolipid biosynthesis—globo and isoglobo series—may play significant parts in E. coli infection (Figure 2B).

3.2. MCODE Analysis

By analyzing the protein–protein interaction network (PPI) represented by 277 DEGs using STRING, we discovered that the majority of increased proteins established an extensive network of interactions. To further analyze the subnetwork of differentially expressed genes, the molecular complex detection (MCODE) plug-in (version 1.5.1) was used within the Cytoscape software (version 3.8.0). Next, we proceeded to choose the three most prominent protein complex modules, as depicted in Figure 3A–C. The key or pivot modules in this context are MCODE1, MCODE2, and MCODE3. MCODE1 contained CXCL8, CXCL2, CSF2, CSF1, CD86, CD83, CD68, CD36, C5AR1, TNF, and IL1R1. MCODE2 contained IL6R, IL3RA, IER3, HMOX1, HBEGF, EGR3, EGR1, END1, CXCL5, CD9, and CD63. MCODE3 included FOSL1, HSPA1B, and HSPA1A.

3.3. Weighted Gene Coexpression Network Analysis (WGCNA) of E. coli Infection and Other Bacterial Infections

Cluster analysis was performed on the gene expression profiles of the E. coli infection group and other bacterial infection groups (Figure 4A). To avoid the network from expanding, we selected a soft threshold (b = 6) (Figure 4B). The expression matrix was, next, transformed into an adjacency matrix, and finally, it was transformed into a topographical matrix. Genes were subjected to cluster analysis using the average linkage phylogenetic clustering method. The minimal number of genes needed for each gene network module was established as fifty with reference to the hybrid dynamic pruning tree as the benchmark. Seven modules were identified with these criteria: height = 0.10 and depth split = 2. The highest correlation with sepsis was MEgreen, r = −0.62 and p = 3 × 10−4 (Figure 4C). The clustering tree diagram is shown in Figure 4D. The genes associated with the MEgreen module and E. coli infection were significant with COR = 0.49 and p = 7.8 × 10−12 (Figure 4E).

3.4. Potential E. coli Hub Genes

Table 1 lists the nine genes screened in Figure 2. The four genes in the WGCNA MEgreen module were HSPA1B, CXCL3, PPAP2B, and TNF (Table 1). HSPA1B and TNF were found in the first three subnetworks of MCODE. We investigated the identification of the above genes in the GSE6269 dataset for the occurrence and development of E. coli infection via ROC curve analysis. The findings revealed the following AUCs for the four genes: HSPA1B AUC: 0.7038, CXCL3 AUC: 0.4718, PPAP2B AUC: 0.4671, and TNF AUC: 0.7116 (Figure 5A). In the validation set, compared to the other bacterial infection groups, we discovered that the E. coli infection group had a greater TNF level (p < 0.05), while the HSPA1B level was diminished in comparison to the other bacterial groups (p < 0.01) (Figure 5B–C).

3.5. Immune Cell Infiltration

The proportions of various immune cell types in the peripheral blood of the E. coli-infected and other bacterial infection groups were assessed using CiberSort. GEO transcriptional microarray information was used to analyze the infiltration rate of immune cells. Among 73 specimens, there were 44 cases in the other bacterial infection group and 29 cases in the E. coli infection group, which met the CiberSort criterion (p-value < 0.05). The ratio of immune cells was significantly different within the groups (Figure 6A). We performed a correlation investigation of the immune cells in the E. coli infection group and discovered numerous combinations of immune cells with positive and negative correlations (Figure 6B), with the correlation’s degree indicated by the score. The synergy between monocytes and resting mast cells was the strongest, and the competition between monocytes and resting NK cells was the strongest. In contrast to the other categories of bacterial infections, the E. coli infection group had significantly increased naive B cells and naive CD4 T cells and significantly reduced plasma cells and neutrophils (Figure 6C).

3.6. Relationship between Immunological Cells and Hub Genes

In the present research, we examined the relationship in the E. coli infection dataset between the hub gene and immune cell infiltration, thus further exploring the potential molecular mechanisms by which the hub gene affects E. coli infection. The two genes were both closely associated with immune cells, according to a correlation investigation with immune cells. Furthermore, Figure 7A–B’s data demonstrate an adverse association between TNF and regulatory T cells (Tregs) and an upward association with activated memory CD4 T cells (p-value < 0.05). HSPA1B demonstrated an adverse relationship with M1 macrophages and a positive relationship with naive CD4 T cells (p-value < 0.05).

