Toward precision medicine: Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

doi:10.21203/rs.3.rs-4983859/v1

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Toward precision medicine: Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

https://doi.org/10.21203/rs.3.rs-4983859/v1

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Background

The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS).

Methods

Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016–2017. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination (RFE) classification and cross-validation with a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein-protein interaction network was analyzed with String software.

Results

277 patients were included (141 with sepsis and 136 with NISIRS). After performing RFE, 30 proteins (SERPINA4, ITIH1, ITIH3, SERPINA3, F12, FN1, SERPINA6, APOE, GSN, C3, SERPINF1, C5, LBP, CD14, FCN3, C6, C1RL, PRDX2, APOB, PPBP, SAA1, VWF, LRG1, AFM, BTD, ORM1, RBP4, LUM, COL1A1, CA1) demonstrated an association with sepsis compared to patients with NISIRS with an accuracy of 0.49 ± 0.035, precision of 0.967 ± 0.037, specificity of 0.910 ± 0.103, sensitivity of 0.964 ± 0.035 and an area under the curve (AUC) of 0.937. Of these PPBP, V1RL, C5, vWF and SERPINA4 have a greater association with Sepsis compared to NISIRS.

Conclusion

There are proteomic patterns associated with sepsis compared to NISIRS with different strength of association. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutic targets in the future.

Sepsis

Septic shock

SIRS

Proteomics

Omics

Diagnosis

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, 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 [12–14].

A deep understanding of the molecular and cellular mechanisms involved in sepsis essential for more accurate and early diagnosis, as well as the development of new therapeutic strategies [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 has 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 sepsis study 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].

The hypothesis of this study is 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.

Study design and ethical approval

This is a prospective, observational, single-center study with two study populations. One 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 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 who presented with two or more of the following variables: (1) White blood cell count > 12,000/mm3 or < 4,000/mm3, or > 10% immature cells, (2) the presence of hyperthermia (axillary temperature > 38.3 ºC) or hypothermia (axillary temperature < 36.0ºC), and/or tachycardia (> 100 beats per minute), tachypnea (> 30 breaths per minute), or desaturation (SpO2 < 90%) and (3) absence of infection. The inclusion criteria for the septic patients group encompassed adult patients ≥ 18 years of age with suspected or documented infrction and the presence of, at least, one of the following sets of variables, as outline by ISC: (1) an acute alteration in the level of consciousness not explained by other clinical conditions, or (2) the presence of hyperthermia (axillary temperature > 38.3 ºC) or hypothermia (axillary temperature < 36 ºC), and/or tachycardia (> 110 beats per minute), tachypnea (> 30 breaths per minute) or desaturation (SpO2 < 90%), as well as arterial hypotension (systolic blood pressure < 90 mmHg or mean arterial pressure < 65 mmHg or > 40 mmHg decreased in baseline systolic blood pressure).

Exclusion criteria include non-adult patients, pregnant women or patients in 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 (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.

Protein study

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 Mann-Whitney U test (p < 0.05) was used to assess differences between distributions. The Benjamini-Hochberg procedure was applied to control the FDR. Statistical analyses were conducted in Python 3.8 using the pandas, sklearn, spicy, and statsmodels libraries.

Protein selection

Protein selection was carried out in three steps. In the first step, the data was split into training and test sets, containing 80% and 20% of the data, respectively. Variables containing over 25% missing values (65 out of 177) were removed. Subsequently, variables were standardized using a z-score, adjusted using training data, and applied to both training and test sets. Missing values were imputed using the KNN method, fitted to the training data, and applied to both training and test sets. Finally, RFE classification was applied using logistic regression with cross-validated classifier for SIRS and sepsis. This method was executed with 10-fold cross-validation, accumulating each result and ultimately ranking each protein based on its elimination rate.

In a second step, 60 lists of proteins were created - the first list containing only the top-ranking protein, the second with the top two ranking proteins, and so on - and evaluated with 100-fold cross-validation using a support vector classifier (SVC). Performance metrics including accuracy, precision, recall, F1 score, and area under the curve (AUC) were used. For each protein list, the mean of 100 iterations was reported along with the spread of Accuracy, Precision, Recall, F1 score, and AUC, in order to select the best protein list for developing the classification model. The significance of the resulting classifier was tested using a permutation test score with the scikit-learn 1.0.2 package implemented in Python.

