Comparative metabolomic analysis reveals shared and unique features of COVID-19 cytokine storm and surgical sepsis

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Metabolomics may contribute to understanding the specific pathobiology of these two syndromes. The aim of this study was to compare serum metabolomic profiles in CS associated with COVID-19 vs. septic surgery patients. In retrospective cross-sectional study serum samples from patients with CS associated with COVID-19, with and without comorbidity as well as serum samples from patients with surgical sepsis were investigated. A targeted metabolomic analysis was carried out in all samples using LC-MS/MS method. Analysis revealed that similar alterations in serum metabolome of patients with COVID-19 and in surgical septic patients were associated with amino acid metabolism, nitrogen metabolism, inflammatory status, methionine cycle and glycolysis. The most significant difference was identified for the serum levels of metabolites of kynurenine synthesis, tricarboxylic acid cycle, as well as for gamma-aminobutyric acid and niacinamide. The metabolic pathway of cysteine and methionine metabolism was disturbed significantly in COVID-19 and surgical septic patients. For the first time, the similarities and differences between the serum metabolomic profiles of patients with CS associated with COVID-19 and patients with surgical sepsis were investigated for the patients from northwest of Russian Federation. Biological sciences/Biochemistry Health sciences/Pathogenesis COVID-19 cytokine storm sepsis targeted metabolomic analysis LC-MS/MS metabolic pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction According to data as of January 14, 2024, the past SARS-CoV-2 pandemic claimed more than 7 million lives [ 1 ]. Despite a significant number of publications on COVID-19 (391381 in PubMed as of January 29, 2024), many questions related to the pathophysiology of the disease remain unresolved. One extensively discussed syndrome linked to the illness is the cytokine storm (CS), observed in a substantial number of COVID-19 patients. CS, a systemic inflammatory state characterized by immune cell hyperactivation and uncontrolled cytokine release, is not exclusive to COVID-19. It is known that it can be triggered by various factors such as infections, tumor processes, autoimmune conditions, and more [ 2 ]. CS can precipitate acute respiratory distress syndrome (ARDS) or multiple organ dysfunction, which can potentially be fatal [ 3 ]. The clinical manifestations of COVID-19-associated CS and its consequences are similar to the manifestations of the acute phase of sepsis [ 4 ]. Sepsis, according to the Third International Consensus Definitions Task Force (Sepsis-3), is a life-threatening organ dysfunction caused by dysregulation of the host's response to infection [ 5 ]. Traditionally, bacterial infection was considered the major cause of sepsis [ 6 ]. The COVID-19 pandemic has led to a reassessment of the role of viruses in the occurrence of sepsis because multi-organ dysfunction caused by CS in COVID-19 largely corresponds to the concept of Sepsis-3 and is currently considered as viral sepsis [ 7 ]. Sepsis caused by CS associated with COVID-19 exhibits distinctive characteristics, even though its clinical symptoms typically resemble those of bacterial sepsis. COVID-19 is distinguished by a less pronounced and more prolonged occurrence of systemic multi-organ inflammation [ 8 , 9 ], an accelerated onset of acute respiratory distress syndrome (ARDS), reduced levels of inflammatory markers like IL-6 [ 10 ], and an immune signature due to differences in response to bacterial and viral infection [ 4 ]. This study focuses on comparing targeted metabolic profiles in the blood serum of patients with surgical sepsis (SS) and those with CS associated with COVID-19. The clinical data of patients with CS do not fully meet the Sepsis-3 criteria due to the patients’ blood being collected at an early stage of disease development (before treatment), when the rate of development of multiple organ dysfunction in this group is quite low yet (SOFA ≤ 2). Variations in the SOFA scores between groups are significant due to the late manifestation of multiple organ dysfunctions in COVID-19 patients, the opposite to the explosive course of the disease, which is a characteristic of bacterial sepsis [ 9 ]. But current hypotheses suggest a connection between CS, caused by COVID-19, and the occurrence of viral sepsis [ 11 ]. That's why the comparison of patients with COVID-19 associated with CS and patients with SS seems reasonable. However, detailed knowledge about the pathophysiology of severe cases of COVID-19 associated with CS and bacterial sepsis is still insufficient. Comparative metabolome studies may help identify the differential and common pathobiological characteristics of these syndromes and supplement the knowledge base. Comorbidities can make the course of COVID-19 more severe and enhance CS [ 12 , 13 ]. In this regard, it would be interesting to determine how comorbidity would affect the results of comparing the metabolomic profiles of patients with COVID-19 and surgical septic patients. This study is one of the first metabolomic studies using a biobank and a significant amount of samples from COVID-19 patients with cytokine storm and surgical sepsis from St. Petersburg and the Leningrad region (Russian Federation). The study was carried out on serum material collected strictly before the beginning of treatment, which made it possible to obtain fairly “clean” serum samples without additional drug interference in the patients’ metabolome. The aim of this study was to compare the serum metabolomic profiles of patients with COVID-19-associated CS with the serum metabolomic profiles of septic patients after surgery. 2. Material and methods 2.1. Participants Frozen blood serum was used from the collection of the biobank of the St. Petersburg State Healthcare Establishment "City Hospital No. 40". The study was conducted within the framework of the research project "Biobanking and biomedical research of human tissue and fluid samples" and was approved by the Expert Council on Ethics of the St. Petersburg State Healthcare Establishment "City Hospital No. 40" (session No. 119, February 9, 2017). A total of 234 patients who underwent treatment at City Hospital No. 40 took part in the current retrospective study of the metabolomics profile. Samples were collected during hospital stays from January 2018 to March 2021 for septic patients and from May 2020 to June 2021 for COVID-19 patients. All patients underwent standard examinations, according to clinical recommendations and according to diagnosis. Written informed consent for sample collection and placing samples in a biobank for subsequent use for scientific purposes and for the results' publication was obtained from all patients. The study was conducted following the World Medical Association’s Code of Ethics (Declaration of Helsinki) for experiments involving humans. The patients were grouped as follows: 1. COVID(-): COVID-19 patients with CS without comorbidity (n=40). 2. COVID(+): COVID-19 patients with CS with comorbidity (n=43) 3. Sepsis: SS patients (n=41). 4. Control: healthy volunteers (n=110). COVID-19 was diagnosed by a polymerase chain reaction (PCR) of nasopharyngeal swabs. CS was determined according to the following conditions: ferritin > 485 μg/L, C-reactive protein > 50 mg/L, d-dimer > 2.5 μg/mL, interleukin-6 > 25 pg/mL, LDH > 550 U/L. Comorbidities were determined based on the patient’s self-report on admission and confirmed, if necessary, by further examinations. The Charlton Comorbidity Index (CCI) was calculated for each patient with COVID-19 [14]. Patients with CCI≤2 were included in the COVID(-) group, and patients with CCI≥5 were included in the COVID(+) group. The diagnosis of sepsis was made according to the Sepsis-3 consensus criteria and the SOFA scale [5]. The cause of sepsis in this group was bacterial infection as complication after abdominal surgery.The Control group was chosen during regular preventive screenings. The criteria for inclusion in the group were the absence of COVID-19, confirmed with PCR, and the absence of sepsis. The general inclusion criterion was age over 18 years. Since our study is conducted in a real-world setting, we didn't employ any additional inclusion or exclusion criteria. 2.2. Study design The objective of this study was to compare the serum metabolomic profiles of patients with CS associated with COVID-19 and patients with sepsis. Metabolomic profiles were obtained using target LC-MS/MS analyses. The study solved the following tasks: 1) t-SNE clustering of patients. 2) Comparison of serum metabolomes of patients of the COVID(-) and COVID(+) groups: identification of common and differentially represented metabolites that alter levels relative to the Control group. 3) Identification of metabolites differentially represented in the serum of the Sepsis group compared to the Control. 4) Comparison of serum metabolomic profiles of patients of the Sepsis groups with serum metabolomes of patients in the COVID(-) and COVID(+) groups: identification of common and differentially represented metabolites. 2.3. Sample collection and storage Blood samples from patients diagnosed with COVID-19 were collected within day of hospital admission and prior to treatment initiation. Blood samples from septic patients were collected when they were admitted to the intensive care unit (ICU) before starting antibiotic treatment. The blood samples for the Control group were obtained during a routine examination of volunteers. All samples were collected into Vacutest tubes (Gel and clot act., Vacutest Kima S.R.L., Italy). After centrifugation for 10 min at 4⁰C, 2,200 rpm, the serum was collected and immediately frozen at -80⁰C. All samples were annotated, indicating the stage of the disease, gender, age, etc. Before analysis, the frozen samples were slowly warmed to room temperature and thoroughly mixed. 2.4. Metabolomic profiling of serum samples The preparation of all blood serum samples for targeted metabolome studies was carried out in duplicate. 2-(N-morpholino)ethanesulfonic acid hydrate (MES) (CAS: 4432-31-9; cat no. M8250, Sigma-Aldrich) and L-methionine sulfone (CAS: 7314-32-1, cat No. M0876, Sigma-Aldrich) were used as internal standards. The samples were thawed at room temperature, and 100 μl of an ice-cold mixture of internal standards of a fixed concentration (25 μg/ml) in acetonitrile (cat no. 9012.2500GL, LC-MS-grade, J.T. Baker) was added to 50 μl of serum. The mixture was vortexes and incubated for 10 min at room temperature, then centrifuged (Centrifuge 5810R, Eppendorf) at 12000 g for 10 min at 4 °C. An aliquot of 80 uL supernatant was diluted with Milli-Q - water acidified with formic acid (cat No. 533002, for LC-MS LiChropur, >99%, Merck) to pH 2 and analyzed using the LC-MS/MS method. The prepared standard solutions and extracts were stored at -20 ⁰С. Targeted metabolite profiling was performed using a liquid chromatography-mass spectrometer with a triple quadrupole LCMS-8050 (Shimadzu) along with a Nexera X2 (Shimadzu) chromatography system. The analysis was carried out following the established "LC/MS/MS Method Package for Primary Metabolites" by Shimadzu, utilizing the multiple reaction monitoring mode. This method allows simultaneous analysis of 98 analytes of the main chemical classes of clinically significant low-molecular compounds, including amino acids, organic acids, nucleotides, nucleosides, and coenzymes (Supplementary Table S1, S3). An analytical column Discovery HS F5-3 (150 x 2.1 mm, 3 mkm) (Supelco, Merck) and SecurityGuard “SupelGuard Discovery HS F5-3” (20 x 2.1 mm, 3 mkm, Supelco) were used to separate the analytes. Mass-spectrometry parameters and chromatographic conditions were meticulously set in accordance with the guidelines provided in the manual for the "LC/MS/MS Method Package for Primary Metabolites" method. Briefly, both ESI (+) and ESI (−) modes used water (LC–MS grade) as mobile phase (A) and ACN as mobile phase (B), and formic acid was used as mobile phase modifier. The gradient program was changed from 0% to 95% (B). Chromatographic and mass spectrometric parameters, as set out in the manual, are given in Table 2 in Supplement. Data collection and processing were performed using LabSolutions software. Metabolites were identified based on chromatographic retention time, m/z values of product ions, and their intensity ratios. Only chromatographic peaks with a peak-to-noise cutoff ratio ≥ 10 were considered. The content of the investigated substances was determined using the internal standard method, which considers the response (area) of the analyte connections in relation to the response (area) of the internal standard. All level measurements are given in arbitrary units of the content of the internal standard (arbitrary units — a.u.). The results correspond to the average of two parallel measurements of the same sample. To ensure quality control (QA/QC), control samples were analyzed along with experimental samples to monitor instrument performance and facilitate chromatographic alignment. Control samples consisted of extracts of averaged blood serum samples with internal standards, prepared following the same procedure as the experimental samples. The averaged serum, derived from thoroughly mixed blood serum samples from seven donors, was aliquoted in 200 μl portions and frozen at -80 °C for later analysis. An appropriate volume of Milli-Q water passed through all stages of sample preparation was used as a “blank” sample. Additionally, all solvents used in sample preparation were also analyzed. We utilized quality control samples to assess the reproducibility and stability of the prepared extracts on both intra- and inter-day bases. The internal standard solutions and control extracts were examined over a 10-day period, when they were stored at -20 °C between analyses and once at +5 °C for 24 hours. Overall process variability was determined by calculating the median RSD for IS and all endogenous metabolites (i.e., non-instrumental standards) present in control samples. The discrepancy between parallel measurements remained within 15% of the average values, with average daily deviations below 20%. Quality control samples were examined after every 50-60 experimental sample injections in order to ensure the consistency of the chromatographic retention times and responses of the studied compounds. Instrument variability was determined by calculating the median relative standard deviation (RSD) of the internal standards added to each sample during extraction. The scatter of the obtained values was evaluated during averaging, ensuring a maximum difference of 20% between the averaged parallel measurements. If this threshold was exceeded 25%, the sample was reanalyzed, and the initial result was disregarded. 