REG1A may be a new biological marker for diagnosing sepsis

doi:10.21203/rs.3.rs-3840154/v1

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REG1A may be a new biological marker for diagnosing sepsis

https://doi.org/10.21203/rs.3.rs-3840154/v1

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Objective

The aim of this study was to analyze the proteomic mass spectrometry of sepsis patients as well as normal subjects, and then to explore the characteristic proteins related to the pathogenesis of sepsis, and then to provide the basis for the clinical diagnosis and treatment of sepsis.

METHODS

Peripheral blood specimens from sepsis patients (n = 17) and normal subjects (n = 8) were analyzed by proteomic mass spectrometry sequencing, and the R-based Integrated Differential Expression and Pathway Analysis (iDEP) (http://bioinformatics.sdstate.edu/idep/) web tool was used to screen and analyze differentially expressed genes (iDEPs) and low-expressed genes (iDEPs). Screening analysis finalized the differentially expressed genes (DEGs, log2FC ≥ 1, P value ≤ 0.05). Differential proteins were screened for potential core proteins by enrichment analysis and combined with the subjects' work characterization curves (ROC curves), and finally the analysis results were verified by ELISA experiments.

RESULTS

Differential proteins were finally screened by the analysis, and the results were validated by ELISA to clarify that REG1A has a guiding significance in the diagnosis, differential diagnosis, and prognosis of sepsis disease.

CONCLUSION

Bioinformatics analysis of protein expression profiles in this study identified that the protein REG1A may represent a molecular mechanism for the onset, progression, and risk prediction of sepsis.

Biological sciences/Immunology/Inflammation/Sepsis

Biological sciences/Computational biology and bioinformatics/Protein analysis

Biological sciences/Computational biology and bioinformatics/Protein function predictions

Biological sciences/Computational biology and bioinformatics/Proteome informatics

Sepsis

Proteomics

Diagnosis

Prognosis

Sepsis is a disease with high morbidity and mortality [1, 2], defined as life-threatening organ dysfunction due to dysregulation of the host response to infection [3]. Sepsis is the leading cause of death worldwide, yet there is no effective treatment [4]. Of the 120,371 patients hospitalised with sepsis, 6611 (5.5%) were previously healthy and 105,455 (87.6%) had community-onset sepsis. Short-term mortality rates were 11.7% and 28.2% for previously healthy and those with comorbidities, 11.0% for 25.2% for community-onset sepsis, and 22.0% and 48.7% for hospital-onset sepsis, respectively [5]. Early detection and prompt initiation of appropriate treatment within the first few hours of sepsis onset improves outcomes [6]. Biomarkers play an important role in early diagnosis and risk stratification of sepsis, guiding antibiotic use, severity and prognosis, and efficacy assessment [7]. A new study reported that we identified a dynamically changing haematological-immunological profile of sepsis, including lymphopenia, reduced dendritic cell frequency and myeloid HLA-DR expression, even though C-reactive protein, a common clinical marker of inflammation, is not elevated, which is characteristic of sepsis [8]. Mid-region adrenomedullinogen (MR-proADM) is a marker of infection-induced oxidative stress, reflecting severity proportional to organ damage [9].Elevated C-reactive protein to albumin ratio (CAR) is associated with increased mortality and poor prognosis in patients with sepsis [10]. The neutrophil index (DNI) has shown better validity in differentiating septic febrile patients from non-septic febrile patients [11]. A new biomarker is urgently needed for proper diagnosis and treatment of sepsis at an early stage.

Proteins are the basis of life activities, and various pathological states of the organism are often accompanied by protein dysfunction. Thanks to the development of high-throughput liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology and the construction of related protein measurement instruments, proteomics has become an indispensable and important tool for studying biological processes at the protein level [12]. In this study, we aimed to find potential novel biomarkers for the diagnosis and treatment of sepsis.

The REG gene family consists of at least four major members, REG I, II, III and IV, which play a role in tissue inflammation, tumourigenesis and regeneration by promoting cell proliferation, differentiation and inhibiting apoptosis, as reported in several studies [13]. REG1A has been reported to be associated with the development of oesophageal cancer [14], melanoma [15] and inflammatory bowel disease [16], but there is still a gap in the studies on REG1A in sepsis, so it is necessary to explore the expression and role of REG1A in sepsis.