The impact of the protein on sepsis and SIRS was assessed through the extraction of the most similar SVC (that is, the SVC closest to the mean of results from the 100 experiments conducted). The coefficients of the SVC were analyzed using their additive Shapley explanations (shape values in summary). Proteins with positive Shapley values were associated with sepsis, while negative Shapley values were associated with SIRS. The strength of association between the Shapley value and the outcome (sepsis and SIRS) was measured by the magnitude of these Shapley values.

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/).

Characteristics of the study population

A total of 277 patients were included in this study, with 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. In 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.
Characteristics	Total (n = 277)	Sepsis (n = 141)	NISIRS (n = 136)	P
Male, n (%)	162 (58.48)	85 (60.28)	77 (56.61)	0.53
Age years (m ± SD)	63.38 ± 15.61	63.9 ± 15.6	62.8 ± 15.5	0.54
SOFA score median (25th,75th)	5 (3,7)	7 (5,8)	3 (2,6)	< 0.05
Norepinephrine, n(%)	121 (43.68)	76 (53.90)	45 (33.08)	< 0.05
ICU admission, n (%)	206 (74.4)	70 (49.64)	136 (100)	< 0.05
Mechanical Ventilation, n (%)	177 (63.9)	41 (29.07)	136 (100)	< 0.05
Leucocytes x 10⁶, (mean ± SD)	14118 ± 9149	13501 ± 11021	14757 ± 661	0.25
Platelets x 10⁹, median (25th,75th)	130.85 (116.0-227.5)	184.00(114.0-278.5)	157.00(119.5-195.2)	< 0.05
Lactate mmol/L, median (25th,75th)	1.9 (1.4,3.1)	2.5 (1.8,4.1)	1.5 (1.0,1.9)	< 0.05
CRP mg/dL, (mean ± SD)	38.96 ± 21.89	38.96 ± 21.89	4.68 ± 2.90	< 0.05
Mortality, n(%)	35 (12.6)	33 (24.2)	2 (1.4)	< 0.05

CRP: C-reactive protein.

Proteomic study results

Initially, a total of 110 proteins were identified through mass spectrometry for the proteomic evaluation of differences between SIRS and Sepsis. Among them, 30 proteins in septic patients showed statistical significance, through RFE, with an accuracy of 0.49 ± 0.035, precision of 0.967 ± 0.037, specificity of 0.910 ± 0.103, sensitivity of 0.964 ± 0.035, and an AUC of 0.937 ± 0.053. The analyzed proteins are presented in Table 2.

Table 2

Proteins analyzed and their relationship with sepsis
Proteins associated to sepsis
C5 - Complement C5 alpha' chain C6 - Complement component C6 APOE - Apolipoprotein E C1RL - Complement C1r subcomponent-like protein FCN3 - Ficolin-3 GSN - Gelsolin C3 - Complement C3c alpha' chain fragment 1 SERPINA3 - Alpha-1-antichymotrypsin LBP - Lipopolysaccharide-binding protein SERPINF1 - Pigment epithelium-derived factor ITIH3 - Inter-alpha-trypsin inhibitor heavy chain H3 CD14 - Monocyte differentiation antigen CD14 ITIH1 - Inter-alpha-trypsin inhibitor heavy chain H1 RBP4 - Plasma retinol-binding protein ORM1 - Alpha-1-acid glycoprotein 1	PPBP - Connective tissue-activating peptide III SERPINA4 - Kallistatin VWF - Von Willebrand antigen 2 COL1A1 - Collagen alpha-1 chain FN1 - Fibronectin CA1 - Carbonic anhydrase 1 LUM - Lumican PRDX2 - Peroxiredoxin-2 SERPINA6 - Corticosteroid-binding globulin LRG1 - Leucine rich alpha-2-glycoprotein 1 F12 - Coagulation factor XIIa heavy chain BTD - Biotinidase SAA1 - Serum amyloid protein A AFM - Afamin APOB - Apolipoprotein B-100

Of the 30 proteins found related to sepsis, twelve are involved in the regulation of proteolysis (SERPINA4, ITIH1, ITIH3, SERPINA3, F12, FN1, SERPINA6, APOE, GSN, C3, SERPINF1, C5), nine in innate immune response (LBP, CD14, F12, FCN3, C3, GSN, C5, C6, C1RL), five in complement activation (C1RL, C3, C5, C6, FCN3), five in response to lipopolysaccharides (LBP, CD14, PRDX2, APOB, PPBP), four in blood coagulation (F12, SAA1, FN1, VWF), two in lipid metabolism (APOE, APOB), and eight proteins serve other functions (LRG1, AFM, BTD, ORM1, RBP4, LUM, COL1A1, CA1). The relationship among these proteins is presented in Fig. 1.