2.5. Statistical analysis To test the hypothesis of normal data distribution, the Shapiro-Wilk test was used. Data were transformed using Median & Quantile Absolute Deviation based Z-Score. The Z-Score transformation was applied solely to provide a more visually interpretable representation of the data in tabular form. To identify intergroup differences in the concentration levels of the studied metabolites, assessed through a.u., a nonparametric one-way analysis of variance was performed using the Kruskal-Wallis test; the Mann-Whitney test was used as a post-hoc analysis. The data was clustered using the t-distributed stochastic neighbor embedding (t-SNE) method, and the resulting data was visualized in two-dimensional space. The following parameters were used for the t-SNE analysis: Perplexity = 30, Learning Rate = 500, and Number of Iterations = 2000. The log fold change (lfc) was used as a measure reflecting the difference in the range of values between samples; descriptive statistics are presented by the median (Me) and interquartile range [Q1-Q3]. The difference between samples was considered significant at p0.5. A Volcano plot was used to visualize the test results. Data processing and statistical analysis were performed using the R programming language version 4.3.1 and the Python programming language version 3.12. Spearman's rank correlation coefficient was used for the correlation analysis. Metabolic pathway analysis was performed using the MetaboAnalyst 6.0 package. 3. Results 3.1. Patient characteristics Table 1 Patient demographic and clinical characteristics. Characteristic COVID(-) (n = 40) COVID(+) (n = 43) Sepsis (n = 41) Control (n = 110) Age, y 51 ± 9.8 74 ± 9 65.2 ± 16.1 48.1 ± 14.5 Sex (Male), n 24 23 19 77 CCI 1.5 ± 0.7 7.7 ± 3 - - SOFA 2 ± 0.5 1.5 ± 1.2 7.3 ± 3.6 - Leukocytes cells, 10 9 /L 7.9 ± 4.3 8.6 ± 4.8 16.8 ± 10.8 - Neutrophils, 10 9 /L 5.9 ± 3.6 7.1 ± 4.6 13.7 ± 9.3 - Lymphocytes, 10 9 /L 1.4 ± 0.7 1 ± 0.5 0.8 ± 0.4 - IL-6, pg/mL 258.2 ± 742.7 346.6 ± 763.3 - - Creatinine, µmol/L 117.6 ± 106.3 112.8 ± 61.5 213.8 ± 149.8 CCI - Charlson Comorbidity Index; SOFA - Sequential Organ Failure Assessment; “-” - data not available Table 1 presents the demographic and clinical characteristics of the patient groups included in the study. The average age of comorbid patients was slightly higher than the average age in other groups (mean 74 ± 9 y vs. 51 ± 9.8 y for COVID(-), 65.2 ± 16 y for Sepsis and 48.1 ± 14 y for Control). The imbalance can be explained by the fact that comorbidities usually arise at a later age. 3.2. The general characteristics of the changes in the serum metabolome of COVID-19 and Sepsis groups. The levels of 100 compounds were studied using the LC/MS/MS procedure (Supplementary Table S1 , S3). 83 compounds whose results were differed from zero were taken for the further analysis (Supplementary Table S4). The graphical result is presented on a heatmap (Fig. 1 ). There was a significant difference in age of the patients in the studied groups. To answer the question about the influence of age factor on the metabolites levels we conducted the correlation analysis (Supplementary Table S4). As all can see for most of the metabolites the correlation with age of the patients is closer to weak. The metabolites with most growing levels with age are cytosine, L-acetylcarnitine, DL-Dopa (ρ 0.45–0.48). The week decline of the level with age (ρ = -0.43) was shown for glutathione. t-SNE clustering of patients was performed based on the obtained metabolomic data (Supplementary Table S4). It revealed 3 clear clusters groups: Sepsis, Control and COVID-19 (Fig. 2 ). Interestingly, the COVID(-) and COVID(+) groups formed approximately one cluster, practically not separated. The Control group was notably segregated, situated far away from the other clusters. Additionally, data from sepsis patients was also well separated from others, while being in proximity to a cluster of COVID-19 patients. We conducted the correlation analysis of to investigate the point of changing the relations of metabolites in groups and found that COVID(-), COVID(+) and Sepsis groups differed significantly in functioning of metabolome which reflected in changing of correlation links between the groups (Fig. 3 ). It was rather unexpectedly because the t-SNE clustering demonstrated that COVID(-) and COVID(+) groups belonged to one cluster. 3.3. COVID(-) vs. COVID(+) groups metabolomics For detalization, we compared the metabolomic profiles of COVID(-) and COVID(+) patients (Supplementary Table S4, Fig. 4 , 5 ). 62 and 67 metabolites were changed compared to the Control in COVID(-) and COVID(+) groups (p < 0.05), respectively. A decline of 38 metabolites along with rise of 24 compounds was recorded in COVID(-) serum; the most prominent changes are presented in Fig. 4 AD. While for COVID(+) group, 45 metabolites showed a decrease and 22 metabolites exhibited an increase compared to the Control group (Fig. 4 BD ). Among 9 compounds mostly increased in the COVID(-) group, 6 metabolites were also risen in the COVID(+) group. These were dimethylglycine, L-acetylcarnitine, L-kynurenine, L-phenylalanine, L-cystathionine, adenosine monophosphate (Fig. 4 AB). Among 25 metabolites mostly decreased in the COVID(+) group, the levels of 17 compounds also fell in the COVID(-) group. These were L-histidine, citrulline, ornithine, uridine, uric acid, L-arginine, asymmetric dimethylarginine, pantothenic acid, L-threonine, 4-hydroxyproline, choline, allantoin, inosine, glycine, L-leucine, acetylcholine chloride, L-isoleucine (Fig. 4 AB). In spite of the presence of some common characteristics of metabolomics changes, there were 29 compounds, which revealed significant (p < 0.05) differences in the levels between the COVID(-) and COVID(+) groups (Supplementary Table S4, Fig. 5 A ). 15 metabolites exhibited higher levels in the COVID(+) group compared to the COVID(-) group. L-kynurenine, L-lactic acid, L-alanine, uric acid, uracil, carnosine, ornithine, and norepinephrine were the most prominent. Conversely, 14 metabolites showed decreased levels in the COVID(+) group, with L-proline and serine being the most notable (Fig. 5 A). Interestingly, some of these compounds changed their levels in series from Control to COVID(-) and then to COVID(+) group. For example, L-kynurenine and ornithine revealed such dynamics (Supplementary Table S4). To evaluate the potential metabolomic shifts that resulted from changes in measured compounds, an enriched pathway analysis was held. We identified the top 8 common pathways from the KEGG database that were most significantly dysregulated in the COVID(-) and COVID(+) groups compared with the Control group (Fig. 6 AB; Supplementary Table S5 AB). The four of these pathways were uniquely disturbed in each group (Fig. 3 C; Supplementary Table S5 D). The metabolism of cysteine and methionine showed the largest difference, being more disrupted in COVID(+) vs. COVID(-) patients (Fig. 7 A ; Supplementary Table S5 AB). These data, in consistency with t-SNE clustering (p. 3.2), demonstrated that the difference between COVID(+) and COVID(-) groups was not substantial and had rather quantitative than qualitative character. 3.4. Sepsis group metabolomics In comparing the Sepsis and Control groups, we found differences in the levels of 75 metabolites (p < 0.05) (Supplementary Table S4, Fig. 4 CD). Among these metabolites, 35 exhibited elevated levels and 40 metabolites displayed decreased levels. Enriched pathway analysis identified the top 10 pathways in the KEGG database that were potentially disturbed in the Sepsis group (Fig. 6 C; Supplementary Table S5 C). These pathways included arginine biosynthesis; cysteine and methionine metabolism; alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism; citrate (TCA) cycle, arginine and proline metabolism; pyrimidine metabolism; tyrosine metabolism; histidine metabolism, and glutathione metabolism. 3.5. Sepsis vs. COVID-19 groups metabolomics Comparison of the Control group with the Sepsis, COVID(-), and COVID(+) groups indicated significant differences in metabolite abundance (p ≤ 0.05). Among the groups, the greatest number of changes in metabolite levels (74) was observed in the Sepsis vs. Control comparison. In the comparison of COVID(-) vs. Control, 60 metabolites showed varying levels, while 65 metabolites displayed differences in the COVID(+) vs. Control comparison (Supplementary Table S4, Fig. 4 E). The levels of 53 metabolites changed significantly (p ≤ 0.05) both for COVID(-) and Sepsis groups relative to the Control group. Within these groups, the levels of 11 metabolites increased, and 22 metabolites decreased in both groups. Similarly, the levels of 57 metabolites showed significant changes in both the COVID(+) and Sepsis groups relative to the Control group; the levels of 14 metabolites increased and 25 metabolites decreased in both groups (Supplementary Table S4). The levels of 47 metabolites changed in all three groups. All three groups exhibited increased levels of 11 metabolites relative to the Control. The most prominent were dimethylglycine, L-acetylcarnitine, L-cystathionine, adenosine monophosphate. Additionally, 19 metabolites displayed lower levels compared to the controls across all groups. The most prominent are L-histidine, citrulline, ornithine, uridine, pantothenic acid, L-threonine, choline, inosine, L-leucine, and L-isoleucine (Fig. 4 ABC ). Comparison of the metabolomic profiles of the Sepsis and COVID(-) groups showed that the levels of 60 metabolites were significantly different (p < 0.05) (Supplementary Table S4). Among these, the levels of 38 metabolites showed higher levels in the Sepsis group, and 22 metabolites displayed lower levels in the Sepsis group. The most significant changes were displayed on Fig. 5 B. The comparison of metabolomic profiles between the Sepsis and COVID(+) groups showed that the levels of 56 metabolites were significantly different (p < 0.05) (Supplementary Table S4). Of these, in the Sepsis group, the levels of 36 metabolites were higher and the levels of 20 metabolites were lower. The most significant changes were displayed on Fig. 5 C. The levels of 36 metabolites were significantly different (p ≤ 0.01) in both COVID-19 groups relative to the Sepsis group; of these, the levels of 28 metabolites were higher in the serum of septic patients (Supplementary Table S4, Fig. 5 BC). The most prominent were acetylcholine chloride, L-histidine, uric acid, allantoin, 4-hydroxyproline, asymmetric dimethylarginine, creatine, creatinine, citric acid, methionine sulfoxide, guanosine, L-carnitine, L-cystathionine, carnosine. The levels of 12 compounds were lower in the serum of septic patients compared to COVID-19 one. The most prominent were L-proline, L-aspartic acid, L-tryptophan, niacinamide, L-tyrosine, L-phenylalanine, L-glutamic acid (Fig. 5 BC). Some metabolites levels changed solely in the Sepsis or COVID-19 groups. So as 4-hydroxyproline, isocitric and pyruvic acids, procollagen-5-hydroxylisine, creatine, creatnine, SAM, acetylcholine chlorid, cytric acid, serotonine, symmetric dimethiylarginine significantly increased, and L-aspartic acid, L-glutamic acid, L-proline, L-lysine, GABA, niacinamide significantly decreased exclusively in the Sepsis group. In contrast to the Sepsis group, both groups of patients with COVID-19 were characterized by a significant increase in citicoloine, 5-thymidilic acid, GABA, nicotinic acid, L-phenylalanine, histamine and a decrease in allantoin, 4-hydroxiproline, isocytric and pyruvic acids, procollagen-5-hydroxylisine, acetylcholine chloride, symmetric and asymmetric dimethylarginine, L-methionine, uric acid, and L-lactic acid (Supplementary Table S4). Enriched pathway analysis of the KEGG databases identified eight metabolic pathways, the differences in which for surgical septic and COVID-19 (COVID(-) and COVID(+) patients (p ≤ 0.05) (Fig. 7 BC ; Supplementary Table S5 EF). These pathways remained consistent when comparing Sepsis with both COVID(-) and COVID(+). There were cysteine and methionine metabolism, histidine metabolism, arginine and proline metabolism, arginine biosynthesis pathway, aspartate, glutamate, and alanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and pyrimidine metabolism. 