3.7. Clinical Features of E. coli Infection

We enumerated the specific values of the indicators for which the differences were significant (Table 2). The age of patients infected with E. coli was notably lower than that of patients affected by other Gram-positive bacteria. For biochemical indicators, the peripheral albumin and globulin contents of patients infected with E. coli were significantly lower than those of patients with other bacterial infections, while total bilirubin and indirect bilirubin were significantly higher than those of patients with other bacterial infections. In addition, the levels of adenosine deaminase, prealbumin, and CK in the E. coli infection group were significantly lower than those in the Gram-positive coccus infection group (p < 0.05). For blood cell counts, the proportion of neutrophils in the E. coli infection group was significantly lower than that in the Gram-positive bacteria infection group, while the proportion of monocytes was significantly higher than that in the Gram-positive bacteria infection group (p < 0.05). Interleukin 2 (IL-2) in the E. coli infection group was higher than that in the Gram-positive bacteria infection group (p = 0.023). The TNF level of the clinical E. coli infection group was also slightly lower than that of the Gram-positive bacterial infection group, but the difference was not significant (p = 0.332).

3.8. Combined Analysis with Clinical Practice

We conducted multicriteria ROC joint analysis of IL2, TNF, and HSPA1B. In differentiating healthy and E. coli infection groups, the AUC values of IL2 were 0.7299, HSPA1B was 0.7931, TNF was 0.8218, and the combination of the three was 0.8793. In differentiating E. coli infection from other bacterial infections, the AUC values of IL2 were 0.5846, HSPA1B was 0.7038, TNF was 0.7116, and the combined value of the three was 0.71 (Figure 8A–D). We found that, for the identification of E. coli infection and other infection groups, the addition of IL2 did not increase the AUC of the other two genes. However, IL2 increased the area under the curve of two other genes when distinguishing the healthy group from the E.-coli-infected group. Furthermore, we functionally analyzed all five genes using KEGG and GO and found that they were highly expressed in many pathways, such as cytokine–cytokine receptor interaction, membrane raft, the positive regulation of interleukin-8 production, and the positive regulation of tyrosine phosphorylation of STAT protein (Figure 9A,B).

4. Discussion

Sepsis is a life-threatening systemic inflammatory response mainly caused by a dysregulated response to pathogen infection, accompanied by severe inflammation and multiple-organ dysfunction [3,26]. As a global public health problem, sepsis can lead to higher mortality in critically ill patients [27]. Early diagnosis and timely and effective intervention are the keys to improving the prognosis and reducing the mortality of septic patients [28]. In addition, sepsis is the most common and serious disease among neonates. Escherichia coli, especially parenteral pathogenic E. coli, is the most common cause of death in neonatal sepsis due to its strong adaptability and strong pathogenic ability [29,30,31,32]. We retrospectively analyzed children with sepsis in our hospital, and the results showed that sepsis patients infected with E. coli accounted for 70.3% of all septic patients infected with Gram-negative bacteria. This reflects that E. coli is the main Gram-negative infectious bacteria in clinical practice. The high incidence of early-onset Escherichia coli sepsis suggests that we should pay attention to the immune features of E. coli infection, especially in neonates or young children [33]. Similarly, we also found that the age of the population infected with E. coli was significantly lower than that of the population infected with other bacteria. In summary, improving the understanding of the changes in the gene expression characteristics and clinical indicators of E. coli sepsis can help us understand the pathogenesis of infection and promote early diagnosis and treatment [12].
This study evaluated the difference between the cohorts with sepsis caused by E. coli infection and other bacterial infections and explored the key modules based on WGCNA. Based on DEGs, WGCNA, and MCODE, key genes associated with E. coli infection were discovered, including CXCL3, HSPA1B, TNF, and PLPP3. Then, we validated these signature genes in the external validation set and found that TNF and HSPA1B had greater values. At the same time, the correlation between immune cell infiltration and signature genes in septic patients infected with E. coli was analyzed using the CiberSort algorithm. In addition, we retrospectively collected data on clinical indicators from a total of 109 septic patients. The clinical index characteristics of patients infected with E. coli were observed and compared. We used the IL-2 proteins with significant differences in clinical indicators and the abovementioned key genes in the external validation set to analyze their correlation with E. coli infection.
