key: cord-0856533-iy339xx1
authors: Aceti, Antonio; Margarucci, Lory Marika; Scaramucci, Elena; Orsini, Massimiliano; Salerno, Gerardo; Di Sante, Gabriele; Gianfranceschi, Gianluca; Di Liddo, Rosa; Valeriani, Federica; Ria, Francesco; Simmaco, Maurizio; Parnigotto, Pier Paolo; Vitali, Matteo; Romano Spica, Vincenzo; Michetti, Fabrizio
title: Serum S100B protein as a marker of severity in Covid-19 patients
date: 2020-10-29
journal: Sci Rep
DOI: 10.1038/s41598-020-75618-0
sha: 47f951dd2a8ae511c44f1b97c82c2a33511bcdf7
doc_id: 856533
cord_uid: iy339xx1

SARS-CoV-2 infection shows a wide-ranging clinical severity, requiring prognostic markers. We focused on S100B, a calcium-binding protein present in biological fluids, being a reliable biomarker in disorders having inflammatory processes as common basis and RAGE as main receptor. Since Covid-19 is characterized by a potent inflammatory response also involving RAGE, we tested if S100B serum levels were related to disease severity. Serum samples (n = 74) were collected from hospitalized SARS-CoV-2 positive patients admitted to Covid center. Illness severity was established by admission clinical criteria and Covid risk score. Treatment protocols followed WHO guidelines available at the time. Circulating S100B was determined by ELISA assay. Statistical analysis used Pearson’s χ(2) test, t-Test, and ANOVA, ANCOVA, Linear Regression. S100B was detected in serum from Covid-19 patients, significantly correlating with disease severity as shown both by the level of intensity of care (p < 0.006) as well by the value of Covid score (Multiple R-squared: 0.3751); the correlation between Covid-Score and S100B was 0.61 (p < 0.01). S100B concentration was associated with inflammation markers (Ferritin, C-Reactive Protein, Procalcitonin), and organ damage markers (Alanine Aminotransferase, Creatinine). Serum S100B plays a role in Covid-19 and can represent a marker of clinical severity in Sars-CoV-2 infected patients.

| (2020) 10:18665 | https://doi.org/10.1038/s41598-020-75618-0 www.nature.com/scientificreports/ pathological conditions, reasonably referred to inflammatory processes 12 . Based on these findings, S100B is considered a reliable biomarker for a variety of neural and non-neural pathological conditions, even displaying a predictive role [14] [15] [16] [17] .

Indeed, S100B appears to share similar characteristics to DAMPs molecules, including the interaction with RAGE and, once released in the microenvironment, the active participation to the inflammatory tissue reaction to damage 10, 18 . Interestingly, the S100B-RAGE axes has been shown to participate in pulmonary inflammatory processes 19, 20 .

Thus, in the light of the notion that SARS CoV-2 infection can induce a severe acute respiratory syndrome with a complex pattern of clinical manifestations characterized by a potent inflammatory response 18 ,21-23 we tested the possibility that S100B could be present in detectable amounts in serum of Covid-19 patients, as well as the possible relationship between severity of the disease and increase in S100B serum levels.

Results S100B is present in serum of Covid patients, correlating with disease severity. S100B was detected at concentrations over the LOD in the serum of 19 patients out of 74 (25.7%), ranging from 0.25 to 29.46 ng/mL. Results obtained by non-parametric Wilcox test showed a positive significant association (p < 0.001) between S100B serum concentrations and the severity of the disease as measured based on HIC or LIC wards where the Covid-19 patients were hospitalized. S100B levels showed a higher mean value in HIC than in LIC (8.80 ± 10.24 and 0.62 ± 2.10 ng/mL, respectively; p < 0.006), as reported in Table 1 and Fig. 1 .

No statistically significant differences were observed for other variable and in particular among the groups for age, gender and number of days of hospitalization to the date of blood sampling. The Wilcox test showed no statistically significant differences between S100B levels and gender.