When applying the SVC model and analyzing its additive shape values, it was observed that the presence of 7 proteins had a stronger association with sepsis than the rest. In terms of association strength, these were PPBP (+ 0.09), C1RL (+ 0.08), C5 (+ 0.07), VWF (+ 0.05), SERPINA4 (+ 0.05), GSN (+ 0.04), and LBP (+ 0.04). Conversely, 5 proteins exhibited a weaker association with the septic process. These were ITIH3 (+ 0.01), CAI (+ 0.01), RBP4 (+ 0.00), PRDX2 (+ 0.00), and SERPINA6 (+ 0.00) (Fig. 2).

This study demonstrates the existence of proteomic patterns associated with sepsis in comparison to patients with NISIRS. Thirty proteins have been detected with varying degrees of association with sepsis. Among these, those with the strongest association are PPBP (+ 0.09), CRL1 (+ 0.08), C5 (+ 0.07), VWF (+ 0.05), and SERPINA4 (+ 0.05), while proteins with weaker associations include RBP4 (0), PRX2 (0), and SERPINA6 (0). Furthermore, the different proteins observed in this study participate in various physiological metabolic pathways, highlighting sepsis as a highly complex process involving diverse biological processes.

Early diagnosis and prognosis of sepsis are crucial in medical research. Mass spectrometry (MS) is a highly powerful and sensitive analytical technique that enables the identification and measurement of molecules based on their mass. Using MS, it is possible to obtain information about multiple identified molecules simultaneously through a targeted approach or even analyze hundreds or thousands of compounds using a broader approach. In the initial research stages, non-targeted MS approaches are often employed to compare samples from different populations. Once a compound that exhibits differential levels between these populations is identified, a targeted approach can be used in a later stage to thoroughly analyze the suspected biomarkers [22].

Proteolysis

For decades, it has been known that sepsis induces proteolysis [23]. Bauzá-Martínez et al. observed that the plasma of patients with septic shock showed an approximately three-fold increase in the total peptide count compared to healthy individuals, indicating elevated proteolysis above physiological levels in septic shock [24].

SERPINA4 or Kallistatin is a glycoprotein with protective activity in vascular injury due to its anti-inflammatory and antioxidant effects [25]. In our study we have observed that this protein has an important role in sepsis with a shap value of 0.05. Chao et al. had already observed low levels of Kallistatin in 10 sepsis patients [26]. Lin et al. also observed reduced levels of Kallistatin in 54 patients with Community-Acquired Pneumonia, establishing a direct correlation between the levels of this glycoprotein and the severity of sepsis [27].

GSN or Gelsolin is a protein that inhibits cell apoptosis and can regulate macrophage function in inflammatory processes [28] and that in our study has an intermediate role in sepsis (+ 0.04). Halis et al. observed a significant decrease in plasma gelsolin levels in septic patients. After resolution of the septic process, these levels normalized, concluding that plasma gelsolin could be a useful marker for severe sepsis. [29]. It has even been observed, in a murine study, that the administration of recombinant plasma gelsolin can modulate the inflammatory response while increasing the host's antibacterial activity [30].

FN1 or Fibronectin, also in our study with an intermediate role in sepsis (+ 0.03), is a high molecular weight glycoprotein involved in many processes, including cell adhesion, proliferation, embryonic development, and matrix remodeling [31], with its primary functions related to the host's response to infection [32]. Ruiz Martín et al. observed a decrease in plasma FN1 levels in septic patients compared to a control group [33]. The same conclusion was reached by Lemańska-Perek et al. in 71 septic patients compared to a control group. Furthermore, lower FN1 levels were associated with higher mortality, giving FN1 a prognostic role [34].

Alpha-1 Antitrypsin or SERPINA3, in our study with a shap value of 0.03, is a glycoprotein whose main function is to inhibit serine proteases such as chymotrypsins, cathepsin G, and mast cell chymases by binding to them in a stable complex, preventing their proteolytic activity [35]. Čaval et al. observed changes in the glycosylation of this protein at various stages in septic patients. These changes are even observed in the early stages of sepsis, leading to its dysfunctionality and, consequently, proteolytic activation [36]. Sehgal et al observed that in cirrhotic patients with sepsis, the SERPINA3 protein was downregulated, leading to increased activation and autophagy of neutrophils [37].