4. Discussion We compared the serum metabolome of patients with COVID-19 related CS vs. surgical sepsis. Both syndromes stem from infections, with COVID-19 linked to a viral infection and surgical sepsis to a bacterial one. These conditions could lead to an increased inflammatory response, posing notable risks to the patients involved. We carried out this study to improve understanding of the pathophysiology that underlies these hyperinflammatory processes. The presence of comorbidities can make the course of COVID-19 more severe [ 12 , 13 ]. To examine the impact of concomitant illnesses on metabolomic alterations and their relevance in relation to the Sepsis group, two patient groups with CS were formed: one without comorbidities (COVID(-)) and the other presenting comorbidities (COVID(+)). The metabolomic profiles of these groups were also compared. Our research uncovered parallel shifts in the metabolome both the COVID(+) and COVID(-) group. The levels of almost all amino acids, including proteogenic and glycogenic types, as well as crucial markers of energy metabolism like pyruvate, lactate, and TCA cycle acids, experienced a significant decrease. Other researchers have also reported a reduction in the levels of these metabolites in patients with CS [ 15 , 16 ]. Furthermore, it was shown that there was a link between the reduction in amino acid levels and the increasing of cytokines in the plasma [ 17 ]. In this study, patients experienced more significant changes in certain energy-related metabolites within the COVID(+) group, where the IL6 level was elevated. Both COVID-19 groups were characterized by decreased carnitine along with increased acetylcarnitine levels, which might indicate an energy deficiency and impaired transport in mitochondria. Similar changes were found by other authors [ 18 , 19 , 20 ]. Also a reduction in the levels of arginine, citrulline, and ornithine indicates a significant disturbance in nitrogen metabolism in COVID-19 patients, aligning with findings from other researchers [ 21 , 22 ]. The pathway of tryptophan degradation, linked to inflammation modulation, underwent a common alteration in both groups of COVID-19, shifting towards kynurenine synthesis. These results in elevated kynurenine levels and decreased levels of tryptophan and serotonin are consistent with the existing data [ 18 ]. In COVID-19 patients, elevated levels of some other metabolites linked to the inflammatory response were observed. These included the inflammatory mediator histamine and the neurotransmitter gamma-aminobutyric acid (GABA), known to possess anti-inflammatory properties [ 23 ]. We assumed that the increase in GABA levels was compensatory. The disparities between the COVID(-) and COVID(+) groups can essentially be considered as inconsequential. The most significant variations in the dynamics of changes were observed with L-proline and serine. Their levels sharply rose in the COVID(-) group and approached the control level in the COVID(+) group. It could be supposed that the alterations in these amino acids in patients with comorbidities were linked to the features of accompanying illnesses and their therapies. So we can propose that the main changes in metabolomics profiles in both groups of COVID-19 patients were due to the virus infection, not comorbidities. This proposal is aligned with the conclusion in [ 24 ]. Nevertheless one interesting feature is connected with correlation analysis which have demonstrated that relations of studied metabolites were differed in COVID(-) and COVID(+) groups. We suppose that it was the influence of comorbidity on the functioning of organism – with less changes levels we saw significant discrepancies in connections between metabolites in COVID(-) and COVID(+) groups.The obtained data on the metabolomics of surgical septic patients, presented in Table S4 and Fig. 4 C, were consistent with the data of other researchers. This change in the level of proteogenic and glycogenic amino acids was considered one of the characteristic features of sepsis [ 25 ]. The drop in the level of serum amino acids was explained by their use as a substrate for the TCA cycle and glycolysis for the energy demand sharply increasing during sepsis [ 26 ]. Energy imbalance in the Sepsis group was confirmed in our study by increased levels of oxoglutaric and citric acids, metabolites of the TCA cycle. Another feature of sepsis is a violation of beta-oxidation of fatty acids in mitochondria and an increase in the level of acetylcarnitines [ 27 , 28 ]. In our study, L-acetylcarnitine was elevated in surgical septic patients. Also, surgical septic patients showed elevated kynurenine levels and declined in tryptophan and serotonin levels, suggesting a redirection of tryptophan degradation toward kynurenine synthesis. These findings aligned with the data presented in [ 27 ]. The inflammatory marker procollagen 5-hydroxy-L-lysine was also significantly increased in the Sepsis group. Similar results were obtained in the study [ 29 ]. The rise in dopamine and acetylcholine chloride levels could be attributed to the compensatory anti-inflammatory impact of these metabolites [ 30 , 31 ]. In addition, we detected a significant change in the levels of compounds in surgical septic patients associated with the development of oxidative stress - decreased levels of glutathione and increased levels of its precursor gamma-glutamylcysteine, ophthalmic acid and symmetric dimethylarginine, noted by other authors [ 26 , 32 , 33 ]. Overall, the metabolomic profile data of patients with COVID-19 and surgical sepsis aligned with findings from other researchers. We observed a similarity in the direction of level changes for various metabolites when comparing the metabolomic profiles of patients with COVID-19 and surgical sepsis, across the Control - COVID(-) - COVID(+) - Sepsis series (Fig. 4 ABCD ). For example, the levels of L-alanine, ornithine, and uric acid sequentially increased. It could be linked to higher breakdown of proteins and nucleic acids caused by cell death and inflammation due to a more severe course of pathological process in the COVID(+) group than in the COVID(-) group and more inflammation and oxidative stress in the Sepsis group than in COVID-19 patients [ 26 , 34 ]. An increase in uric acid levels was also a marker of gradual deterioration of renal function in the series Control - COVID(-) - COVID(+) - Sepsis [ 35 ]. This was confirmed by clinical analysis of creatinine levels of patients (Table 1 ). The inflammatory marker kynurenine showed elevated levels across all three groups. Surprisingly, in the Sepsis group, its level was the lowest, while in the groups of patients with COVID-19, its level was notably higher, with the COVID(+) group displaying the highest level. Another participant in the tryptophan degradation pathway, serotonin, exhibited the opposite alteration. Its level was highest in the Sepsis group, and lowest in the COVID(+) group. This fact required additional examination to comprehend the involvement of the kynurenine pathway in hyperinflammatory conditions of diverse origins. The level of the antioxidant carnosine sequentially increased in the series COVID(-) - COVID(+) - Sepsis. Moreover, its level in the COVID(-) group was lower than the level in the Control group, but significantly higher in the COVID(+) and Sepsis groups. Also in the series of COVID(-), COVID(+), and Sepsis, a consistent rise in levels was noted for norepinephrine, a metabolite known for its reported anti-inflammatory properties [ 36 ]. At the same time, the levels of the precursor of norepinephrine in the synthesis pathway, L-tyrosine, altered in opposite way and fell proportionally in the series. This suggests that there could be compensatory production of carnosine and norepinephrine as inflammation and oxidative stress escalated within the sequence Control - COVID(-) - COVID(+) - Sepsis. The sequential increase in lactic acid in the series COVID(-) - COVID(+) - Sepsis looked explicable. As lactic acid levels rose in the examined groups, it indicated a transition towards glycolysis, signaling a shift in energy metabolism. In all three groups, it was noteworthy that the level of lactic acid remained lower compared to the Control group, despite documented elevations in lactic acid levels observed in sepsis and severe cases of COVID-19 [ 37 , 38 ]. Considerable alterations in all groups were found for the methionine metabolism. In the progression from COVID(-) to COVID(+) to Sepsis, a notable alteration in the concentrations of two metabolites associated with the methionine cycle was detected: a rise in SAH levels and a decline in serine levels. It should be noted that a decrease in serine levels could impact both methionine synthesis and the pathway for producing the antioxidant glutathione, which exhibited similarly low levels across all three groups. In the progression from COVID(-) to COVID(+) to Sepsis, the methionine sulfoxide, another participant in the methionine cycle, showed a steady rise in levels. We assumed that this was necessary to maintain the level of methionine, as a key metabolite in many processes, when the level of serine fell. Levels of several metabolites fell to a similar extent in patients with COVID-19 and in the Sepsis group (Fig. 4 ABCD). Firstly, it is the decrease in amino acid levels observed in both the Sepsis group and patients with COVID-19. This level drop might indicate a heightened demand for oxidative sources to energy cycles across all groups [ 16 ]. Levels of glutathione and choline, known for their ability to decrease oxidative stress and inflammation, diminished equally in individuals with surgical sepsis and with COVID-19. So, while there were resemblances in metabolomic profiles, the distinctions in them for Sepsis and COVID-19 patients were also significant. Figure 4 E schematically demonstrated the number of metabolites that significantly changed their levels in the three groups. The greatest number of such metabolites belonged to the Sepsis group (74); the COVID(-) and COVID(+) groups differed slightly in this feature (60 and 65 metabolites, respectively), but the difference between both COVID-19 groups and Sepsis group was a lot more. It might be explained by the different dynamics of both pathological processes. As noted, sepsis characterizes by more “explosive” course than CSS, which also leads to severe consequences, but more slowly [ 34 ]. For the intergroup comparison of Sepsis vs. COVID-19, 28 metabolites showed a significant increase (p ≤ 0.01) compared to both groups of COVID-19 patients (p. 3.4). These included metabolites related to mitochondrial function, markers of oxidative stress, collagen distruction, methionine and transsulfuration cycles, and renal failure. The most prominent were differences in the levels of metabolites in the TCA cycle. It likely reflected the biological basis of both syndromes. COVID-19 causes in most cases: lung injury at the beginning of the infection and oxygen deficiency as a consequence. So the suppression of the TCA cycle occurred. surgical sepsis is not necessary related to the hard lung injury [ 34 ], especially at the early stage, as for our patients. In the absence of hypooxigenation, the TCA cycle upregulates in order to satisfy the increased needs in energy production for maintenance of hypperinflamation and immune function. Some metabolites exhibited a significant (p ≤ 0.01) downregulation in surgical septic compared to COVID-19 patients (Fig. 5 BC). The lower levels of several amino acids in the Sepsis group might be associated with the greatest need for energy and more intensive utilization of amino acids in energetic cycles. But the most significant changes were noted in the profiles of GABA and niacinamide. These metabolites were increased in the COVID(-) and COVID(+) groups, but their levels decreased by numerous orders of magnitude in the Sepsis group. GABA might reveal an anti-inflammatory function [ 23 ], and niacinamide was the precursor in synthesis of NAD. Such alterations might indicate a significantly higher level of inflammation and energy problems in surgical septic patients compared to patients with COVID-19. To evaluate the potential changes in the metabolomics pathway, we carried out an enriched analysis of the KEGG databases. The analysis revealed that certain metabolic pathways shared among the COVID(-), COVID(+), and Sepsis groups were notably disrupted. These pathways included: glycine, serine and threonine metabolism; arginine metabolism; cysteine and methionine metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; glutathione metabolism; tyrosine metabolism; and histidine metabolism (Fig. 6 ). Among these, the cysteine and methionine metabolism pathway was one of the most altered in all groups. By assessing the level of changes in this pathway based on the number of metabolites analyzed, the p -value, and the impact factor, one could see greater impairment in the COVID(+) and Sepsis groups than in the COVID(-) group. The cysteine and methionine metabolism pathway is one of the most important in the body. It is associated both with maintaining the level of methionine, which is a donor of methyl groups, and with the transformations of sulfur-containing amino acids responsible for the redox potential. In our study, the arginine biosynthesis and histidine metabolism pathways were also altered similarly in all three groups. The TCA cycle was disturbed only in the COVID(+) and Sepsis groups, but to a greater extent in the Sepsis group as assessed by the number of analyzed metabolites, p-value and impact factor. This analysis confirmed that significant metabolic changes in the serum of COVID-19 and surgical septic patients were primarily linked to amino acid metabolism and alterations in redox potential and energy cycle, with metabolic irregularities amplifying from COVID(-) to COVID(+) to Sepsis. This study possessed several limitations. Firstly, we carried out targeted metabolomic exploration, so our results did not cover metabolomic changes as completely as a non-targeted metabolomic study can. Secondly, there was a substantial disparity in SOFA levels between the patient groups of COVID-19 and Sepsis. This incongruity arose from our deliberate collection of blood samples from COVID-19 patients prior to treatment initiation to mitigate the impact of antibiotics, glucocorticoids, and similar factors. So, the observed SOFA discrepancy appeared due to the slower progression of multiple organ failure in COVID-19 compared to surgical sepsis. Lastly, our study reflected real-world conditions, resulting in variations in age parameters across the groups under comparison. It is imperative to consider these limitations when evaluating the findings of this research. 