IL-2 is a growth factor for T cells, and it plays a promotional role in T-cell growth and transformation [34]. IL-2 maximizes the toxic effects of T cells and increases the activity of natural killer cells. Lower levels of IL-2 were observed in the E. coli group in the present study, which may predict the downregulation of T-cell proliferation. In addition, IL-2 induced the gene expression of TNF-α in monocytes and macrophages [35], and TNF was mentioned in our study.
TNF is expressed on activated macrophages and lymphocytes, among other cell types, and it can induce cell survival or death of cell lines [36]. As a pleiotropic cytokine, TNF-α has strong proinflammatory and immunomodulatory properties [37]. Some studies have found that after continuous venous hemofiltration for severe sepsis, the secretion level of TNF-α was significantly reduced in patients [38]. In septic patients with bone marrow stromal cell (BMSC) transplantation failure, TNF-α was significantly upregulated [39]. Therefore, TNF is often associated with the inflammatory state of sepsis. However, we found higher expression in E. coli-infected sepsis, which may reflect a stronger inflammatory response and immune response. Our index analysis of clinical septic patients also showed that E. coli had a higher expression level of TNF-α, but no statistically significant difference was observed (Table 1).
Heat shock 70 kDa protein 1B (HSPA1B), also known as heat shock 70 kDa protein 2 (HSP70-2), belongs to the heat shock protein family [40]. It responds to organisms under proteotoxic stress by enhancing cell viability and promoting protein damage repair [41]. In addition, under cellular stress conditions, it is critical for maintaining protein homeostasis, such as assisting in protein folding [42]. Studies have found that HSPA1B-179C > T affects the production of HSP70 and is a key determinant of individual susceptibility to various inflammatory diseases [43]. In addition, HSP70 secreted into the extracellular environment can also lead to the upregulation of the expression of proinflammatory factors such as TNF by stimulating signal transduction cascade reactions [44]. This finding also suggests that HSPA1B may have a potential effect on TNF, the first signature gene we found.
In addition to analyzing the hub genes, we also analyzed the common indicators of clinical patients. One study found that the total bilirubin content of Gram-negative bacteria (22.5 μ mol/L) in infected patients was higher than that of Gram-positive bacteria (13.8 μ mol/L) [45]. This was also similar to our finding. We found that the total bilirubin and indirect bilirubin in patients with E. coli infection were significantly higher than those in patients with other bacterial infections (p = 0.033, 0.031). In addition, we performed immune cell infiltration analysis on the GSE6269 data and found that neutrophil counts in E. coli infection were significantly lower than those in other bacterial infections. This was also consistent with the result of our clinical retrospective analysis of data; that is, the proportion of neutrophils in patients with E. coli infectious sepsis was lower than that of patients with other bacterial infections. The depletion of peripheral B cells is associated with immunosuppression and immune system abnormalities during sepsis. Some studies have found that there is a high level of naive B cells in septic shock patients [46]. However, we found that naive B cells were high in E.-coli-infection-associated sepsis, which may be related to the depletion of peripheral blood B cells.
We analyzed the E. coli infectivity signature genes based on published data and combined this analysis with the clinical indicators commonly used in our hospital. For patients with sepsis caused by E. coli infection, early diagnosis and appropriate treatment are critical to improve clinical outcomes. By combining the information of signature genes and clinical indicators, we can establish a more accurate prediction model for the diagnosis of septic sepsis risk assessment and provide more individualized treatment plans.
Our research also faced specific constraints. The data included in this investigation were obtained from openly accessible databases and were derived from a limited sample size, potentially introducing selection bias. However, the validity of our investigation was verified using an independent validation database. Additionally, the number of patients with clinical sepsis was small, and the data were obtained only from a single center and, thus, have only reference value. Lastly, to confirm the study’s findings even further, more clinical samples and molecular tests are needed.