Innate immune response

In our study, C1RL or Component C1r subcomponent-like shows a significant association with sepsis (+ 0.08). The function of this protein is not clear, but it appears to be involved, among other, in the proteolytic cleavage of a proform of haptoglobin [38]. Haptoglobin is a glycoprotein whose concentration increases during inflammation and also has several immunomodulatory functions, including the regulation of T cell-mediated immune responses [39]. Although this protein has been associated with the development of several types of cancers [40, 41], in this study we describe for first time the association of C1RL with sepsis. LBP or Lipopolysaccharide Binding Protein is an acute-phase protein that binds to lipopolysaccharides (LPS), a component of the outer wall of gram-negative bacteria, and triggers immune response activation [42]. Our study demonstrates an intermediate role in sepsis (+ 0.04). This protein has been studied in multiple studies as a sepsis biomarker [43–45], although in a meta-analysis, LBP showed low sensitivity and specificity in sepsis detection, so it is not recommended in clinical practice as a standalone biomarker [46].

FCN3 or Ficolin-3, in our study with a low association in sepsis (+ 0.02), is a protein produced in various organs of the body that binds to a wide spectrum of microorganisms, suggesting its involvement in host defense against a broad range of microbial infections [47]. Schlapbach et al. observed that low FCN3 concentration was associated with a higher risk of neutropenic fever, particularly with bacteremia, in children undergoing cancer chemotherapy. Therefore, low levels of FCN3 represent a new risk factor for chemotherapy-related infections [48]. Snipsøyr et al. studied patients with infectious endocarditis and observed a downregulation of this protein [49].

CD14 (low association in sepsis with a shap value of + 0.01) is an antigen present on the membrane of monocytes, macrophages, and dendritic cells, whose function is to bind to the lipopolysaccharides of Gram-negative bacteria [50]. Chen et al. conducted a murine study where, in mice induced with sepsis after cecal puncture, an elevation in CD14 expression was observed [51]. Furthermore, Edge et al. demonstrated that combined inhibition of C5 and CD14 efficiently attenuates organ inflammation induced by E. coli sepsis in pigs [52].

Complement activation

Another group of proteins that play a significant role in sepsis is the complement system, a key component of the innate immune system against pathogens [53]. In our study we have observed that there are various complement-associated proteins that have varying degrees of association with sepsis. Just as CRL1 (+ 0.08), already mentioned in the previous section as C5 (+ 0.05) play an important role in sepsis with respect to patients with NISIRS, others such as C6 (+ 0.03), C3 (+ 0.02) and FCN3 (0.02), its association with sepsis is minor. Complement activation (via the classical or alternative pathway) triggers proteases that cleave C3 and C5 into C3a and C5a, leading to the formation of the terminal complement complex. This complex binds to infected cells and induces cell lysis [54]. More than two decades ago, Stöve et al. observed that C3a levels and the C3a/C3 ratios, during the first 24 hours following the clinical onset of sepsis, were significantly higher in sepsis patients compared to those with SIRS. C3 levels for septic or SIRS patients were significantly lower than those of healthy donors. Interestingly, the C3a levels of septic patients decreased with appropriate treatment. Furthermore, they found that complement activation was much lower in SIRS patients than in septic patients [55]. De Nooijer et al. also observed that in septic patients, there is a decrease in complement components C3 and C5 and an increase in C3a, C3c, C5a [56]. In addition, no differences were observed in complement factor concentrations between patients with different immunological endotypes: hyperinflammation or immunoparalysis. Consequently, no correlation was observed with other inflammatory parameters, disease severity, or mortality.

C6 is the complement component that binds to complement C5b along with C7, C8, and C9, forming the membrane attack complex. This complex creates a pore in the membrane, leading to pathogen lysis [57]. Several studies demonstrate that patients with a deficiency in complement component C6 are associated with increased susceptibility to meningococcal infection. Petersen et al. observed that thirteen patients who had experienced at least one episode, typically two or more episodes, of bacteremia caused by Neisseria meningitidis or Neisseria gonorrhoeae had a deficiency in complement components C6, C7, or C8 [58]. Ellison et al. evaluated the complement system in 20 patients who presented with a first episode of meningococcal meningitis, meningococcemia, or meningococcal pericarditis and found that three of these six patients had a deficiency in a terminal pathway protein, C6 and C8 [59]. In contrast, a murine study involving mice in which the C6 protein had been eliminated observed defective innate responses in surface adhesion molecules, the generation of superoxide anion, the appearance of reactive oxygen species, and histone release after PMN activation, along with defective phagocytosis. When these mice were subjected to polymicrobial sepsis, they exhibited reduced organ dysfunction due to lower levels of proinflammatory cytokines and chemokines in the plasma and lower histone levels in the plasma [60].

Response to lipopolysaccharides

Lipopolysaccharides (LPS) are molecules present in the outer membrane of Gram-negative bacteria. LPS can be recognized by toll-like receptor 4, found in monocytes, macrophages, neutrophils, and dendritic cells, activating the immune system [61]. Of all the proteins found in our study, PPBP or Connective tissue-activating peptide III is the one with the strongest association with sepsis (+ 0.09). This is a peptide with demonstrated antimicrobial capabilities found inside platelets [62]. Tang et al, using proteomic techniques, were able to isolate 7 proteins, among which PPBP was included. In vitro, they were exposed to cultures with different bacteria (E. coli and Staphylococcus aureus) and fungi (Cryptococcus neoformans and Candida albicans) and observed that there was good antimicrobial activity against the first three germs with no activity against C. albicans [63]. Smith et al analyzed the expression of PPBP in patients with chronic cavitary pulmonary aspergillosis (CCPA) and allergic bronchopulmonary aspergillosis (ABPA), observing a significant increase in PPBP expression and protein levels (ABPA − 19.7 times and CCPA − 27.7 times) compared to control groups. They conclude that in patients with the described characteristics, PPBP levels could be used as a diagnostic biomarker in these types of patients [64].

Blood coagulation

VWF or Von Willebrand factor, with a shap value in our study of + 0.05, is a protein with prothrombotic and proinflammatory capabilities. Additionally, VWF serves as a binding site for ligands of bacteria that cause potentially life-threatening local and systemic infections, such as Staphylococcus aureus and Streptococcus pneumoniae [65]. In a study comparing forty patients with severe sepsis and septic shock with forty healthy controls, Kremer Hovinga et al observed that VWF antigen levels were significantly higher in septic patients compared to controls, with no correlation with disease severity, organ dysfunction, or outcome [66]. Singh et al reached the same conclusion in a recent study comparing septic and non-septic patients, although the difference from the previous study is that VWF antigen levels were higher in non-surviving patients compared to those who survived sepsis [67].

In our study, F12 or Coagulation factor XIIa heavy chain shows a weak association with sepsis compared to patients with NISIRS (+ 0.01). However, some studies suggest that this protein contributes to the host's pathological response to certain infectious organisms, leading to septic processes. Matsumoto et al observed, in a murine study, that the administration of a protease from Serratia marcescens resulted in activation of F12 and prekallikrein in guinea pigs, increasing vascular permeability due to inflammatory processes [68]. Kaminishi et al, in an in vitro study, exposed normal human plasma to proteases from Candida albicans, Pseudomonas aeruginosa and Serratia marcescens, observing that F12 activation occurred, leading to thrombin formation. This suggests that systemic blood circulation can be affected by the activation of the blood coagulation cascade by microbial infections, especially in septic patients, facilitating disseminated intravascular coagulation and multiorgan failure [69].

Lipid metabolism

The role of lipoproteins in sepsis is increasingly recognized as a fundamental aspect of the host's early response to infection [70]. In our study, two lipoproteins are associated with sepsis, APOE and APOB, with the former showing the strongest association (+ 0.03). Fu et al conducted a study with 279 pediatric patients with various infections compared to 58 healthy controls. They observed that serum ApoE levels increased significantly in cases of bacterial infections, including sepsis, bacterial meningitis, and bacterial pneumonia, compared to healthy controls. No significantly elevated serum ApoE levels were observed in patients with aseptic meningitis or mycoplasma pneumonia [71]. Wang et al, also in a pediatric study, found that ApoE levels in cerebrospinal fluid (CSF) increased significantly in patients with bacterial meningitis (with an optimal cutoff value of CSF ApoE > 1.7 mg/L with a sensitivity of 85% and specificity of 100%), and serum ApoE was markedly elevated in patients with sepsis or bacterial meningitis (with an optimal serum cutoff value of > 42 mg/L with a sensitivity of 80% and specificity of 93%) compared to patients with other infections and uninfected children [72]. In a proteomics study on sepsis, Li et al identified several proteins, including APOE. They observed that the expression of this lipoprotein is increased in comparison to non-septic patients with an AUC of 0.619 (95% CI: 0.510–0.719) [73]. Tripp et al, in a study with rats, injected live colonies of Escherichia coli and found that ApoB production increased 2.6 times in the septic groups compared to uninfected rat controls (P = 0.037), while ApoE production decreased 2.9 times in the control group (P = 0.036) [74]. Conversely, Kumaraswamy observed that both severe sepsis and NISIRS led to a decrease in blood APOB levels, although in septic patients, it was a significantly greater decrease [75].

Other proteins

In our patients, we have observed other proteins with physiological functions different from the ones mentioned earlier. ORM1 or Alpha-1-acid glycoprotein 1 is the protein, in this section, that has a stronger association with sepsis (+ 0.03). It is a protein produced by the liver and peripheral tissues in response to systemic inflammation with immunomodulatory and substance transport capabilities related to inflammation [76]. Astrup et al, in a murine study, observed an increase of approximately 400–500 times in ORM1 in rats with cerebral infarction and cerebral abscesses compared to those with cerebral infarction alone [77]. In human studies, Lu et al observed that ORM1 expression was upregulated in sepsis patients compared to patients without sepsis upon discharge [78]. Similar results have been observed in a study with 25 sepsis patients compared to 25 healthy controls [79].

LUM or Lumican is a leucine-rich proteoglycan that binds to collagen and modifies the structure of collagen-rich connective tissues, in our study with an intermediate strength of association in sepsis of + 0.03. Subsequent studies have shown that it regulates the detection of bacterial lipopolysaccharides through the Toll-like receptor 4 signaling pathway and the innate immune response [80]. Maiti et al measured the abundance of LUM in the plasma of 11 sepsis patients and 17 healthy individuals and found a small but consistent increase in sepsis patients (26.41 ± 1.54 ng/ml versus 21.0 ± 1.19 ng/ml) [81].

In our study, LGR1 or Leucine rich alpha-2-glycoprotein 1 has a low association with sepsis (+ 0.02). Gong et al conducted a proteomic study on samples from 89 sepsis patients and 67 healthy controls. They observed that in septic patients, there were a series of genes upregulated upon discharge that are closely related and cooperate to respond to different types of insults, including those expressing LGR1 and ORM1 [82]. Similar results regarding LGR1 were found by Lai et al, comparing 233 sepsis patients and 70 healthy controls with AUCs exceeding 0.95 [83].

This study has several limitations. The first and main is that it only allows us to determine the degree of association that these proteins have with sepsis compared to patients with NISIRS, but we do not know the concentrations at which they are present in the patients. We cannot determine whether the expression is upregulated or downregulated compared to other studies.

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, 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.

ABPA

Allergic bronchopulmonary aspergil·losis

AUC

Area under the curve

CCPA

Chronic cavitary pulmonary aspergil·losis

CRP

C-reactive protein

CSF

Cerebrospinal fluid

FDR

False discovery rate

ISC

Intra-hospitalalty sepsis code

KNN

K-nearest neighbor

LPS

Lipopolysaccharides

Mass spectrometry

NISIRS

Non-infectious systemic inflammatory response syndrome

PCT

procalcitonin

qSOFA

Quick SOFA

RFE

Recursive feature elimination

SIRS

Systemic inflammatory response syndrome

SOFA

Sequential Organ Failure Assessment

SVC

Support vector classifier

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).

Availability of data and materials

Not applicable.

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.

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No competing interests reported.

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Toward precision medicine: Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

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Abstract

Background

Methods

Results

Conclusion

Figures

INTRODUCTION

METHOD

Study design and ethical approval

Inclusion and exclusion criteria

Data collection and biomarker measurements

Protein study

Protein extraction and quantification

Protein digestion and peptide 10-plex TMT labeling

NanoLC-(Orbitrap)MS/MS analysis

Protein identification/quantification

STATISTICAL ANALYSIS

Protein selection

RESULTS

Characteristics of the study population

Proteomic study results

DISCUSSION

Proteolysis

Innate immune response

Complement activation

Response to lipopolysaccharides

Blood coagulation

Lipid metabolism

Other proteins

CONCLUSION

Abbreviations

Declarations

References

Additional Declarations

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