5. Conclusion This study used biobank samples of COVID-19 patients with cytokine storm and surgical sepsis from St. Petersburg and the Leningrad region (Russian Federation) and represented one of the first serum comparative metabolomic studies in this geographical area. The serum of patients with COVID-19 and surgical sepsis showed significant changes in various metabolites linked to amino acid metabolism, nitrogen metabolism, inflammation, the folate and methionine cycles, and glycolysis. Differences between COVID(-) and COVID(+) groups were not significant. Changes in metabolite levels tended to increase consistently from COVID(-) to COVID(+) to Sepsis groups, with more pronounced changes in the Sepsis group. The most significant differences between surgical septic and COVID-19 patients appeared in metabolites related to kynurenine synthesis, niacinamide, the TCA cycle, and GABA. Across all groups, there were significant alterations in the cysteine and methionine metabolism pathways. So, our study revealed common and different features of the metabolomic profiles of patients with surgical sepsis and CS associated with COVID-19. Abbreviations CS, cytokine storm; ARDS, acute respiratory distress syndrome; SS, surgical sepsis; PCR, polymerase chain reaction; CCI, Charlson Comorbidity Index; SOFA, Sequential Organ Failure Assessment; ICU, intensive care unit; MES, 2-(N-morpholino)ethanesulfonic acid hydrate; QA/QC, quality control; RSD, relative standard deviation; SAH, S-Adenosylhomocysteine; SAM, S-Adenosylmethionine; GABA, Gamma-Aminobutyric acid; TCA, Tricarboxylic acid cycle Declarations Ethics approval and consent to participate The study was conducted following the World Medical Association’s Code of Ethics (Declaration of Helsinki) for experiments involving humans and was approved by the Expert Council on Ethics of the St. Petersburg State Healthcare Establishment "City Hospital No. 40" (session No. 119, February 9, 2017). Written informed consent was obtained from all subjects involved in the study. Consent for publication All authors agree to the publication of the manuscript. Data availability statement All experimental details, results, and materials are available in the text and supplementary file. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Supported by Saint Petersburg State University, project ID: 95412780 CRediT authorship contribution statemen t Russkikh Ia.V.: Investigation, validation, data curation, writing, editing. Popov O.S.: Conceptualization, investigation, data curation, formal analysis, visualization, writing, editing. Klochkova Т.G.: conceptualization, writing, editing, corresponding author. Sushentseva N.N.: Conceptualization, methodology, writing, editing. Apalko S.V.: Conceptualization, project administration, writing, editing. Asinovskaya А.Yu.: Resources, project administration, supervision. Mosenko S.V.: Data curation, resources. Sarana А.М.: Supervision, project administration. Shcherbak S.G.: Resources, supervision, project administration. Acknowledgments Not applicable. References WHO. WHO Coronavirus (COVID-19) Dashboard; WHO: 2024. https://data.who.int/dashboards/ COVID-19/deaths, 2024 (accessed 14 January 2024). Fajgenbaum, D. C., June, C. H. Cytokine storm. N Engl J Med . 383 , 2255-2273 (2020). doi:10.1056/NEJMra2026131 Cron, R. Q., Goyal, G., Chatham, W. W. Cytokine storm syndrome. Annu Rev Med . 74, 321-337 (2023). doi:10.1146/annurev-med-042921-112837 Jarczak, D., Nierhaus, A. Cytokine storm-definition, causes, and implications. Int J Mol Sci . 23 , 11740 (2022). doi:10.3390/ijms231911740 Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). 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The key role of Warburg effect in SARS-CoV-2 replication and associated inflammatory response. Biochimie . 180 , 169-177 (2021). doi:10.1016/j.biochi.2020.11.010 Liu, J., Zhou, G., Wang, X., Liu, D. Metabolic reprogramming consequences of sepsis: adaptations and contradictions. Cell Mol Life Sci . 79 , 456 (2022). doi:10.1007/s00018-022-04490-0 Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Supplementary data Supplementary file includes baseline information on the list of studied compounds, chromatographic and mass spectrometric parameters, MRM transitions used for the studied compounds, primary statistics data, results of correlation with age analysis, pathway analysis tables. 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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-5339115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":370761664,"identity":"30d53a59-8907-4d45-87cc-fdacd2a1dab0","order_by":0,"name":"Iana Russkih","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Iana","middleName":"","lastName":"Russkih","suffix":""},{"id":370761665,"identity":"d346b6c0-d023-4659-a069-fb4d906477c6","order_by":1,"name":"Oleg Popov","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Oleg","middleName":"","lastName":"Popov","suffix":""},{"id":370761666,"identity":"c2a0cd06-2478-4b99-92de-309020615ad3","order_by":2,"name":"Tatiana Klochkova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIie3PIQ7CMBSA4S4kxTSZ7TLSM5RMILlKlyZTwyO7kNSRWbgFEtkqzA4w2RkUAsmSCjoxBUmLQ/RXFe9L3wMgFvvDtj0XIBHMPRdasRACsZ4JZI7QAJI1M0FUgSCSJ40ZrxWht+6pjLVgc/KsB0lyWB+7uqDd7qJKScGq9xOZJ3JfnsVEBAUY+0g+k/ZhFLMhJGsmUpctroFiMIRg7W6RVZHiO3W3FAgj4yNcm1FyAlM+DC9LCF56fvkI/Tgfi8VisW+9ASnbQ+UcfjacAAAAAElFTkSuQmCC","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":true,"prefix":"","firstName":"Tatiana","middleName":"","lastName":"Klochkova","suffix":""},{"id":370761667,"identity":"d68082fc-42dc-43dc-9cae-dc97794a5fec","order_by":3,"name":"Natalia Sushentseva","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Sushentseva","suffix":""},{"id":370761668,"identity":"4757a890-67e9-4dc0-b4fc-36f159bc5e9f","order_by":4,"name":"Svetlana Apalko","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Svetlana","middleName":"","lastName":"Apalko","suffix":""},{"id":370761669,"identity":"31b5dca7-672f-4863-a72a-82088ba79ded","order_by":5,"name":"Anna Asinovskaya","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Asinovskaya","suffix":""},{"id":370761670,"identity":"03a909bb-813d-48a6-96c1-8c17cc63b41f","order_by":6,"name":"Sergey Mosenko","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Sergey","middleName":"","lastName":"Mosenko","suffix":""},{"id":370761671,"identity":"61d3b199-8e5b-4672-9c67-3ca0fca67287","order_by":7,"name":"Andrey Sarana","email":"","orcid":"","institution":"Saint-Petersburg State University","correspondingAuthor":false,"prefix":"","firstName":"Andrey","middleName":"","lastName":"Sarana","suffix":""},{"id":370761672,"identity":"7c6716d1-d055-491f-8d90-43692b08e2ae","order_by":8,"name":"Sergey Shcherbak","email":"","orcid":"","institution":"City Hospital No. 40","correspondingAuthor":false,"prefix":"","firstName":"Sergey","middleName":"","lastName":"Shcherbak","suffix":""}],"badges":[],"createdAt":"2024-10-26 22:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5339115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5339115/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90426-0","type":"published","date":"2025-02-24T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68353363,"identity":"9cc18077-e1be-484d-81db-94a81acb8f21","added_by":"auto","created_at":"2024-11-06 11:04:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117458,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of metabolites concentrations for each patient. Data normalized using Z-score normalization. Patients devided into groups: Sepsis, COVID(-), COVID(+) and Control. The borders between groups are designated by white lines across the heatmap\u003c/p\u003e","description":"","filename":"Binder41.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/009f98f969124e7781957419.png"},{"id":68353371,"identity":"38d4b909-b96a-4408-819b-e7e4db99db70","added_by":"auto","created_at":"2024-11-06 11:04:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222003,"visible":true,"origin":"","legend":"\u003cp\u003et-SNE clustering of patients based on the LC-MS/MS analysis\u003c/p\u003e","description":"","filename":"Binder42.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/f2f3ddd4e3a1266bd46c7806.png"},{"id":68353364,"identity":"786f3075-5129-411f-9979-11625dd0fa8c","added_by":"auto","created_at":"2024-11-06 11:04:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2160552,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of the metabolites concentrations in groups. The color of each cell represents the level of Spearman's rank correlation coefficient from -1 to 1. \u003cstrong\u003eA\u003c/strong\u003e. COVID(-) group. \u003cstrong\u003eB\u003c/strong\u003e. COVID(+) group. \u003cstrong\u003eC\u003c/strong\u003e. Sepsis group\u003c/p\u003e","description":"","filename":"Binder43.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/0475c55703982d9a64b9136f.png"},{"id":68354971,"identity":"18c98d9b-b7d5-4741-8636-e64df4acae93","added_by":"auto","created_at":"2024-11-06 11:12:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210624,"visible":true,"origin":"","legend":"\u003cp\u003eMain abnormal metabolites in COVID(-), COVID(+) and Sepsis group in comparison with the Control. Volcano plots parametres: Log2 fold change cutoff 0.5; p-value cutoff 0.05. \u003cstrong\u003eA\u003c/strong\u003e. COVID(-) vs Control. \u003cstrong\u003eB\u003c/strong\u003e. COVID(+) vs Control. \u003cstrong\u003eC\u003c/strong\u003e. Sepsis vs Control. \u003cstrong\u003eD\u003c/strong\u003e. Heat map of concentration of metabolites in COVID(-), COVID(+) and Sepsis groups. The color of each cell represents logarithm of fold change of metabolites with concentration of metabolites in control group, and p values were calculated using Mann–Whitney U test, *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001. \u003cstrong\u003eE\u003c/strong\u003e. The Venn diagram shows the overall quantity of differentialy abundant in comparison with Control metabolites in COVID(-), COVID(+) and Sepsis group highlighting 47 metabolites common to all, 7 metabolites common to COVID(-) and COVID(+), 6 common to COVID(-) and Sepsis, 10 common for COVID(+) and Sepsis, 1 unique for \u0026nbsp;COVID(+) and 11 unique for Sepsis\u003c/p\u003e","description":"","filename":"Binder44.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/db0046fa47fb118b72638b8a.png"},{"id":68353365,"identity":"dee9d785-9a63-4daa-9327-e28ab1f6b2cf","added_by":"auto","created_at":"2024-11-06 11:04:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":521661,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the COVID(-), COVID(+) and Sepsis groups. The volcano plots show most significant differentially abundant metabolites. Log2 fold change cutoff 0.5; p-value cutoff 0.05. A. COVID(-) vs COVID(+). B. Sepsis vs COVID(-). C. Sepsis vs COVID(+)\u003c/p\u003e","description":"","filename":"Binder45.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/fce4a15cf47fbe6ae753b234.png"},{"id":68353370,"identity":"69aa8815-4202-4e5a-8fc7-b7a1a6322112","added_by":"auto","created_at":"2024-11-06 11:04:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":353473,"visible":true,"origin":"","legend":"\u003cp\u003eThe main KEGG pathways disturbed in groups. \u003cstrong\u003eA – C\u003c/strong\u003e: the pathways view according to the p-values and the pathway impact values. Dots present the most relevant pathways. The pathway impact is presented by size and p-value by color. \u003cstrong\u003eA\u003c/strong\u003e. COVID(-) vs. Control. \u003cstrong\u003eB\u003c/strong\u003e. COVID(+) vs. Control. \u003cstrong\u003eC\u003c/strong\u003e. Sepsis vs. Control. \u003cstrong\u003eD – F\u003c/strong\u003e: a summary plots for quantitative enrichment analysis showing the top 25 enriched terms for each group. \u003cstrong\u003eD\u003c/strong\u003e. COVID(-). \u003cstrong\u003eE\u003c/strong\u003e. COVID(+). \u003cstrong\u003eF\u003c/strong\u003e. Sepsis\u003c/p\u003e","description":"","filename":"Binder46.