Author Contributions

Conceptualization, Q.Y.; methodology, Q.S.; validation, Q.S., D.C. and X.R.; formal analysis, S.C.; investigation, Q.S.; resources, Q.S.; data curation, S.C.; writing—original draft preparation, Q.S.; writing—review and editing, Q.S.; visualization, X.R.; supervision, D.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Children’s Hospital affiliated with Zhejiang University (2023-IRB-0107-P-01) (6 June 2023).

Informed Consent Statement

Informed consent was obtained from all subjects or their legal guardians involved in the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to [The data contains patient privacy, is stored in the hospital database, and is not publicly available for a short period of time].

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Standing up to Infectious Disease. Nat. Microbiol. 2019, 4, 1.
  2. Rudd, K.E.; Johnson, S.C.; Agesa, K.M.; Shackelford, K.A.; Tsoi, D.; Kievlan, D.R.; Colombara, D.V.; Ikuta, K.S.; Kissoon, N.; Finfer, S.; et al. Global, Regional, and National Sepsis Incidence and Mortality, 1990–2017: Analysis for the Global Burden of Disease Study. Lancet 2020, 395, 200–211. [Google Scholar] [CrossRef] [PubMed]
  3. Markwart, R.; Saito, H.; Harder, T.; Tomczyk, S.; Cassini, A.; Fleischmann-Struzek, C.; Reichert, F.; Eckmanns, T.; Allegranzi, B. Epidemiology and Burden of Sepsis Acquired in Hospitals and Intensive Care Units: A Systematic Review and Meta-Analysis. Intensive Care Med. 2020, 46, 1536–1551. [Google Scholar] [CrossRef] [PubMed]
  4. Ye, Q.; Shao, W.X.; Wang, Q.Q.; Mao, J.H. An Imbalance of T Cell Subgroups Exists in Children with Sepsis. Microbes Infect. 2019, 21, 386–392. [Google Scholar] [CrossRef] [PubMed]
  5. Ramilo, O.; Allman, W.; Chung, W.; Mejias, A.; Ardura, M.; Glaser, C.; Wittkowski, K.M.; Piqueras, B.; Banchereau, J.; Palucka, A.K.; et al. Gene Expression Patterns in Blood Leukocytes Discriminate Patients with Acute Infections. Blood 2007, 109, 2066–2077. [Google Scholar] [CrossRef]
  6. Faix, J.D. Biomarkers of Sepsis. Crit. Rev. Clin. Lab. Sci. 2013, 50, 23–36. [Google Scholar] [CrossRef] [PubMed]
  7. Glik, J.; Kawecki, M.; Gaździk, T.; Nowak, M. The Impact of the Types of Microorganisms Isolated from Blood and Wounds on the Results of Treatment in Burn Patients with Sepsis. Pol. Prz. Chir. 2012, 84, 6–16. [Google Scholar] [CrossRef]
  8. Croxen, M.A.; Law, R.J.; Scholz, R.; Keeney, K.M.; Wlodarska, M.; Finlay, B.B. Recent Advances in Understanding Enteric Pathogenic Escherichia Coli. Clin. Microbiol. Rev. 2013, 26, 822–880. [Google Scholar] [CrossRef]
  9. Geurtsen, J.; de Been, M.; Weerdenburg, E.; Zomer, A.; McNally, A.; Poolman, J. Genomics and Pathotypes of the Many Faces of Escherichia Coli. FEMS Microbiol. Rev. 2022, 46, fuac031. [Google Scholar] [CrossRef] [PubMed]
  10. Hernandez-Pastor, L.; Geurtsen, J.; Baugh, B.; El Khoury, A.C.; Kalu, N.; Krishnarajah, G.; Gauthier-Loiselle, M.; Bungay, R.; Cloutier, M.; Saade, E. Economic Burden of Invasive Escherichia Coli Disease among Older Adult Patients Treated in Hospitals in the United States. J. Manag. Care Spec. Pharm. 2023, 29, 873–883. [Google Scholar] [CrossRef]
  11. Alshammari, M.K.; Alsanad, A.H.; Alnusayri, R.J.; Alanazi, A.S.; Shamakhi, F.Q.; Alshahrani, K.M.; Alshahrani, A.M.; Yahya, G.; Alshahrani, A.A.; Alshahrani, T.S.; et al. Risk and Diagnostic Factors and Therapy Outcome of Neonatal Early Onset Sepsis in Icu Patients of Saudi Arabia: A Systematic Review and Meta Analysis. Front. Pediatr. 2023, 11, 1206389. [Google Scholar] [CrossRef]
  12. Abernethy, J.K.; Johnson, A.P.; Guy, R.; Hinton, N.; Sheridan, E.A.; Hope, R.J. Thirty Day All-Cause Mortality in Patients with Escherichia Coli Bacteraemia in England. Clin. Microbiol. Infect. 2015, 21, 251.e1–251.e8. [Google Scholar] [CrossRef] [PubMed]
  13. Martin, C.M.; Priestap, F.; Fisher, H.; Fowler, R.A.; Heyland, D.K.; Keenan, S.P.; Longo, C.J.; Morrison, T.; Bentley, D.; Antman, N. A Prospective, Observational Registry of Patients with Severe Sepsis: The Canadian Sepsis Treatment and Response Registry. Crit. Care Med. 2009, 37, 81–88. [Google Scholar] [CrossRef] [PubMed]
