Dynamic alterations and clinical implications of the plasma proteome in pediatric sepsis

Dynamic alterations and clinical implications of the plasma proteome in pediatric sepsis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic alterations and clinical implications of the plasma proteome in pediatric sepsis Shiyuan Fan, Xinglv Liu, Zichi Zhao, Yanjuan Liu, Yu Jiang, Saizhen Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6146492/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jul, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background Current sepsis biomarkers have limitations, but mass spectrometry-based proteomics can identify patients at high risk of mortality or organ dysfunction, identify the molecular mechanisms of pediatric sepsis, and reveal personalized biomarkers and therapeutic strategies, with high-risk cohorts benefiting from early and accurate identification through clinical biomarkers. Methods The young mice were randomly divided into sepsis and sham groups(D0), and then the plasma was dissected at D0, day 1(D1), day3(D3) and day7(D7) after surgery for additional protein identification by liquid chromatography-mass spectrometry (LC/MS) proteomics. Subsequently, data from 66 cases of children diagnosed with sepsis upon admission to PICU at Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University) were gathered. Dynamic plasma samples (D1, D3, D7) were obtained for ELISA verification and correlation analysis of the candidate biomarkers to determine the clinical significance of sepsis candidate plasma biomarkers. Results Among the 6578 proteins identified, the septic mice groups (D1, D3, D7) demonstrated 161 differently upregulated plasma proteins. The main enriched pathways in the KEGG study were related to complement and coagulation cascades, focal adhesion, and phagosomes. ELISA test results indicated that among pediatric patients, the five candidate biomarkers (ANTⅢ, CFD, Col1α1, EGFR, Thbs1) all showed varying degrees of decrease in diagnosing sepsis. Correlation study results suggested that ATⅢ was adversely linked with IgA, IgG, IgM, C3, with Pearson's coefficients of -0.543, -0.217, -0.526, -0.128, respectively. CFD was positively connected with IgA, IgG, IgM, and negatively correlated with C3. Col1α1, CFD, EGFR, and Thbs1 demonstrated negative correlation with suppressive CD8 + cells, while Col1α1, EGFR, and Thbs1 showed positive correlation with B cells (CD19+). Furthermore, Col1α1, CFD, EGFR, and Thbs1 revealed positive connection with CD4+/CD8+. Additionally, ATⅢ demonstrated positive connection with PT, APTT, INR, D-Dimer, Fbg, while Col1α1, EGFR showed negative association with PT, APTT, INR, D-Dimer, Fbg, and CFD was favorably connected with Fbg, and Thbs1 showed positive correlation with D-Dimer. Conclusion Within one week of sepsis onset, 161 proteins revealed alterations in young mice, with the complement and coagulation cascades, focal adhesion, and phagosome pathways showing the most significant correlations. All prospective markers reduced following the recognition of sepsis and were associated with coagulation and immunological function in pediatric patients. Children Sepsis Plasma Mass spectrometry Proteome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Sepsis, defined as life-threatening organ failure induced by an aberrant host response to infection, remains a substantial clinical challenge 1 . The criteria for pediatric sepsis proposed by the International Pediatric Sepsis Consensus Conference in 2005 have been widely used in clinical practices, research, quality improvement programs, and policy frameworks 2 . In January 2024, the Pediatric Sepsis Definition Task Force of the Society of Critical Care Medicine developed the most recent international consensus guidelines for pediatric sepsis and septic shock 3 . Sepsis remains a common cause of pediatric mortality and pediatric intensive care unit (PICU) admissions 4 . Sepsis is expected to have impacted 48.9 million individuals worldwide in 2017, with 20.3 million cases comprising children under the age of five 5 . As a result, sensitive early clinical indicators are critical for aiding early screening, identification, and intervention in high-risk people. Early and exact identification of at-risk populations is crucial for maximizing treatments among those who will benefit most from prompt therapeutic measures 6 . In the early stages of hospitalization, distinguishing between sepsis and sterile inflammation could be challenging. Furthermore, the microbiological identification of pathogens does not necessarily mean that they are the cause of sepsis, because microbiological laboratories cannot distinguish between colonizing organisms and infectious pathogens 7 . Given the complexity of infection and host-pathogen interactions, a single biomarker is unlikely to yield sufficient precision for infection diagnosis 8 . Furthermore, host-pathogen interactions during the microbial cell lifecycle, such as cellular entrance, pathogen reproduction and dissemination, and host defense mechanisms, entail a number of metabolic modifications that can be detected using key proteins and metabolic products 9 . Advanced mass spectrometry (MS) techniques enable the collecting of molecular insights into diseases from both the host and pathogen perspectives. Proteomics may simultaneously evaluate many proteins, resulting in diagnostic and prognostic protein signatures. The future of proteomics hinges on its application in precision medicine. Plasma proteomics research in pediatric sepsis is still in its early phases, with no studies showing the dynamic changes in plasma proteins. Plasma proteome profiling in sepsis has demonstrated that the changes in protein abundance found in a mouse sepsis model largely correspond with the trends documented in human sepsis literature 10 . Consequently, the strong proteomic signals derived from the murine sepsis model are expected to enhance and advance future human-targeted proteome research. In order to screen and uncover possible plasma biomarkers for pediatric sepsis, we performed proteome investigations of plasma protein expression profiles in young sepsis murine models using liquid chromatography-mass spectrometry (LC/MS)-based techniques. Concurrently, we collected data on pediatric sepsis patients initially identified and treated in the PICU of Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University). Fundamental admission details and laboratory findings were recorded, and dynamic plasma samples (D1, D3, and D7) were collected. Consequently, ELISA was employed to investigate potential plasma biomarkers of sepsis, aiming to identify distinctive plasma biomarkers for pediatric sepsis and assess their clinical significance within this setting. Materials and methods Design of experiments Two distinct experiments were conducted. Experiment 1 established a gold-standard animal model of pediatric sepsis via the cecal ligation and puncture (CLP). Proteomic techniques were employed to identify potential plasma biomarkers for sepsis. In Experiment 2, the potential biomarkers were validated in plasma samples from pediatric patients with sepsis via ELISA. Proteomic analysis, ELISA validation, and clinical evaluation were conducted in a blinded way. Animals experiment Male C57BL/6J mice were procured from Hunan Saike Jingda Experimental Animal Co., Ltd., aged 3–4 weeks, with a body weight of 11.4 ± 0.9 g. The animals were randomly apportioned into a sham operation group (D0) and a sepsis group (CLP), further subdivided into Day 1 (D1), Day 3 (D3), and Day 7 (D7) groups. Before the experiment, all animals were acclimatized for a week in SPF animal facilities under controlled conditions of 25–26°C and a 12:12 h light-dark cycle. Water and food were provided ad libitum. Mice in the CLP subgroups were euthanized on D1, D3, and D7 post-CLP, whereas the sham group was euthanized 24 hours after the sham procedure. CLP has emerged as the predominant model for experimental sepsis and is considered the gold standard in sepsis research 11 , 12 . Mice were anesthetized using 2–3% isoflurane (Ringpu Bio, China). The cecum was exposed utilizing a midline surgical incision and ligated by a 4.0 silk suture. Then, a 21G needle penetrated the cecum and the abdomen was closed in layers with 4.0 sutures. The cecum was mobilized without the employment of CLP for the sham-operated animals. Resuscitated animals by subcutaneous injection of a prewarmed normal saline (37°C, 50mL/kg) at the end of the surgical procedures, then mice were returned to cages immediately where accessed to water and food in freedom, with a temperature-controlled environment (22°C) for 12h light and dark cycles. The plasma was obtained post-anesthesia, and blood was extracted from the enucleated eye with an anticoagulant-treated tube to collect roughly 1 ml of blood (n = 11 per group). Subsequently, blood was centrifuged at 4°C for 15 minutes at 3000 rpm and stored at -80°C until required. Proteomic analysis After equilibrating the samples to room temperature, 10 µl of plasma from each sample was taken and subjected to high-abundance protein depletion using a protein centrifuge column pool. The plasma samples were then diluted with 8 M urea for disulfide reduction and alkylation. Proteins were digested with trypsin (ThermoScientific, US) at 37°C overnight, followed by peptide purification and desalting. The desalted peptides were dried under vacuum and reconstituted in mass spectrometry buffer. The samples were subjected to chromatographic separation and analyzed by an Orbitrap Exploris 240 (ThermoScientific, US) mass spectrometer (MS). The analysis was conducted over 150 minutes in positive ion mode, with a precursor ion scan range of 350–1200 m/z and a MS1 resolution of 60,000. The AGC target was set to Standard. Peptide and fragment m/z ratios were acquired using the following parameters: Data-independent acquisition (DIA) was set to Top N, with N set to 30. MS2 activation was performed using HCD, with an isolation window of 1.6 m/z. The MS2 resolution was set to 15,000, Microscans to 1, and the ion dynamic exclusion time was set to Auto, with a normalized collision energy of 30%. This study employed a label-free quantitative proteomics approach using DIA-MS1 data integration, a methodology that does not rely on labeled reagents. The DIA-NN software (version 1.8.1) was used to intelligently identify peptide characteristics from the raw data. Following data collection, by setting the grouping, database, and post-translational modification types, DIA-NN computed peak integration intensities to provide label-free quantification data. Additionally, the software filtered the data based on the false discovery rate (FDR) principle, automatically matched the data to the database, and thus yielded highly reliable qualitative results. Finally, the UniProt database was employed to consolidate all quantitative and qualitative results, completing the comprehensive proteomic data analysis. Bioinformatics analysis In the MS assay, each sample underwent three replicates of global protein quantification, yielding three quantitative values. The final quantitative value for each sample was determined as the mean of these three replicates. The ratio of the final quantitative values between different samples was then calculated as the differential expression level Fold Change (FC) for the comparison groups.Analysis and volcano plot generation were carried out using relevant R packages in R version 4.3.1. The FC threshold of > 0.5 or <-0.5, combined with P < 0.05, was set to determine the upregulated or downregulated protein expression changes in the plasma. Enrichment analysis for GO annotations and KEGG pathways was conducted using the pathview and clusterProfiler R packages provided by the microbioinformatics platform for pathway-based data integration and visualization ( https://www.bioinformatics.com.cn ). Protein-protein interaction (PPI) data for differentially expressed genes was obtained from the online STRING database ( http://string-db.org ). The MetaboAnalyst 6.0 platform ( https://www.metaboanalyst.ca/MetaboAnalyst/ ) was used for biomarker feature analysis and pattern prediction. Patient recruitment This study recruited pediatric patients diagnosed with sepsis in the PICU of Hunan