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/4d444a917cbf339eb33f577e.png"},{"id":68353369,"identity":"786ed844-0013-46dd-a823-0dfe659c3186","added_by":"auto","created_at":"2024-11-06 11:04:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":327685,"visible":true,"origin":"","legend":"\u003cp\u003eThe main KEGG pathways disturbed between groups. \u003cstrong\u003eA – C\u003c/strong\u003e: the pathways view according to the p-values and the pathway impact values. Dots present the most relevant pathways. The pathway impact is presented by size and p-value by color. \u003cstrong\u003eA\u003c/strong\u003e. COVID(-) vs. COVID(+). \u003cstrong\u003eB\u003c/strong\u003e. COVID(-) vs. Sepsis. \u003cstrong\u003eC\u003c/strong\u003e. COVID(+) vs. Sepsis. \u003cstrong\u003eD – F\u003c/strong\u003e: a summary plots for quantitative enrichment analysis showing the top 25 enriched terms for each group. \u003cstrong\u003eD\u003c/strong\u003e. COVID(-) vs. COVID(+). \u003cstrong\u003eE\u003c/strong\u003e. COVID(-) vs. Sepsis. \u003cstrong\u003eF\u003c/strong\u003e. COVID(+) vs. Sepsis\u003c/p\u003e","description":"","filename":"Binder47.png","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/07f424a5e301d5e66696970f.png"},{"id":77622313,"identity":"335dd7d3-4f14-41cb-93bd-aafd9e0c26de","added_by":"auto","created_at":"2025-03-03 16:04:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4877980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/88839ff1-1a69-4953-9395-ae3ef6dcfd2e.pdf"},{"id":68355320,"identity":"98c28578-cccf-4705-837a-1f55838c8da0","added_by":"auto","created_at":"2024-11-06 11:20:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":78877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary file includes baseline information on the list of studied compounds, chromatographic and mass spectrometric parameters, MRM transitions used for the studied compounds, primary statistics data, results of correlation with age analysis, pathway analysis tables.\u003c/p\u003e","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-5339115/v1/e8e93c486a1284b0a5f29686.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative metabolomic analysis reveals shared and unique features of COVID-19 cytokine storm and surgical sepsis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to data as of January 14, 2024, the past SARS-CoV-2 pandemic claimed more than 7\u0026nbsp;million lives [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite a significant number of publications on COVID-19 (391381 in PubMed as of January 29, 2024), many questions related to the pathophysiology of the disease remain unresolved.\u003c/p\u003e \u003cp\u003eOne extensively discussed syndrome linked to the illness is the cytokine storm (CS), observed in a substantial number of COVID-19 patients. CS, a systemic inflammatory state characterized by immune cell hyperactivation and uncontrolled cytokine release, is not exclusive to COVID-19. It is known that it can be triggered by various factors such as infections, tumor processes, autoimmune conditions, and more [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CS can precipitate acute respiratory distress syndrome (ARDS) or multiple organ dysfunction, which can potentially be fatal [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe clinical manifestations of COVID-19-associated CS and its consequences are similar to the manifestations of the acute phase of sepsis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sepsis, according to the Third International Consensus Definitions Task Force (Sepsis-3), is a life-threatening organ dysfunction caused by dysregulation of the host's response to infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Traditionally, bacterial infection was considered the major cause of sepsis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The COVID-19 pandemic has led to a reassessment of the role of viruses in the occurrence of sepsis because multi-organ dysfunction caused by CS in COVID-19 largely corresponds to the concept of Sepsis-3 and is currently considered as viral sepsis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSepsis caused by CS associated with COVID-19 exhibits distinctive characteristics, even though its clinical symptoms typically resemble those of bacterial sepsis. COVID-19 is distinguished by a less pronounced and more prolonged occurrence of systemic multi-organ inflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], an accelerated onset of acute respiratory distress syndrome (ARDS), reduced levels of inflammatory markers like IL-6 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and an immune signature due to differences in response to bacterial and viral infection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study focuses on comparing targeted metabolic profiles in the blood serum of patients with surgical sepsis (SS) and those with CS associated with COVID-19. The clinical data of patients with CS do not fully meet the Sepsis-3 criteria due to the patients\u0026rsquo; blood being collected at an early stage of disease development (before treatment), when the rate of development of multiple organ dysfunction in this group is quite low yet (SOFA\u0026thinsp;\u0026le;\u0026thinsp;2). Variations in the SOFA scores between groups are significant due to the late manifestation of multiple organ dysfunctions in COVID-19 patients, the opposite to the explosive course of the disease, which is a characteristic of bacterial sepsis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. But current hypotheses suggest a connection between CS, caused by COVID-19, and the occurrence of viral sepsis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. That's why the comparison of patients with COVID-19 associated with CS and patients with SS seems reasonable. However, detailed knowledge about the pathophysiology of severe cases of COVID-19 associated with CS and bacterial sepsis is still insufficient. Comparative metabolome studies may help identify the differential and common pathobiological characteristics of these syndromes and supplement the knowledge base.\u003c/p\u003e \u003cp\u003eComorbidities can make the course of COVID-19 more severe and enhance CS [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this regard, it would be interesting to determine how comorbidity would affect the results of comparing the metabolomic profiles of patients with COVID-19 and surgical septic patients.\u003c/p\u003e \u003cp\u003eThis study is one of the first metabolomic studies using a biobank and a significant amount of samples from COVID-19 patients with cytokine storm and surgical sepsis from St. Petersburg and the Leningrad region (Russian Federation). The study was carried out on serum material collected strictly before the beginning of treatment, which made it possible to obtain fairly \u0026ldquo;clean\u0026rdquo; serum samples without additional drug interference in the patients\u0026rsquo; metabolome. The aim of this study was to compare the serum metabolomic profiles of patients with COVID-19-associated CS with the serum metabolomic profiles of septic patients after surgery.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e\u003cem\u003e2.1. Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrozen blood serum was used from the collection of the biobank of the St. Petersburg State Healthcare Establishment \"City Hospital No. 40\". The study was conducted within the framework of the research project \"Biobanking and biomedical research of human tissue and fluid samples\" and was approved by the Expert Council on Ethics of the St. Petersburg State Healthcare Establishment \"City Hospital No. 40\" (session No. 119, February 9, 2017). A total of 234 patients who underwent treatment at City Hospital No. 40 took part in the current retrospective study of the metabolomics profile. Samples were collected during hospital stays from January 2018 to March 2021 for septic patients and from May 2020 to June 2021 for COVID-19 patients. All patients underwent standard examinations, according to clinical recommendations and according to diagnosis. Written informed consent for sample collection and placing samples in a biobank for subsequent use for scientific purposes and for the results' publication was obtained from all patients. The study was conducted following the World Medical Association’s Code of Ethics (Declaration of Helsinki) for experiments involving humans. \u003c/p\u003e\n\u003cp\u003eThe patients were grouped as follows:\u003c/p\u003e\n\u003cp\u003e1. COVID(-): COVID-19 patients with CS without comorbidity (n=40).\u003c/p\u003e\n\u003cp\u003e2. COVID(+): COVID-19 patients with CS with comorbidity (n=43)\u003c/p\u003e\n\u003cp\u003e3. Sepsis: SS patients (n=41).\u003c/p\u003e\n\u003cp\u003e4. Control: healthy volunteers (n=110).\u003c/p\u003e\n\u003cp\u003eCOVID-19 was diagnosed by a polymerase chain reaction (PCR) of nasopharyngeal swabs. CS was determined according to the following conditions: ferritin \u0026gt; 485 μg/L, C-reactive protein \u0026gt; 50 mg/L, d-dimer \u0026gt; 2.5 μg/mL, interleukin-6 \u0026gt; 25 pg/mL, LDH \u0026gt; 550 U/L.\u003c/p\u003e\n\u003cp\u003eComorbidities were determined based on the patient’s self-report on admission and confirmed, if necessary, by further examinations. The Charlton Comorbidity Index (CCI) was calculated for each patient with COVID-19 [14]. Patients with CCI≤2 were included in the COVID(-) group, and patients with CCI≥5 were included in the COVID(+) group.\u003c/p\u003e\n\u003cp\u003eThe diagnosis of sepsis was made according to the Sepsis-3 consensus criteria and the SOFA scale [5]. The cause of sepsis in this group was bacterial infection as complication after abdominal surgery.The Control group was chosen during regular preventive screenings. The criteria for inclusion in the group were the absence of COVID-19, confirmed with PCR, and the absence of sepsis. The general inclusion criterion was age over 18 years. Since our study is conducted in a real-world setting, we didn't employ any additional inclusion or exclusion criteria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2. Study design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to compare the serum metabolomic profiles of patients with CS associated with COVID-19 and patients with sepsis. Metabolomic profiles were obtained using target LC-MS/MS analyses. The study solved the following tasks: \u003c/p\u003e\n\u003cp\u003e1) t-SNE clustering of patients.\u003c/p\u003e\n\u003cp\u003e2) Comparison of serum metabolomes of patients of the COVID(-) and COVID(+) groups: identification of common and differentially represented metabolites that alter levels relative to the Control group.\u003c/p\u003e\n\u003cp\u003e3) Identification of metabolites differentially represented in the serum of the Sepsis group compared to the Control.\u003c/p\u003e\n\u003cp\u003e4) Comparison of serum metabolomic profiles of patients of the Sepsis groups with serum metabolomes of patients in the COVID(-) and COVID(+) groups: identification of common and differentially represented metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Sample collection and storage\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples from patients diagnosed with COVID-19 were collected within day of hospital admission and prior to treatment initiation. Blood samples from septic patients were collected when they were admitted to the intensive care unit (ICU) before starting antibiotic treatment. The blood samples for the Control group were obtained during a routine examination of volunteers. All samples were collected into Vacutest tubes (Gel and clot act., Vacutest Kima S.R.L., Italy). After centrifugation for 10 min at 4⁰C, 2,200 rpm, the serum was collected and immediately frozen at -80⁰C. All samples were annotated, indicating the stage of the disease, gender, age, etc. Before analysis, the frozen samples were slowly warmed to room temperature and thoroughly mixed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4. Metabolomic profiling of serum samples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe preparation of all blood serum samples for targeted metabolome studies was carried out in duplicate. 2-(N-morpholino)ethanesulfonic acid hydrate (MES) (CAS: 4432-31-9; cat no. M8250, Sigma-Aldrich) and L-methionine sulfone (CAS: 7314-32-1, cat No. M0876, Sigma-Aldrich) were used as internal standards. The samples were thawed at room temperature, and 100 μl of an ice-cold mixture of internal standards of a fixed concentration (25 μg/ml) in acetonitrile (cat no. 9012.2500GL, LC-MS-grade, J.T. Baker) was added to 50 μl of serum. The mixture was vortexes and incubated for 10 min at room temperature, then centrifuged (Centrifuge 5810R, Eppendorf) at 12000 g for 10 min at 4 °C. An aliquot of 80 uL supernatant was diluted with Milli-Q - water acidified with formic acid (cat No. 533002, for LC-MS LiChropur, \u0026gt;99%, Merck) to pH 2 and analyzed using the LC-MS/MS method. The prepared standard solutions and extracts were stored at -20 ⁰С.\u003c/p\u003e\n\u003cp\u003eTargeted metabolite profiling was performed using a liquid chromatography-mass spectrometer with a triple quadrupole LCMS-8050 (Shimadzu) along with a Nexera X2 (Shimadzu) chromatography system. The analysis was carried out following the established \"LC/MS/MS Method Package for Primary Metabolites\" by Shimadzu, utilizing the multiple reaction monitoring mode. This method allows simultaneous analysis of 98 analytes of the main chemical classes of clinically significant low-molecular compounds, including amino acids, organic acids, nucleotides, nucleosides, and coenzymes (Supplementary Table S1, S3). An analytical column Discovery HS F5-3 (150 x 2.1 mm, 3 mkm) (Supelco, Merck) and SecurityGuard “SupelGuard Discovery HS F5-3” (20 x 2.1 mm, 3 mkm, Supelco) were used to separate the analytes. Mass-spectrometry parameters and chromatographic conditions were meticulously set in accordance with the guidelines provided in the manual for the \"LC/MS/MS Method Package for Primary Metabolites\" method. Briefly, both ESI (+) and ESI (−) modes used water (LC–MS grade) as mobile phase (A) and ACN as mobile phase (B), and formic acid was used as mobile phase modifier. The gradient program was changed from 0% to 95% (B). Chromatographic and mass spectrometric parameters, as set out in the manual, are given in Table 2 in Supplement. Data collection and processing were performed using LabSolutions software. \u003c/p\u003e\n\u003cp\u003eMetabolites were identified based on chromatographic retention time, \u003cem\u003em/z\u003c/em\u003e values of product ions, and their intensity ratios. Only chromatographic peaks with a peak-to-noise cutoff ratio ≥ 10 were considered. The content of the investigated substances was determined using the internal standard method, which considers the response (area) of the analyte connections in relation to the response (area) of the internal standard. All level measurements are given in arbitrary units of the content of the internal standard (arbitrary units — a.u.). The results correspond to the average of two parallel measurements of the same sample. \u003c/p\u003e\n\u003cp\u003eTo ensure quality control (QA/QC), control samples were analyzed along with experimental samples to monitor instrument performance and facilitate chromatographic alignment. Control samples consisted of extracts of averaged blood serum samples with internal standards, prepared following the same procedure as the experimental samples. The averaged serum, derived from thoroughly mixed blood serum samples from seven donors, was aliquoted in 200 μl portions and frozen at -80 °C for later analysis. An appropriate volume of Milli-Q water passed through all stages of sample preparation was used as a “blank” sample. Additionally, all solvents used in sample preparation were also analyzed.