Patient recruitment and clinical data collection

In the preliminary stage of this research, sepsis patients (n=17) and normal human volunteers (n=8) in the acute intensive care unit (EICU) of the Affiliated Hospital of Southwest Medical University (SWMU) from January 2019 to December 2020 were collected under the support of the Sichuan Provincial Clinical Key Specialty Construction Project and the municipal project [Luzhou Municipal People's Government-Southwest Medical University Science and Technology Strategic Cooperation Applied Basic Research Project (NO.2021LZXNYD-J13)]. The inclusion criteria were as follows: 1. Compliance with the diagnostic criteria for sepsis 3.0 proposed by the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM) in 2016; 2. Age between 16 and 75 years old; and 3. Agreement of subjects or their legal representatives to participate in the study and signing of an informed consent form. The exclusion criteria were as follows: 1, patients younger than 16 years of age; 2, those with a previous history of organ failure (i.e., heart failure, respiratory failure, liver failure, renal failure, etc.); 3, those with previous immune system disorders; 4, those with previous hematological disorders; and 5, patients who did not wish to participate in the study. The exclusion criteria were as follows: 1. those with previous organ failure, immune system diseases, and blood system diseases; 2. those who were not willing to participate in this study.

This study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (approval number: KY2018029). All patients and volunteers who participated in the study informed themselves or their legal representatives and obtained written informed consent.

Sample collection

A total of 5 ml each of peripheral blood specimens from 8 normal individuals and 17 patients with septic shock were collected in this study, and the specimens were stored in the -80°C refrigerator of the Biological Sample Bank of the Affiliated Hospital of Southwest Medical University. Proteomic mass spectrometry analysis was performed on 8 normal human specimens and 17 specimens from patients with sepsis.

Ethical statement

Our study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (approval number: KY2018029). All patients and volunteers participating in the study informed themselves or their legal representatives and obtained written informed consent, and we confirm that all studies involving human research participants during the course of the experiment were conducted in accordance with the Declaration of Helsinki.

Access to raw data

The datasets generated and analysed during the current study are available in the CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) with accession number CNP0002611 repository (https://db.cngb.org/).

Proteomics

17 samples from the sepsis group and 8 samples from normal group were analyzed by DIA proteomics techniques. We performed accurate and highly reproducible quantitative mass spectrometric analysis of a large number of proteins using a Q-Exactive HF (Thermo Fisher Scientific, San Jose, CA) liquid-mass spectrometer. Subsequently, Ratio values and P-values of protein expression changes in 25 samples were identified and quantified using the mProphet algorithm, based on spectral libraries constructed in a traditional data-dependent acquisition (DDA) mode [17].

Data quality control

The data quality was assessed by log-logging the sequenced dataset using the online analysis software idep.96 (http://bioinformatics.sdstate.edu/idep/), where the principal component analysis plot (PCA, Fig1).

Differential Expression Protein Screening

After logarithmic preprocessing of the raw data, the differential proteins in the sepsis and normal control groups were identified by the online analysis software idep.96. We defined as differential proteins the values of proteins that got up-regulated and down-regulated under this condition by setting the screening parameter, Fold change value (FC value) > 2 and p < 0.05.

Gene Ontology (GO) and Pathway Enrichment Analysis

GO is a popular method for classifying gene expression and its properties, including molecular functions, biological processes (BPs) and cellular components, and it provides researchers with a comprehensive functional annotation tool for integrating functionally important genes with specific functions. The Database for Annotation, Visualisation and Integrated Discovery (DAVID) is used to identify highly representative GO categories with P-values <0.05 in BPs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is an online database collecting information on genomic, biochemical and enzymatic pathways. DEGs were mapped to the KEGG database and screened for significantly related pathways. GO functions and KEGG pathways were analysed using DAVID and the results visualised using the OmicShare online tool based on R Voice[18].

ELISA

In order to verify the authenticity as well as accuracy of the changes in REG1A protein in the normal group and in sepsis, we further validated the index by ELISA experiments. We collected peripheral blood samples from 53 sepsis patients as well as 12 normal subjects for the experiment. The experiment was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (Grant No. KY2018029). All patients and volunteers who participated in the study informed themselves or their legal representatives and obtained written informed consent.