Provincial People’s Hospital (the First Affiliated Hospital of Hunan Normal University) between January 1, 2023, and December 31, 2023. The selection was based on the definitions of sepsis and related pediatric organ dysfunction standards established at the 2005 International Pediatric Sepsis Consensus Conference. The inclusion criteria consisted of children aged between 28 days and 18 years who were diagnosed with sepsis. The exclusion criteria included: 1) children with genetic metabolic disorders or chromosomal diseases; 2) children with primary immunodeficiencies; 3) children who had been on long-term corticosteroids or immunosuppressants; 4) children with malignant tumors; 5) children who were transferred to other medical institutions for various reasons during hospitalization; and 6) children with incomplete clinical data. Plasma samples were collected and preserved as follows: On D1, D3, and D7 of hospitalization, 2 mL of blood was drawn from each patient into EDTA purple cap tubes in the morning. The collected samples were allowed to sit for 30 minutes before being centrifuged (3000rpm ,10min) within 2 hours. The supernatant was then stored at -80°C for further analysis. ELISA validation The plasma samples, after being retrieved from the − 80℃ freezer, is thawed in a 4℃ refrigerator. The blood and intratumour levels of ANTⅢ, CFD ,Col1α1, EGFR and Thbs1 were detected with ELISA kits (MultiSciences) following the manufacturer’s instructions. Statistical Analysis Statistical analysis and data visualization were conducted using software GraphPad Prism 9.0 and SPSS 25.0. Data were presented as mean ± SD. Differences between two groups were assessed using Student's t-test, while differences among multiple groups were analyzed using one-way ANOVA. P < 0.05 was considered statistically significant, and P < 0.01 was considered highly statistically significant. Results Differentially expressed proteins (DEPs) in sepsis plasma According to our findings (Fig. 1 A-C), MS identified 6,578 plasma proteins in septic young mice. On D1 of sepsis, there were 505 DEPs in the plasma, including 343 proteins that were upregulated and 162 proteins that were downregulated. On D3, a total of 706 DEPs were identified, with 337 proteins upregulated and 369 proteins downregulated. On D7, 483 DEPs were identified, comprising 244 upregulated proteins and 239 downregulated proteins. Our results further revealed that among the 6,578 proteins identified, there were 161 proteins exhibiting dynamic changes in the plasma of septic mice over D1, D3, and D7 (Fig. 1 D). To investigate the relationships among the discovered DEPs, PPI network was created using the STRING database, demonstrating the complicated interactions among many genes (Fig. 1 E). Characteristics of DEPs In this study, functional enrichment analysis was done to identify the discovered DEPs according to their functions using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The ten most significantly enriched pathways are: complement and coagulation cascades, focal adhesion, phagosome, amoebiasis, bladder cancer, COVID-19, proteoglycans in cancer, proteasome, prion diseases, extracellular matrix-receptor interaction, and cell adhesion molecules (Fig. 2 D). The most significantly enriched proteins were connected to complement and coagulation cascades, focal adhesion, and phagosome pathways. In the GO analysis (Fig. 2 C), molecular function was primarily enriched for extracellular matrix structural constituent, serine-type endopeptidase inhibitor activity, and heparin binding. For cellular component, the top three enriched phrases were collagen-containing extracellular matrix, high-density lipoprotein particle, and collagen trimers (Fig. 2 B). Biological process enrichment was especially noticeable in terms of acute-phase response, negative regulation of hydrolase activity, and regulation of extracellular matrix organization (Fig. 2 A). Supervised correlation analysis of proteomic signatures Proteomic analysis of 161 proteins involved in dynamic alterations related with sepsis plasma. Partial least squares discriminant analysis (PLS-DA) demonstrated substantial variations in gene expression at several time periods following CLP therapy, with strong intra-group homogeneity (Fig. 3 A). Subsequently, we performed variable importance in projection (VIP) ranking on the 161 differentially expressed plasma proteins, with the top 20 proteins depicted in Fig. 3 B. Additionally, we selected the top 25 proteins associated with the three most significantly enriched KEGG pathways for correlation analysis (Fig. 3 C), showing the Pearson correlation coefficients as a heatmap. Our data suggested that most proteins had substantial relationships, with deeper hues indicating greater linkages (red indicating positive correlation and blue indicating negative correlation). Concurrently, we examined the predictive capacity of the model for these 25 proteins; the Q2 value for individual proteins exceeded 0.5 (Fig. 3 D), whereas evaluations comprising two or more proteins neared 1. Collectively, our results implied that these 25 proteins demonstrated significant predictive capabilities, with the inclusion of numerous proteins increasing the model's predictive ability. Candidate plasma biomarkers The characterisation of the plasma proteome demonstrating significant differential expression during sepsis (Fig. 2 E) principally comprises three major types of proteins: (1) Proteases involved in the complement and coagulation cascades, including serpina1e, A2m, ATⅢ, C1ra, CFD, C8a, C8b, F13b, Klkb1, Masp1, and Mbl2; (2) Extracellular proteins participating in focal adhesion processes, such as Actn1, Col1α1, Col1α2, EGFR, Flnc, Tga8, Lamc1, Thbs1, and Flt4; (3) Proteins associated with phagosome activity, including Actn1, Col1α1, Col1α2, C8a, C8b, and Lamc1. Based on the computed VIP scores, we identified five possible DEPs (ATⅢ, CFD, Col1α1, EGFR, and Thbs1) that demonstrated high correlation coefficients among the majority of proteins in the top three pathways. We calculated the abundance of all proteins in each sample (log2 transformed) and found that the candidate plasma biomarkers demonstrated varying degrees of decrease following the onset of sepsis, with trends in both the validation cohort and training cohort showing remarkable similarity (Fig. 4 and Fig. 5 ). Characteristics of the study population This study enrolled a total of 66 pediatric subjects, with demographic and clinical characteristics of the patients described in table1. The average age of the participants was 42.4 months, with males forming 63.6%, and the mean APACHEⅡ score was 12.68. Laboratory testing revealed that the white blood cell (WBC) count was (11.12 ± 0.94) × 10⁹/L on D1, decreasing by D3 (P value of 0.0107). The percentage of neutrophils was highest on D1, subsequently dropping (P values of 0.0005 and 0.0445, respectively). Platelets revealed abnormal biphasic alterations, initially dropping at the onset of sepsis, followed by a recovery in platelet count and thrombocytosis. C-reactive protein (CRP) and procalcitonin (PCT) gradually normalized over the course of the disease, with no significant statistical differences found between time periods. Immune function indices, including lymphocyte subsets and immunoglobulins (A, G, M), were lower than age-specific reference values. ELISA examination outcomes In the ELISA testing, plasma samples from 66 septic pediatric patients were evaluated for the expression levels of five potential biomarkers. The results indicated (Table 2, Fig. 5 ) that all five putative sepsis plasma biomarkers displayed various degrees of decline upon detection of sepsis, thereafter demonstrating a steady recovery trend over time. Correlation analysis of candidate biomarkers A correlation analysis (using Pearson correlation coefficients) was undertaken between the five screened putative biomarkers and common laboratory indices. The five putative sepsis plasma biomarkers (ATⅢ, CFD, Col1α1, EGFR, Thbs1) revealed certain relationships with clinical laboratory indices related to hematology, biochemistry, blood gas analysis, coagulation function, and immune function (Fig. 6 ). Enrichment pathways demonstrated considerable enrichment of proteins linked with coagulation and complement. Therefore, we focused our analysis on associations between coagulation function and immunological function. The results showed that ATⅢ was negatively linked with IgA, IgG, IgM, and C3, with Pearson indices R of -0.543, -0.217, -0.526, and − 0.128, respectively. CFD was positively connected with IgA, IgG, and IgM, with correlation indices of 0.384, 0.270, and 0.304, respectively, and negatively correlated with C3 (-0.267). Col1α1, CFD, EGFR, and Thbs1 were negatively connected with inhibitory CD8 + cells (-0.177, -0.058, -0.284, and − 0.209, respectively), while Col1α1, EGFR, and Thbs1 were favorably correlated with B cells (CD19+) (0.312, 0.063, and 0.273, respectively). Col1α1, CFD, EGFR, and Thbs1 were favorably linked with the CD4+/CD8 + ratio (0.175, 0.135, 0.384, and 0.150, respectively). ATⅢ was favorably connected with PT, APTT, INR, D-Dimer, and Fbg, while Col1α1 and EGFR were negatively correlated with PT, APTT, INR, D-Dimer, and Fbg. CFD was positively connected with Fbg (0.137), and Thbs1 was positively correlated with D-Dimer (0.138). Discussion Childhood is a specific life stage where biological pathways are developmentally linked, necessitating unique considerations and specialized treatments under illness situations. As spatial proteomics and high-throughput proteomics technology have evolved, proteomics can now precisely quantify hundreds of proteins and their abundances to unravel probable molecular pathways behind pediatric sepsis and to identify tailored biomarkers and therapy methods. Our MS results with prior studies 10 , demonstrating that the plasma proteome involved with coagulation and complement undergoes large abundance changes during sepsis. Indeed, the complement system, as a quick and powerful immune surveillance mechanism, exerts enormous influence on both healthy and changed host cells, as well as invading pathogens. Upon recognition of pathogen-associated molecular patterns (PAMPs) during microbial invasion, the host triggers one or more complement activation pathways aimed at eradicating the microbial intruders. By clearing cellular debris and infectious microbes, coordinating immune responses, and transmitting "danger" signals, the complement system makes significant contributions to homeostasis. Its interactions with overall immunity and other endogenous pathways contribute to its dual role in maintaining homeostasis and disease 13 . Furthermore, during infection, the interplay between the complement and coagulation systems increases local coagulation and limits the spread of microorganisms via the circulation 13 . Antithrombin III (AT III) is the primary inhibitor of the coagulation cascade, synthesized by the liver as a glycoprotein that irreversibly inhibits serine proteases (Xa and Ⅱa) in a 1:1 ratio, forming a protease-AT III complex that is needed for anticoagulation 14 . Increasing data shows that AT III possesses anti-inflammatory 15 – 17 and renoprotective effects 18 – 21 . Recent research revealed that AT III could inhibit the serine protease TMPRSS2, thereby decreasing viral load in COVID-19 patients and early SARS-CoV-2 infection 22 . Beyond its anticoagulant effects, AT III affects the immune system and may alter endothelial cell activity 23 . There is emerging evidence that AT III plays a significant role in both anticoagulation and anti-inflammatory effects 16 . AT III is a marker of DIC in sepsis patients and is significant for prognosis 24 , 25 , with levels below 50–60% often tied to worse outcomes, and levels below 20% being associated with fatal results 26 . Overall, our data supported the hypothesis that AT III may serve more as an inflammatory response regulator than a coagulation inhibitor. Inthorn et al. 27 observed that AT III supplementation greatly improved respiratory, hepatic, and renal failure while also reducing certain inflammatory markers. As a result, it has been hypothesized that AT III supplementation could be applied to regulate coagulopathy and better the clinical results of sepsis-induced inflammatory