Finally, the number of patients positive for serum S100B was significantly higher in HIC (p < 0.01). This result was confirmed by both Welch-ANOVA model (p < 0.01) and Games-Howell test (p < 0.01). The incremental trend of S100B vs clinical severity measured by Covid-score was statistically significant. Figure 2 shows the distribution of S100B concentration and Covid Score in all patients (A) and the subgroup of patient where S100B was detected in the serum (B). Most patients admitted in LIC ward show a presence of S100B below the LOD, even if with different values of Covid Score (A). Additional information is reported in supplementary material (Figs. S1 and S2). When considering only those samples over the LOD, a hypothetical trendline was extrapolated (B), suggesting a theorical distribution. Both trendlines show significant correlations between S100B and the score for evaluating clinical risk (multiple R-squared 0.4369 and 0.3751, respectively), supporting a putative role of S100B as a marker for clinical severity. S100B correlates with several blood markers. A positive and significant correlation was observed between S100B and Ferritin concentrations (p < 0.01, 74 Observations), as well as for other parameters (Table 2) , and in particular PCT (p < 0.01), d-Dimer (p < 0.05) and CRP (p < 0.1). The ANCOVA model displayed the marginal effects of the CRP and the hospitalization ward (HIC as corner point) variables on the S100B levels, showing a positive and statistically significant relation (p < 0.05 and p < 0.01, respectively). Similar results were found for PCT (p < 0.01) and ALT (p < 0.01). In order to estimate the marginal effects of the measured parameters, a stepwise selection procedure was carried out, showing ALT and PCT variables as the best predictors of the S100B levels. A further linear regression was used to verify the single and interactive effects of CRP, ALT, PCT variables, showing a significant interaction with ALT and PCT (p < 0.001) and a correlation with CRP (Fig. 3) . The whole www.nature.com/scientificreports/ of the results suggests that both S100B presence and levels correlate with the severity of the disease, the trend of CRP and Ferritin values and inflammatory status, as well as with other key parameters of Covid-19 severity, such as PCT, d-Dimer, ALT. Moreover, we observed a significant correlation between S100B and CRE levels (p < 0.01) in the subgroup of patients with high levels of ALT (> 40 IU/L, n = 19) 18 , thus suggesting the possibility of an independent association with a liver and/or kidney damage.

In this study, we investigated the levels of circulating S100B in serum of patients affected by SARS-CoV-2 virus at various stages of the disease. S100B serum concentrations resulted correlated with the severity of the disease, as indicated by clinical/laboratory parameters. The major part of data at present available on the S100B protein as a biomarker and pathogenic factor deals with disorders primarily related to the nervous system whereas disorders primarily related to other systems having been essentially disregarded 12 : thus, present results appear to enlarge the field of investigation on this protein and its potentials in Covid-19 and, more in general, infectious diseases. Indeed, the discrete distribution of S100B in definite extra-neural cell types offers the basis for a functional/ pathogenic role of the protein in extra-neural tissues, which at present has not been exhaustively investigated. This study was performed during the epidemic peak, with the advantage of collecting homogeneous data from the same outbreak but with all the restrictions present while managing the emergency, including the limits in sample size and in the number of additional clinical or laboratory parameters to be assessed. However, the acquired data were stressed by an accurate statistical analysis, strongly reporting a role for S100B in Covid-19.

The increased levels of S100B are reasonably related to inflammatory processes, as also supported by its significant correlation with CRP, which is known to be a recognized inflammatory hallmark 24 . S100B is known to participate in inflammatory processes 25, 26 which are also known to be raised during SARS-CoV-2 disease 19, 21 . Interestingly, in this respect we observed that S100B levels are correlated with indicators of distress involving non-neural districts, such as ALT, d-Dimer, PCT, suggesting, in this case, a wider and systemic valence for S100B as a putative biomarker.

The source of increased serum S100B in SARS-CoV-2 patients remains to be identified. Since information indicating a prevalent involvement of the nervous system in pathogenic processes of SARS-CoV-2 at present is lacking 27 , it seems unlikely that in this case the protein is primarily released from this tissue, which at present is regarded to be the natural source of the protein in biological fluids in the major part of pathological conditions already known. Among the cell types which are known to express and putatively release the protein, adipocytes, dendritic and lymphoid cell types [28] [29] [30] [31] appear to be putative sources for serum S100B in this disease. Adipocytes are known to secrete inflammatory cytokines which play a recognized role in crosstalk with the immune www.nature.com/scientificreports/ system, which at present are known to be especially relevant in processes leading to obesity 32, 33 . Since the role(s) of molecules secreted by this intriguing cell type, including S100B, is at present largely unknown, this finding might add a novel element deserving interest. In the case of dendritic and lymphoid cell types, their role in inflammatory processes is widely known 34, 35 , so that the mechanistic involvement of S100B in their function would merely increase the breadth of knowledge in this respect. It is interesting to note that, under pulmonary inflammation, S100B has been reported to be upregulated in bronchiolar epithelial cells and airway dendritic cells 36, 37 . Moreover, as shown in alveolar cell types, S100B can stimulate the secretion of pro-inflammatory cytokines, that are commonly involved in lung inflammation, following a similar process suspected to be present also in Covid-19 [19] [20] [21] [22] 36, 37 . Additional studies will be needed in order to define the source of serum S100B in patients affected by SARS-CoV-2, but the finding of increased serum levels of the protein, correlated with the gravity of the disease and inflammatory processes, offers a novel biomarker potentially useful to monitor the disease. In addition, in the light of growing evidence candidating S100B as an active factor in inflammatory processes 12 , the present findings may even propose the protein as a therapeutic target to counteract the potent inflammatory processes characterizing this infectious disease.