\u003c/p\u003e\n\u003cp\u003eWe utilized quality control samples to assess the reproducibility and stability of the prepared extracts on both intra- and inter-day bases. The internal standard solutions and control extracts were examined over a 10-day period, when they were stored at -20 °C between analyses and once at +5 °C for 24 hours. Overall process variability was determined by calculating the median RSD for IS and all endogenous metabolites (i.e., non-instrumental standards) present in control samples. The discrepancy between parallel measurements remained within 15% of the average values, with average daily deviations below 20%. Quality control samples were examined after every 50-60 experimental sample injections in order to ensure the consistency of the chromatographic retention times and responses of the studied compounds. Instrument variability was determined by calculating the median relative standard deviation (RSD) of the internal standards added to each sample during extraction. The scatter of the obtained values was evaluated during averaging, ensuring a maximum difference of 20% between the averaged parallel measurements. If this threshold was exceeded 25%, the sample was reanalyzed, and the initial result was disregarded.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5. Statistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo test the hypothesis of normal data distribution, the Shapiro-Wilk test was used. Data were transformed using Median \u0026amp; Quantile Absolute Deviation based Z-Score. The Z-Score transformation was applied solely to provide a more visually interpretable representation of the data in tabular form. To identify intergroup differences in the concentration levels of the studied metabolites, assessed through a.u., a nonparametric one-way analysis of variance was performed using the Kruskal-Wallis test; the Mann-Whitney test was used as a post-hoc analysis. The data was clustered using the t-distributed stochastic neighbor embedding (t-SNE) method, and the resulting data was visualized in two-dimensional space. The following parameters were used for the t-SNE analysis: Perplexity = 30, Learning Rate = 500, and Number of Iterations = 2000.\u003c/p\u003e\n\u003cp\u003eThe log fold change (lfc) was used as a measure reflecting the difference in the range of values between samples; descriptive statistics are presented by the median (Me) and interquartile range [Q1-Q3]. The difference between samples was considered significant at p\u0026lt;0.05 and |lfc|\u0026gt;0.5. A Volcano plot was used to visualize the test results. Data processing and statistical analysis were performed using the R programming language version 4.3.1 and the Python programming language version 3.12. Spearman's rank correlation coefficient was used for the correlation analysis. Metabolic pathway analysis was performed using the MetaboAnalyst 6.0 package.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient characteristics\u003c/h2\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\u003ePatient demographic and clinical characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOVID(-)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID(+)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;110)\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\u003eAge, y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (Male), n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeukocytes cells, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophils, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocytes, 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-6, pg/mL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258.2\u0026thinsp;\u0026plusmn;\u0026thinsp;742.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346.6\u0026thinsp;\u0026plusmn;\u0026thinsp;763.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine, \u0026micro;mol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.6\u0026thinsp;\u0026plusmn;\u0026thinsp;106.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.8\u0026thinsp;\u0026plusmn;\u0026thinsp;61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213.8\u0026thinsp;\u0026plusmn;\u0026thinsp;149.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCCI - Charlson Comorbidity Index; SOFA - Sequential Organ Failure Assessment; \u0026ldquo;-\u0026rdquo; - data not available\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and clinical characteristics of the patient groups included in the study. The average age of comorbid patients was slightly higher than the average age in other groups (mean 74\u0026thinsp;\u0026plusmn;\u0026thinsp;9 y vs. 51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 y for COVID(-), 65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16 y for Sepsis and 48.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14 y for Control). The imbalance can be explained by the fact that comorbidities usually arise at a later age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The general characteristics of the changes in the serum metabolome of COVID-19 and Sepsis groups.\u003c/h2\u003e \u003cp\u003eThe levels of 100 compounds were studied using the LC/MS/MS procedure (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S3). 83 compounds whose results were differed from zero were taken for the further analysis (Supplementary Table S4). The graphical result is presented on a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was a significant difference in age of the patients in the studied groups. To answer the question about the influence of age factor on the metabolites levels we conducted the correlation analysis (Supplementary Table S4). As all can see for most of the metabolites the correlation with age of the patients is closer to weak. The metabolites with most growing levels with age are cytosine, L-acetylcarnitine, DL-Dopa (ρ 0.45\u0026ndash;0.48). The week decline of the level with age (ρ = -0.43) was shown for glutathione. t-SNE clustering of patients was performed based on the obtained metabolomic data (Supplementary Table S4). It revealed 3 clear clusters groups: Sepsis, Control and COVID-19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, the COVID(-) and COVID(+) groups formed approximately one cluster, practically not separated. The Control group was notably segregated, situated far away from the other clusters. Additionally, data from sepsis patients was also well separated from others, while being in proximity to a cluster of COVID-19 patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted the correlation analysis of to investigate the point of changing the relations of metabolites in groups and found that COVID(-), COVID(+) and Sepsis groups differed significantly in functioning of metabolome which reflected in changing of correlation links between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It was rather unexpectedly because the t-SNE clustering demonstrated that COVID(-) and COVID(+) groups belonged to one cluster.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. COVID(-) vs. COVID(+) groups metabolomics\u003c/h2\u003e \u003cp\u003eFor detalization, we compared the metabolomic profiles of COVID(-) and COVID(+) patients (Supplementary Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ). 62 and 67 metabolites were changed compared to the Control in COVID(-) and COVID(+) groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively. A decline of 38 metabolites along with rise of 24 compounds was recorded in COVID(-) serum; the most prominent changes are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eAD. While for COVID(+) group, 45 metabolites showed a decrease and 22 metabolites exhibited an increase compared to the Control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e BD ). Among 9 compounds mostly increased in the COVID(-) group, 6 metabolites were also risen in the COVID(+) group. These were dimethylglycine, L-acetylcarnitine, L-kynurenine, L-phenylalanine, L-cystathionine, adenosine monophosphate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e AB). Among 25 metabolites mostly decreased in the COVID(+) group, the levels of 17 compounds also fell in the COVID(-) group. These were L-histidine, citrulline, ornithine, uridine, uric acid, L-arginine, asymmetric dimethylarginine, pantothenic acid, L-threonine, 4-hydroxyproline, choline, allantoin, inosine, glycine, L-leucine, acetylcholine chloride, L-isoleucine (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e AB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn spite of the presence of some common characteristics of metabolomics changes, there were 29 compounds, which revealed significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) differences in the levels between the COVID(-) and COVID(+) groups (Supplementary Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA ). 15 metabolites exhibited higher levels in the COVID(+) group compared to the COVID(-) group. L-kynurenine, L-lactic acid, L-alanine, uric acid, uracil, carnosine, ornithine, and norepinephrine were the most prominent. Conversely, 14 metabolites showed decreased levels in the COVID(+) group, with L-proline and serine being the most notable (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Interestingly, some of these compounds changed their levels in series from Control to COVID(-) and then to COVID(+) group. For example, L-kynurenine and ornithine revealed such dynamics (Supplementary Table S4).\u003c/p\u003e \u003cp\u003eTo evaluate the potential metabolomic shifts that resulted from changes in measured compounds, an enriched pathway analysis was held. We identified the top 8 common pathways from the KEGG database that were most significantly dysregulated in the COVID(-) and COVID(+) groups compared with the Control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e AB; Supplementary Table S5 AB). The four of these pathways were uniquely disturbed in each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; Supplementary Table S5 D). The metabolism of cysteine and methionine showed the largest difference, being more disrupted in COVID(+) vs. COVID(-) patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA ; Supplementary Table S5 AB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese data, in consistency with t-SNE clustering (p. 3.2), demonstrated that the difference between COVID(+) and COVID(-) groups was not substantial and had rather quantitative than qualitative character.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Sepsis group metabolomics\u003c/h2\u003e \u003cp\u003eIn comparing the Sepsis and Control groups, we found differences in the levels of 75 metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e CD). Among these metabolites, 35 exhibited elevated levels and 40 metabolites displayed decreased levels. Enriched pathway analysis identified the top 10 pathways in the KEGG database that were potentially disturbed in the Sepsis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC; Supplementary Table S5 C). These pathways included arginine biosynthesis; cysteine and methionine metabolism; alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism; citrate (TCA) cycle, arginine and proline metabolism; pyrimidine metabolism; tyrosine metabolism; histidine metabolism, and glutathione metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Sepsis vs. COVID-19 groups metabolomics\u003c/h2\u003e \u003cp\u003eComparison of the Control group with the Sepsis, COVID(-), and COVID(+) groups indicated significant differences in metabolite abundance (p\u0026thinsp;\u0026le;\u0026thinsp;0.05). Among the groups, the greatest number of changes in metabolite levels (74) was observed in the Sepsis vs. Control comparison. In the comparison of COVID(-) vs. Control, 60 metabolites showed varying levels, while 65 metabolites displayed differences in the COVID(+) vs. Control comparison (Supplementary Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe levels of 53 metabolites changed significantly (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) both for COVID(-) and Sepsis groups relative to the Control group. Within these groups, the levels of 11 metabolites increased, and 22 metabolites decreased in both groups. Similarly, the levels of 57 metabolites showed significant changes in both the COVID(+) and Sepsis groups relative to the Control group; the levels of 14 metabolites increased and 25 metabolites decreased in both groups (Supplementary Table S4). The levels of 47 metabolites changed in all three groups. All three groups exhibited increased levels of 11 metabolites relative to the Control. The most prominent were dimethylglycine, L-acetylcarnitine, L-cystathionine, adenosine monophosphate. Additionally, 19 metabolites displayed lower levels compared to the controls across all groups. The most prominent are L-histidine, citrulline, ornithine, uridine, pantothenic acid, L-threonine, choline, inosine, L-leucine, and L-isoleucine (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ABC ).