  14. Wong, H.R.; Shanley, T.P.; Sakthivel, B.; Cvijanovich, N.; Lin, R.; Allen, G.L.; Thomas, N.J.; Doctor, A.; Kalyanaraman, M.; Tofil, N.M.; et al. Genome-Level Expression Profiles in Pediatric Septic Shock Indicate a Role for Altered Zinc Homeostasis in Poor Outcome. Physiol. Genom. 2007, 30, 146–155. [Google Scholar] [CrossRef] [PubMed]
  15. Dickinson, P.; Smith, C.L.; Forster, T.; Craigon, M.; Ross, A.J.; Khondoker, M.R.; Ivens, A.; Lynn, D.J.; Orme, J.; Jackson, A.; et al. Whole Blood Gene Expression Profiling of Neonates with Confirmed Bacterial Sepsis. Genome Data 2015, 3, 41–48. [Google Scholar] [CrossRef] [PubMed]
  16. Spaeder, M.C.; Moorman, J.R.; Tran, C.A.; Keim-Malpass, J.; Zschaebitz, J.V.; Lake, D.E.; Clark, M.T. Predictive Analytics in the Pediatric Intensive Care Unit for Early Identification of Sepsis: Capturing the Context of Age. Pediatr. Res. 2019, 86, 655–661. [Google Scholar] [CrossRef] [PubMed]
  17. Dix, A.; Hünniger, K.; Weber, M.; Guthke, R.; Kurzai, O.; Linde, J. Biomarker-Based Classification of Bacterial and Fungal Whole-Blood Infections in a Genome-Wide Expression Study. Front. Microbiol. 2015, 6, 171. [Google Scholar] [CrossRef] [PubMed]
  18. Singer, M.; Deutschman, C.S.; Seymour, C.W.; Shankar-Hari, M.; Annane, D.; Bauer, M.; Bellomo, R.; Bernard, G.R.; Chiche, J.D.; Coopersmith, C.M.; et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016, 315, 801–810. [Google Scholar] [CrossRef]
  19. Xie, R.; Li, B.; Jia, L.; Li, Y. Identification of Core Genes and Pathways in Melanoma Metastasis Via Bioinformatics Analysis. Int. J. Mol. Sci. 2022, 23, 794. [Google Scholar] [CrossRef]
  20. Langfelder, P.; Horvath, S. Wgcna: An R Package for Weighted Correlation Network Analysis. BMC Bioinform. 2008, 9, 559. [Google Scholar] [CrossRef]
  21. Zhang, B.; Horvath, S. A General Framework for Weighted Gene Co-Expression Network Analysis. Stat. Appl. Genet. Mol. Biol. 2005, 4, 17. [Google Scholar] [CrossRef]
  22. Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 2015, 12, 453–457. [Google Scholar] [CrossRef]
  23. Chen, B.; Khodadoust, M.S.; Liu, C.L.; Newman, A.M.; Alizadeh, A.A. Profiling Tumor Infiltrating Immune Cells with Cibersort. Methods Mol. Biol. 2018, 1711, 243–259. [Google Scholar]
  24. Dai, W.; Zheng, P.; Luo, D.; Xie, Q.; Liu, F.; Shao, Q.; Zhao, N.; Qian, K. Lpin1 Is a Regulatory Factor Associated with Immune Response and Inflammation in Sepsis. Front. Immunol. 2022, 13, 820164. [Google Scholar] [CrossRef] [PubMed]
  25. Gentles, A.J.; Newman, A.M.; Liu, C.L.; Bratman, S.V.; Feng, W.; Kim, D.; Nair, V.S.; Xu, Y.; Khuong, A.; Hoang, C.D.; et al. The Prognostic Landscape of Genes and Infiltrating Immune Cells across Human Cancers. Nat. Med. 2015, 21, 938–945. [Google Scholar] [CrossRef]
  26. Angus, D.C.; van der Poll, T. Severe Sepsis and Septic Shock. N. Engl. J. Med. 2013, 369, 840–851. [Google Scholar] [CrossRef] [PubMed]
  27. Abe, T.; Yamakawa, K.; Ogura, H.; Kushimoto, S.; Saitoh, D.; Fujishima, S.; Otomo, Y.; Kotani, J.; Umemura, Y.; Sakamoto, Y.; et al. Epidemiology of Sepsis and Septic Shock in Intensive Care Units between Sepsis-2 and Sepsis-3 Populations: Sepsis Prognostication in Intensive Care Unit and Emergency Room (Spice-Icu). J. Intensive Care 2020, 8, 44. [Google Scholar] [CrossRef]
  28. Rivers, E.; Nguyen, B.; Havstad, S.; Ressler, J.; Muzzin, A.; Knoblich, B.; Peterson, E.; Tomlanovich, M. Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock. N. Engl. J. Med. 2001, 345, 1368–1377. [Google Scholar] [CrossRef] [PubMed]
  29. Simonsen, K.A.; Anderson-Berry, A.L.; Delair, S.F.; Davies, H.D. Early-Onset Neonatal Sepsis. Clin. Microbiol. Rev. 2014, 27, 21–47. [Google Scholar] [CrossRef] [PubMed]
  30. Doenhardt, M.; Seipolt, B.; Mense, L.; Winkler, J.L.; Thürmer, A.; Rüdiger, M.; Berner, R.; Armann, J. Neonatal and Young Infant Sepsis by Group B Streptococci and Escherichia Coli: A Single-Center Retrospective Analysis in Germany-Gbs Screening Implementation Gaps and Reduction in Antibiotic Resistance. Eur. J. Pediatr. 2020, 179, 1769–1777. [Google Scholar] [CrossRef] [PubMed]
  31. Chakraborty, A.; Adhikari, P.; Shenoy, S.; Saralaya, V. Genomic Analysis and Clinical Importance of Escherichia Coli Isolate from Patients with Sepsis. Indian J. Pathol. Microbiol. 2015, 58, 22–26. [Google Scholar] [CrossRef] [PubMed]
