Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome | 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 Article Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome Adolfo Ruiz-Sanmartín, Vicent Ribas, David Suñol, Luis Chiscano-Camón, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7056801/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. This study aims to identify protein biomarkers that differentiate sepsis from non-infectious systemic inflammatory response syndrome (NISIRS). This is a prospective and observational study, conducted between 2016 and 2017, it included 277 patients (141 with sepsis, 136 with NISIRS). Plasma proteins were analyzed using mass spectrometry and evaluated through recursive feature elimination and cross-validation with a vector classifier. Twenty-five proteins showed statistically significant differences, with high diagnostic performance (sensitivity: 0.973, specificity: 0.920, accuracy: 0.960, AUC: 0.985). Fourteen proteins (VWF, PPBP, C5, C1RL, FCN3, SAA2, ORM1, ITIH3, GSN, C1QA, CA1, CFB, C3, LBP) were more associated with sepsis, while eleven (FN1, IGFALS, SERPINA4, APOE, APOH, C6, SERPINA3, AHSG, LUM, ITIH2, SAA1) were related with NISIRS. The study found upregulation of several proteins in sepsis (C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP and ITIH3) and downregulation of others (SERPINA4 and AHSG). These findings highlight distinct proteomic patterns between sepsis and NISIRS. Advances in understanding these protein changes may allow for the identification of new biomarkers in the future. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Sepsis Septic shock SIRS Proteomics Omics Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Sepsis is known as a clinical syndrome where life-threatening organ dysfunction occurs due to a dysregulated host response to infection. The severity of sepsis varies significantly with the response and degree of organ dysfunction. Severe cases of sepsis, during which hypotension persists even after adequate fluid resuscitation and lactate levels > 2 mmol/L and the patient needs vasoactive support, are classified as septic shock [1]. Despite advances in diagnosis and treatment, sepsis remains one of the leading causes of morbidity and mortality worldwide, with a mortality rate ranging around 30-50% [2,3]. Current decisions regarding sepsis diagnosis and treatment are primarily based on Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA), but their sensitivity and accuracy are known to be lacking [4,5]. C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and other biomarkers are also used for sepsis detection. Most of these biomarkers can reflect the immune system's state and stages of the inflammatory cascade, being protein molecules with negatively regulated gene expression. CRP is frequently used to identify infections and sepsis. However, CRP cannot accurately reflect the severity of infection and sepsis because it increases during a minor infection or remains elevated even after the temporal course of the infection. Additionally, CRP levels can also rise during an inflammatory response to non-infectious events, trauma, tumorigenesis, or surgical interventions. These findings suggested that CRP lacks specificity as an early-stage sepsis biomarker [6,7]. PCT is likely the best-suited biomarker for infection at present, and it has even been proposed as a prognostic factor for sepsis progression [8] and a guide for antibiotic treatment duration [9]. However, it is hindered by false positives in non-infectious inflammation settings and a rather delayed induction (4 to 12 hours with a half-life of 22 to 35 hours) during the host's response to infection [10,11]. Other biomarkers such as presepsin or pro-ADM have also been proposed as promising biomarkers in sepsis [12-13]. A deep understanding of the molecular and cellular mechanisms involved in sepsis is essential for more accurate and early diagnosis, as well as the development of new therapeutic strategies [14-15]. In this context, proteomics (a discipline of molecular biology that studies the complete set of proteins expressed in a cell, tissue or organ) has emerged as a powerful and promising tool in the study of complex protein interactions underlying sepsis [16]. The use of techniques like two-dimensional electrophoresis, liquid chromatography, and mass spectrometry (MS) have led to the identification of specific biomarkers for early diagnosis and prognosis of sepsis. These biomarkers can assist clinicians in swiftly identifying high-risk patients and making more precise therapeutic decisions. The main goal of proteomics in the study of sepsis is to identify specific biomarkers and key molecular pathways involved in disease progression and prognosis. The identification of accurate and sensitive biomarkers would enable early diagnosis and more effective monitoring of sepsis, potentially improving clinical outcomes and reducing associated mortality rates [17-19]. We hypothesized that there are proteomic patterns in patients with sepsis that differentiate them from patients with NISIRS. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and NISIRS. METHOD Study design and ethical approval This is a prospective, observational, single-center study with two study populations. One study group with septic patients who met the criteria for activation of the Vall d'Hebron University Hospital in-hospital Sepsis code [20] (ISC) between April 2016 and January 2018. The second study group included patients admitted to the Intensive Care Unit who met criteria for Systemic Inflammatory Response Syndrome (SIRS) without evidence of infection [21]. The study was approved by the Clinical Research Ethics Committee of Vall d'Hebron University Hospital [PR (AG) 11-2016, PR (AG) 336-2016, PR (AG) 210/2017], and written informed consent was obtained from all participants. The study fully adhered to the General Data Protection Regulation (Regulation (EU) 2016/679) and was conducted in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments. Inclusion and exclusion criteria The inclusion criteria for patients with NISIRS were adult patients ≥ 18 years of age who presented with two or more of the following variables: (1) white blood cell count >12,000/mm3 or 10% immature cells, (2) the presence of hyperthermia (axillary temperature >38.3ºC) or hypothermia (axillary temperature 100 beats per minute), tachypnea (>30 breath per minute) and (3) absence of infection. The inclusion criteria for the septic patients group encompassed adult patients ≥ 18 years of age with suspected or documented infection and the presence of, at least, one of the following sets of variables, as outlined by de ISC [20]: (1) an acute alteration in the level of consciousness not explained by other clinical conditions, or (2) the presence of hyperthermia (axillary temperature >38.3ºC) or hypothermia (axillary temperature 100 beats per minute), tachypnea (>30 breath per minute) or desaturation (SpO2 <90%), as well as arterial hypotension (systolic blood pressure <90 mmHg or mean arterial pressure 40 mmHg decreased in baseline systolic blood pressure). Exclusion criteria include non-adult patients, pregnant women or patients from whom a blood sample or written informed consent could not be obtained. Data collection and biomarker measurements Following patient enrollment in the study, demographic data were recorded, and a venous or arterial blood sample was obtained at the time of the initial visit for routine laboratory value assessments. Additionally, samples were collected for microbiological cultures in patients suspected of having sepsis. Clinical scores, such as SOFA, were retrospectively calculated whenever feasible at the time of enrollment. Measurements of CRP using an immunoturbidimetric test and lactate using an enzymatic color test were performed on these samples. The collected samples were frozen at -80ºC and stored in a Sepsis Bank of Vall d'Hebron University Hospital Biobank with appropriate ethics approval for subsequent analysis in accordance with clinical laboratory protocols. PROTEOMIC ANALYSIS BY MASS SPECTROMETRY The proteomic study was performed from plasma samples collected in Vacutainer K2E EDTA tubes (Becton Dickinson-Plymouth, United Kingdom) by the Proteomics and Metabolomics Area of the Center for Omic Sciences, a Joint Unit between Rovira i Virgili University and Eurecat (Reus, Spain). Protein extraction and quantification Prior to proteomic analysis, depletion of the seven most abundant plasma proteins (albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin, and fibrinogen) was performed to increase the number of identified/quantified proteins. Therefore, 12 μl of each sample was passed twice through the Agilent Technologies Human-7 Multiple Affinity Removal Spin cartridge and flow-through fractions were collected for proteomic analysis following the manufacturer's protocol. Flow-through fractions were concentrated, and buffer was exchanged to approximately 100 µl of 6 M urea in 50 mM ammonium bicarbonate using 5K MWCO spin columns (Agilent 5185-5991). Protein digestion and peptide 10-plex TMT labeling Thirty micrograms of total protein (quantified by Bradford’s method) were reduced with 4mM 1.4-Dithiothreitol for 1h at 37°C and alkylated with 8 mM iodoacetamide for 30 min at 25ºC in the dark. Afterwards, samples were overnight digested (pH 8.0, 37ºC) with sequencing-grade trypsin (Promega) at enzyme: protein ratio of 1:50. Digestion was quenched by acidification with 1% (v/v) formic acid and peptides were desalted on Oasis HLB SPE column (Waters) before TMT 10-plex labelling (Thermo Fisher) following manufacturer instructions. To normalize all samples in the study along the different TMT-multiplexed batches used, a pool containing all the samples was labelled with a TMT-126 tag and included in each TMT batch. The different TMT 10-plex batches were desalted on Oasis HLB SPE columns before the nanoLC-MS analysis. NanoLC-(Orbitrap)MS/MS analysis Labelled and multiplexed peptides were loaded on a trap nano-column (100 μm I.D.; 2cm length; 5μm particle diameter, Thermo Fisher Scientific, San José, CA, USA) and separated onto a C-18 reversed phase nano-column (75μm I.D.; 15cm length; 3μm particle diameter, Nikkyo Technos Co. LTD, Japan) on an EASY-II nanoLC from Thermo Fisher. The chromatographic separation was performed with a 180 min gradient using Milli-Q water (0.1% formic acid) and acetonitrile (0.1% formic acid) as mobile phase at a flow rate of 300 nL/min. Mass spectrometry analyses were performed on an LTQ-Orbitrap Velos Pro from Thermo Fisher by an enhanced FT-resolution MS spectrum (R=30,000 FHMW) followed by a data dependent FT-MS/MS acquisition (R=15,000 FHMW, 40% HCD) from the most intense ten parent ions with a charge state rejection of one and dynamic exclusion of 0.5 min. Protein identification/quantification Protein identification/quantification was performed on Proteome Discoverer software v.1.4.0.288 (Thermo Fisher). For protein identification, all MS and MS/MS spectra were analyzed using Mascot search engine (v.2.5). Mascot was set up to search SwissProt_2018_03. fasta database (557012 entries), restricting for Human taxonomy (20317 sequences) and assuming trypsin digestion. Two missed cleavages were allowed and an error of 0.02 Da for FT-MS/MS fragmentation mass and 10.0 ppm for a FT-MS parent ion mass were allowed. TMT-10plex was set as quantification modification and oxidation of methionine and acetylation of N-termini were set as dynamic modifications, whereas carbamidometylation of cysteine was set as static modifications. The false discovery rate (FDR) and protein probabilities were calculated by Perclorator. For protein quantification, the ratios between each TMT-label against 126-TMT label were used and quantification results were normalized based on protein median. The results are a ratio of reporter ions abundance and are dimensionless. STATISTICAL ANALYSIS Demographic, clinical, and laboratory data were reported as mean ± standard deviation or median with interquartile