\u003c/p\u003e \u003cp\u003eComparison of the metabolomic profiles of the Sepsis and COVID(-) groups showed that the levels of 60 metabolites were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table S4). Among these, the levels of 38 metabolites showed higher levels in the Sepsis group, and 22 metabolites displayed lower levels in the Sepsis group. The most significant changes were displayed on Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eThe comparison of metabolomic profiles between the Sepsis and COVID(+) groups showed that the levels of 56 metabolites were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table S4). Of these, in the Sepsis group, the levels of 36 metabolites were higher and the levels of 20 metabolites were lower. The most significant changes were displayed on Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003eThe levels of 36 metabolites were significantly different (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) in both COVID-19 groups relative to the Sepsis group; of these, the levels of 28 metabolites were higher in the serum of septic patients (Supplementary Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e BC). The most prominent were acetylcholine chloride, L-histidine, uric acid, allantoin, 4-hydroxyproline, asymmetric dimethylarginine, creatine, creatinine, citric acid, methionine sulfoxide, guanosine, L-carnitine, L-cystathionine, carnosine. The levels of 12 compounds were lower in the serum of septic patients compared to COVID-19 one. The most prominent were L-proline, L-aspartic acid, L-tryptophan, niacinamide, L-tyrosine, L-phenylalanine, L-glutamic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e BC).\u003c/p\u003e \u003cp\u003eSome metabolites levels changed solely in the Sepsis or COVID-19 groups. So as 4-hydroxyproline, isocitric and pyruvic acids, procollagen-5-hydroxylisine, creatine, creatnine, SAM, acetylcholine chlorid, cytric acid, serotonine, symmetric dimethiylarginine significantly increased, and L-aspartic acid, L-glutamic acid, L-proline, L-lysine, GABA, niacinamide significantly decreased exclusively in the Sepsis group. In contrast to the Sepsis group, both groups of patients with COVID-19 were characterized by a significant increase in citicoloine, 5-thymidilic acid, GABA, nicotinic acid, L-phenylalanine, histamine and a decrease in allantoin, 4-hydroxiproline, isocytric and pyruvic acids, procollagen-5-hydroxylisine, acetylcholine chloride, symmetric and asymmetric dimethylarginine, L-methionine, uric acid, and L-lactic acid (Supplementary Table S4).\u003c/p\u003e \u003cp\u003eEnriched pathway analysis of the KEGG databases identified eight metabolic pathways, the differences in which for surgical septic and COVID-19 (COVID(-) and COVID(+) patients (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e BC ; Supplementary Table S5 EF). These pathways remained consistent when comparing Sepsis with both COVID(-) and COVID(+). There were cysteine and methionine metabolism, histidine metabolism, arginine and proline metabolism, arginine biosynthesis pathway, aspartate, glutamate, and alanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and pyrimidine metabolism.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe compared the serum metabolome of patients with COVID-19 related CS vs. surgical sepsis. Both syndromes stem from infections, with COVID-19 linked to a viral infection and surgical sepsis to a bacterial one. These conditions could lead to an increased inflammatory response, posing notable risks to the patients involved. We carried out this study to improve understanding of the pathophysiology that underlies these hyperinflammatory processes.\u003c/p\u003e \u003cp\u003eThe presence of comorbidities can make the course of COVID-19 more severe [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To examine the impact of concomitant illnesses on metabolomic alterations and their relevance in relation to the Sepsis group, two patient groups with CS were formed: one without comorbidities (COVID(-)) and the other presenting comorbidities (COVID(+)). The metabolomic profiles of these groups were also compared.\u003c/p\u003e \u003cp\u003eOur research uncovered parallel shifts in the metabolome both the COVID(+) and COVID(-) group. The levels of almost all amino acids, including proteogenic and glycogenic types, as well as crucial markers of energy metabolism like pyruvate, lactate, and TCA cycle acids, experienced a significant decrease. Other researchers have also reported a reduction in the levels of these metabolites in patients with CS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, it was shown that there was a link between the reduction in amino acid levels and the increasing of cytokines in the plasma [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, patients experienced more significant changes in certain energy-related metabolites within the COVID(+) group, where the IL6 level was elevated. Both COVID-19 groups were characterized by decreased carnitine along with increased acetylcarnitine levels, which might indicate an energy deficiency and impaired transport in mitochondria. Similar changes were found by other authors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Also a reduction in the levels of arginine, citrulline, and ornithine indicates a significant disturbance in nitrogen metabolism in COVID-19 patients, aligning with findings from other researchers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The pathway of tryptophan degradation, linked to inflammation modulation, underwent a common alteration in both groups of COVID-19, shifting towards kynurenine synthesis. These results in elevated kynurenine levels and decreased levels of tryptophan and serotonin are consistent with the existing data [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In COVID-19 patients, elevated levels of some other metabolites linked to the inflammatory response were observed. These included the inflammatory mediator histamine and the neurotransmitter gamma-aminobutyric acid (GABA), known to possess anti-inflammatory properties [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We assumed that the increase in GABA levels was compensatory. The disparities between the COVID(-) and COVID(+) groups can essentially be considered as inconsequential. The most significant variations in the dynamics of changes were observed with L-proline and serine. Their levels sharply rose in the COVID(-) group and approached the control level in the COVID(+) group. It could be supposed that the alterations in these amino acids in patients with comorbidities were linked to the features of accompanying illnesses and their therapies. So we can propose that the main changes in metabolomics profiles in both groups of COVID-19 patients were due to the virus infection, not comorbidities. This proposal is aligned with the conclusion in [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Nevertheless one interesting feature is connected with correlation analysis which have demonstrated that relations of studied metabolites were differed in COVID(-) and COVID(+) groups. We suppose that it was the influence of comorbidity on the functioning of organism \u0026ndash; with less changes levels we saw significant discrepancies in connections between metabolites in COVID(-) and COVID(+) groups.The obtained data on the metabolomics of surgical septic patients, presented in Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, were consistent with the data of other researchers. This change in the level of proteogenic and glycogenic amino acids was considered one of the characteristic features of sepsis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The drop in the level of serum amino acids was explained by their use as a substrate for the TCA cycle and glycolysis for the energy demand sharply increasing during sepsis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Energy imbalance in the Sepsis group was confirmed in our study by increased levels of oxoglutaric and citric acids, metabolites of the TCA cycle.\u003c/p\u003e \u003cp\u003eAnother feature of sepsis is a violation of beta-oxidation of fatty acids in mitochondria and an increase in the level of acetylcarnitines [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In our study, L-acetylcarnitine was elevated in surgical septic patients. Also, surgical septic patients showed elevated kynurenine levels and declined in tryptophan and serotonin levels, suggesting a redirection of tryptophan degradation toward kynurenine synthesis. These findings aligned with the data presented in [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The inflammatory marker procollagen 5-hydroxy-L-lysine was also significantly increased in the Sepsis group. Similar results were obtained in the study [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The rise in dopamine and acetylcholine chloride levels could be attributed to the compensatory anti-inflammatory impact of these metabolites [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, we detected a significant change in the levels of compounds in surgical septic patients associated with the development of oxidative stress - decreased levels of glutathione and increased levels of its precursor gamma-glutamylcysteine, ophthalmic acid and symmetric dimethylarginine, noted by other authors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Overall, the metabolomic profile data of patients with COVID-19 and surgical sepsis aligned with findings from other researchers.\u003c/p\u003e \u003cp\u003eWe observed a similarity in the direction of level changes for various metabolites when comparing the metabolomic profiles of patients with COVID-19 and surgical sepsis, across the Control - COVID(-) - COVID(+) - Sepsis series (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ABCD ). For example, the levels of L-alanine, ornithine, and uric acid sequentially increased. It could be linked to higher breakdown of proteins and nucleic acids caused by cell death and inflammation due to a more severe course of pathological process in the COVID(+) group than in the COVID(-) group and more inflammation and oxidative stress in the Sepsis group than in COVID-19 patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. An increase in uric acid levels was also a marker of gradual deterioration of renal function in the series Control - COVID(-) - COVID(+) - Sepsis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This was confirmed by clinical analysis of creatinine levels of patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inflammatory marker kynurenine showed elevated levels across all three groups. Surprisingly, in the Sepsis group, its level was the lowest, while in the groups of patients with COVID-19, its level was notably higher, with the COVID(+) group displaying the highest level. Another participant in the tryptophan degradation pathway, serotonin, exhibited the opposite alteration. Its level was highest in the Sepsis group, and lowest in the COVID(+) group. This fact required additional examination to comprehend the involvement of the kynurenine pathway in hyperinflammatory conditions of diverse origins. The level of the antioxidant carnosine sequentially increased in the series COVID(-) - COVID(+) - Sepsis. Moreover, its level in the COVID(-) group was lower than the level in the Control group, but significantly higher in the COVID(+) and Sepsis groups. Also in the series of COVID(-), COVID(+), and Sepsis, a consistent rise in levels was noted for norepinephrine, a metabolite known for its reported anti-inflammatory properties [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. At the same time, the levels of the precursor of norepinephrine in the synthesis pathway, L-tyrosine, altered in opposite way and fell proportionally in the series. This suggests that there could be compensatory production of carnosine and norepinephrine as inflammation and oxidative stress escalated within the sequence Control - COVID(-) - COVID(+) - Sepsis.\u003c/p\u003e \u003cp\u003eThe sequential increase in lactic acid in the series COVID(-) - COVID(+) - Sepsis looked explicable. As lactic acid levels rose in the examined groups, it indicated a transition towards glycolysis, signaling a shift in energy metabolism. In all three groups, it was noteworthy that the level of lactic acid remained lower compared to the Control group, despite documented elevations in lactic acid levels observed in sepsis and severe cases of COVID-19 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsiderable alterations in all groups were found for the methionine metabolism. In the progression from COVID(-) to COVID(+) to Sepsis, a notable alteration in the concentrations of two metabolites associated with the methionine cycle was detected: a rise in SAH levels and a decline in serine levels. It should be noted that a decrease in serine levels could impact both methionine synthesis and the pathway for producing the antioxidant glutathione, which exhibited similarly low levels across all three groups. In the progression from COVID(-) to COVID(+) to Sepsis, the methionine sulfoxide, another participant in the methionine cycle, showed a steady rise in levels. We assumed that this was necessary to maintain the level of methionine, as a key metabolite in many processes, when the level of serine fell.