syndromes 28 . Our findings demonstrated that AT III in plasma dropped and then progressively returned in the detection of pediatric sepsis, while the association between AT III expression levels in plasma and sepsis prognosis need additional validation through investigation. The complement system is a proteolytic cascade that can be activated by the classical, lectin, and alternative pathways. Complement factor D (CFD) amplifies all complement-mediated bactericidal actions and plays a vital part in the membrane assault complex directed against bacteria, mediated by mannose-binding lectin 29 – 31 . Evidence suggested that the complement system also served protective roles in the lungs, where its loss led to greater activation of NF-κB and exacerbated pulmonary inflammation 32 . Research studies revealed that lower expression of CFD may facilitate the action of IL-17A in promoting obesity-related airway hyperreactivity 33 . Furthermore, CFD in the gut has a critical function in the clearance of Escherichia coli, hence maintaining intestinal homeostasis 34 . Our data revealed that in the context of sepsis, CFD levels decline to a minimum on D3 and rebound by D7. Recent studies have also demonstrated that CFD is negatively connected with the pro-inflammatory cytokine IL-6 and positively correlated with the anti-inflammatory cytokine IL-4 35 , suggesting that CFD may function as an anti-inflammatory factor. However, the particular mechanisms and signaling pathways through which CFD exerts its anti-inflammatory benefits require additional experimental clarification. Col1α1, the primary constituent of type I collagen, is broadly distributed in parenchymal organs and interstitial connective tissues across the body 36 . Col1α1 is crucial for controlling intercellular adhesion and differentiation, as well as fortifying diverse tissues in the body 37 . Col1α1 controls cell proliferation, metastasis, invasion, and angiogenesis 38 , 39 . It also plays a role in epithelial-mesenchymal transition, which is intricately associated to the genesis and spread of cancer 40 . Further research is needed to explore the link between Col1α1 gene variation and sepsis. Sepsis, a complicated disease characterized by a range of pathogens (mainly bacteria) and a dysregulated host response to infection, commonly leads to respiratory muscle injury in persons with severe sepsis. In mice, sepsis produces diaphragm dysfunction via activating TLR, NF-κB, and TNF signaling pathways, as well as blocking oxidative phosphorylation, cardiac contraction, and citric acid cycle pathways. Recent research on the molecular mechanisms of sepsis-induced diaphragm dysfunction demonstrated that the Col1α1 gene, connected to cellular adhesion, is downregulated 24 hours after lipopolysaccharide (LPS) injection 41 . Col1α1 mutations in animal models of osteogenesis imperfecta led to reduced diaphragm mass and contractility 42 . Thus, sepsis-induced lung injury can impede diaphragm and skeletal muscle contraction, resulting in decreased circulatory blood flow and respiratory failure. Our study demonstrated reduced plasma Col1α1 expression levels after sepsis, indicating a probable relationship with pediatric sepsis-induced lung injury. Further research is needed to validate this. The ErbB family contains the archetypal members, epidermal growth factor receptor (EGFR)/ErbB1, ErbB2, ErbB3, and ErbB4, which are cell surface growth factor receptors found in numerous developing mammalian organs. Previous experiments have showed that overexpressing EGFR and ErbB4 protects mice from acute pancreatitis 43 . In vitro and in vivo investigations revealed that EGFR is intricately connected to the maintenance and repair of normal epithelial cells 44 . ErbB1 inactivation induces hemorrhagic enteritis, which is analogous to necrotizing enterocolitis (NEC) 45 . Furthermore, previous preclinical and clinical studies have demonstrated that NRG1-ErbBs signaling has cytoprotective, anti-inflammatory, and anti-remodeling effects 46 – 48 , and decreased mRNA expression levels of ErbB2 and ErbB4 receptors may result from irreversible cardiomyocyte loss within the infarcted area. ErbB1 is notably concentrated in the nuclei of highly proliferative cells 49 , showing that nuclear ErbB receptors engage in stimulating gene expression, with cell surface ErbB1 receptor activation promoting the migration of the full-length transmembrane ErbB1 receptor to the nucleus. ErbB activation in cultured intestinal cells promotes cell fates predicted to be protective during inflammation. In colonic epithelial cells, EGFR activation promotes proliferation 50 , lowers cytokine-induced apoptosis 51 , and speeds migration/wound repair 52 , 53 , both in vitro and in vivo. Increasing data suggested that ErbB signaling deficits occur under a range of intestinal inflammatory settings and may be causally connected 54 – 56 . As a result, replacing or reactivating ligands and receptors may directly promote mucosal healing. Our data demonstrated that EGFR is expressed at low levels in plasma during the early stages of sepsis, however its likely involvement in these illnesses is unknown, and it may be implicated in the endothelial response to infection/inflammation. Thrombospondin1(Thbs1) modulates TGF-β activation, impacting wound healing, proliferation, differentiation, and cytokine responses 57 . Thbs1 has been found as a component of a risk categorization model for pediatric septic shock that combines multiple biomarkers 58 . A single-center cohort study suggested 59 that Thbs1 is a possible prognostic marker for poor outcomes in neonatal patients with pneumonia sepsis, demonstrating that developmental age alters Thbs1's biological function during sepsis. In human cells, Thbs1 suppresses IL-1β and caspase-1 mRNA, not LPS-induced NLRP3 60 . Previous research has demonstrated that Thbs1 has context-specific pro- and anti-inflammatory actions 61 – 63 , with Thbs1 produced in response to inflammation, supporting the resolution of inflammatory processes and facilitating phagocytosis of wounded cells 64 , 65 . During the early phases of injury and inflammation, high levels of Thbs1 promote dendritic cell tolerogenicity to antigens, therefore halting the inflammatory response. Thbs1 regulates the creation and activation of pro-inflammatory cytokine IL-1β in human and murine macrophages. Thbs1 suppresses IL-1β mRNA induction in an NF-κB/AP-1-dependent mechanism 60 . Thbs1 levels suppress the production of LPS-induced IL-1β mRNA and protein in human macrophages. Our findings revealed that Thbs1 is expressed at low levels in the plasma of children with sepsis, making it a viable biomarker for sepsis in this population. Thbs1 interacts with proteins on cell membranes and in the extracellular matrix, increasing platelet aggregation, angiogenesis, wound healing, and immunological responses 66 . As a result, the biological function of Thbs1 during sepsis deserves deeper research. Coagulation activation during infection may be helpful 67 , 68 , as it increases platelet activation through the production of thrombin, enhancing the creation of microthrombi in inflammatory and infectious responses. This condition, known as immunothrombosis, tries to entrap invading microorganisms and restrict their dissemination. The increase of platelet activation by thrombin generation further boosts microthrombi formation, which is crucial for trapping invading pathogens and restricting their dissemination 69 , 70 . Research results revealed 71 that thrombocytopenia at the time of admission was associated with higher mortality and deregulation of host responses during sepsis. Notably, the kinetics of platelets during sepsis generally exhibit a biphasic pattern, defined by an initial fall within the first few days (1–4 days), followed by a subsequent increase in platelet counts 72 . Our findings revealed that platelet counts began to rise after day three. Studies have indicated that the absence of this biphasic response may lead to prolonged thrombocytopenia, which corresponds with poor prognosis and higher 28-day mortality 72 , 73 . Lymphocytopenia induced by sepsis is a transient event, with lymphocyte numbers eventually returning to pre-sepsis levels. A growing body of evidence supported the notion that the immune status of patients is related to the ultimate outcome of sepsis. CD4 + T lymphocytes can activate B cells and effector T cells through the release of various factors, thereby upregulating immune function, while CD8 + T lymphocytes possess surveillance and cytotoxic functions. Studies have found 74 , 75 that a decreased CD4+/CD8 + ratio is closely associated with immunosuppression and poor prognosis. The number and function of natural killer (NK) cells also appear to play a significant role in this disease 76 . B cells (CD19+) are crucial for both adaptive and innate immune responses 77 , 78 . In response to infectious agents, B cells not only secrete IgM and IgG but also phagocytize, process, and present antigens to T cells to generate humoral immunity 78 , 79 . Previous studies have reported 80 an increase in B cell numbers in whole blood of sepsis patients, whereas most other studies show a decrease in B cell numbers in severe sepsis patients 81 – 84 . The variation in reported B cell counts across different studies may be due to the timing of blood collection after patient admission. It has been found 80 that IgM plays a critical role in preventing secondary infections following sepsis, with elderly patients exhibiting increased colonization of Gram-negative bacteria and candida albicans when IgM levels are reduced. Additionally, septic shock patients often present with hypogammaglobulinemia 85 , and levels of IgG, IgM, and IgA at the time of diagnosis are directly associated with survival rates 86 . Our findings indicated that, at the time of sepsis identification, lymphocyte subsets and gamma globulin levels were lower than normal reference values, which may reflect impaired immune function during sepsis. Therefore, the value of lymphocyte subsets and serum gamma globulin in the diagnosis and prognosis assessment of sepsis may still require substantial further research to confirm. Limitations Our study also highlighted a few limitations. Firstly, the validation cohort of this work utilized plasma from experimental animals with a disease model to screen for potential markers. Although these experimental animals share a comparable genetic background with people, and characteristics such as body weight and age can be easily controlled to strengthen the comparability of the trials, they do not fully reflect the heterogeneity of the human situation. Secondly, when acquiring clinical laboratory parameters, some data (e.g., immune cells, cytokines, etc.) lacked information from the intermediate and late stages of sepsis (D3 and D7), which hindered a thorough display of their dynamic changes during sepsis. Therefore, future research utilizing plasma from pediatric sepsis patients for MS analysis could better understand the dynamic changes in plasma proteins and the underlying pathophysiological mechanisms during pediatric sepsis. Conclusion In this proteomic examination of plasma in septic young mice, we detected 161 significantly increased proteins at distinct time intervals throughout the first week of sepsis (D1, D3, D7). According to KEGG pathway enrichment analysis, the complement and coagulation cascades were most closely correlated to the differentially expressed proteins, followed by focal adhesion and phagocyte-related proteins. All five possible plasma indicators declined to varied degrees at the time of sepsis diagnosis and were linked with coagulation and immunological activity. As a result, more validation studies are needed to establish the causal link between sepsis and increased or decreased expression of any newly found proteins. Declarations Ethics approval and consent to participate The Ethics Committee of Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University) approved this study (2022-139). Consent to Participate declaration: not applicable. Clinical trial number Not applicable Consent for publication Not applicable. Availability of data and materials The datasets of the current study are available from the corresponding author upon reasonable request. Competing interests No authors have any potential conflicts of interest. Funding The Natural Science Foundation of Hunan Province (2023JJ60101); Financial Plan for the High-Level Health Personnel Program of Hunan Province (20230508-1033). A uthor Contributions ZSZ and JY conceived and designed the project, FSY acquired the data and wrote the paper, FSY and LYJ acquired and analyzed the data, LXL and ZZC collected the study samples and acquired the data, ZSZ, JY and LYJ have drafted the manuscript and thoroughly edited it for important intellectual content. Acknowledgments Not applicable. References Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA . Feb 23 2016;315(8):801 – 10. doi: 10.1001/jama.2016.0287 Goldstein B, Giroir B, Randolph A, International Consensus Conference on Pediatric S. International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics. Pediatr Crit Care Med . Jan 2005;6(1):2–8. doi: 10.1097/01.PCC.0000149131.72248.E6 Schlapbach LJ, Watson RS, Sorce LR, et al. International Consensus Criteria for Pediatric Sepsis and Septic Shock. 