Through differential protein analysis and enrichment analysis, we finally identified the differential protein REG1A, which was up-regulated in sepsis patients. In order to verify the accuracy of the above results, we further performed ELISA on peripheral blood serum samples from 53 sepsis patients and 12 normal patients.

Serum dilutions were prepared by centrifuging the blood samples from sepsis patients and normal subjects and taking 1 ml of the upper serum layer, and seven different concentrations of human REG1A standard and sample dilutions (37°C, incubation for 90 min), biotin anti-human REG1A antibody working solution dilutions (37°C, incubation for 60 min) were added sequentially into 96-well enzyme-labelled plates according to the instructions of the ELISA kit, ABC working solution (37℃, incubated for 30min), TMB colour development solution (37℃, incubated for 20min, protected from light), and termination solution, and finally, the O.D. values were measured in an enzyme marker (450nm) and the expression value data of each sample were calculated.

Receiver operating characteristic

The receiver operating characteristic (ROC) plot is an alternative way of presenting the risk distributions of diseased and non-diseased individuals. In the ROC plot, the separation of the risk distributions is indicated by the area between the ROC curve and the diagonal. The more separation between the risk distributions of the diseased and non-diseased individuals, the larger the area between the ROC curve and the diagonal, and the higher the AUC.

Differential Expression Expression Protein Screening

The data after log logarithmization were subjected to differential gene analysis by the online analysis software idep.951 (http://bioinformatics.sdstate.edu/idep/), and the parameters were set to FC > 2 and p < 0.05. 1569 differential proteins were obtained under this screening condition, and according to the logFC value to distinguish them into 209 up-regulated proteins and 31 down-regulated proteins (Fig2a), and visualized them using volcano plots (Fig2b).

Enrichment analysis

Enrichment analysis is a method of analyzing protein expression information; enrichment is the process of classifying proteins according to a priori knowledge, i.e., proteomic annotation information. After proteins are categorized, it can help to cognitively search for proteins that share some aspect of commonality (e.g., function, composition, etc.). We screened biomarkers related to the pathogenesis of sepsis by using the DAVID online software database (https://david.ncifcrf.gov/) and pooling GO analysis to annotate the function of differentially expressed proteins, which were visualized using the Ouyi BioCloud platform online tool (Fig3a). Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis was used to screen the signaling pathways involved in the differentially expressed proteins (Fig3b).

ELISA

The data were visualized and analyzed by Xiantao Academic Online Analysis Software (https://www.xiantaozi.com/), and the results are shown in Fig4. The figure shows that the expression values of protein REG1A in normal group and sepsis group are obviously different and statistically significant, so we can think that the difference protein obtained from bioinformatics analysis of sepsis and normal group is real and reliable and can be a guide for the diagnosis of sepsis. Therefore, we can conclude that the difference in protein expression between sepsis and normal proteomics by bioinformatics analysis is real and reliable and can be a guide for the diagnosis of sepsis.

Receiver operating characteristic

From the results of ELISA experiments, we can clarify that the expression level of REG1A is significantly different between sepsis patients and normal subjects. To further investigate the diagnostic efficiency of this differential protein, we generated the receiver operating characteristic curve (ROC curve) and calculated the Area Under Curve (AUC) for each included characteristic, according to the Swets criterion [19], AUC<0.5 indicates that the test has no diagnostic value, AUC of 0.8-0.9 indicates that the test has no diagnostic value, and AUC of 0.8-0.9 indicates that the test has no diagnostic value. 0.5 indicates that the test has no diagnostic value, AUC of 0.8-0.9 indicates that the test has good accuracy, and AUC>0.9 indicates that the diagnostic test has high accuracy, the results are shown in Fig5.