\u003c/p\u003e \u003cp\u003eLevels of several metabolites fell to a similar extent in patients with COVID-19 and in the Sepsis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ABCD). Firstly, it is the decrease in amino acid levels observed in both the Sepsis group and patients with COVID-19. This level drop might indicate a heightened demand for oxidative sources to energy cycles across all groups [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Levels of glutathione and choline, known for their ability to decrease oxidative stress and inflammation, diminished equally in individuals with surgical sepsis and with COVID-19.\u003c/p\u003e \u003cp\u003eSo, while there were resemblances in metabolomic profiles, the distinctions in them for Sepsis and COVID-19 patients were also significant. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE schematically demonstrated the number of metabolites that significantly changed their levels in the three groups. The greatest number of such metabolites belonged to the Sepsis group (74); the COVID(-) and COVID(+) groups differed slightly in this feature (60 and 65 metabolites, respectively), but the difference between both COVID-19 groups and Sepsis group was a lot more. It might be explained by the different dynamics of both pathological processes. As noted, sepsis characterizes by more \u0026ldquo;explosive\u0026rdquo; course than CSS, which also leads to severe consequences, but more slowly [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the intergroup comparison of Sepsis vs. COVID-19, 28 metabolites showed a significant increase (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) compared to both groups of COVID-19 patients (p. 3.4). These included metabolites related to mitochondrial function, markers of oxidative stress, collagen distruction, methionine and transsulfuration cycles, and renal failure. The most prominent were differences in the levels of metabolites in the TCA cycle. It likely reflected the biological basis of both syndromes. COVID-19 causes in most cases: lung injury at the beginning of the infection and oxygen deficiency as a consequence. So the suppression of the TCA cycle occurred. surgical sepsis is not necessary related to the hard lung injury [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], especially at the early stage, as for our patients. In the absence of hypooxigenation, the TCA cycle upregulates in order to satisfy the increased needs in energy production for maintenance of hypperinflamation and immune function.\u003c/p\u003e \u003cp\u003eSome metabolites exhibited a significant (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) downregulation in surgical septic compared to COVID-19 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e BC). The lower levels of several amino acids in the Sepsis group might be associated with the greatest need for energy and more intensive utilization of amino acids in energetic cycles. But the most significant changes were noted in the profiles of GABA and niacinamide. These metabolites were increased in the COVID(-) and COVID(+) groups, but their levels decreased by numerous orders of magnitude in the Sepsis group. GABA might reveal an anti-inflammatory function [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and niacinamide was the precursor in synthesis of NAD. Such alterations might indicate a significantly higher level of inflammation and energy problems in surgical septic patients compared to patients with COVID-19.\u003c/p\u003e \u003cp\u003eTo evaluate the potential changes in the metabolomics pathway, we carried out an enriched analysis of the KEGG databases. The analysis revealed that certain metabolic pathways shared among the COVID(-), COVID(+), and Sepsis groups were notably disrupted. These pathways included: glycine, serine and threonine metabolism; arginine metabolism; cysteine and methionine metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; glutathione metabolism; tyrosine metabolism; and histidine metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among these, the cysteine and methionine metabolism pathway was one of the most altered in all groups. By assessing the level of changes in this pathway based on the number of metabolites analyzed, the \u003cem\u003ep\u003c/em\u003e-value, and the impact factor, one could see greater impairment in the COVID(+) and Sepsis groups than in the COVID(-) group. The cysteine and methionine metabolism pathway is one of the most important in the body. It is associated both with maintaining the level of methionine, which is a donor of methyl groups, and with the transformations of sulfur-containing amino acids responsible for the redox potential. In our study, the arginine biosynthesis and histidine metabolism pathways were also altered similarly in all three groups. The TCA cycle was disturbed only in the COVID(+) and Sepsis groups, but to a greater extent in the Sepsis group as assessed by the number of analyzed metabolites, p-value and impact factor. This analysis confirmed that significant metabolic changes in the serum of COVID-19 and surgical septic patients were primarily linked to amino acid metabolism and alterations in redox potential and energy cycle, with metabolic irregularities amplifying from COVID(-) to COVID(+) to Sepsis.\u003c/p\u003e \u003cp\u003eThis study possessed several limitations. Firstly, we carried out targeted metabolomic exploration, so our results did not cover metabolomic changes as completely as a non-targeted metabolomic study can. Secondly, there was a substantial disparity in SOFA levels between the patient groups of COVID-19 and Sepsis. This incongruity arose from our deliberate collection of blood samples from COVID-19 patients prior to treatment initiation to mitigate the impact of antibiotics, glucocorticoids, and similar factors. So, the observed SOFA discrepancy appeared due to the slower progression of multiple organ failure in COVID-19 compared to surgical sepsis. Lastly, our study reflected real-world conditions, resulting in variations in age parameters across the groups under comparison. It is imperative to consider these limitations when evaluating the findings of this research.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study used biobank samples of COVID-19 patients with cytokine storm and surgical sepsis from St. Petersburg and the Leningrad region (Russian Federation) and represented one of the first serum comparative metabolomic studies in this geographical area. The serum of patients with COVID-19 and surgical sepsis showed significant changes in various metabolites linked to amino acid metabolism, nitrogen metabolism, inflammation, the folate and methionine cycles, and glycolysis. Differences between COVID(-) and COVID(+) groups were not significant. Changes in metabolite levels tended to increase consistently from COVID(-) to COVID(+) to Sepsis groups, with more pronounced changes in the Sepsis group. The most significant differences between surgical septic and COVID-19 patients appeared in metabolites related to kynurenine synthesis, niacinamide, the TCA cycle, and GABA. Across all groups, there were significant alterations in the cysteine and methionine metabolism pathways. So, our study revealed common and different features of the metabolomic profiles of patients with surgical sepsis and CS associated with COVID-19.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCS, cytokine storm; ARDS, acute respiratory distress syndrome; SS, surgical sepsis; PCR, polymerase chain reaction; CCI, Charlson Comorbidity Index; SOFA, Sequential Organ Failure Assessment; ICU, intensive care unit; MES, 2-(N-morpholino)ethanesulfonic acid hydrate; QA/QC, quality control; RSD, relative standard deviation; SAH, S-Adenosylhomocysteine; SAM, S-Adenosylmethionine; GABA, Gamma-Aminobutyric acid; TCA, Tricarboxylic acid cycle\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted following the World Medical Association’s Code of Ethics (Declaration of Helsinki) for experiments involving humans and was approved by the Expert Council on Ethics of the St. Petersburg State Healthcare Establishment \"City Hospital No. 40\" (session No. 119, February 9, 2017). Written informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to the publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental details, results, and materials are available in the text and supplementary file.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by Saint Petersburg State University, project ID: 95412780\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statemen\u003c/strong\u003et\u003c/p\u003e\n\u003cp\u003eRusskikh Ia.V.: Investigation, validation, data curation, writing, editing. Popov O.S.: Conceptualization, investigation, data curation, formal analysis, visualization, writing, editing. Klochkova\u0026nbsp;Т.G.: conceptualization, writing, editing, corresponding author. Sushentseva N.N.: Conceptualization, methodology, writing, editing. Apalko S.V.: Conceptualization, project administration, writing, editing. Asinovskaya А.Yu.: Resources, project administration, supervision. Mosenko S.V.: Data curation, resources. Sarana\u0026nbsp;А.М.: Supervision, project administration. Shcherbak S.G.: Resources, supervision, project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. WHO Coronavirus (COVID-19) Dashboard; WHO: 2024. https://data.who.int/dashboards/ COVID-19/deaths, 2024 (accessed 14 January 2024).\u003c/li\u003e\n\u003cli\u003eFajgenbaum, D. C., June, C. H. 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Metabolic reprogramming consequences of sepsis: adaptations and contradictions. \u003cem\u003eCell Mol Life Sci\u003c/em\u003e. \u003cstrong\u003e79\u003c/strong\u003e, 456 (2022). doi:10.1007/s00018-022-04490-0\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, cytokine storm, sepsis, targeted metabolomic analysis, LC-MS/MS, metabolic pathways ","lastPublishedDoi":"10.21203/rs.3.rs-5339115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5339115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe clinical manifestations of the cytokine storm (CS) associated with COVID-19 resemble the acute phase of sepsis. Metabolomics may contribute to understanding the specific pathobiology of these two syndromes. The aim of this study was to compare serum metabolomic profiles in CS associated with COVID-19 vs. septic surgery patients. In retrospective cross-sectional study serum samples from patients with CS associated with COVID-19, with and without comorbidity as well as serum samples from patients with surgical sepsis were investigated. A targeted metabolomic analysis was carried out in all samples using LC-MS/MS method. Analysis revealed that similar alterations in serum metabolome of patients with COVID-19 and in surgical septic patients were associated with amino acid metabolism, nitrogen metabolism, inflammatory status, methionine cycle and glycolysis. The most significant difference was identified for the serum levels of metabolites of kynurenine synthesis, tricarboxylic acid cycle, as well as for gamma-aminobutyric acid and niacinamide. The metabolic pathway of cysteine and methionine metabolism was disturbed significantly in COVID-19 and surgical septic patients. For the first time, the similarities and differences between the serum metabolomic profiles of patients with CS associated with COVID-19 and patients with surgical sepsis were investigated for the patients from northwest of Russian Federation.\u003c/p\u003e","manuscriptTitle":"Comparative metabolomic analysis reveals shared and unique features of COVID-19 cytokine storm and surgical sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 11:03:59","doi":"10.21203/rs.3.rs-5339115/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-04T03:40:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-03T17:03:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65112381703914499798667517022493165659","date":"2024-11-30T14:48:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-21T22:31:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19189086803727898732085376795922066179","date":"2024-11-15T12:19:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-15T09:45:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-14T04:26:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-14T03:55:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-14T03:46:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-26T22:00:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23ea2efc-7fcb-4599-979c-56d2964f1475","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39459047,"name":"Biological sciences/Biochemistry"},{"id":39459048,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2025-03-03T15:59:10+00:00","versionOfRecord":{"articleIdentity":"rs-5339115","link":"https://doi.org/10.1038/s41598-025-90426-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-24 15:57:00","publishedOnDateReadable":"February 24th, 2025"},"versionCreatedAt":"2024-11-06 11:03:59","video":"","vorDoi":"10.1038/s41598-025-90426-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90426-0","workflowStages":[]},"version":"v1","identity":"rs-5339115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5339115","identity":"rs-5339115","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}