  32. MacKinnon, M.C.; McEwen, S.A.; Pearl, D.L.; Lyytikäinen, O.; Jacobsson, G.; Collignon, P.; Gregson, D.B.; Valiquette, L.; Laupland, K.B. Increasing Incidence and Antimicrobial Resistance in Escherichia coli Bloodstream Infections: A Multinational Population-Based Cohort Study. Antimicrob. Resist. Infect. Control 2021, 10, 131. [Google Scholar] [CrossRef] [PubMed]
  33. Wang, X.; Chan PH, Y.; Lau HY, S.; Tsoi, K.; Lam, H.S. Epidemiologic Changes of Neonatal Early-Onset Sepsis after the Implementation of Universal Maternal Screening for Group B Streptococcus in Hong Kong. Pediatr. Infect. Dis. J. 2023, 42, 914–920. [Google Scholar] [CrossRef]
  34. Abbas, A.K.; Trotta, E.; Simeonov, D.R.; Marson, A.; Bluestone, J.A. Revisiting Il-2: Biology and Therapeutic Prospects. Sci. Immunol. 2018, 3, eaat1482. [Google Scholar] [CrossRef] [PubMed]
  35. Costa, R.T.; Araújo, O.R.; Brunialti MK, C.; Assunção MS, C.; Azevedo LC, P.; Freitas, F.; Salomão, R. T Helper Type Cytokines in Sepsis: Time-Shared Variance and Correlation with Organ Dysfunction and Hospital Mortality. Braz. J. Infect. Dis. 2019, 23, 79–85. [Google Scholar] [CrossRef]
  36. Horiuchi, T.; Mitoma, H.; Harashima, S.; Tsukamoto, H.; Shimoda, T. Transmembrane Tnf-Alpha: Structure, Function and Interaction with Anti-Tnf Agents. Rheumatology 2010, 49, 1215–1228. [Google Scholar] [CrossRef]
  37. Holtmann, M.H.; Neurath, M.F. Differential Tnf-Signaling in Chronic Inflammatory Disorders. Curr. Mol. Med. 2004, 4, 439–444. [Google Scholar] [CrossRef]
  38. Gao, Y.; Huang, X.; Yang, Y.; Lei, Z.; Chen, Q.; Guo, X.; Tian, J.; Gao, X. Clinical Analysis of An69st Membrane Continuous Venous Hemofiltration in the Treatment of Severe Sepsis. Open Med. (Wars) 2023, 18, 20230784. [Google Scholar] [CrossRef]
  39. Xiu, G.; Li, X.; Chen, J.; Li, J.; Chen, K.; Liu, P.; Ling, B.; Yang, Y. Role of Serum Inflammatory Cytokines in Sepsis Rats Following Bmscs Transplantation: Protein Microarray Analysis. Cell Transplant. 2023, 32, 9636897231198175. [Google Scholar] [CrossRef]
  40. Rosenzweig, R.; Nillegoda, N.B.; Mayer, M.P.; Bukau, B. The Hsp70 Chaperone Network. Nat. Rev. Mol. Cell. Biol. 2019, 20, 665–680. [Google Scholar] [CrossRef]
  41. Radons, J. The Human Hsp70 Family of Chaperones: Where Do We Stand? Cell Stress Chaperones 2016, 21, 379–404. [Google Scholar] [CrossRef] [PubMed]
  42. Mayer, M.P. Hsp70 Chaperone Dynamics and Molecular Mechanism. Trends Biochem. Sci. 2013, 38, 507–514. [Google Scholar] [CrossRef] [PubMed]
  43. Temple, S.E.; Cheong, K.Y.; Ardlie, K.G.; Sayer, D.; Waterer, G.W. The Septic Shock Associated Hspa1b1267 Polymorphism Influences Production of Hspa1a and Hspa1b. Intensive Care Med. 2004, 30, 1761–1767. [Google Scholar] [CrossRef] [PubMed]
  44. Asea, A.; Kraeft, S.K.; Kurt-Jones, E.A.; Stevenson, M.A.; Chen, L.B.; Finberg, R.W.; Koo, G.C.; Calderwood, S.K. Hsp70 Stimulates Cytokine Production through a Cd14-Dependant Pathway, Demonstrating Its Dual Role as a Chaperone and Cytokine. Nat. Med. 2000, 6, 435–442. [Google Scholar] [CrossRef] [PubMed]
  45. Huang, C.; Xiong, H.; Li, W.; Peng, L.; Zheng, Y.; Liao, W.; Zhou, M.; Xu, Y. T Cell Activation Profiles Can Distinguish Gram Negative/Positive Bacterial Sepsis and Are Associated with Icu Discharge. Front. Immunol. 2022, 13, 1058606. [Google Scholar] [CrossRef]
  46. Dong, X.; Tu, H.; Bai, X.; Qin, S.; Li, Z. Intrinsic/Extrinsic Apoptosis and Pyroptosis Contribute to the Selective Depletion of B Cell Subsets in Septic Shock Patients. Shock 2023, 60, 345–353. [Google Scholar] [CrossRef]
Diagnostics 13 03542 g001
Figure 1. A flow chart illustrating the research findings of this study.
Figure 1. A flow chart illustrating the research findings of this study.
Diagnostics 13 03542 g001
Diagnostics 13 03542 g002
Figure 2. The determination of DEGs and their subsequent functional enrichment. (A) The Venn diagram shows the DEGs. The mock vs. ecoli group comprised the genes whose expression levels differed between the E. coli infection group and the healthy model, the mock vs. others group comprised the genes whose expression levels varied between the other bacterial infection groups and the healthy model, and the others vs. ecoli group comprised the differentially expressed genes between other bacteria and E. coli. (B) Top 10 functional enrichment items in the KEGG, MF, CC, and BP analyses of DEGs.