In studies related to esophageal cancer, it was revealed that advanced thoracic esophageal cancer with positive expression of REG1A showed higher sensitivity to chemotherapy, and showed certain advantages in overall survival, disease-specific survival and disease-free survival compared with patients with negative expression of REG1A[14]. Studies of melanoma have found that patients with high REG1A expression have longer disease-specific survival than those with low REG1A expression[15], surprisingly, no association was found between REG1A expression levels and survival in pancreatic cancer[20]. REG1A has been extensively studied not only in tumor-related diseases, but also in inflammatory diseases. In a study on inflammatory bowel disease [IBD] in mice, the researchers adopted RNA sequencing was performed on colon biopsies from Crohn's disease [CD] patients, ulcerative colitis [UC] patients and non-IBD controls, and continuing to build the genes shown to be significantly dysregulated in human IBD were used to study gene expression in colons from a piroxicam-accelerated colitis interleukin-10 knockout [PAC IL-10 k.o.], an adoptive transfer [AdTr] and a dextran sulfate sodium [DSS] colitis mouse model. Finally, it was found that IBD and experimental mouse colitis had a high degree of similarity in the colonic transcription profile[16]. We obtained the same high expression of REG1A in the serum of sepsis patients by proteomic analysis and validation by ELISA assay. Another report about inflammatory bowel disease [IBD] said that REG1A was substantially increased in the inflamed colorectal tissues of IBD patients. And the aberrantly expressed REG1A in intestinal epithelial cells (IEC) prominently inhibited inflammatory responses, promoted cell proliferation and suppressed epithelial apoptosis. Mechanically, IL-6 and IL-22 markedly activated REG1A transcription through triggering JAK/STAT3 signaling pathway. In addition, overexpression of REG1A in mice by systematic delivery of REG1A lentivirus remarkably alleviated DSS-induced inflammatory injury and maintained the integrity of intestinal mucosal barrier. Taken together, our data demonstrated that the novel proliferative factor REG1A controlled by IL-6/IL-22-JAK-STAT3 signaling may provide a promising therapeutic target for patients with IBD[21]. This study reveals the mechanism of action of REG1A in IBD, and further research is needed to determine whether this mechanism of action is also present in the pathogenesis of sepsis. The progression of chronic pancreatitis (CP), an inflammatory disease of the pancreas, causes pancreatic stones to form within the pancreatic ductal lumen/parenchyma, which occurs via protein plug formation, and the result is intense and excruciating pain[22,23,24], and a recent study on sepsis found that pancreatic stone protein (PSP) is a promising biomarker in the context of sepsis,during the course of the study, the researchers compared PSP to currently used biomarkers during the study, including white blood cell count (WBC) and C-reactive protein (CRP), and they found that in multivariate regression, PSP was a significant predictor for sepsis, with an area under the curve (AUC) of 0.69. Positive and negative predictive values for this model were 100% and 84.4%, respectively[25]. Meanwhile, in the course of a study on pancreatic stone protein plugs, researchers found that rock protein (Reg1A) was the only protein in the plugs[26]. In conjunction with our study, we found that REG1A could be a biomarker capable of serving as a diagnostic marker for sepsis in clinical practice.

In this study, we found the differential protein REG1A by protein mass spectrometry analysis combined with ELISA, which provides a basic basis for the diagnosis of sepsis. However, due to the limitation of the research fund, we only applied ELISA in the process of experimental validation, and the reliability of the results could be improved if the results could be re-validated by combining with Western blot. Secondly, the limitation of the sample size may lead to errors in the analysis results, so we need to further expand the sample size to reduce the errors. Finally, we have not done further research on the pathogenesis of REG1A in sepsis, so the pathogenesis of REG1A can be further explored in subsequent studies.

Author contributions statement

Yuzhou Shen and Yang Li was responsible for manuscript writing, Guowen Tao was responsible for data collection ,Wenhao Chen was responsible for data analysis, Chenglin Wang and Shilin Li were responsible for experimental of mice ,at last Muhu Chen and Yingchun Hu were responsible for final review of the article.

Additional information

All the authors have no conflict of interest for this study.

Fund

The support of the Sichuan Provincial Clinical Key Specialty Construction Project and a city-level project [Luzhou Municipal People's Government - Southwest Medical University Science and Technology Strategic Cooperation Applied Basic Research (NO.2021LZXNYD-J13)].

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No competing interests reported.

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REG1A may be a new biological marker for diagnosing sepsis

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Abstract

Objective

METHODS

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CONCLUSION

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Introduction

Materials and methods

Results

Discussion

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