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Characteristics Total patients（N=66） (mean ± SD) p value Male，n(%) 42（63.6） - Age, month 42.4 - APACHEII 12.68 - Laboratory findings WBC（109/L） D1 11.12±0.94 - D3 8.16±0.52 0.0107 D7 8.62±0.52 0.9128 N（%） D1 63.31±2.68 - D3 48.72±2.79 0.0005 D7 38.57±2.97 0.0445 PLT（109/L） D1 293.42±17.63 - D3 330.56±19.47 0.4338 D7 434.23±32.61 0.0063 CRP(mg/L) D1 31.66±5.63 - D3 19.97±6.44 0.0247 D7 2.29±0.5 0.0741 PCT(ng/ml) D1 3.59±1.41 - D3 1.38±0.38 0.5791 D7 0.22±0.08 0.9078 PT（s） 11.04±0.13 - APTT（s） 30.34±0.96 - INR 0.97±0.01 - D-Dimer（mg/L) 1.49±0.33 - Fbg(g/L) 4.03±0.38 - IgA(g/L) 0.78±0.09 - IgG(g/L) 8.16±0.41 - IgM(g/L) 0.97±0.06 - C3(g/L) 1.08±0.04 - C4(g/L) 0.44±0.13 - CD3+% 55.18±2.03 - CD4+% 30.48±1.58 - CD8+% 20.3±1.15 - CD19+% 30.3±2.14 - CD16+CD56+% 12.16±1.7 - CD3+CD4+CD8+% 0.75±0.12 - CD4+/CD8+T% 1.7±0.14 - CD3+CD4-CD8-% 8.65±0.79 - IL-2(pg/ml) 15.58±14.84 - IL-4(pg/ml) 2.24±0.42 - IL-6(pg/ml) 307.53±182.19 - IL-10(pg/ml) 39.57±33.18 - TNF-α(pg/ml) 30.1±5.74 - INF-γ(pg/ml) 90.24±39.98 - Note Fbg—Fibrinogen, INR—International Normalized Ratio, PT—Prothrombin Time, APTT—Activated Partial Thromboplastin Time, IgA—Immunoglobulin A, IgG—Immunoglobulin G, IgM—Immunoglobulin M, C3—Complement 3, C4—Complement 4.WBC—White Blood Cell, N—Neutrophil, PLT—Platelet.The P values relate to the statistical analysis results for D 3 vs D 1 and D 7 vs D 3 Table.2 Candidate biomarkers (mean ± SD) Protein Time(day) Concentration p value D1 570.48±25.61 - ATⅢ(μg/ml) D3 551.69±29.38 0.8945 D7 665.65±32.41 0.0198 D1 313.01±29.07 - Col1α1(ng/ml) D3 351.94±35.71 0.6948 D7 599.17±37.07 ＜0.0001 D1 1360.14±134.25 - CFD (ng/ml) D3 913.19±59.74 0.0019 D7 981.61±64.62 0.8376 D1 81.54±4.54 - EGFR（ng/ml) D3 87.56±5.29 0.6861 D7 111.43±5.04 0.0036 D1 32.87±3.26 - Thbs1(μg/ml) D3 24.31±2.15 0.1296 D7 34.76±3.52 0.0441 Note The P values relate to the statistical analysis results for Day 3 vs Day 1 and Day 7 vs Day 3. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6146492","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423861424,"identity":"55c92c49-d9dc-450d-bca4-10d007bff2c4","order_by":0,"name":"Shiyuan Fan","email":"","orcid":"","institution":"Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Shiyuan","middleName":"","lastName":"Fan","suffix":""},{"id":423861425,"identity":"cf27c5c8-94c2-494d-b77c-aa2b2ba7ff2d","order_by":1,"name":"Xinglv Liu","email":"","orcid":"","institution":"Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Xinglv","middleName":"","lastName":"Liu","suffix":""},{"id":423861426,"identity":"db8a2ef9-0634-4715-b283-9fb2c7805c02","order_by":2,"name":"Zichi Zhao","email":"","orcid":"","institution":"Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Zichi","middleName":"","lastName":"Zhao","suffix":""},{"id":423861427,"identity":"aecfd1d9-a5cf-44d3-a837-8af28a5f0dbb","order_by":3,"name":"Yanjuan Liu","email":"","orcid":"","institution":"Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Yanjuan","middleName":"","lastName":"Liu","suffix":""},{"id":423861428,"identity":"2e556220-3668-442b-a87f-8a5e6d605675","order_by":4,"name":"Yu Jiang","email":"","orcid":"","institution":"Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jiang","suffix":""},{"id":423861429,"identity":"202b78be-788c-4b07-9cfe-e0f67046cfc7","order_by":5,"name":"Saizhen Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBAC+/kHEox//LDh4Sdai4EEw4Nixp40GckG4rUwPvjMwHbYxuAAsVrMpZsTNxfwMPMYH0/ewPCjYhthLZZzjiUbz7Bg4zE786yAsefMbSKsOZCTZsADBGY3cgyYGduI0pL//QcPmwSP8QxitRjcSEgw5mEz4DGQIFaLZM+BBMOZPQk8EkC/HCTKL/zsDQkGH378t+dvT9744EcFMX5BgATiowahhVQdo2AUjIJRMEIAAMVZPXz1V57IAAAAAElFTkSuQmCC","orcid":"","institution":"Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University)","correspondingAuthor":true,"prefix":"","firstName":"Saizhen","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2025-03-03 13:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6146492/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6146492/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-02933-5","type":"published","date":"2025-07-23T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78243447,"identity":"8feb22bf-048c-4dc2-9a12-fa89b6452e19","added_by":"auto","created_at":"2025-03-11 09:11:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123716,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEPs in Sepsis Plasma. A-C, volcano plot of pairwise comparisons of the plasma proteomes of D1, D3, and D7 after CLP against control. The analyses were performed using the student’s t test (permutation FDR 0.05). Up- and down-regulated proteins were highlighted in red and blue, respectively. The plots indicated the gene names of the represented proteins. Light gray data points represent proteins with non-significant P-values (P \u0026gt; 0.05) and/or insignificant fold changes (-0.5 \u0026lt; FC \u0026lt; 0.5). D, DEPs in septic plasma that alter with time by Venn plot. E, PPI network illustrating the interactions among proteins that exhibit dynamic changes during sepsis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/aa41ed98c230613ba8ac49e3.png"},{"id":78241149,"identity":"def18017-907f-45e3-a426-b646799de8e5","added_by":"auto","created_at":"2025-03-11 09:03:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70797,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment function of elevated DEPs via GO and KEGG. A-C, Enrichment of GO. D-E, Enrichment of KEGG.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/af3c4e6264184ba65ff5ebe0.png"},{"id":78243445,"identity":"88174d2e-0b49-442f-93dd-7b4300dbd6a8","added_by":"auto","created_at":"2025-03-11 09:11:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20854,"visible":true,"origin":"","legend":"\u003cp\u003eSupervised correlation analysis of proteomic signatures. A, Partial least squares discriminant analysis (PLS-DA) multidimensional scaling plot of core proteins at different time points. B, Variable Importance in the Projection (VIP) scores. C, Correlation analysis, represented as a Pearson correlation coefficient heatmap. D, Assessment of the predictive capability of the variable model.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/c7167ea638571c041212958e.png"},{"id":78243446,"identity":"1998c603-ac4d-4530-aff6-c1a75cb6e195","added_by":"auto","created_at":"2025-03-11 09:11:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27023,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in potential biomarkers at several time points within one week in sepsis mice. Violin plot showing the differences in co-differential proteins(ATⅢ, CFD, Col1α1, EGFR, and Thbs1) within one week in sepsis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/b49dc32066dff88fabdb7024.png"},{"id":78244496,"identity":"72a929a2-c0d2-4690-87c6-d11ecfe69878","added_by":"auto","created_at":"2025-03-11 09:19:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33924,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in potential biomarkers(ATⅢ, CFD, Col1α1, EGFR, and Thbs1) at several time points within one week in sepsis pediatric.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/245e22dc400c7b470eef4d40.png"},{"id":78241164,"identity":"ea4d9be8-cb98-45b7-ba12-7fbde8757d11","added_by":"auto","created_at":"2025-03-11 09:03:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":202965,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients between potential biomarkers and clinical laboratory indicators. A-B, correlation coefficients of D1. C, correlation coefficients of D3. D, correlation coefficients of D7.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/137c582e54e497eb1076efea.png"},{"id":87756858,"identity":"301c4fac-c7fa-4330-8ba1-edae4dc61e3a","added_by":"auto","created_at":"2025-07-28 16:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1549850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6146492/v1/c01544d1-3d51-49c8-a1c6-6e91731f0459.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic alterations and clinical implications of the plasma proteome in pediatric sepsis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, defined as life-threatening organ failure induced by an aberrant host response to infection, remains a substantial clinical challenge\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The criteria for pediatric sepsis proposed by the International Pediatric Sepsis Consensus Conference in 2005 have been widely used in clinical practices, research, quality improvement programs, and policy frameworks\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In January 2024, the Pediatric Sepsis Definition Task Force of the Society of Critical Care Medicine developed the most recent international consensus guidelines for pediatric sepsis and septic shock\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Sepsis remains a common cause of pediatric mortality and pediatric intensive care unit (PICU) admissions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Sepsis is expected to have impacted 48.9\u0026nbsp;million individuals worldwide in 2017, with 20.3\u0026nbsp;million cases comprising children under the age of five\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a result, sensitive early clinical indicators are critical for aiding early screening, identification, and intervention in high-risk people. Early and exact identification of at-risk populations is crucial for maximizing treatments among those who will benefit most from prompt therapeutic measures\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the early stages of hospitalization, distinguishing between sepsis and sterile inflammation could be challenging. Furthermore, the microbiological identification of pathogens does not necessarily mean that they are the cause of sepsis, because microbiological laboratories cannot distinguish between colonizing organisms and infectious pathogens\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Given the complexity of infection and host-pathogen interactions, a single biomarker is unlikely to yield sufficient precision for infection diagnosis\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, host-pathogen interactions during the microbial cell lifecycle, such as cellular entrance, pathogen reproduction and dissemination, and host defense mechanisms, entail a number of metabolic modifications that can be detected using key proteins and metabolic products\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Advanced mass spectrometry (MS) techniques enable the collecting of molecular insights into diseases from both the host and pathogen perspectives. Proteomics may simultaneously evaluate many proteins, resulting in diagnostic and prognostic protein signatures. The future of proteomics hinges on its application in precision medicine.\u003c/p\u003e \u003cp\u003ePlasma proteomics research in pediatric sepsis is still in its early phases, with no studies showing the dynamic changes in plasma proteins. Plasma proteome profiling in sepsis has demonstrated that the changes in protein abundance found in a mouse sepsis model largely correspond with the trends documented in human sepsis literature\u003csup\u003e10\u003c/sup\u003e. Consequently, the strong proteomic signals derived from the murine sepsis model are expected to enhance and advance future human-targeted proteome research. In order to screen and uncover possible plasma biomarkers for pediatric sepsis, we performed proteome investigations of plasma protein expression profiles in young sepsis murine models using liquid chromatography-mass spectrometry (LC/MS)-based techniques. Concurrently, we collected data on pediatric sepsis patients initially identified and treated in the PICU of Hunan Provincial People\u0026apos;s Hospital (The First Affiliated Hospital of Hunan Normal University). Fundamental admission details and laboratory findings were recorded, and dynamic plasma samples (D1, D3, and D7) were collected. Consequently, ELISA was employed to investigate potential plasma biomarkers of sepsis, aiming to identify distinctive plasma biomarkers for pediatric sepsis and assess their clinical significance within this setting.\u003c/p\u003e\n"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign of experiments\u003c/h2\u003e \u003cp\u003eTwo distinct experiments were conducted. Experiment 1 established a gold-standard animal model of pediatric sepsis via the cecal ligation and puncture (CLP). Proteomic techniques were employed to identify potential plasma biomarkers for sepsis. In Experiment 2, the potential biomarkers were validated in plasma samples from pediatric patients with sepsis via ELISA. Proteomic analysis, ELISA validation, and clinical evaluation were conducted in a blinded way.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnimals experiment\u003c/h3\u003e\n\u003cp\u003eMale C57BL/6J mice were procured from Hunan Saike Jingda Experimental Animal Co., Ltd., aged 3\u0026ndash;4 weeks, with a body weight of 11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 g. The animals were randomly apportioned into a sham operation group (D0) and a sepsis group (CLP), further subdivided into Day 1 (D1), Day 3 (D3), and Day 7 (D7) groups. Before the experiment, all animals were acclimatized for a week in SPF animal facilities under controlled conditions of 25\u0026ndash;26\u0026deg;C and a 12:12 h light-dark cycle. Water and food were provided ad libitum. Mice in the CLP subgroups were euthanized on D1, D3, and D7 post-CLP, whereas the sham group was euthanized 24 hours after the sham procedure.\u003c/p\u003e \u003cp\u003eCLP has emerged as the predominant model for experimental sepsis and is considered the gold standard in sepsis research\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Mice were anesthetized using 2\u0026ndash;3% isoflurane (Ringpu Bio, China). The cecum was exposed utilizing a midline surgical incision and ligated by a 4.0 silk suture. Then, a 21G needle penetrated the cecum and the abdomen was closed in layers with 4.0 sutures. The cecum was mobilized without the employment of CLP for the sham-operated animals. Resuscitated animals by subcutaneous injection of a prewarmed normal saline (37\u0026deg;C, 50mL/kg) at the end of the surgical procedures, then mice were returned to cages immediately where accessed to water and food in freedom, with a temperature-controlled environment (22\u0026deg;C) for 12h light and dark cycles. The plasma was obtained post-anesthesia, and blood was extracted from the enucleated eye with an anticoagulant-treated tube to collect roughly 1 ml of blood (n\u0026thinsp;=\u0026thinsp;11 per group). Subsequently, blood was centrifuged at 4\u0026deg;C for 15 minutes at 3000 rpm and stored at -80\u0026deg;C until required.\u003c/p\u003e\n\u003ch3\u003eProteomic analysis\u003c/h3\u003e\n\u003cp\u003eAfter equilibrating the samples to room temperature, 10 \u0026micro;l of plasma from each sample was taken and subjected to high-abundance protein depletion using a protein centrifuge column pool. The plasma samples were then diluted with 8 M urea for disulfide reduction and alkylation. Proteins were digested with trypsin (ThermoScientific, US) at 37\u0026deg;C overnight, followed by peptide purification and desalting. The desalted peptides were dried under vacuum and reconstituted in mass spectrometry buffer. The samples were subjected to chromatographic separation and analyzed by an Orbitrap Exploris 240 (ThermoScientific, US) mass spectrometer (MS). The analysis was conducted over 150 minutes in positive ion mode, with a precursor ion scan range of 350\u0026ndash;1200 m/z and a MS1 resolution of 60,000. The AGC target was set to Standard. Peptide and fragment m/z ratios were acquired using the following parameters: Data-independent acquisition (DIA) was set to Top N, with N set to 30. MS2 activation was performed using HCD, with an isolation window of 1.6 m/z. The MS2 resolution was set to 15,000, Microscans to 1, and the ion dynamic exclusion time was set to Auto, with a normalized collision energy of 30%.\u003c/p\u003e \u003cp\u003eThis study employed a label-free quantitative proteomics approach using DIA-MS1 data integration, a methodology that does not rely on labeled reagents. The DIA-NN software (version 1.8.1) was used to intelligently identify peptide characteristics from the raw data. Following data collection, by setting the grouping, database, and post-translational modification types, DIA-NN computed peak integration intensities to provide label-free quantification data. Additionally, the software filtered the data based on the false discovery rate (FDR) principle, automatically matched the data to the database, and thus yielded highly reliable qualitative results. Finally, the UniProt database was employed to consolidate all quantitative and qualitative results, completing the comprehensive proteomic data analysis.\u003c/p\u003e\n\u003ch3\u003eBioinformatics analysis\u003c/h3\u003e\n\u003cp\u003eIn the MS assay, each sample underwent three replicates of global protein quantification, yielding three quantitative values. The final quantitative value for each sample was determined as the mean of these three replicates. The ratio of the final quantitative values between different samples was then calculated as the differential expression level Fold Change (FC) for the comparison groups.Analysis and volcano plot generation were carried out using relevant R packages in R version 4.3.1. The FC threshold of \u0026gt;\u0026thinsp;0.5 or \u0026lt;-0.5, combined with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, was set to determine the upregulated or downregulated protein expression changes in the plasma.\u003c/p\u003e \u003cp\u003eEnrichment analysis for GO annotations and KEGG pathways was conducted using the pathview and clusterProfiler R packages provided by the microbioinformatics platform for pathway-based data integration and visualization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Protein-protein interaction (PPI) data for differentially expressed genes was obtained from the online STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The MetaboAnalyst 6.0 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/MetaboAnalyst/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/MetaboAnalyst/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for biomarker feature analysis and pattern prediction.\u003c/p\u003e\n\u003ch3\u003ePatient recruitment\u003c/h3\u003e\n\u003cp\u003eThis study recruited pediatric patients diagnosed with sepsis in the PICU of Hunan Provincial People\u0026rsquo;s Hospital (the First Affiliated Hospital of Hunan Normal University) between January 1, 2023, and December 31, 2023. The selection was based on the definitions of sepsis and related pediatric organ dysfunction standards established at the 2005 International Pediatric Sepsis Consensus Conference. The inclusion criteria consisted of children aged between 28 days and 18 years who were diagnosed with sepsis. The exclusion criteria included: 1) children with genetic metabolic disorders or chromosomal diseases; 2) children with primary immunodeficiencies; 3) children who had been on long-term corticosteroids or immunosuppressants; 4) children with malignant tumors; 5) children who were transferred to other medical institutions for various reasons during hospitalization; and 6) children with incomplete clinical data.\u003c/p\u003e \u003cp\u003ePlasma samples were collected and preserved as follows: On D1, D3, and D7 of hospitalization, 2 mL of blood was drawn from each patient into EDTA purple cap tubes in the morning. The collected samples were allowed to sit for 30 minutes before being centrifuged (3000rpm ,10min) within 2 hours. The supernatant was then stored at -80\u0026deg;C for further analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eELISA validation\u003c/h2\u003e \u003cp\u003eThe plasma samples, after being retrieved from the \u0026minus;\u0026thinsp;80℃ freezer, is thawed in a 4℃ refrigerator. The blood and intratumour levels of ANTⅢ, CFD ,Col1α1, EGFR and Thbs1 were detected with ELISA kits (MultiSciences) following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and data visualization were conducted using software GraphPad Prism 9.0 and SPSS 25.0. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Differences between two groups were assessed using Student's t-test, while differences among multiple groups were analyzed using one-way ANOVA. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was considered highly statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed proteins (DEPs) in sepsis plasma\u003c/h2\u003e \u003cp\u003eAccording to our findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C), MS identified 6,578 plasma proteins in septic young mice. On D1 of sepsis, there were 505 DEPs in the plasma, including 343 proteins that were upregulated and 162 proteins that were downregulated. On D3, a total of 706 DEPs were identified, with 337 proteins upregulated and 369 proteins downregulated. On D7, 483 DEPs were identified, comprising 244 upregulated proteins and 239 downregulated proteins. Our results further revealed that among the 6,578 proteins identified, there were 161 proteins exhibiting dynamic changes in the plasma of septic mice over D1, D3, and D7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). To investigate the relationships among the discovered DEPs, PPI network was created using the STRING database, demonstrating the complicated interactions among many genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of DEPs\u003c/h2\u003e \u003cp\u003eIn this study, functional enrichment analysis was done to identify the discovered DEPs according to their functions using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The ten most significantly enriched pathways are: complement and coagulation cascades, focal adhesion, phagosome, amoebiasis, bladder cancer, COVID-19, proteoglycans in cancer, proteasome, prion diseases, extracellular matrix-receptor interaction, and cell adhesion molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The most significantly enriched proteins were connected to complement and coagulation cascades, focal adhesion, and phagosome pathways. In the GO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), molecular function was primarily enriched for extracellular matrix structural constituent, serine-type endopeptidase inhibitor activity, and heparin binding. For cellular component, the top three enriched phrases were collagen-containing extracellular matrix, high-density lipoprotein particle, and collagen trimers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Biological process enrichment was especially noticeable in terms of acute-phase response, negative regulation of hydrolase activity, and regulation of extracellular matrix organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSupervised correlation analysis of proteomic signatures\u003c/h2\u003e \u003cp\u003eProteomic analysis of 161 proteins involved in dynamic alterations related with sepsis plasma. Partial least squares discriminant analysis (PLS-DA) demonstrated substantial variations in gene expression at several time periods following CLP therapy, with strong intra-group homogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, we performed variable importance in projection (VIP) ranking on the 161 differentially expressed plasma proteins, with the top 20 proteins depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. Additionally, we selected the top 25 proteins associated with the three most significantly enriched KEGG pathways for correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), showing the Pearson correlation coefficients as a heatmap. Our data suggested that most proteins had substantial relationships, with deeper hues indicating greater linkages (red indicating positive correlation and blue indicating negative correlation). Concurrently, we examined the predictive capacity of the model for these 25 proteins; the Q2 value for individual proteins exceeded 0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), whereas evaluations comprising two or more proteins neared 1. Collectively, our results implied that these 25 proteins demonstrated significant predictive capabilities, with the inclusion of numerous proteins increasing the model's predictive ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCandidate plasma biomarkers\u003c/h2\u003e \u003cp\u003eThe characterisation of the plasma proteome demonstrating significant differential expression during sepsis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) principally comprises three major types of proteins: (1) Proteases involved in the complement and coagulation cascades, including serpina1e, A2m, ATⅢ, C1ra, CFD, C8a, C8b, F13b, Klkb1, Masp1, and Mbl2; (2) Extracellular proteins participating in focal adhesion processes, such as Actn1, Col1α1, Col1α2, EGFR, Flnc, Tga8, Lamc1, Thbs1, and Flt4; (3) Proteins associated with phagosome activity, including Actn1, Col1α1, Col1α2, C8a, C8b, and Lamc1. Based on the computed VIP scores, we identified five possible DEPs (ATⅢ, CFD, Col1α1, EGFR, and Thbs1) that demonstrated high correlation coefficients among the majority of proteins in the top three pathways. We calculated the abundance of all proteins in each sample (log2 transformed) and found that the candidate plasma biomarkers demonstrated varying degrees of decrease following the onset of sepsis, with trends in both the validation cohort and training cohort showing remarkable similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eThis study enrolled a total of 66 pediatric subjects, with demographic and clinical characteristics of the patients described in table1. The average age of the participants was 42.4 months, with males forming 63.6%, and the mean APACHEⅡ score was 12.68. Laboratory testing revealed that the white blood cell (WBC) count was (11.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94) \u0026times; 10⁹/L on D1, decreasing by D3 (P value of 0.0107). The percentage of neutrophils was highest on D1, subsequently dropping (P values of 0.0005 and 0.0445, respectively). Platelets revealed abnormal biphasic alterations, initially dropping at the onset of sepsis, followed by a recovery in platelet count and thrombocytosis. C-reactive protein (CRP) and procalcitonin (PCT) gradually normalized over the course of the disease, with no significant statistical differences found between time periods. Immune function indices, including lymphocyte subsets and immunoglobulins (A, G, M), were lower than age-specific reference values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eELISA examination outcomes\u003c/h2\u003e \u003cp\u003eIn the ELISA testing, plasma samples from 66 septic pediatric patients were evaluated for the expression levels of five potential biomarkers. The results indicated (Table\u0026nbsp;2, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) that all five putative sepsis plasma biomarkers displayed various degrees of decline upon detection of sepsis, thereafter demonstrating a steady recovery trend over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of candidate biomarkers\u003c/h2\u003e \u003cp\u003eA correlation analysis (using Pearson correlation coefficients) was undertaken between the five screened putative biomarkers and common laboratory indices. The five putative sepsis plasma biomarkers (ATⅢ, CFD, Col1α1, EGFR, Thbs1) revealed certain relationships with clinical laboratory indices related to hematology, biochemistry, blood gas analysis, coagulation function, and immune function (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Enrichment pathways demonstrated considerable enrichment of proteins linked with coagulation and complement. Therefore, we focused our analysis on associations between coagulation function and immunological function. The results showed that ATⅢ was negatively linked with IgA, IgG, IgM, and C3, with Pearson indices R of -0.543, -0.217, -0.526, and \u0026minus;\u0026thinsp;0.128, respectively. CFD was positively connected with IgA, IgG, and IgM, with correlation indices of 0.384, 0.270, and 0.304, respectively, and negatively correlated with C3 (-0.267). Col1α1, CFD, EGFR, and Thbs1 were negatively connected with inhibitory CD8\u0026thinsp;+\u0026thinsp;cells (-0.177, -0.058, -0.284, and \u0026minus;\u0026thinsp;0.209, respectively), while Col1α1, EGFR, and Thbs1 were favorably correlated with B cells (CD19+) (0.312, 0.063, and 0.273, respectively). Col1α1, CFD, EGFR, and Thbs1 were favorably linked with the CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio (0.175, 0.135, 0.384, and 0.150, respectively). ATⅢ was favorably connected with PT, APTT, INR, D-Dimer, and Fbg, while Col1α1 and EGFR were negatively correlated with PT, APTT, INR, D-Dimer, and Fbg. CFD was positively connected with Fbg (0.137), and Thbs1 was positively correlated with D-Dimer (0.138).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eChildhood is a specific life stage where biological pathways are developmentally linked, necessitating unique considerations and specialized treatments under illness situations. As spatial proteomics and high-throughput proteomics technology have evolved, proteomics can now precisely quantify hundreds of proteins and their abundances to unravel probable molecular pathways behind pediatric sepsis and to identify tailored biomarkers and therapy methods. Our MS results with prior studies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, demonstrating that the plasma proteome involved with coagulation and complement undergoes large abundance changes during sepsis. Indeed, the complement system, as a quick and powerful immune surveillance mechanism, exerts enormous influence on both healthy and changed host cells, as well as invading pathogens. Upon recognition of pathogen-associated molecular patterns (PAMPs) during microbial invasion, the host triggers one or more complement activation pathways aimed at eradicating the microbial intruders. By clearing cellular debris and infectious microbes, coordinating immune responses, and transmitting \"danger\" signals, the complement system makes significant contributions to homeostasis. Its interactions with overall immunity and other endogenous pathways contribute to its dual role in maintaining homeostasis and disease\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, during infection, the interplay between the complement and coagulation systems increases local coagulation and limits the spread of microorganisms via the circulation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAntithrombin III (AT III) is the primary inhibitor of the coagulation cascade, synthesized by the liver as a glycoprotein that irreversibly inhibits serine proteases (Xa and Ⅱa) in a 1:1 ratio, forming a protease-AT III complex that is needed for anticoagulation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Increasing data shows that AT III possesses anti-inflammatory\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and renoprotective effects\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Recent research revealed that AT III could inhibit the serine protease TMPRSS2, thereby decreasing viral load in COVID-19 patients and early SARS-CoV-2 infection\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Beyond its anticoagulant effects, AT III affects the immune system and may alter endothelial cell activity\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. There is emerging evidence that AT III plays a significant role in both anticoagulation and anti-inflammatory effects\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. AT III is a marker of DIC in sepsis patients and is significant for prognosis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, with levels below 50\u0026ndash;60% often tied to worse outcomes, and levels below 20% being associated with fatal results\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Overall, our data supported the hypothesis that AT III may serve more as an inflammatory response regulator than a coagulation inhibitor. Inthorn et al.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e observed that AT III supplementation greatly improved respiratory, hepatic, and renal failure while also reducing certain inflammatory markers. As a result, it has been hypothesized that AT III supplementation could be applied to regulate coagulopathy and better the clinical results of sepsis-induced inflammatory syndromes\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Our findings demonstrated that AT III in plasma dropped and then progressively returned in the detection of pediatric sepsis, while the association between AT III expression levels in plasma and sepsis prognosis need additional validation through investigation.\u003c/p\u003e \u003cp\u003eThe complement system is a proteolytic cascade that can be activated by the classical, lectin, and alternative pathways. Complement factor D (CFD) amplifies all complement-mediated bactericidal actions and plays a vital part in the membrane assault complex directed against bacteria, mediated by mannose-binding lectin\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Evidence suggested that the complement system also served protective roles in the lungs, where its loss led to greater activation of NF-κB and exacerbated pulmonary inflammation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Research studies revealed that lower expression of CFD may facilitate the action of IL-17A in promoting obesity-related airway hyperreactivity\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Furthermore, CFD in the gut has a critical function in the clearance of Escherichia coli, hence maintaining intestinal homeostasis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Our data revealed that in the context of sepsis, CFD levels decline to a minimum on D3 and rebound by D7. Recent studies have also demonstrated that CFD is negatively connected with the pro-inflammatory cytokine IL-6 and positively correlated with the anti-inflammatory cytokine IL-4\u003csup\u003e35\u003c/sup\u003e, suggesting that CFD may function as an anti-inflammatory factor. However, the particular mechanisms and signaling pathways through which CFD exerts its anti-inflammatory benefits require additional experimental clarification.\u003c/p\u003e \u003cp\u003eCol1α1, the primary constituent of type I collagen, is broadly distributed in parenchymal organs and interstitial connective tissues across the body\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Col1α1 is crucial for controlling intercellular adhesion and differentiation, as well as fortifying diverse tissues in the body\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Col1α1 controls cell proliferation, metastasis, invasion, and angiogenesis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. It also plays a role in epithelial-mesenchymal transition, which is intricately associated to the genesis and spread of cancer\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Further research is needed to explore the link between Col1α1 gene variation and sepsis. Sepsis, a complicated disease characterized by a range of pathogens (mainly bacteria) and a dysregulated host response to infection, commonly leads to respiratory muscle injury in persons with severe sepsis. In mice, sepsis produces diaphragm dysfunction via activating TLR, NF-κB, and TNF signaling pathways, as well as blocking oxidative phosphorylation, cardiac contraction, and citric acid cycle pathways. Recent research on the molecular mechanisms of sepsis-induced diaphragm dysfunction demonstrated that the Col1α1 gene, connected to cellular adhesion, is downregulated 24 hours after lipopolysaccharide (LPS) injection\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Col1α1 mutations in animal models of osteogenesis imperfecta led to reduced diaphragm mass and contractility\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Thus, sepsis-induced lung injury can impede diaphragm and skeletal muscle contraction, resulting in decreased circulatory blood flow and respiratory failure. Our study demonstrated reduced plasma Col1α1 expression levels after sepsis, indicating a probable relationship with pediatric sepsis-induced lung injury. Further research is needed to validate this.\u003c/p\u003e \u003cp\u003eThe ErbB family contains the archetypal members, epidermal growth factor receptor (EGFR)/ErbB1, ErbB2, ErbB3, and ErbB4, which are cell surface growth factor receptors found in numerous developing mammalian organs. Previous experiments have showed that overexpressing EGFR and ErbB4 protects mice from acute pancreatitis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In vitro and in vivo investigations revealed that EGFR is intricately connected to the maintenance and repair of normal epithelial cells\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. ErbB1 inactivation induces hemorrhagic enteritis, which is analogous to necrotizing enterocolitis (NEC)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Furthermore, previous preclinical and clinical studies have demonstrated that NRG1-ErbBs signaling has cytoprotective, anti-inflammatory, and anti-remodeling effects\u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and decreased mRNA expression levels of ErbB2 and ErbB4 receptors may result from irreversible cardiomyocyte loss within the infarcted area. ErbB1 is notably concentrated in the nuclei of highly proliferative cells\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, showing that nuclear ErbB receptors engage in stimulating gene expression, with cell surface ErbB1 receptor activation promoting the migration of the full-length transmembrane ErbB1 receptor to the nucleus. ErbB activation in cultured intestinal cells promotes cell fates predicted to be protective during inflammation. In colonic epithelial cells, EGFR activation promotes proliferation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, lowers cytokine-induced apoptosis\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and speeds migration/wound repair\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, both in vitro and in vivo. Increasing data suggested that ErbB signaling deficits occur under a range of intestinal inflammatory settings and may be causally connected\u003csup\u003e\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. As a result, replacing or reactivating ligands and receptors may directly promote mucosal healing. Our data demonstrated that EGFR is expressed at low levels in plasma during the early stages of sepsis, however its likely involvement in these illnesses is unknown, and it may be implicated in the endothelial response to infection/inflammation.\u003c/p\u003e \u003cp\u003eThrombospondin1(Thbs1) modulates TGF-β activation, impacting wound healing, proliferation, differentiation, and cytokine responses\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Thbs1 has been found as a component of a risk categorization model for pediatric septic shock that combines multiple biomarkers\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. A single-center cohort study suggested\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e that Thbs1 is a possible prognostic marker for poor outcomes in neonatal patients with pneumonia sepsis, demonstrating that developmental age alters Thbs1's biological function during sepsis. In human cells, Thbs1 suppresses IL-1β and caspase-1 mRNA, not LPS-induced NLRP3\u003csup\u003e60\u003c/sup\u003e. Previous research has demonstrated that Thbs1 has context-specific pro- and anti-inflammatory actions\u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, with Thbs1 produced in response to inflammation, supporting the resolution of inflammatory processes and facilitating phagocytosis of wounded cells\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. During the early phases of injury and inflammation, high levels of Thbs1 promote dendritic cell tolerogenicity to antigens, therefore halting the inflammatory response. Thbs1 regulates the creation and activation of pro-inflammatory cytokine IL-1β in human and murine macrophages. Thbs1 suppresses IL-1β mRNA induction in an NF-κB/AP-1-dependent mechanism\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Thbs1 levels suppress the production of LPS-induced IL-1β mRNA and protein in human macrophages. Our findings revealed that Thbs1 is expressed at low levels in the plasma of children with sepsis, making it a viable biomarker for sepsis in this population. Thbs1 interacts with proteins on cell membranes and in the extracellular matrix, increasing platelet aggregation, angiogenesis, wound healing, and immunological responses\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. As a result, the biological function of Thbs1 during sepsis deserves deeper research.\u003c/p\u003e \u003cp\u003eCoagulation activation during infection may be helpful\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, as it increases platelet activation through the production of thrombin, enhancing the creation of microthrombi in inflammatory and infectious responses. This condition, known as immunothrombosis, tries to entrap invading microorganisms and restrict their dissemination. The increase of platelet activation by thrombin generation further boosts microthrombi formation, which is crucial for trapping invading pathogens and restricting their dissemination\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Research results revealed\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e that thrombocytopenia at the time of admission was associated with higher mortality and deregulation of host responses during sepsis. Notably, the kinetics of platelets during sepsis generally exhibit a biphasic pattern, defined by an initial fall within the first few days (1\u0026ndash;4 days), followed by a subsequent increase in platelet counts\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Our findings revealed that platelet counts began to rise after day three. Studies have indicated that the absence of this biphasic response may lead to prolonged thrombocytopenia, which corresponds with poor prognosis and higher 28-day mortality\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLymphocytopenia induced by sepsis is a transient event, with lymphocyte numbers eventually returning to pre-sepsis levels. A growing body of evidence supported the notion that the immune status of patients is related to the ultimate outcome of sepsis. CD4\u0026thinsp;+\u0026thinsp;T lymphocytes can activate B cells and effector T cells through the release of various factors, thereby upregulating immune function, while CD8\u0026thinsp;+\u0026thinsp;T lymphocytes possess surveillance and cytotoxic functions. Studies have found\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e that a decreased CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio is closely associated with immunosuppression and poor prognosis. The number and function of natural killer (NK) cells also appear to play a significant role in this disease\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. B cells (CD19+) are crucial for both adaptive and innate immune responses\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. In response to infectious agents, B cells not only secrete IgM and IgG but also phagocytize, process, and present antigens to T cells to generate humoral immunity\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Previous studies have reported\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e an increase in B cell numbers in whole blood of sepsis patients, whereas most other studies show a decrease in B cell numbers in severe sepsis patients\u003csup\u003e\u003cspan additionalcitationids=\"CR82 CR83\" citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. The variation in reported B cell counts across different studies may be due to the timing of blood collection after patient admission. It has been found\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e that IgM plays a critical role in preventing secondary infections following sepsis, with elderly patients exhibiting increased colonization of Gram-negative bacteria and candida albicans when IgM levels are reduced. Additionally, septic shock patients often present with hypogammaglobulinemia\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, and levels of IgG, IgM, and IgA at the time of diagnosis are directly associated with survival rates\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Our findings indicated that, at the time of sepsis identification, lymphocyte subsets and gamma globulin levels were lower than normal reference values, which may reflect impaired immune function during sepsis. Therefore, the value of lymphocyte subsets and serum gamma globulin in the diagnosis and prognosis assessment of sepsis may still require substantial further research to confirm.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study also highlighted a few limitations. Firstly, the validation cohort of this work utilized plasma from experimental animals with a disease model to screen for potential markers. Although these experimental animals share a comparable genetic background with people, and characteristics such as body weight and age can be easily controlled to strengthen the comparability of the trials, they do not fully reflect the heterogeneity of the human situation. Secondly, when acquiring clinical laboratory parameters, some data (e.g., immune cells, cytokines, etc.) lacked information from the intermediate and late stages of sepsis (D3 and D7), which hindered a thorough display of their dynamic changes during sepsis. Therefore, future research utilizing plasma from pediatric sepsis patients for MS analysis could better understand the dynamic changes in plasma proteins and the underlying pathophysiological mechanisms during pediatric sepsis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this proteomic examination of plasma in septic young mice, we detected 161 significantly increased proteins at distinct time intervals throughout the first week of sepsis (D1, D3, D7). According to KEGG pathway enrichment analysis, the complement and coagulation cascades were most closely correlated to the differentially expressed proteins, followed by focal adhesion and phagocyte-related proteins. All five possible plasma indicators declined to varied degrees at the time of sepsis diagnosis and were linked with coagulation and immunological activity. As a result, more validation studies are needed to establish the causal link between sepsis and increased or decreased expression of any newly found proteins.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Hunan Provincial People\u0026rsquo;s Hospital (The First Affiliated Hospital of Hunan Normal University) approved this study (2022-139). Consent to Participate declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets of the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo authors have any potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Natural Science Foundation of Hunan Province (2023JJ60101); Financial Plan for the High-Level Health Personnel Program of Hunan Province (20230508-1033).\u003c/p\u003e\n\u003cp\u003eA\u003cstrong\u003euthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZSZ and JY conceived and designed the project, FSY acquired the data and wrote the paper, FSY and LYJ acquired and analyzed the data, LXL and ZZC collected the study samples and acquired the data, ZSZ, JY and LYJ have drafted the manuscript and thoroughly edited it for important intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, et al. 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Biochem Biophys Res Commun. Sep 5 2003;308(4):840\u0026ndash;6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0006-291x(03)01482-7\u003c/span\u003e\u003cspan address=\"10.1016/s0006-291x(03)01482-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHotchkiss RS, Tinsley KW, Swanson PE, et al. Sepsis-induced apoptosis causes progressive profound depletion of B and CD4\u0026thinsp;+\u0026thinsp;T lymphocytes in humans. J Immunol. Jun 1 2001;166(11):6952\u0026ndash;63. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4049/jimmunol.166.11.6952\u003c/span\u003e\u003cspan address=\"10.4049/jimmunol.166.11.6952\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenet F, Gebeile R, Bancel J, et al. 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Dec 2012;27(6):616\u0026ndash;22. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcrc.2012.08.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jcrc.2012.08.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"503\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 503px;\"\u003e\n \u003cp\u003eTable.1 Characteristics of the study population.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal patients（N=66）\u003c/p\u003e\n \u003cp\u003e(mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMale，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42（63.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eAge, month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eAPACHEII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 503px;\"\u003e\n \u003cp\u003eLaboratory findings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eWBC（109/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.12\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.16\u0026plusmn;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.62\u0026plusmn;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eN（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63.31\u0026plusmn;2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48.72\u0026plusmn;2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38.57\u0026plusmn;2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePLT（109/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e293.42\u0026plusmn;17.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e330.56\u0026plusmn;19.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e434.23\u0026plusmn;32.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31.66\u0026plusmn;5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.97\u0026plusmn;6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.29\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePCT(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.59\u0026plusmn;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.38\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.22\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePT（s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.04\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAPTT（s）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.34\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eD-Dimer（mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.49\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eFbg(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.03\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIgG(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.16\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIgM(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eC3(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eC4(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.44\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD3+%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e55.18\u0026plusmn;2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4+%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.48\u0026plusmn;1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD8+%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.3\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD19+%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.3\u0026plusmn;2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD16+CD56+%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.16\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD3+CD4+CD8+%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD4+/CD8+T%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.7\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCD3+CD4-CD8-%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.65\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIL-2(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.58\u0026plusmn;14.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIL-4(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.24\u0026plusmn;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIL-6(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e307.53\u0026plusmn;182.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eIL-10(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39.57\u0026plusmn;33.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eTNF-\u0026alpha;(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30.1\u0026plusmn;5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eINF-\u0026gamma;(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e90.24\u0026plusmn;39.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote Fbg\u0026mdash;Fibrinogen, INR\u0026mdash;International Normalized Ratio, PT\u0026mdash;Prothrombin Time, APTT\u0026mdash;Activated Partial Thromboplastin Time, IgA\u0026mdash;Immunoglobulin A, IgG\u0026mdash;Immunoglobulin G, IgM\u0026mdash;Immunoglobulin M, C3\u0026mdash;Complement 3, C4\u0026mdash;Complement 4.WBC\u0026mdash;White Blood Cell, N\u0026mdash;Neutrophil, PLT\u0026mdash;Platelet.The P values relate to the statistical analysis results for D 3 vs D 1 and D 7 vs D 3\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"487\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 417px;\"\u003e\n \u003cp\u003eTable.2 Candidate biomarkers (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eTime(day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003eConcentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e570.48\u0026plusmn;25.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eATⅢ(\u0026mu;g/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e551.69\u0026plusmn;29.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.8945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e665.65\u0026plusmn;32.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e313.01\u0026plusmn;29.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eCol1\u0026alpha;1(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e351.94\u0026plusmn;35.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.6948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e599.17\u0026plusmn;37.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1360.14\u0026plusmn;134.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eCFD (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n 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119px;\"\u003e\n \u003cp\u003eEGFR（ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e87.56\u0026plusmn;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.6861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e111.43\u0026plusmn;5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.87\u0026plusmn;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eThbs1(\u0026mu;g/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e24.31\u0026plusmn;2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.1296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158px;\"\u003e\n \u003cp\u003e34.76\u0026plusmn;3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote The P values relate to the statistical analysis results for Day 3 vs Day 1 and Day 7 vs Day 3.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Children, Sepsis, Plasma, Mass spectrometry, Proteome","lastPublishedDoi":"10.21203/rs.3.rs-6146492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6146492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCurrent sepsis biomarkers have limitations, but mass spectrometry-based proteomics can identify patients at high risk of mortality or organ dysfunction, identify the molecular mechanisms of pediatric sepsis, and reveal personalized biomarkers and therapeutic strategies, with high-risk cohorts benefiting from early and accurate identification through clinical biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe young mice were randomly divided into sepsis and sham groups(D0), and then the plasma was dissected at D0, day 1(D1), day3(D3) and day7(D7) after surgery for additional protein identification by liquid chromatography-mass spectrometry (LC/MS) proteomics. Subsequently, data from 66 cases of children diagnosed with sepsis upon admission to PICU at Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University) were gathered. Dynamic plasma samples (D1, D3, D7) were obtained for ELISA verification and correlation analysis of the candidate biomarkers to determine the clinical significance of sepsis candidate plasma biomarkers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 6578 proteins identified, the septic mice groups (D1, D3, D7) demonstrated 161 differently upregulated plasma proteins. The main enriched pathways in the KEGG study were related to complement and coagulation cascades, focal adhesion, and phagosomes. ELISA test results indicated that among pediatric patients, the five candidate biomarkers (ANTⅢ, CFD, Col1α1, EGFR, Thbs1) all showed varying degrees of decrease in diagnosing sepsis. Correlation study results suggested that ATⅢ was adversely linked with IgA, IgG, IgM, C3, with Pearson's coefficients of -0.543, -0.217, -0.526, -0.128, respectively. CFD was positively connected with IgA, IgG, IgM, and negatively correlated with C3. Col1α1, CFD, EGFR, and Thbs1 demonstrated negative correlation with suppressive CD8\u0026thinsp;+\u0026thinsp;cells, while Col1α1, EGFR, and Thbs1 showed positive correlation with B cells (CD19+). Furthermore, Col1α1, CFD, EGFR, and Thbs1 revealed positive connection with CD4+/CD8+. Additionally, ATⅢ demonstrated positive connection with PT, APTT, INR, D-Dimer, Fbg, while Col1α1, EGFR showed negative association with PT, APTT, INR, D-Dimer, Fbg, and CFD was favorably connected with Fbg, and Thbs1 showed positive correlation with D-Dimer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWithin one week of sepsis onset, 161 proteins revealed alterations in young mice, with the complement and coagulation cascades, focal adhesion, and phagosome pathways showing the most significant correlations. All prospective markers reduced following the recognition of sepsis and were associated with coagulation and immunological function in pediatric patients.\u003c/p\u003e","manuscriptTitle":"Dynamic alterations and clinical implications of the plasma proteome in pediatric sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 09:03:25","doi":"10.21203/rs.3.rs-6146492/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-12T18:53:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T11:34:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270422666719459988359830190293296221830","date":"2025-06-09T11:39:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169126391573967582441265765298645167166","date":"2025-05-01T01:29:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T15:42:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65112381703914499798667517022493165659","date":"2025-04-15T15:34:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254763195297377933015972812819534629959","date":"2025-04-11T07:48:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T07:12:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-04T09:10:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-04T08:14:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-03-03T12:57:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac38c84e-bb9b-476c-b6d4-f82f3043b40b","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:05:58+00:00","versionOfRecord":{"articleIdentity":"rs-6146492","link":"https://doi.org/10.1186/s40001-025-02933-5","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-07-23 15:57:49","publishedOnDateReadable":"July 23rd, 2025"},"versionCreatedAt":"2025-03-11 09:03:25","video":"","vorDoi":"10.1186/s40001-025-02933-5","vorDoiUrl":"https://doi.org/10.1186/s40001-025-02933-5","workflowStages":[]},"version":"v1","identity":"rs-6146492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6146492","identity":"rs-6146492","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}