Figure 2. The determination of DEGs and their subsequent functional enrichment. (A) The Venn diagram shows the DEGs. The mock vs. ecoli group comprised the genes whose expression levels differed between the E. coli infection group and the healthy model, the mock vs. others group comprised the genes whose expression levels varied between the other bacterial infection groups and the healthy model, and the others vs. ecoli group comprised the differentially expressed genes between other bacteria and E. coli. (B) Top 10 functional enrichment items in the KEGG, MF, CC, and BP analyses of DEGs.
Diagnostics 13 03542 g002
Diagnostics 13 03542 g003
Figure 3. MCODE analysis built around data collected via the PPI network. (A) MCODE1. (B) MCODE2. (C) MCODE3.
Figure 3. MCODE analysis built around data collected via the PPI network. (A) MCODE1. (B) MCODE2. (C) MCODE3.
Diagnostics 13 03542 g003
Diagnostics 13 03542 g004
Figure 4. Module construction and assessment of WGCNA. (A) Dendrogram for specimen clustering utilizing Euclidean distances. (B) Analysis of the network architecture with a variety of different soft thresholds. (C) Module–trait association. A module is represented by each row, while a group is represented by each column. (D) Using module color assignment and topology connect, a clustered tree comprising various genes was constructed. (E) The relevance of members in the MEgreen module and sepsis.
Figure 4. Module construction and assessment of WGCNA. (A) Dendrogram for specimen clustering utilizing Euclidean distances. (B) Analysis of the network architecture with a variety of different soft thresholds. (C) Module–trait association. A module is represented by each row, while a group is represented by each column. (D) Using module color assignment and topology connect, a clustered tree comprising various genes was constructed. (E) The relevance of members in the MEgreen module and sepsis.
Diagnostics 13 03542 g004
Diagnostics 13 03542 g005
Figure 5. The performance of the hub genes in GSE6269. (A) ROC curve of the hub genes. (B,C) The expression of hub genes between E. coli and other cohorts. The Others group was the other bacterial infections group. *, p < 0.05; **, p < 0.01.
Figure 5. The performance of the hub genes in GSE6269. (A) ROC curve of the hub genes. (B,C) The expression of hub genes between E. coli and other cohorts. The Others group was the other bacterial infections group. *, p < 0.05; **, p < 0.01.
Diagnostics 13 03542 g005
Diagnostics 13 03542 g006
Figure 6. Assessment of the invasion of immune cells and correlation studies. (A) Bar plot showing the percentages of 22 distinct immune cells in specimens from patients with sepsis. (B) The correlation heatmap of 22 various forms of immune cell infiltration. Blue indicates a positive correlation, and red indicates an adverse association. A greater correlation is indicated by a deeper color. (C) The expression of 22 various forms of immune cells in the E. coli infection group and the other bacterial infection groups. The other bacterial group is shown in blue, while the E. coli group is shown in red. **, p < 0.01; ***, p < 0.001.
Figure 6. Assessment of the invasion of immune cells and correlation studies. (A) Bar plot showing the percentages of 22 distinct immune cells in specimens from patients with sepsis. (B) The correlation heatmap of 22 various forms of immune cell infiltration. Blue indicates a positive correlation, and red indicates an adverse association. A greater correlation is indicated by a deeper color. (C) The expression of 22 various forms of immune cells in the E. coli infection group and the other bacterial infection groups. The other bacterial group is shown in blue, while the E. coli group is shown in red. **, p < 0.01; ***, p < 0.001.
Diagnostics 13 03542 g006
Diagnostics 13 03542 g007
Figure 7. Correlation between immune cells and two genes. (A) Correlation between immune cells and HSPA1B. (B) Correlation between immune cells and TNF.
Figure 7. Correlation between immune cells and two genes. (A) Correlation between immune cells and HSPA1B. (B) Correlation between immune cells and TNF.
Diagnostics 13 03542 g007
Diagnostics 13 03542 g008