range as appropriate, and categorical variables as numbers and percentages. The Student's t-test was used for parametric quantitative variables, Mann-Whitney U test for non-parametric quantitative variables, and Chi-square test for qualitative variables. Statistical significance was determined at p < 0.05. The statistical analysis was performed using SPSS 18.0 software (SPSS Inc., Chicago, IL, USA). In the proteomic study, prior to conducting any statistical analysis, each protein was standardized, and missing values were imputed using the k-nearest neighbor (KNN) method for proteins with less than 25% missing values. Proteins with major missing assignments were excluded from the study. The Kruskal-Wallis method with Benjamini-Hochberg false discovered rate (FDR) correction test (p < 0.05) was used to assess differences between distributions. Statistical analyses were conducted in Python 3.8 using the pandas, sklearn, spicy, and stats models libraries. Protein selection Protein selection was carried out in five steps. In the first step data availability was assessed for completeness and consistency. In this step, data with a missingness percentage greater than 25% has been censored. This process filtered out 65 out of 177 proteins. After that, two different datasets have been generated: one where missing values have been imputed with the k-nearest neighbors (KNN) method and a second one with no imputation. The second step consisted in a statistical analysis with the Kruskal-Wallis method with Benjamini-Hochberg false discovery rate (FDR) correction over the two datasets. The statistical analysis yielded the same list of 78 proteins with statistically significant expression values between Sepsis and NISIRS. The third step consists of a recursive feature elimination (RFE) with a logistic regression over the two datasets generated in the first step outlined above. RFE was applied with a 10-fold cross validation approach with stratified 80-20% splits for training and validation. The resulting predictions in validation between the two datasets were assessed with the MacNemar statistical test with a p-value of 0.3. Since there are no statistically significant differences between the results for the two datasets, it was decided to continue the experimental setup with the imputed dataset. The fourth step consisted in assessing the discriminative power of the protein list obtained in the previous step. This step has been implemented with a logistic regression with a 10-fold cross validation approach with stratified 80-20% splits where accuracy, sensitivity, specificity, and AUC have been reported with 95% confidence intervals (95% CI). The logistic regression coefficients have also been reported with 95% confidence intervals, z-score, and p-values. In the fifth and final step, the coefficients of the logistic regression were analyzed using their additive Shapley explanations (SHAP values in summary). Proteins with positive Shapley values were associated with sepsis, while negative Shapley values were associated with NISIRS. The strength of association between the Shapley value and the outcome (sepsis and NISIRS) was measured by the magnitude of these Shapley values (Fig 1). Protein selection was performed in Python 3.8 using the standard libraries pandas and scikit-learn. The protein-protein interaction network was analyzed using String v 11.0b software (https://string-db.org/). RESULTS Characteristics of the study population A total of 277 patients were included in this study; 141 patients in the sepsis group and 136 in the NISIRS group. The demographic and clinical data of the patients are shown in Table 1 . For the sepsis group, the most common infection focus was urinary 49 (34.8%), followed by respiratory 47 (33.3%), and abdominal 44 (31.2%). In the NISIRS group, 107 (78.67%) patients had been admitted post-cardiac surgery, 13 (9.55%) were lung transplant recipients, 5 (3.67%) were liver transplant recipients, 4 (2.95%) had hemorrhagic shock, 3 (2.20%) were kidney transplant recipients, 2 (1.47%) were polytrauma patients, 1 (0.75%) had splenic hematoma, and 1 (0.75%) patient had acute pancreatitis. Table 1 Characteristics of the study population. CRP: C-reactive protein. Characteristics Total (n = 277) Sepsis (n = 141) NISIRS (n = 136) Male n (%) 162 (58.48) 85 (60.28) 77 (56.61) Age years (m ± SD) 63.38 ± 15.61 63.9 ± 15.6 62.8 ± 15.5 SOFA score median (25th -75th) 5 (3–7) 7 (5–8) 3 (2–6) Norepinephrine n (%) 121 (43.68) 76 (53.90) 45 (33.08) ICU admission n (%) 206 (74.4) 70 (49.64) 136 (100) Mechanical Ventilation n (%) 177 (63.9) 41 (29.07) 136 (100) Leucocytes x 10 6 (mean ± SD) 14118 ± 9149 13501 ± 11021 14757 ± 661 Platelets x 10 9 median (25th -75th) 130.85 (116.0-227.5) 184.00(114.0-278.5) 157.00(119.5-195.2) Lactate mmol/L median (25th -75th) 1.9 (1.4–3.1) 2.5 (1.8–4.1) 1.5 (1.0-1.9) CRP mg/dL (mean ± SD) 14.61 ± 11.90 21.77 ± 12.58 7.58 ± 5.06 Mortality n (%) 35 (12.6) 33 (24.2) 2 (1.4) Proteomic study results Initially, a total of 110 proteins were identified by MS for differential proteomic evaluation between NISIRS and Sepsis. Among them, 25 proteins in the study patient cohort showed statistical significance, with an accuracy of 0.960 (95% CI: 0.936–0.983), specificity of 0.920 (95% CI: 0.859–0.980), sensitivity of 0.973 (95% CI: 0.945–1.00), and an AUC of 0.985 (95% CI: 0.972–0.997). The analyzed proteins are presented in Table 2 . Table 2 Differentiated proteins analyzed between sepsis and NISIRS Proteins studied in sepsis and NISIRS patients C1RL - Complement C1r subcomponent-like protein C3 - Complement C3c alpha' chain fragment 1 C5 - Complement C5 alpha' chain C6 - Complement component C6 CFB – Complement factor B Ba fragment APOE - Apolipoprotein E APOH – Beta-2-glycoprotein 1 FCN3 - Ficolin-3 GSN - Gelsolin SERPINA3 - Alpha-1-antichymotrypsin SERPINA4 - Kallistatin LBP - Lipopolysaccharide-binding protein ITIH2 – Inter-alpha-trypsin inhibitor heavy chain H2 ITIH3 - Inter-alpha-trypsin inhibitor heavy chain H3 PPBP - Connective tissue-activating peptide III VWF - Von Willebrand antigen 2 AHSG - Alpha-2-HS-glycoprotein chain A FN1 - Fibronectin CA1 - Carbonic anhydrase 1 LUM - Lumican SAA1 - Serum amyloid protein A SAA2 – Serum amylod A-2 protein ORM1 - Alpha-1-acid glycoprotein 1 IGFALS – Insulin-like growth factor-binding protein complex acid labile subunit C1QA – Complement C1q subcomponent subunit A Of the 25 proteins found in this study, ten are involved in the regulation of proteolysis (SERPINA4, ITIH2, ITIH3, SERPINA3, FN1, APOE, C3, C5, GSN and AHSG), eight in innate immune response (LBP, FCN3, C3, C5, C6, GSN, CFB and C1RL), seven in complement activation (C1RL, C3, C5, C6, FCN3, C1QA and CFB), two in response to lipopolysaccharides (LBP and PPBP), four in blood coagulation (APOH, SAA1, FN1, VWF), two in lipid metabolism (APOE, APOH), and six proteins serve other functions (PPBP, ORM1, IGFALS, CA1, SAA2 and LUM). The relationship among these proteins is presented in Fig. 2 . When applying logistic regression model and analyzing its additive shape values, it was observed that the presence of 7 proteins with a higher association strength in the group of patients analyzed (Sepsis and NISIRS patients): PPBP (0.96), VWF (0.77), FN1 (0.57), CA1 (0.46), SERPINA4 (0.44), SAA2 (0.44) and IGFALS (0.42). In contrast, proteins that show lower differential association strength between septic patients and patients with NSIRS are SAA1 (0.14), C6 (0.14), C3 (0.09) and ITIH2 (0.09) (Fig. 3 ). After performing the analysis of logistic regression coefficients using their additive Shapley explanations, we detected that the main proteins with the greatest association with sepsis are VWF (+ 1.32), PPBP (+ 1.31), C5 (+ 1.08), C1RL (+ 1.07), SAA2 (0.65), ORM1 (+ 0.64) and ITIH3 (0.64). The proteins that presented the highest negative value and, therefore, present the greatest association with NISIRS are FN1 (-1.30), IGFALS (-1.08), SERPINA4 (-1.04), APOE (-0.88), APOH (-0.82) and C6 (-0.80) (Fig. 4 ). Finally, the comparative proteomic analysis performed using mass spectrometry coupled with the Tandem Mass Tag (TMT) relative quantification method revealed an upregulation of the proteins C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP, and ITIH3. In contrast, the proteins SERPINA4 and AHSG exhibited downregulation. DISCUSSION In this study, using proteomics techniques, we identified specific proteomic patterns associated with sepsis by comparing them with NISIRS patients. Twenty-five proteins were found with varying degrees of association with sepsis and NISIRS. The most relevant proteins associated with sepsis were VWF (1,32), PPBP (1,31), C5 (1,08), C1RL (1,07) and FCN3 (1,04). In contrast, the proteins found to have a greater relationship with NIRS were FN1 (-1,30), IGFALS (-1,08), SERPINA4 (-1,04) and APOE (-0,88). Moreover, the results reveal that the proteins C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP, and ITIH3 are upregulated, many of which are involved in immunological, inflammatory, and complement activation processes, suggesting a systemic activation associated with the pathophysiological condition in sepsis. In contrast, the proteins SERPINA4 and AHSG are found downregulated; both are known to play inhibitory roles in proteolytic and inflammatory cascades, which may reflect a loss of regulatory mechanisms or an uncontrolled pro-inflammatory state. In recent years, multiple studies have observed distinct proteomic patterns in patients with sepsis, although few have compared them with groups having NISIRS. The most important one, conducted by Mi Y et al, analyzed samples from 1,182 septic patients, 225 patients with SIRS, and 152 healthy donors. Among other proteins, they observed that SAA1, SAA2, VWF, FGB, FGA, LCN2, S100A9, S100A12, FGL1, ORM1, CD14, MMP2, COL6A1, TNC, LBP, SERPINA1, HP, and COL1A2 were highly expressed in patients with sepsis, whereas APOA1, APOA2, APOC1, APOC3, SERPIND1, VTN, HRG, KNG1, TTR, and PON1 were highly expressed in patients with SIRS. The most prominent functions of the highly expressed proteins in sepsis include regulation of systemic inflammation, lipopolysaccharide (LPS) response, innate immune activation, coagulation, and proteolysis, among others. [ 22 ]. Five of the upregulated proteins observed in our study (CFB, VWF, SAA2, ORM1 and LBP) coincide with the results of Mi Y. Shen et al. examined the plasma of 25 patients with SIRS and 25 with sepsis and found that seven proteins showed an increase in the plasma of patients with sepsis compared to SIRS, while three showed a decrease. The upregulated proteins included C4, CRP, plasminogen precursor, apolipoprotein A-II, plasma protease inhibitor C1 precursor, transthyretin precursor, and serum amyloid component P precursor. It was found that the APOA1 precursor, antithrombin-III precursor, and serum amyloid A-4 protein precursor were downregulated. This study revealed that complement and coagulation cascades were the most relevant pathways found to be altered in these samples [ 23 ]. None of these proteins were observed in our study. It is possible that the lack of knowledge regarding the clinical and biological characteristics of patients with sepsis and SIRS in that study, along with the small sample size analyzed, may have influenced the identification of a proteomic pattern that differs from our results. These limitations could have affected the representativeness and reproducibility of the findings, which may explain the discrepancies observed. Su et al. conducted an analysis of urine from 15 patients with sepsis and 15 patients with SIRS. They studied a total of 130 proteins, of which 34 showed differential expression (with an increase greater than 1.5 times) and were associated with processes of inflammation, immunity, and structural or cytoskeletal aspects [ 24 ]. The authors, like in our study, find that proteins such as CA1 and C3 play a role in sepsis. Furthermore, we have observed that among these two proteins, CA1 exhibits a relatively stronger association with sepsis (0.62 vs. 0.56, respectively). More than twenty years ago, Stöve et al noted that the levels of C3a and the C3a/C3 ratio in the first 24 hours after the onset of sepsis were significantly higher in septic patients than in those with SIRS, while the levels of C3 in both groups were lower than in healthy donors. Additionally, the levels of C3a in septic patients decreased with appropriate treatment, and complement activation was much lower in patients with SIRS than in those with sepsis [ 25 ]. Most published studies on proteomics in sepsis have compared patients with healthy controls. For example, Chen Q et al. conducted a proteomic characterization in these groups and observed the expression of various proteins involved in different metabolic pathways, such as the acute inflammatory response, platelet degranulation, and the activation of myeloid cells in the immune response, among others. Among the analyzed proteins, they observed increased expression of LBP, S10A8, ORM1, FIBB, CRP, AACT, SAA1, and SAA2 [ 26 ]. Some of these were also detected in our study; however, we found that SAA1 is more associated with non-infectious inflammation patients. Lu et al. observed that ORM1 expression was upregulated in septic patients compared to non-septic patients at the time of discharge [ 27 ]. Liang X et al. compared the proteome of a total of 114 patients with sepsis and 62 normal controls in a study. They detected that 81 proteins were involved in different metabolic pathways, such as the acute inflammatory response, platelet degranulation, immune response, and lipid metabolism, among others. Of these proteins, CRP, SAA1, LBP, and A2GL presented highly specific expression levels and could be used as sensitive biomarkers to diagnose patients with sepsis [ 28 ]. García-Obregón S et al. analyzed samples from 85 patients with sepsis and 67 healthy donors. After proteomic analysis, they found 6 proteins responsible for inflammation and immune response. Among them, SAA1 was upregulated in septic patients while three other observed proteins (APOE, FHR1 and Hb-β) did not find differences between septic patients and healthy volunteers [ 29 ]. In our study, we have observed that APOE is a biomarker that is more associated with NISIRS than with sepsis. Soares AJ et al. examined alterations in the plasma proteome in ten adults affected by sepsis caused by Acinetobacter baumannii compared to matched healthy controls. A total of 19 proteins were identified that belong to the inflammation/coagulation pathways and the kallikrein-kinin system. Of these, 10 were upregulated (including C3 and a group of SAA proteins) and 9 were decreased (including AHSG) [ 30 ], although in our study this protein is more associated with NISIRS. In all these studies, the absence of a comparative group of patients with NISIRS (as included in our study) represents a significant limitation. This prevents the results from being exclusively attributed to sepsis, as they could be influenced by the underlying systemic inflammatory response. Despite the fact that in most of the reviewed studies the observed proteins belong to different metabolic pathways, some studies have detected specific proteomes of particular metabolic pathways. LPS are molecules located in the outer membrane of gram-negative bacteria and act as potent activators of the immune system by being recognized by toll-like receptor 4, present in cells such as monocytes, macrophages, neutrophils, and dendritic cells [ 31 ]. In our study, we identified two proteins associated with the response to lipopolysaccharide stimulation: PPBP and LBP. Among them, PPBP shows a strong association with sepsis (1,31), while LBP's association is significantly weaker (0,51). In a study conducted by Qia WJ et al., LPS was administered to healthy volunteers, and through proteomic techniques, an upregulation of several inflammatory mediators was observed, such as lipopolysaccharide-binding protein (LBP), CRP, serum amyloid A (SAA), and VWF. Furthermore, analysis at 9 hours after the first LPS injection showed that these three proteins continued to be positively regulated consistently [ 32 ]. Kwon OK et al. observed that 19 proteins were secreted by endothelial cells after being stimulated with LPS including C3 and AHSG [ 33 ]. Jiao J et al., in a study conducted with rats subjected to cecal ligation and puncture (CLP), found 47 proteins, among which PPBP, Ficolin 1, APOB, LBP, and SERPINA3 were upregulated, while C3 was downregulated in septic rats compared to control rats, correlating closely with the diagnosis of sepsis [ 34 ]. In our study, we observed that PPBP is the protein that presents the highest association with sepsis and has been proposed as a biomarker for sepsis [ 35 ]. The role of lipoproteins in sepsis is increasingly recognized as a fundamental aspect of the early host response to infection [ 36 ]. In a proteomic study on sepsis, Li et al. observed several proteins, including APOE. They observed that the expression of this lipoprotein increases compared to non-septic patients, with an AUC of 0.619 (95% CI: 0.510–0.719) [ 37 ]. Additionally, other proteins that act in different metabolic pathways, such as CA1 and SAA1, were also found to be upregulated. Cao et al. collected plasma from patients with community-acquired pneumonia (CAP) aged 50–65 years and 70–85 years, with and without sepsis, as samples for semi-quantitative plasma proteomics. They identified fifty-eight proteins involved in various metabolic pathways. Regarding lipid metabolism, they observed an increase in expression of Apo B100 and Apo E, while Apo C and Apo A were downregulated. Furthermore, in the age group analysis, ApoE levels were higher in younger adults, while ApoA and ApoC were higher in the older age group [ 38 ]. In another study involving 27 patients hospitalized for hospital-acquired pneumonia (HAP), the plasma proteome study found that lipid metabolism emerged as the main altered function in these patients compared to healthy volunteers, with High density lipoprotein (HDL) as the central node in the network analysis, interacting with downregulated proteins such as APOA4, APOB, APOC1, APOL1, SAA4, and Paraoxonase 1 (PON1). Validation tests showed reduced plasma levels of total cholesterol, HDL-C, LDL-C, non-HDL cholesterol, apolipoproteins ApoA1 and ApoB100, and PON1 in HAP patients [ 39 ]. Kumaraswamy et al. observed that both severe sepsis and NISIRS led to a decrease in blood levels of APOB, although in septic patients, the decrease was significantly greater [ 40 ]. In our study, two lipoproteins (APOE and APOH) were found, although we observed that their role is greater in NISIRS than in sepsis, so future studies are necessary to confirm or not the findings. In sepsis, impaired blood coagulation and thrombus formation are an aspect of the host defense mechanisms [ 41 ]. In our study, we observed four proteins associated with coagulation, of which von Willebrand factor (VWF) plays the most significant role in sepsis. In the previously mentioned study by Cao et al., they also found the expression of proteins associated with coagulation pathways. Elevated levels of fibrinogen alpha chain, fibrinogen beta chain, fibrinogen gamma chain, and VWF were found in young adults who developed severe sepsis. Additionally, they found lower levels of antithrombin III (ATIII). Elevated levels of VWF are associated with increased mortality in sepsis [ 38 ]. In a study involving septic patients and healthy controls, Kremer Hovinga et al. found significantly higher levels of VWF antigen in septic patients, with no relation to severity or prognosis [ 42 ]. Singh et al. confirmed these findings, additionally observing that VWF levels were higher in those who did not survive sepsis [ 43 ]. Liu and collaborators utilized two-dimensional electrophoresis (2-DE) and MALDI-TOF-MS techniques to identify platelet proteins whose expression differs between patients with sepsis and healthy individuals. The study results showed that five platelet proteins had increased expression in patients with sepsis: EFCAB7 (involved in calcium ion binding), actin (part of the cytoskeleton), IL-1b (a cytokine), GPIX (a membrane receptor), and GPIIb (an integrin). These proteins participate in inflammatory response processes and coagulation activation, emphasizing the fundamental role of platelets in the inflammation and coagulation triggered by sepsis [ 44 ]. In a study on patients with sepsis due to CAP, several proteins related to the inflammatory response were found, including SAA1, ORM1, ATM, SERPINA1, SERPINA3, CRP, and LBP (upregulated) and F2, GSN, and APOE (downregulated). Some of these proteins are also key molecules in coagulation and bleeding processes [ 45 ]. In sepsis, the inflammatory reaction can damage organs such as the lungs, heart, and kidneys, increasing the risk of mortality. The biological processes that trigger this damage are not yet fully understood, and without specific biomarkers to diagnose or predict the progression of the disease, it can advance to a state of septic shock and even be fatal. To find these biomarkers, various studies have analyzed the proteome in the organs that become damaged during sepsis. Star BS et al. observed nine proteins, including carbonic anhydrase (CA1), which allowed differentiation between patients with sepsis admitted to the emergency department who developed acute kidney injury (AKI) within the first 24 hours and those septic patients who did not present AKI, suggesting that these could act as early biomarkers for the detection of sepsis-AKI [ 46 ]. Hashida et al. studied 20 patients with AKI upon admission to the ICU who received continuous renal replacement therapy, 10 of whom had sepsis. Protein was extracted from the adsorbates of the hemofilter and analyzed using proteomic techniques, identifying 197 proteins, of which 9 were found in all studied septic patients compared to non-septic patients [ 47 ]. Hinkelbein J et al. conducted a study in rats where they examined the proteome of cardiac tissue after subjecting the animals to a sepsis model induced by CLP. In their findings, they identified 12 proteins in the heart that showed significant alterations. Of these proteins, five (acyl-CoA synthetase 2-like, E1 component of 2-oxoglutarate dehydrogenase, oxoglutarate dehydrogenase, 2-oxoglutarate dehydrogenase complex, and succinate coenzyme A ligase) showed reduced expression 48 hours after sepsis was initiated. The decrease in these proteins is associated with a reduced capacity of the heart to produce energy adequately [ 48 ]. Mi Y et al developed a prediction model, identifying a small set of plasma proteins (USP15, COL1A2, APOA2, MAP1A, GNMT, TSPAN11, LCP1 and ALB) capable of classifying patients with sepsis into three subgroups of patients based on severity (SOFA, APACHE II) and mortality with high sensitivity, specificity and accuracy [ 22 ]. Our study did not aim to analyze the variation in protein expression in relation to organ dysfunction. Recently, our group has already determinated proteomic patterns associated with organ dysfunction and mortality in sepsis [ 19 ]. This study has several limitations. The main one is that it only allows us to establish the association of these proteins with sepsis in comparison to patients with NISIRS, without providing information on their specific concentrations in patients. This limitation serves as a starting point for future research, which could include validation using techniques such as ELISA or targeted MS to accurately quantify these proteins. Second, this is a single-center study. Our results only have internal validity due to the demographic characteristics of the patients. Therefore, it would be necessary to conduct multicenter studies to validate and generalize the findings found in this study. Third, in septic patients group, samples are collected upon sepsis code activation, and although they are taken early in the course of sepsis, we cannot rule out that all patients present the same stage of evolution at a pathophysiological level at the time of sample collection, which could affect the results. CONCLUSION The findings of the present study suggest that there are differences between the proteomic profile of sepsis and non-infectious SIRS. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutics targets based in precision medicine in the future. Declarations Availability of data and materials The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request. Acknowledgements To Toni del Pino, Rosa Ras and Pol Herrero from the Proteomics and Metabolomics Area of the Center for Omic Sciences (COS), a Joint between Rovira I Virgili University and Eurecat (Reus, Spain), for their contribution to the proteomics analysis . Samples and data from patients included in this study were provided by Sepsis Bank of the Vall d’Hebron University Hospital Biobank (PT20/00107), integrated in the Spanish National Biobanks Network, and they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committees. The authors kindly appreciate the generous donation of samples and clinical data of the donors of the Sepsis Bank of HUVH Biobank. Author contributions A.R-S, J.C.R-R.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing; V.R.: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft , Writing – review & editing; D.S.: Formal analysis, Methodology; L.C-C, L.M., I.B.: Data curation, Formal analysis, Investigation, Validation; N.L., J.J.G., M.D.C.: Validation; N.C.: Formal analysis, Investigation, Methodology, Validation, Writing – original draft; R.F.: : Supervision, Validation, Writing – review & editing. Funding This study has been funded by Eurecat 2017 Research Projects (Health Forecast 2.0. Omic stratification of patients with sepsis and septic shock). Ethics approval and consent to participate The study was approved by the Clinical Research Ethics Committee of Vall d'Hebron University Hospital [PR (AG) 11-2016, PR (AG) 336-2016, PR (AG) 210/2017], and written informed consent was obtained from all participants. Consent for publication Consent to publish has been obtained from patients or their relatives. Competing interests All authors declare no conflicts of interest. Author details 1. Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain. 2. Intensive Care Department, Vall d'Hebron University Hospital, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain. 3. Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d’Hebron Research Institute, Barcelona, Spain. 4. Eurecat, Centre Tecnològic de Catalunya, Digital Health Unit, Barcelona, Spain. 5. Department of Clinical Microbiology, Vall d'Hebron University Hospital, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain. 6. Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit URV-EURECAT, Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain. 7. Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Barcelona, Spain. 8. CIBERINFEC, ISCIII – CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain. 9. Post-cardiac Surgery Unit. Department of Intensive Care, Vall d'Hebron University Hospital, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain. References Singer, M., Deutschman, C. S. & Seymour, C. W. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315 , 801–810 (2016). Lorencio Cárdenas, C. et al. Trends in mortality in septic patients organ failure during 15 years. Crit. Care . 26 , 302 (2022). Yébenes, J. C. et al. Epidemiology of sepsis in Catalonia: analysis of incidence and outcomes in a European setting. Ann. Intensive Care . 7 , 19 (2017). Vincent, J. L. et al. The SOFA (Sepsis- related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Work- ing Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 22 , 707–710 (1996). Jiang, J., Yang, J., Mei, J., Jin, Y. & Lu, Y. Head-to-head comparison of qSOFA and SIRS criteria in predicting the mortality of infected patients in the emergency department: a meta-analysis. Scand. J. Trauma. Resusc. Emerg. Med. 26 , 56 (2018). Ginn, A. N., Halliday, C. L., Douglas, A. P. & Chen, S. C. PCR-based tests for the early diagnosis of sepsis. Where do we stand? Curr. Opin. Infect. Dis. 30 , 565–572 (2017). Komiya, K. et al. Plasma C-reactive protein levels are associated with mortality in elderly with acute lung injury. J Crit. Care . 27 , 524e1–524e6 (2012). Ruiz-Rodríguez, J. C. et al. Usefulness of procalcitonin clearance as a prognostic biomarker in septic shock. A prospective pilot study. Med. 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[Pathophysiology of septic shock. Med. Intensiva (Engl Ed. 46 (Suppl 1), 1–13 (2022). Ruiz-Rodriguez, J. C. et al. Precision medicine in sepsis and septic shock: From omics to clinical tools. World J. Crit. Care Med. 11 , 1–21 (2022). List, E. O. et al. The use of proteomics to study infectious diseases. Infect. Disord Drug Targets . 8 , 31–45 (2008). Liu, X., Ren, H. & Peng, D. Sepsis biomarkers: an omics perspective. Front. Med. 8 , 58–67 (2014). Blangy-Letheule, A., Persello, A., Rozec, B., Waard, M. & Lauzier, B. New approaches to identify sepsis biomarkers: the importance of model and sample source for mass spectrometry. Oxid. Med. Cell Longev . 6681073 (2020). (2020). Ruiz-Sanmartín, A. et al. Characterization of a proteomic profile associated with organ dysfunction and mortality of sepsis and septic shock. PLoS One . 17 , e0278708 (2022). Baldirà, J. et al. Biomarkers and clinical scores to aid the identification of disease severity and intensive care requirement following activation of an in-hospital sepsis code. Ann. Intensive Care . 10 , 7 (2020). Bone, R. C. et al. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit. Care Med . 20, 864 – 74 (1992). Mi, Y. et al. High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response. Sci. Transl Med. 16 , eadh0185 (2024). Shen, Z. et al. Sepsis plasma protein profiling with immunodepletion, three-dimensional liquid chromatography tandem mass spectrometry, and spectrum counting. Proteome Res. 5 , 3154–3160 (2006). Su, L. et al. Urinary proteomics analysis for sepsis biomarkers with iTRAQ labeling and two-dimensional liquid chromatography-tandem mass spectrometry. Trauma. Acute Care Surg. 74 , 940–945 (2013). Stöve, S. et al. Circulating complement proteins in patients with sepsis or systemic inflammatory response syndrome. Clin. Diagn. Lab. Immunol. 3 , 175–183 (1996). Chen, Q. et al. Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis. J. Transl Med. 20 , 123 (2022). Lu, J. et al. Two gene set variation indexes as potential diagnostic tool for sepsis. Am. J. Transl Res. 12 , 2749–2759 (2020). Liang, X. et al. Serum proteomics reveals disorder of lipoprotein metabolism in sepsis. Life Sci. Alliance . 4 , e202101091 (2021). Garcia-Obregon, S. et al. Identification of a panel of serum protein markers in early stage of sepsis and its validation in a cohort of patients. J. Microbiol. Immunol. Infect. 51 , 465–472 (2018). Soares, A. J. et al. Differential proteomics of the plasma of individuals with sepsis caused by Acinetobacter baumannii. J. Proteom. 73 , 267–278 (2009). Wang, X., Quinn, P. J. & Lipopolysaccharide Biosynthetic pathway and structure modification. Prog Lipid Res. 49 , 97–107 (2010). Qian, W. J. et al. Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry. Proteomics 5 , 572–584 (2005). Kwon, O. K. et al. In-depth proteomics approach of secretome to identify novel biomarker for sepsis in LPS-stimulated endothelial cells. Electrophoresis 36 , 2851–2858 (2015). Jiao, J. et al. Identification of potential biomarkers by serum proteomics analysis in rats with sepsis. Shock 42 , 75–81 (2014). Smith, N. L., Bromley, M. J., Denning, D. W., Simpson, A. & Bowyer, P. Elevated levels of the neutrophil chemoattractant pro-platelet basic protein in macrophages from individuals with chronic and allergic aspergillosis. J. Infect. Dis. 211 , 651–660 (2015). Harris, H. W., Gosnell, J. E. & Kumwenda, Z. L. The lipemia of sepsis: triglyceride-rich lipoproteins as agents of innate immunity. J. Endotoxin Res. 6 , 421–430 (2000). Li, M. et al. Identification of novel biomarkers for sepsis diagnosis via serum proteomic analysis using iTRAQ-2D-LC-MS/MS. J. Clin. Lab. Anal. 36 , e24142 (2022). Cao, Z., Yende, S., Kellum, J. A., Angus, D. C. & Robinson, R. A. Proteomics reveals age-related differences in the host immune response to sepsis. J. Proteome Res. 13 , 422–432 (2014). Sharma, N. K. et al. Lipid metabolism impairment in patients with sepsis secondary to hospital acquired pneumonia, a proteomic analysis. Clin. Proteom. 16 , 29 (2019). Kumaraswamy, S. B., Linder, A., Åkesson, P. & Dahlbäck, B. Decreased plasma concentrations of apolipoprotein M in sepsis and systemic inflammatory response syndromes. Crit. Care . 16 , R60 (2012). Ono, T. et al. Severe secondary deficiency of von Willebrand factor-cleaving protease (ADAMTS13) in patients with sepsis-induced disseminated intravascular coagulation: its correlation with development of renal failure. Blood 107, 528 – 34 (2006). Kremer Hovinga, J. A. et al. ADAMTS-13, von Willebrand factor and related parameters in severe sepsis and septic shock. J. Thromb. Haemost . 5 , 2284–2290 (2007). Singh, K. et al. Characterization of ADAMTS13 and von Willebrand factor levels in septic and non-septic ICU patients. PLoS One . 16 , e0247017 (2021). Liu, J., Li, J. & Den, X. Proteomic analysis of diferential protein expression in platelets of septic patients. Mol. Biol. Rep. 41 , 3179–3185 (2014). Sharma, N. K. et al. Proteomic study revealed cellular assembly and lipid metabolism dysregulation in sepsis secondary to community-acquired pneumonia. Sci. Rep. 7 , 15606 (2017). Star, B. S. et al. Plasma proteomic characterization of the development of acute kidney injury in early sepsis patients. Sci. Rep. 12 , 19705 (2022). Hashida, T. et al. Proteome analysis of hemofilter adsorbates to identify novel substances of sepsis: a pilot study. J. Artif. Organs . 20 , 132–137 (2017). Hinkelbein, J., Kalenka, A., Schubert, C., Peterka, A. & Feldmann, R. E. Jr. Proteome and metabolome alterations in heart and liver indicate compromised energy production during sepsis. Protein Pept. Lett. 17 , 18–31 (2010). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7056801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489819432,"identity":"a354a0c8-07a9-461f-931b-4512c900491b","order_by":0,"name":"Adolfo 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of\u003cstrong\u003e \u003c/strong\u003eProtein Section.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7056801/v1/b2698bffffc21d5d7a5c83b5.png"},{"id":87694597,"identity":"397f04c4-e831-458d-82c9-139cf56f5059","added_by":"auto","created_at":"2025-07-28 05:46:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4156882,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of physiological interactions between the different proteins analyzed (strings) (\u003ca href=\"https://string-db.org/\"\u003ehttps://string-db.org/\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7056801/v1/9e4b3f1e3aed0b89f22269fa.png"},{"id":87694594,"identity":"6d059c05-2dfd-485d-8a28-5d808e6c58ed","added_by":"auto","created_at":"2025-07-28 05:46:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":383508,"visible":true,"origin":"","legend":"\u003cp\u003eSHAP values graphic for logistic regression coefficients assessing the association strength of each protein in the overall set of patients studied.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7056801/v1/7dca3e5a04b351ba4746dde4.png"},{"id":87694600,"identity":"46f9840c-7bd8-4a24-b197-2ed29025b3e5","added_by":"auto","created_at":"2025-07-28 05:46:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":340228,"visible":true,"origin":"","legend":"\u003cp\u003eCoefficient values indicating tendency towards Sepsis (Blue) or NISIRS (Orange).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7056801/v1/130a66aff3fa300846001d0d.png"},{"id":90066018,"identity":"bb3b5879-d981-4d88-91e8-4217912e5457","added_by":"auto","created_at":"2025-08-28 05:33:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6352194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7056801/v1/59e42ada-59ef-475e-be94-5b89b8c719b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp;Sepsis is known as a clinical syndrome where life-threatening organ dysfunction occurs due to a dysregulated host response to infection. The severity of sepsis varies significantly with the response and degree of organ dysfunction. Severe cases of sepsis, during which hypotension persists even after adequate fluid resuscitation and lactate levels \u0026gt; 2 mmol/L and the patient needs vasoactive support, are classified as septic shock [1]. Despite advances in diagnosis and treatment, sepsis remains one of the leading causes of morbidity and mortality worldwide, with a mortality rate ranging around 30-50% [2,3].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Current decisions regarding sepsis diagnosis and treatment are primarily based on Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA), but their sensitivity and accuracy are known to be lacking [4,5]. C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and other biomarkers are also used for sepsis detection. Most of these biomarkers can reflect the immune system\u0026apos;s state and stages of the inflammatory cascade, being protein molecules with negatively regulated gene expression. CRP is frequently used to identify infections and sepsis. However, CRP cannot accurately reflect the severity of infection and sepsis because it increases during a minor infection or remains elevated even after the temporal course of the infection. Additionally, CRP levels can also rise during an inflammatory response to non-infectious events, trauma, tumorigenesis, or surgical interventions. These findings suggested that CRP lacks specificity as an early-stage sepsis biomarker [6,7]. PCT is likely the best-suited biomarker for infection at present, and it has even been proposed as a prognostic factor for sepsis progression [8] and a guide for antibiotic treatment duration [9]. However, it is hindered by false positives in non-infectious inflammation settings and a rather delayed induction (4 to 12 hours with a half-life of 22 to 35 hours) during the host\u0026apos;s response to infection [10,11]. Other biomarkers such as presepsin or pro-ADM have also been proposed as promising biomarkers in sepsis [12-13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; A deep understanding of the molecular and cellular mechanisms involved in sepsis is essential for more accurate and early diagnosis, as well as the development of new therapeutic strategies [14-15]. In this context, proteomics (a discipline of molecular biology that studies the complete set of proteins expressed in a cell, tissue or organ) has emerged as a powerful and promising tool in the study of complex protein interactions underlying sepsis [16]. The use of techniques like two-dimensional electrophoresis, liquid chromatography, and mass spectrometry (MS) have led to the identification of specific biomarkers for early diagnosis and prognosis of sepsis. These biomarkers can assist clinicians in swiftly identifying high-risk patients and making more precise therapeutic decisions. The main goal of proteomics in the study of sepsis is to identify specific biomarkers and key molecular pathways involved in disease progression and prognosis. The identification of accurate and sensitive biomarkers would enable early diagnosis and more effective monitoring of sepsis, potentially improving clinical outcomes and reducing associated mortality rates [17-19].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; We hypothesized that there are proteomic patterns in patients with sepsis that differentiate them from patients with NISIRS. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and NISIRS.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e\u003cstrong\u003eStudy design and ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; This is a prospective, observational, single-center study with two study populations. One study group with septic patients who met the criteria for activation of the Vall d'Hebron University Hospital in-hospital Sepsis code [20] (ISC) between April 2016 and January 2018. The second study group included patients admitted to the Intensive Care Unit who met criteria for Systemic Inflammatory Response Syndrome (SIRS) without evidence of infection [21]. The study was approved by the Clinical Research Ethics Committee of Vall d'Hebron University Hospital [PR (AG) 11-2016, PR (AG) 336-2016, PR (AG) 210/2017], and written informed consent was obtained from all participants. The study fully adhered to the General Data Protection Regulation (Regulation (EU) 2016/679) and was conducted in accordance with the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and exclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria for patients with NISIRS were adult patients ≥ 18 years of age who presented with two or more of the following variables: (1) white blood cell count \u0026gt;12,000/mm3 or \u0026lt;4,000/mm3, or \u0026gt;10% immature cells, (2) the presence of hyperthermia (axillary temperature \u0026gt;38.3ºC) or hypothermia (axillary temperature \u0026lt;36.0ºC), and/or tachycardia (\u0026gt;100 beats per minute), tachypnea (\u0026gt;30 breath per minute) and (3) absence of infection. The inclusion criteria for the septic patients group encompassed adult patients ≥ 18 years of age with suspected or documented infection and the presence of, at least, one of the following sets of variables, as outlined by de ISC [20]: (1) an acute alteration in the level of consciousness not explained by other clinical conditions, or (2) the presence of hyperthermia (axillary temperature \u0026gt;38.3ºC) or hypothermia (axillary temperature \u0026lt;36.0ºC), and/or tachycardia (\u0026gt;100 beats per minute), tachypnea (\u0026gt;30 breath per minute) or desaturation (SpO2 \u0026lt;90%), as well as arterial hypotension (systolic blood pressure \u0026lt;90 mmHg or mean arterial pressure \u0026lt;65 mmHg or \u0026gt;40 mmHg decreased in baseline systolic blood pressure).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Exclusion criteria include non-adult patients, pregnant women or patients from whom a blood sample or written informed consent could not be obtained.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData collection and biomarker measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Following patient enrollment in the study, demographic data were recorded, and a venous or arterial blood sample was obtained at the time of the initial visit for routine laboratory value assessments. Additionally, samples were collected for microbiological cultures in patients suspected of having sepsis. Clinical scores, such as SOFA, were retrospectively calculated whenever feasible at the time of enrollment. Measurements of CRP using an immunoturbidimetric test and lactate using an enzymatic color test were performed on these samples. The collected samples were frozen at -80ºC and stored in a Sepsis Bank of Vall d'Hebron University Hospital Biobank with appropriate ethics approval for subsequent analysis in accordance with clinical laboratory protocols.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePROTEOMIC ANALYSIS BY MASS SPECTROMETRY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The proteomic study was performed from plasma samples collected in Vacutainer K2E EDTA tubes (Becton Dickinson-Plymouth, United Kingdom) by the Proteomics and Metabolomics Area of the Center for Omic Sciences, a Joint Unit between Rovira i Virgili University and Eurecat (Reus, Spain).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein extraction and quantification\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Prior to proteomic analysis, depletion of the seven most abundant plasma proteins (albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin, and fibrinogen) was performed to increase the number of identified/quantified proteins. Therefore, 12 μl of each sample was passed twice through the Agilent Technologies Human-7 Multiple Affinity Removal Spin cartridge and flow-through fractions were collected for proteomic analysis following the manufacturer's protocol. Flow-through fractions were concentrated, and buffer was exchanged to approximately 100 µl of 6 M urea in 50 mM ammonium bicarbonate using 5K MWCO spin columns (Agilent 5185-5991).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein digestion and peptide 10-plex TMT labeling\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Thirty micrograms of total protein (quantified by Bradford’s method) were reduced with 4mM 1.4-Dithiothreitol for 1h at 37°C and alkylated with 8 mM iodoacetamide for 30 min at 25ºC in the dark. Afterwards, samples were overnight digested (pH 8.0, 37ºC) with sequencing-grade trypsin (Promega) at enzyme: protein ratio of 1:50. Digestion was quenched by acidification with 1% (v/v) formic acid and peptides were desalted on Oasis HLB SPE column (Waters) before TMT 10-plex labelling (Thermo Fisher) following manufacturer instructions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; To normalize all samples in the study along the different TMT-multiplexed batches used, a pool containing all the samples was labelled with a TMT-126 tag and included in each TMT batch. The different TMT 10-plex batches were desalted on Oasis HLB SPE columns before the nanoLC-MS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNanoLC-(Orbitrap)MS/MS analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Labelled and multiplexed peptides were loaded on a trap nano-column (100 μm I.D.; 2cm length; 5μm particle diameter, Thermo Fisher Scientific, San José, CA, USA) and separated onto a C-18 reversed phase nano-column (75μm I.D.; 15cm length; 3μm particle diameter, Nikkyo Technos Co. LTD, Japan) on an EASY-II nanoLC from Thermo Fisher. The chromatographic separation was performed with a 180 min gradient using Milli-Q water (0.1% formic acid) and acetonitrile (0.1% formic acid) as mobile phase at a flow rate of 300 nL/min.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Mass spectrometry analyses were performed on an LTQ-Orbitrap Velos Pro from Thermo Fisher by an enhanced FT-resolution MS spectrum (R=30,000 FHMW) followed by a data dependent FT-MS/MS acquisition (R=15,000 FHMW, 40% HCD) from the most intense ten parent ions with a charge state rejection of one and dynamic exclusion of 0.5 min.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein identification/quantification\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Protein identification/quantification was performed on Proteome Discoverer software v.1.4.0.288 (Thermo Fisher). For protein identification, all MS and MS/MS spectra were analyzed using Mascot search engine (v.2.5). Mascot was set up to search SwissProt_2018_03. fasta database (557012 entries), restricting for Human taxonomy (20317 sequences) and assuming trypsin digestion. Two missed cleavages were allowed and an error of 0.02 Da for FT-MS/MS fragmentation mass and 10.0 ppm for a FT-MS parent ion mass were allowed. TMT-10plex was set as quantification modification and oxidation of methionine and acetylation of N-termini were set as dynamic modifications, whereas carbamidometylation of cysteine was set as static modifications. The false discovery rate (FDR) and protein probabilities were calculated by Perclorator. For protein quantification, the ratios between each TMT-label against 126-TMT label were used and quantification results were normalized based on protein median. The results are a ratio of reporter ions abundance and are dimensionless.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATISTICAL ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Demographic, clinical, and laboratory data were reported as mean ± standard deviation or median with interquartile range as appropriate, and categorical variables as numbers and percentages. The Student's t-test was used for parametric quantitative variables, Mann-Whitney U test for non-parametric quantitative variables, and Chi-square test for qualitative variables. Statistical significance was determined at p \u0026lt; 0.05. The statistical analysis was performed using SPSS 18.0 software (SPSS Inc., Chicago, IL, USA).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; In the proteomic study, prior to conducting any statistical analysis, each protein was standardized, and missing values were imputed using the k-nearest neighbor (KNN) method for proteins with less than 25% missing values. Proteins with major missing assignments were excluded from the study. The Kruskal-Wallis method with Benjamini-Hochberg false discovered rate (FDR) correction test (p \u0026lt; 0.05) was used to assess differences between distributions. Statistical analyses were conducted in Python 3.8 using the pandas, sklearn, spicy, and stats models libraries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Protein selection was carried out in five steps. In the first step data availability was assessed for completeness and consistency. In this step, data with a missingness percentage greater than 25% has been censored. This process filtered out 65 out of 177 proteins. After that, two different datasets have been generated: one where missing values have been imputed with the k-nearest neighbors (KNN) method and a second one with no imputation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The second step consisted in a statistical analysis with the Kruskal-Wallis method with Benjamini-Hochberg false discovery rate (FDR) correction over the two datasets. The statistical analysis yielded the same list of 78 proteins with statistically significant expression values between Sepsis and NISIRS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The third step consists of a recursive feature elimination (RFE) with a logistic regression over the two datasets generated in the first step outlined above. RFE was applied with a 10-fold cross validation approach with stratified 80-20% splits for training and validation. The resulting predictions in validation between the two datasets were assessed with the MacNemar statistical test with a p-value of 0.3. Since there are no statistically significant differences between the results for the two datasets, it was decided to continue the experimental setup with the imputed dataset.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The fourth step consisted in assessing the discriminative power of the protein list obtained in the previous step. This step has been implemented with a logistic regression with a 10-fold cross validation approach with stratified 80-20% splits where accuracy, sensitivity, specificity, and AUC have been reported with 95% confidence intervals (95% CI). The logistic regression coefficients have also been reported with 95% confidence intervals, z-score, and p-values. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;In the fifth and final step, the coefficients of the logistic regression were analyzed using their additive Shapley explanations (SHAP values in summary). Proteins with positive Shapley values were associated with sepsis, while negative Shapley values were associated with NISIRS. The strength of association between the Shapley value and the outcome (sepsis and NISIRS) was measured by the magnitude of these Shapley values (Fig 1).\u003c/p\u003e\n\u003cp\u003eProtein selection was performed in Python 3.8 using the standard libraries pandas and scikit-learn. The protein-protein interaction network was analyzed using String v 11.0b software (https://string-db.org/).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eCharacteristics of the study population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 277 patients were included in this study; 141 patients in the sepsis group and 136 in the NISIRS group. The demographic and clinical data of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the sepsis group, the most common infection focus was urinary 49 (34.8%), followed by respiratory 47 (33.3%), and abdominal 44 (31.2%). In the NISIRS group, 107 (78.67%) patients had been admitted post-cardiac surgery, 13 (9.55%) were lung transplant recipients, 5 (3.67%) were liver transplant recipients, 4 (2.95%) had hemorrhagic shock, 3 (2.20%) were kidney transplant recipients, 2 (1.47%) were polytrauma patients, 1 (0.75%) had splenic hematoma, and 1 (0.75%) patient had acute pancreatitis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study population. CRP: C-reactive protein.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;277)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSepsis (n\u0026thinsp;=\u0026thinsp;141)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNISIRS (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162 (58.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (60.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (56.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003cp\u003eyears (m\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.38\u0026thinsp;\u0026plusmn;\u0026thinsp;15.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSOFA score\u003c/b\u003e median (25th -75th)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5\u0026ndash;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNorepinephrine\u003c/b\u003e\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (43.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (53.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (33.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU admission\u003c/b\u003e n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (74.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (49.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMechanical Ventilation\u003c/b\u003e n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (29.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLeucocytes x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14118\u0026thinsp;\u0026plusmn;\u0026thinsp;9149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13501\u0026thinsp;\u0026plusmn;\u0026thinsp;11021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14757\u0026thinsp;\u0026plusmn;\u0026thinsp;661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatelets x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e median (25th -75th)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130.85 (116.0-227.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184.00(114.0-278.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157.00(119.5-195.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLactate mmol/L\u003c/b\u003e median (25th -75th)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.9 (1.4\u0026ndash;3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5 (1.8\u0026ndash;4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5 (1.0-1.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCRP mg/dL\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.61\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eProteomic study results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInitially, a total of 110 proteins were identified by MS for differential proteomic evaluation between NISIRS and Sepsis. Among them, 25 proteins in the study patient cohort showed statistical significance, with an accuracy of 0.960 (95% CI: 0.936\u0026ndash;0.983), specificity of 0.920 (95% CI: 0.859\u0026ndash;0.980), sensitivity of 0.973 (95% CI: 0.945\u0026ndash;1.00), and an AUC of 0.985 (95% CI: 0.972\u0026ndash;0.997). The analyzed proteins are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferentiated proteins analyzed between sepsis and NISIRS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eProteins studied in sepsis and NISIRS patients\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC1RL\u003c/b\u003e - Complement C1r subcomponent-like protein\u003c/p\u003e\u003cp\u003e\u003cb\u003eC3\u003c/b\u003e - Complement C3c alpha' chain fragment 1\u003c/p\u003e\u003cp\u003e\u003cb\u003eC5\u003c/b\u003e - Complement C5 alpha' chain\u003c/p\u003e\u003cp\u003e\u003cb\u003eC6\u003c/b\u003e - Complement component C6\u003c/p\u003e\u003cp\u003e\u003cb\u003eCFB\u003c/b\u003e \u0026ndash; Complement factor B Ba fragment\u003c/p\u003e\u003cp\u003e\u003cb\u003eAPOE\u003c/b\u003e - Apolipoprotein E\u003c/p\u003e\u003cp\u003e\u003cb\u003eAPOH\u003c/b\u003e \u0026ndash; Beta-2-glycoprotein 1\u003c/p\u003e\u003cp\u003e\u003cb\u003eFCN3\u003c/b\u003e - Ficolin-3\u003c/p\u003e\u003cp\u003e\u003cb\u003eGSN\u003c/b\u003e - Gelsolin\u003c/p\u003e\u003cp\u003e\u003cb\u003eSERPINA3\u003c/b\u003e - Alpha-1-antichymotrypsin\u003c/p\u003e\u003cp\u003e\u003cb\u003eSERPINA4\u003c/b\u003e - Kallistatin\u003c/p\u003e\u003cp\u003e\u003cb\u003eLBP\u003c/b\u003e - Lipopolysaccharide-binding protein\u003c/p\u003e\u003cp\u003e\u003cb\u003eITIH2\u003c/b\u003e \u0026ndash; Inter-alpha-trypsin inhibitor heavy chain H2\u003c/p\u003e\u003cp\u003e\u003cb\u003eITIH3\u003c/b\u003e - Inter-alpha-trypsin inhibitor heavy chain H3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePPBP\u003c/b\u003e - Connective tissue-activating peptide III\u003c/p\u003e\u003cp\u003e\u003cb\u003eVWF\u003c/b\u003e - Von Willebrand antigen 2\u003c/p\u003e\u003cp\u003e\u003cb\u003eAHSG\u003c/b\u003e - Alpha-2-HS-glycoprotein chain A\u003c/p\u003e\u003cp\u003e\u003cb\u003eFN1\u003c/b\u003e - Fibronectin\u003c/p\u003e\u003cp\u003e\u003cb\u003eCA1\u003c/b\u003e - Carbonic anhydrase 1\u003c/p\u003e\u003cp\u003e\u003cb\u003eLUM\u003c/b\u003e - Lumican\u003c/p\u003e\u003cp\u003e\u003cb\u003eSAA1\u003c/b\u003e - Serum amyloid protein A\u003c/p\u003e\u003cp\u003e\u003cb\u003eSAA2\u003c/b\u003e \u0026ndash; Serum amylod A-2 protein\u003c/p\u003e\u003cp\u003e\u003cb\u003eORM1\u003c/b\u003e - Alpha-1-acid glycoprotein 1\u003c/p\u003e\u003cp\u003e\u003cb\u003eIGFALS\u003c/b\u003e \u0026ndash; Insulin-like growth factor-binding protein complex acid labile subunit\u003c/p\u003e\u003cp\u003e\u003cb\u003eC1QA\u003c/b\u003e \u0026ndash; Complement C1q subcomponent subunit A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOf the 25 proteins found in this study, ten are involved in the regulation of proteolysis (SERPINA4, ITIH2, ITIH3, SERPINA3, FN1, APOE, C3, C5, GSN and AHSG), eight in innate immune response (LBP, FCN3, C3, C5, C6, GSN, CFB and C1RL), seven in complement activation (C1RL, C3, C5, C6, FCN3, C1QA and CFB), two in response to lipopolysaccharides (LBP and PPBP), four in blood coagulation (APOH, SAA1, FN1, VWF), two in lipid metabolism (APOE, APOH), and six proteins serve other functions (PPBP, ORM1, IGFALS, CA1, SAA2 and LUM). The relationship among these proteins is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen applying logistic regression model and analyzing its additive shape values, it was observed that the presence of 7 proteins with a higher association strength in the group of patients analyzed (Sepsis and NISIRS patients): PPBP (0.96), VWF (0.77), FN1 (0.57), CA1 (0.46), SERPINA4 (0.44), SAA2 (0.44) and IGFALS (0.42). In contrast, proteins that show lower differential association strength between septic patients and patients with NSIRS are SAA1 (0.14), C6 (0.14), C3 (0.09) and ITIH2 (0.09) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter performing the analysis of logistic regression coefficients using their additive Shapley explanations, we detected that the main proteins with the greatest association with sepsis are VWF (+\u0026thinsp;1.32), PPBP (+\u0026thinsp;1.31), C5 (+\u0026thinsp;1.08), C1RL (+\u0026thinsp;1.07), SAA2 (0.65), ORM1 (+\u0026thinsp;0.64) and ITIH3 (0.64). The proteins that presented the highest negative value and, therefore, present the greatest association with NISIRS are FN1 (-1.30), IGFALS (-1.08), SERPINA4 (-1.04), APOE (-0.88), APOH (-0.82) and C6 (-0.80) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, the comparative proteomic analysis performed using mass spectrometry coupled with the Tandem Mass Tag (TMT) relative quantification method revealed an upregulation of the proteins C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP, and ITIH3. In contrast, the proteins SERPINA4 and AHSG exhibited downregulation.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, using proteomics techniques, we identified specific proteomic patterns associated with sepsis by comparing them with NISIRS patients. Twenty-five proteins were found with varying degrees of association with sepsis and NISIRS. The most relevant proteins associated with sepsis were VWF (1,32), PPBP (1,31), C5 (1,08), C1RL (1,07) and FCN3 (1,04). In contrast, the proteins found to have a greater relationship with NIRS were FN1 (-1,30), IGFALS (-1,08), SERPINA4 (-1,04) and APOE (-0,88). Moreover, the results reveal that the proteins C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP, and ITIH3 are upregulated, many of which are involved in immunological, inflammatory, and complement activation processes, suggesting a systemic activation associated with the pathophysiological condition in sepsis. In contrast, the proteins SERPINA4 and AHSG are found downregulated; both are known to play inhibitory roles in proteolytic and inflammatory cascades, which may reflect a loss of regulatory mechanisms or an uncontrolled pro-inflammatory state.\u003c/p\u003e\u003cp\u003eIn recent years, multiple studies have observed distinct proteomic patterns in patients with sepsis, although few have compared them with groups having NISIRS. The most important one, conducted by Mi Y et al, analyzed samples from 1,182 septic patients, 225 patients with SIRS, and 152 healthy donors. Among other proteins, they observed that SAA1, SAA2, VWF, FGB, FGA, LCN2, S100A9, S100A12, FGL1, ORM1, CD14, MMP2, COL6A1, TNC, LBP, SERPINA1, HP, and COL1A2 were highly expressed in patients with sepsis, whereas APOA1, APOA2, APOC1, APOC3, SERPIND1, VTN, HRG, KNG1, TTR, and PON1 were highly expressed in patients with SIRS. The most prominent functions of the highly expressed proteins in sepsis include regulation of systemic inflammation, lipopolysaccharide (LPS) response, innate immune activation, coagulation, and proteolysis, among others. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Five of the upregulated proteins observed in our study (CFB, VWF, SAA2, ORM1 and LBP) coincide with the results of Mi Y. Shen et al. examined the plasma of 25 patients with SIRS and 25 with sepsis and found that seven proteins showed an increase in the plasma of patients with sepsis compared to SIRS, while three showed a decrease. The upregulated proteins included C4, CRP, plasminogen precursor, apolipoprotein A-II, plasma protease inhibitor C1 precursor, transthyretin precursor, and serum amyloid component P precursor. It was found that the APOA1 precursor, antithrombin-III precursor, and serum amyloid A-4 protein precursor were downregulated. This study revealed that complement and coagulation cascades were the most relevant pathways found to be altered in these samples [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. None of these proteins were observed in our study. It is possible that the lack of knowledge regarding the clinical and biological characteristics of patients with sepsis and SIRS in that study, along with the small sample size analyzed, may have influenced the identification of a proteomic pattern that differs from our results. These limitations could have affected the representativeness and reproducibility of the findings, which may explain the discrepancies observed. Su et al. conducted an analysis of urine from 15 patients with sepsis and 15 patients with SIRS. They studied a total of 130 proteins, of which 34 showed differential expression (with an increase greater than 1.5 times) and were associated with processes of inflammation, immunity, and structural or cytoskeletal aspects [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The authors, like in our study, find that proteins such as CA1 and C3 play a role in sepsis. Furthermore, we have observed that among these two proteins, CA1 exhibits a relatively stronger association with sepsis (0.62 vs. 0.56, respectively). More than twenty years ago, St\u0026ouml;ve et al noted that the levels of C3a and the C3a/C3 ratio in the first 24 hours after the onset of sepsis were significantly higher in septic patients than in those with SIRS, while the levels of C3 in both groups were lower than in healthy donors. Additionally, the levels of C3a in septic patients decreased with appropriate treatment, and complement activation was much lower in patients with SIRS than in those with sepsis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMost published studies on proteomics in sepsis have compared patients with healthy controls. For example, Chen Q et al. conducted a proteomic characterization in these groups and observed the expression of various proteins involved in different metabolic pathways, such as the acute inflammatory response, platelet degranulation, and the activation of myeloid cells in the immune response, among others. Among the analyzed proteins, they observed increased expression of LBP, S10A8, ORM1, FIBB, CRP, AACT, SAA1, and SAA2 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Some of these were also detected in our study; however, we found that SAA1 is more associated with non-infectious inflammation patients. Lu et al. observed that ORM1 expression was upregulated in septic patients compared to non-septic patients at the time of discharge [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Liang X et al. compared the proteome of a total of 114 patients with sepsis and 62 normal controls in a study. They detected that 81 proteins were involved in different metabolic pathways, such as the acute inflammatory response, platelet degranulation, immune response, and lipid metabolism, among others. Of these proteins, CRP, SAA1, LBP, and A2GL presented highly specific expression levels and could be used as sensitive biomarkers to diagnose patients with sepsis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Garc\u0026iacute;a-Obreg\u0026oacute;n S et al. analyzed samples from 85 patients with sepsis and 67 healthy donors. After proteomic analysis, they found 6 proteins responsible for inflammation and immune response. Among them, SAA1 was upregulated in septic patients while three other observed proteins (APOE, FHR1 and Hb-β) did not find differences between septic patients and healthy volunteers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study, we have observed that APOE is a biomarker that is more associated with NISIRS than with sepsis. Soares AJ et al. examined alterations in the plasma proteome in ten adults affected by sepsis caused by \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e compared to matched healthy controls. A total of 19 proteins were identified that belong to the inflammation/coagulation pathways and the kallikrein-kinin system. Of these, 10 were upregulated (including C3 and a group of SAA proteins) and 9 were decreased (including AHSG) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], although in our study this protein is more associated with NISIRS. In all these studies, the absence of a comparative group of patients with NISIRS (as included in our study) represents a significant limitation. This prevents the results from being exclusively attributed to sepsis, as they could be influenced by the underlying systemic inflammatory response.\u003c/p\u003e\u003cp\u003eDespite the fact that in most of the reviewed studies the observed proteins belong to different metabolic pathways, some studies have detected specific proteomes of particular metabolic pathways. LPS are molecules located in the outer membrane of gram-negative bacteria and act as potent activators of the immune system by being recognized by toll-like receptor 4, present in cells such as monocytes, macrophages, neutrophils, and dendritic cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our study, we identified two proteins associated with the response to lipopolysaccharide stimulation: PPBP and LBP. Among them, PPBP shows a strong association with sepsis (1,31), while LBP's association is significantly weaker (0,51). In a study conducted by Qia WJ et al., LPS was administered to healthy volunteers, and through proteomic techniques, an upregulation of several inflammatory mediators was observed, such as lipopolysaccharide-binding protein (LBP), CRP, serum amyloid A (SAA), and VWF. Furthermore, analysis at 9 hours after the first LPS injection showed that these three proteins continued to be positively regulated consistently [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Kwon OK et al. observed that 19 proteins were secreted by endothelial cells after being stimulated with LPS including C3 and AHSG [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Jiao J et al., in a study conducted with rats subjected to cecal ligation and puncture (CLP), found 47 proteins, among which PPBP, Ficolin 1, APOB, LBP, and SERPINA3 were upregulated, while C3 was downregulated in septic rats compared to control rats, correlating closely with the diagnosis of sepsis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, we observed that PPBP is the protein that presents the highest association with sepsis and has been proposed as a biomarker for sepsis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe role of lipoproteins in sepsis is increasingly recognized as a fundamental aspect of the early host response to infection [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a proteomic study on sepsis, Li et al. observed several proteins, including APOE. They observed that the expression of this lipoprotein increases compared to non-septic patients, with an AUC of 0.619 (95% CI: 0.510\u0026ndash;0.719) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, other proteins that act in different metabolic pathways, such as CA1 and SAA1, were also found to be upregulated. Cao et al. collected plasma from patients with community-acquired pneumonia (CAP) aged 50\u0026ndash;65 years and 70\u0026ndash;85 years, with and without sepsis, as samples for semi-quantitative plasma proteomics. They identified fifty-eight proteins involved in various metabolic pathways. Regarding lipid metabolism, they observed an increase in expression of Apo B100 and Apo E, while Apo C and Apo A were downregulated. Furthermore, in the age group analysis, ApoE levels were higher in younger adults, while ApoA and ApoC were higher in the older age group [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In another study involving 27 patients hospitalized for hospital-acquired pneumonia (HAP), the plasma proteome study found that lipid metabolism emerged as the main altered function in these patients compared to healthy volunteers, with High density lipoprotein (HDL) as the central node in the network analysis, interacting with downregulated proteins such as APOA4, APOB, APOC1, APOL1, SAA4, and Paraoxonase 1 (PON1). Validation tests showed reduced plasma levels of total cholesterol, HDL-C, LDL-C, non-HDL cholesterol, apolipoproteins ApoA1 and ApoB100, and PON1 in HAP patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Kumaraswamy et al. observed that both severe sepsis and NISIRS led to a decrease in blood levels of APOB, although in septic patients, the decrease was significantly greater [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In our study, two lipoproteins (APOE and APOH) were found, although we observed that their role is greater in NISIRS than in sepsis, so future studies are necessary to confirm or not the findings.\u003c/p\u003e\u003cp\u003eIn sepsis, impaired blood coagulation and thrombus formation are an aspect of the host defense mechanisms [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In our study, we observed four proteins associated with coagulation, of which von Willebrand factor (VWF) plays the most significant role in sepsis. In the previously mentioned study by Cao et al., they also found the expression of proteins associated with coagulation pathways. Elevated levels of fibrinogen alpha chain, fibrinogen beta chain, fibrinogen gamma chain, and VWF were found in young adults who developed severe sepsis. Additionally, they found lower levels of antithrombin III (ATIII). Elevated levels of VWF are associated with increased mortality in sepsis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In a study involving septic patients and healthy controls, Kremer Hovinga et al. found significantly higher levels of VWF antigen in septic patients, with no relation to severity or prognosis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Singh et al. confirmed these findings, additionally observing that VWF levels were higher in those who did not survive sepsis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Liu and collaborators utilized two-dimensional electrophoresis (2-DE) and MALDI-TOF-MS techniques to identify platelet proteins whose expression differs between patients with sepsis and healthy individuals. The study results showed that five platelet proteins had increased expression in patients with sepsis: EFCAB7 (involved in calcium ion binding), actin (part of the cytoskeleton), IL-1b (a cytokine), GPIX (a membrane receptor), and GPIIb (an integrin). These proteins participate in inflammatory response processes and coagulation activation, emphasizing the fundamental role of platelets in the inflammation and coagulation triggered by sepsis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In a study on patients with sepsis due to CAP, several proteins related to the inflammatory response were found, including SAA1, ORM1, ATM, SERPINA1, SERPINA3, CRP, and LBP (upregulated) and F2, GSN, and APOE (downregulated). Some of these proteins are also key molecules in coagulation and bleeding processes [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn sepsis, the inflammatory reaction can damage organs such as the lungs, heart, and kidneys, increasing the risk of mortality. The biological processes that trigger this damage are not yet fully understood, and without specific biomarkers to diagnose or predict the progression of the disease, it can advance to a state of septic shock and even be fatal. To find these biomarkers, various studies have analyzed the proteome in the organs that become damaged during sepsis. Star BS et al. observed nine proteins, including carbonic anhydrase (CA1), which allowed differentiation between patients with sepsis admitted to the emergency department who developed acute kidney injury (AKI) within the first 24 hours and those septic patients who did not present AKI, suggesting that these could act as early biomarkers for the detection of sepsis-AKI [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Hashida et al. studied 20 patients with AKI upon admission to the ICU who received continuous renal replacement therapy, 10 of whom had sepsis. Protein was extracted from the adsorbates of the hemofilter and analyzed using proteomic techniques, identifying 197 proteins, of which 9 were found in all studied septic patients compared to non-septic patients [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Hinkelbein J et al. conducted a study in rats where they examined the proteome of cardiac tissue after subjecting the animals to a sepsis model induced by CLP. In their findings, they identified 12 proteins in the heart that showed significant alterations. Of these proteins, five (acyl-CoA synthetase 2-like, E1 component of 2-oxoglutarate dehydrogenase, oxoglutarate dehydrogenase, 2-oxoglutarate dehydrogenase complex, and succinate coenzyme A ligase) showed reduced expression 48 hours after sepsis was initiated. The decrease in these proteins is associated with a reduced capacity of the heart to produce energy adequately [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Mi Y et al developed a prediction model, identifying a small set of plasma proteins (USP15, COL1A2, APOA2, MAP1A, GNMT, TSPAN11, LCP1 and ALB) capable of classifying patients with sepsis into three subgroups of patients based on severity (SOFA, APACHE II) and mortality with high sensitivity, specificity and accuracy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our study did not aim to analyze the variation in protein expression in relation to organ dysfunction. Recently, our group has already determinated proteomic patterns associated with organ dysfunction and mortality in sepsis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has several limitations. The main one is that it only allows us to establish the association of these proteins with sepsis in comparison to patients with NISIRS, without providing information on their specific concentrations in patients. This limitation serves as a starting point for future research, which could include validation using techniques such as ELISA or targeted MS to accurately quantify these proteins. Second, this is a single-center study. Our results only have internal validity due to the demographic characteristics of the patients. Therefore, it would be necessary to conduct multicenter studies to validate and generalize the findings found in this study. Third, in septic patients group, samples are collected upon sepsis code activation, and although they are taken early in the course of sepsis, we cannot rule out that all patients present the same stage of evolution at a pathophysiological level at the time of sample collection, which could affect the results.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings of the present study suggest that there are differences between the proteomic profile of sepsis and non-infectious SIRS. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutics targets based in precision medicine in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; To Toni del Pino, Rosa Ras and Pol Herrero from the Proteomics and Metabolomics Area of the Center for Omic Sciences (COS), a Joint between Rovira I Virgili University and Eurecat (Reus, Spain), for their contribution to the proteomics analysis\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSamples and data from patients included in this study were provided by Sepsis Bank of the Vall d\u0026rsquo;Hebron University Hospital Biobank (PT20/00107), integrated in the Spanish National Biobanks Network, and they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committees. The authors kindly appreciate the generous donation of samples and clinical data of the donors of the Sepsis Bank of HUVH Biobank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.R-S, J.C.R-R.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; V.R.: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing \u0026ndash; original draft , Writing \u0026ndash; review \u0026amp; editing; D.S.: Formal analysis, Methodology; L.C-C, L.M., I.B.: Data curation, Formal analysis, Investigation, Validation; N.L., J.J.G., M.D.C.: Validation; N.C.: Formal analysis, Investigation, Methodology, Validation, Writing \u0026ndash; original draft; R.F.: : Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been funded by Eurecat 2017 Research Projects (Health Forecast 2.0. Omic stratification of patients with sepsis and septic shock).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Clinical Research Ethics Committee of Vall d\u0026apos;Hebron University Hospital [PR (AG) 11-2016, PR (AG) 336-2016, PR (AG) 210/2017], and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish has been obtained from patients or their relatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Departament de Medicina, Universitat Aut\u0026ograve;noma de Barcelona, Barcelona, Spain. 2. Intensive Care Department, Vall d\u0026apos;Hebron University Hospital, Vall d\u0026rsquo;Hebron Barcelona Hospital Campus, Barcelona, Spain. 3. Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d\u0026rsquo;Hebron Research Institute, Barcelona, Spain. 4. Eurecat, Centre Tecnol\u0026ograve;gic de Catalunya, Digital Health Unit, Barcelona, Spain. 5. Department of Clinical Microbiology, Vall d\u0026apos;Hebron University Hospital, Vall d\u0026rsquo;Hebron Barcelona Hospital Campus, Barcelona, Spain. 6. Eurecat, Centre Tecnol\u0026ograve;gic de Catalunya, Centre for Omic Sciences (COS), Joint Unit URV-EURECAT, Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain. 7. Department of Genetics and Microbiology, Universitat Aut\u0026ograve;noma de Barcelona, Barcelona, Spain. 8. CIBERINFEC, ISCIII \u0026ndash; CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain. 9. Post-cardiac Surgery Unit. Department of Intensive Care, Vall d\u0026apos;Hebron University Hospital, Vall d\u0026rsquo;Hebron Barcelona Hospital Campus, Barcelona, Spain.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger, M., Deutschman, C. S. \u0026amp; Seymour, C. W. The third international consensus definitions for sepsis and septic shock (Sepsis-3). \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e315\u003c/b\u003e, 801\u0026ndash;810 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLorencio C\u0026aacute;rdenas, C. et al. Trends in mortality in septic patients organ failure during 15 years. \u003cem\u003eCrit. Care\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 302 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eY\u0026eacute;benes, J. C. et al. Epidemiology of sepsis in Catalonia: analysis of incidence and outcomes in a European setting. \u003cem\u003eAnn. 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Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 19705 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHashida, T. et al. Proteome analysis of hemofilter adsorbates to identify novel substances of sepsis: a pilot study. \u003cem\u003eJ. Artif. Organs\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 132\u0026ndash;137 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHinkelbein, J., Kalenka, A., Schubert, C., Peterka, A. \u0026amp; Feldmann, R. E. Jr. Proteome and metabolome alterations in heart and liver indicate compromised energy production during sepsis. \u003cem\u003eProtein Pept. Lett.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 18\u0026ndash;31 (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, Septic shock, SIRS, Proteomics, Omics, Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7056801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7056801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. This study aims to identify protein biomarkers that differentiate sepsis from non-infectious systemic inflammatory response syndrome (NISIRS). This is a prospective and observational study, conducted between 2016 and 2017, it included 277 patients (141 with sepsis, 136 with NISIRS). Plasma proteins were analyzed using mass spectrometry and evaluated through recursive feature elimination and cross-validation with a vector classifier. Twenty-five proteins showed statistically significant differences, with high diagnostic performance (sensitivity: 0.973, specificity: 0.920, accuracy: 0.960, AUC: 0.985). Fourteen proteins (VWF, PPBP, C5, C1RL, FCN3, SAA2, ORM1, ITIH3, GSN, C1QA, CA1, CFB, C3, LBP) were more associated with sepsis, while eleven (FN1, IGFALS, SERPINA4, APOE, APOH, C6, SERPINA3, AHSG, LUM, ITIH2, SAA1) were related with NISIRS. The study found upregulation of several proteins in sepsis (C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP and ITIH3) and downregulation of others (SERPINA4 and AHSG). These findings highlight distinct proteomic patterns between sepsis and NISIRS. Advances in understanding these protein changes may allow for the identification of new biomarkers in the future.\u003c/p\u003e","manuscriptTitle":"Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 05:46:31","doi":"10.21203/rs.3.rs-7056801/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e6a423e-fa93-4fd3-a3c9-3e4443f16dee","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52004005,"name":"Health sciences/Biomarkers"},{"id":52004006,"name":"Health sciences/Diseases"},{"id":52004007,"name":"Biological sciences/Immunology"},{"id":52004008,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-08-28T05:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 05:46:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7056801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7056801","identity":"rs-7056801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}