In conclusion, increased serum levels of S100B correlate with the severity of Covid-19 and inflammatory processes, offering a novel biomarker potentially beneficial in monitoring the disease course and prognosis. In the light of growing evidence candidating S100B as an active factor in inflammatory processes driven by DAMP and RAGE, the present findings propose this protein as a severity marker and its cellular pathways as candidate targets to unravel pathogenetic mechanisms and counteract the potent inflammatory processes characterizing SARS-CoV-2 infection.

Relationship between Covid-Score and concentration of S100B. The scatterplot shows a positive correlation between S100B concentration (ng/mL) and clinical severity of the disease as represented by Covidscore. Analysis considering all samples (A) or only those with S100B detected in serum (B), from both HIC and LIC wards. Most of the samples with a concentration of S100B below the LOD belongs to the group hospitalized in the LIC wards (A). When considering only patients with S100B over the LOD and from both wards (B), the regression equation was Y = 0.584X−32.778. Figure A is reported as a comparison respect to figure B to highlight the distribution of S100B levels below the LOD and the independent linear regression curves are reported in supplementary materials (S1). The hospitalization ward is indicated for each patient (Red Dots: Low Intensity Care (LIC); Blue Triangles: High Intensive Care (HIC). The linear regression lines and their confidence intervals (95%) are showed by the gray areas. The correlation between Covid-Score and S100B is equal to 0.66 (p < 0.001) (A) and 0.61 (p < 0.01) (B). [4] [5] [6] [7] 9 . Main variables incorporated in the Covid-score included: older age, male sex, comorbidities, respiratory rate, oxygenation, radiographic severity, higher neutrophils, higher CRP and lower albumin at presentation, predicted critical care admission and mortality; in particular: age > 50, male, oxygen saturation < 93%, radiological severity score > 3, neutrophil count > 8.0 × 10 9 /L, CRP > 40 mg/L, albumin < 34 g/L, creatinine > 100 μmol/L, comorbidity and chronic lung disease, ALT > 40 IU/L; Creatinine > 100 μmol/L; D-dimer > 0.5 μg/L; Prothrombin-time > 16 s; Ferritin > 300 μg/L; Procalcitonin > 0.1 ng/mL. Namely, patients recovered in HIC (n = 16) presented: severe pneumonia (fever or suspected respiratory infection, plus one of the following: respiratory rate > 30 breaths/min; severe respiratory distress or SpO2 ≤ 93% on room air); and LIC (n = 58) comprehended a group spanning from uncomplicated disease to pneumonia but without signs of severe pneumonia. In both groups, all patients received treatment in accordance with the guidelines available at the time of the study 1 . Time of hospitalization ranged from 7 to 85 days (average: 29.97). Table 1 shows patients' characteristics together with data obtained on serum samples. Patients were balanced with respect to gender (female = 49%), their age ranging from 32 to 89 (median: 66.0). Serum samples from healthy subjects (n = 5) negative for SARS-CoV-2 detection by PCR and negative by serologic test were used as controls for the ELISA test. Table 2 . Correlation matrix: Pearson correlation coefficients and relative p values. Statistically significant results are highlighted in bold. n number of patients. a S100B protein, ng/mL. b White Blood Cell count per mm 3 . c Lymphocyte count per mm 3 . d Alanine Aminotransferase-IU/L. e Creatinine-mg/dL. f d-Dimer-ng/ mL. g Prothrombin-seconds. h Ferritin-mg/L. i Procalcitonin-ng/mL. j C Reactive Protein-mg/dL. k Patients' age-years. l Period from hospitalization to blood sample collection-days. m Period from blood sample collection to hospital discharge-days. n Covid score-%. + p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001. Laboratory Tests. The serum S100B concentrations were measured by adapting the enzyme-linked immunosorbent assay (ELISA) kit following manufacturer protocols (S100B ELISA Kit, Abcam, England). Samples resulting below the detection limit of the test (LOD, equal to 0.1 ng/mL) were considered negative and assigned a value of 0 ng/mL. From the same serum, a panel of consolidated blood markers was also assessed, including White Blood Cell count (WBC), Lymphocytes (LYM), Alanine Aminotransferase (ALT), Creatinine (CRE), d-Dimer, Prothrombin (PT), Ferritin (FERR), Procalcitonin (PCT), C Reactive Protein (CRP).

Statistical analysis. Continuous variables were S100B serum concentration (ng/mL), WBC (count per mm 3 ), LYM (count per mm 3 ), ALT (IU/L), CRE (mg/dL), d-Dimer (ng/mL), PT (seconds), FERR (ng/mL), PCT (pg/mL), CRP (mg/dL), period from hospitalization to blood sample collection (days), period from sample collection to hospital discharge (days), severity of the disease (Covid score), age (years). Categorical variables were gender (male = 0, female = 1), S100B protein in detectable amount (below LOD = 0 and over LOD = 1), hospitalization ward (LIC = 0 and HIC = 1). Relations between continuous variables were compared by Pearson's correlation. Pearson's χ 2 test was used to compare the frequencies of the categorical variables and non parametric test Wilcox or Kruskal were used to compare the two groups under the non-normality assumption. Linear models (ANOVA, ANCOVA, and Linear Regression) were performed to evaluate the effect of variables observed on concentration of S100B. Whenever the test of homogeneity of variance was not satisfied, the ANOVA results were substituted by those from Welch ANOVA. Tukey test were performed to evaluate the difference of means between groups. If equal variance assumption is violated during the ANOVA process, pairwise comparison was based on the Games-Howell statistics. Stepwise regression was performed to obtain the model with the best predictors. All analyses were considered statistically significant at a p value of less than 0.05, if not differently indicated. All data were analyzed using the R environment for statistical computing (Version 4.0.1).

Received: 25 July 2020; Accepted: 15 October 2020

Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: interim guidance

Clinical characteristics of coronavirus disease 2019 in China

A comparative study on the clinical features of Coronavirus 2019 (COVID-19) pneumonia with other pneumonias

A clinical risk score to identify patients with COVID-19 at high risk of critical care admission or death: an observational cohort study

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019

Prediction for progression risk in patients with COVID-19 pneumonia: The CALL Score

Predictors for Severe COVID-19 Infection

The value of urine biochemical parameters in the prediction of the severity of coronavirus disease 2019

Prediction models for diagnosis and prognosis of Covid-19 infection: systematic review and critical appraisal

DAMP-sensing receptors in sterile inflammation and inflammatory diseases

Alarmins and immunity

The S100B story: from biomarker to active factor in neural injury

Protein S100B: from cancer diagnostics to the evaluation of mild traumatic brain injury

The S-100 antigen in cerebrospinal fluid as a possible index of cell injury in the nervous system

Increase in S-100 protein levels in blood plasma by epinephrine

Increased urinary S100B protein as an early indicator of intraventricular hemorrhage in preterm infants: correlation with the grade of hemorrhage

S100B as a new fecal biomarker of inflammatory bowel diseases

Expression profiling of endogenous secretory receptor for advanced glycation end products in human organs

Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages

SARS-CoV-2-mediated inflammatory response in lungs: should we look at RAGE?

The trinity of COVID-19: immunity, inflammation and intervention

Immunological and inflammatory profiles in mild and severe cases of COVID-19

Updated definitions of healthy ranges for serum alanine aminotransferase levels

Role of C-reactive protein at sites of inflammation and infection

S100B Protein, a damage-associated molecular pattern protein in the brain and heart, and beyond. Cardiovasc Psychiatry Neurol

S100B regulates inflammatory response during osteoarthritis via fibroblast growth factor receptor 1 signalling

Global consortium study of neurological dysfunction in COVID-19 (GCS-NeuroCOVID): study design and rationale

Immunochemical and immunocytochemical study of S-100 protein in rat adipocytes

S-100 protein in "follicular dendritic" cells or rat lymphoid organs. An immunochemical and immunocytochemical study

Human CD8(+) T cells and NK cells express and secrete S100B upon stimulation

Morphologic, flow cytometric, functional, and molecular analyses of S100B positive lymphocytes, unique cytotoxic lymphocytes containing S100B protein

Adipocyte-macrophage cross-talk in obesity

Adipocytes properties and crosstalk with immune system in obesity-related inflammation

Innate lymphoid cells in the initiation, regulation and resolution of inflammation

Dendritic cells in the regulation of immunity and inflammation

The danger signal S100B integrates pathogen-and danger-sensing pathways to restrain inflammation

S100B induces the release of pro-inflammatory cytokines in alveolar type I-like cells

The authors thank Dr. Gianluca Gianfranceschi for the technical support and Tiziana Zilli for the library support;The study was partially supported by Nando-Elsa Peretti Foundation Project (grant assigned to F.M.; NaEPF 2019-041), by Fondazione per la Ricerca Scientifica Termale grant: "Development of innovative strategies for thermal water treatments: nanotechnologies & perspectives for hygiene" (CUP H81I18000070008) and by the Foundation for Biology and Regenerative Medicine, Tissue Engineering and Signaling T.E.S (PPP).

The authors declare no competing interests.

Supplementary information is available for this paper at https ://doi.org/10.1038/s4159 8-020-75618 -0.Correspondence and requests for materials should be addressed to V.R.S.Reprints and permissions information is available at www.nature.com/reprints.Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.