Figure 8. Joint analysis of IL-2 and hub genes. (A) ROCs showing the diagnostic performance of IL-2 and hub genes between E.-coli-infected patients and healthy individuals. (B) Combination of il-2 and hub genes between E.-coli-infected patients and healthy individuals. (C) The diagnostic performance of IL-2 and hub genes between E. coli infections and other bacterial infections. (D) Combination of il-2 and hub genes between E. coli infections and other bacterial infections.
Figure 8. Joint analysis of IL-2 and hub genes. (A) ROCs showing the diagnostic performance of IL-2 and hub genes between E.-coli-infected patients and healthy individuals. (B) Combination of il-2 and hub genes between E.-coli-infected patients and healthy individuals. (C) The diagnostic performance of IL-2 and hub genes between E. coli infections and other bacterial infections. (D) Combination of il-2 and hub genes between E. coli infections and other bacterial infections.
Diagnostics 13 03542 g008
Diagnostics 13 03542 g009
Figure 9. Functional enrichment analysis and PPI network diagram of CXCL3, HSPA1B, IL2, TNF, and PLPP3. (A) PPI network diagram. (B) BP, CC, MF, and KEGG analysis from left to right.
Figure 9. Functional enrichment analysis and PPI network diagram of CXCL3, HSPA1B, IL2, TNF, and PLPP3. (A) PPI network diagram. (B) BP, CC, MF, and KEGG analysis from left to right.
Diagnostics 13 03542 g009
Table 1. Nine candidate hub genes are shown in Figure 2A, and the genes were included in the MEgreen module and MCODE analysis of WGCNA. Four genes were obtained via WGCNA, and two genes were obtained after MCODE analysis.
Table 1. Nine candidate hub genes are shown in Figure 2A, and the genes were included in the MEgreen module and MCODE analysis of WGCNA. Four genes were obtained via WGCNA, and two genes were obtained after MCODE analysis.
Nine Genes in Figure 2AWGCNAMCODE
LOC728830 HSPA1B HSPA1B
EDN1 PPAP2B TNF
HSPA1B CXCL2
PPAP2B TNF
CXCL2
TNF
LOC643930
LOC387763
LOC642093
Table 2. Common clinical indicators of patients with E. coli and other bacterial infections.
Table 2. Common clinical indicators of patients with E. coli and other bacterial infections.
CharacteristicsE. coli (n = 26)G+ (n = 72)P E. coli vs. G+G− (n = 11)P E. coli vs. G−
Gender, F, n (%)9 (34.6)32 (40.5)0.2637 (58.3)0.206
Age (month) median (IQR)2 (0, 11)24 (4, 72) 0.000 10.5 (2.3, 4.5)0.3680
Laboratory parameters median (IQR)
Albumin (g/L)36.4 (33.1, 37.2)39.5 (35.7, 43.3) 0.002 36.5 (33.8, 38.5)0.370
Globulin (g/L)18.3 (16.6, 20.5)23.3 (17.4, 27.3) 0.004 20.0 (18.3, 23.6)0.342
Total bilirubin (mol/L)22.8 (8.7, 137.8)8.6 (5.1, 14.6) 0.033 9.2 (5.5, 75.0)0.071
Indirect bilirubin (mol/L)14.1 (7.0, 122.9)6.7 (3.8, 11.1) 0.031 7.3 (4.3, 28.5) 0.019
Adenosine deaminase (U/L)10.1 (5.5, 13.6)13.95 (9.5, 18.8) 0.033 13.7 (6.3, 22.4)0.173
Prealbumin (g/L)100.3 (61.2, 118.4)139.1 (108.1, 188.6) 0.000 110.9 (91.3, 132.7)0.063
Creatine kinase (U/L)77.4 (50.0, 131.5)123.5 (60.3, 271.8) 0.023 72.0 (59.0, 84.0)0.447
Neutrophil (%)46.8 (37.4, 61.5)61.5 (41.8, 77.1) 0.037 61.0 (21.3, 90.4)0.578
Monocyte (%)9.1 (6.8, 11.9)4.8 (6.7, 8.4) 0.006 5.6 (3.0, 8.6)0.043
TNFα (ng/mL)2.4 (1.9, 6.0)2.1 (1.4, 4.1)0.3321.8 (1.6, 2.1)0.619
IL2 (pg/mL) 2.1 (1.2, 3.1)2.8 (2.4, 4.2) 0.023 2.8 (2.0, 4.85)0.115
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Shao, Q.; Chen, D.; Chen, S.; Ru, X.; Ye, Q. Escherichia coli Infection Sepsis: An Analysis of Specifically Expressed Genes and Clinical Indicators. Diagnostics 2023, 13, 3542. https://doi.org/10.3390/diagnostics13233542

AMA Style

Shao Q, Chen D, Chen S, Ru X, Ye Q. Escherichia coli Infection Sepsis: An Analysis of Specifically Expressed Genes and Clinical Indicators. Diagnostics. 2023; 13(23):3542. https://doi.org/10.3390/diagnostics13233542

Chicago/Turabian Style

Shao, Qingyi, Danlei Chen, Simiao Chen, Xuanwen Ru, and Qing Ye. 2023. "Escherichia coli Infection Sepsis: An Analysis of Specifically Expressed Genes and Clinical Indicators" Diagnostics 13, no. 23: 3542. https://doi.org/10.3390/diagnostics13233542

APA Style

Shao, Q., Chen, D., Chen, S., Ru, X., & Ye, Q. (2023). Escherichia coli Infection Sepsis: An Analysis of Specifically Expressed Genes and Clinical Indicators. Diagnostics, 13(23), 3542. https://doi.org/10.3390/diagnostics13233542

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop