Application of histone modified marker in preparation of sepsis diagnosis product and kit of histone modified marker

Abstract

The invention provides application of a histone modified marker in preparation of a sepsis diagnosis product and a kit of the histone modified marker, and belongs to the field of biomarkers. According to the invention, peripheral blood mononuclear cell acquisition, histone extraction, Western blot detection and data analysis prove that the acetylation modification level (H3K18ac) of the 18th lysine residue of histone H3 can be used as a sepsis assessment marker; the reagent for detecting H3K18ac can be applied to the preparation of a kit for distinguishing infected severe illness from non-infected severe illness, severity evaluation of sepsis or prognosis prediction of sepsis.

Description

Technical Field

[0001] The present invention belongs to the field of biomarkers, relates to histone modification biomarkers, and specifically relates to the application of histone modification biomarkers in the preparation of sepsis diagnostic products and its kits. Background Art

[0002] Sepsis is a systemic inflammatory response caused by infection. Septic shock is a severe form of sepsis, manifested as persistent hypotension and insufficient tissue perfusion. Although the medical community's understanding of it has been deepening, the complexity of sepsis and septic shock and the diversity of clinical manifestations still make early identification and accurate diagnosis difficult. The immune response of sepsis is a dynamic process involving multiple cell types and molecular mechanisms. Epigenetics plays a key role in the heterogeneous response of sepsis. Gene recoding processes such as DNA methylation, histone modification changes, and non-coding RNA transcriptional regulation may partly explain the prognostic differences among different patients.

[0003] Biomarkers are a class of specific substances produced by the body that can reflect the occurrence and development of diseases. Sepsis biomarkers are expected to be used for diagnosing pathogenic infections, the severity of the disease, and the therapeutic effects of drugs. An ideal sepsis biomarker should quickly and specifically distinguish sepsis from other non-infectious inflammatory responses. Currently, a variety of proteins have been reported to be used for the diagnosis of sepsis, including procalcitonin, C-reactive protein, interleukin-6, soluble receptors such as triggering receptor expressed on myeloid cells 1, etc. However, these biomarkers all have certain limitations and lack specificity, and there is no biomarker that can be used alone for the diagnosis of sepsis.

[0004] Protein translational modifications (PTMs) increase the functional diversity of the proteome through the covalent addition of functional groups or proteins, proteolytic cleavage of regulatory subunits, or degradation of the entire protein. PTMs include phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation, lactylation, lipidation, and proteolysis, etc., and are crucial in the research of cell biology and disease treatment and prevention.

[0005] Serum lactate level is used as an important indicator to evaluate the severity and prognosis of sepsis. However, clinically, a management strategy of early volume resuscitation is often adopted for shock patients, resulting in the rapid clearance of serum lactate, which makes it difficult to obtain the accurate serum lactate value during the critical period of the patient's condition. Therefore, it is particularly important to explore biomarkers that are not easily affected by early clinical interventions (such as fluid resuscitation, application of vasoactive drugs, etc.). These biomarkers should not only provide a wider detection time window, but also provide a more reliable tool for clinical condition assessment due to their deep connection with the pathophysiological process.

[0006] The prior art CN114113631B discloses a detection kit for sepsis, which is used for the prognosis of sepsis and includes at least one of the following reagents: a reagent for detecting the level of total protein lactylation; a reagent for detecting the level of histone H3K18 lactylation; a reagent for detecting cytokines; a reagent for detecting the messenger ribonucleic acid level of arginase-1. The disadvantage of this technology is that the diagnostic accuracy is poor.

[0007] Zhang Hong et al. (Zhang Hong, Ni Zheng, Zhang Liying, etc. Significance of rapid emergency PCT detection in evaluating the prognosis of sepsis [J]. Journal of Clinical Emergency, 2015.) found that as the PCT value increases, the mortality rate of sepsis patients shows an increasing trend and the length of hospital stay prolongs. For the prognosis assessment of sepsis patients, the sensitivity of the first PCT value at admission is comparable to that of dynamic PCT monitoring, but the specificity of dynamic PCT monitoring is stronger. The results of rapid emergency PCT detection have certain guiding significance for the judgment of sepsis condition and prognosis assessment; dynamically monitoring the changes of its indicators is more significant for short-term prognosis assessment. The disadvantage of this technology is that it is vulnerable to early clinical intervention and has poor sensitivity and specificity. Summary of the Invention

[0008] In view of the problems existing in the prior art, it is particularly important to explore biomarkers that are not easily affected by early clinical interventions (such as fluid resuscitation, application of vasoactive drugs, etc.). These biomarkers should not only provide a wider detection time window, but also provide a more reliable tool for clinical condition assessment due to their deep connection with pathophysiological processes. Based on this, the present invention provides the application of histone modification biomarkers in the preparation of sepsis diagnostic products and their kits. The present invention proves that the acetylation modification level of lysine residue at position 18 of histone H3 (H3K18ac) can be used as a sepsis assessment biomarker through obtaining peripheral blood mononuclear cells, histone extraction, Western blot detection, and data analysis; the reagent for detecting H3K18ac can be applied to the preparation of a kit for differentiating severe infection from non-severe infection, assessing the severity of sepsis, or predicting the prognosis of sepsis, which is not easily affected by early clinical intervention and has high sensitivity and specificity.

[0009] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] On the one hand, the present invention provides the application of histone modification biomarkers in the preparation of products for etiological diagnosis, condition assessment, or prognosis assessment of severe sepsis, and the histone modification biomarkers include H3K18ac; the H3K18ac is the acetylation level of lysine residue at position 18 of histone H3.

[0011] Preferably, the application is to prepare a kit using an H3K18ac detection reagent; the detection reagent is used for the quantitative or semi - quantitative determination of histone modification markers.

[0012] Preferably, the method for quantitative or semi - quantitative determination includes any one or more of ELISA detection, Dot blot detection, Western blot detection, and immunohistochemical detection.

[0013] Preferably, the application includes determining infectious severe cases and non - infectious severe cases based on the quantitative or semi - quantitative detection results of H3K18ac.

[0014] Preferably, when the application makes a determination based on the detection results, the diagnostic cut - off value is 0.930 - 0.950; when H3K18ac is less than or equal to the cut - off value, it is determined as an infectious severe case; when H3K18ac is greater than the cut - off value, it is determined as a non - infectious severe case.

[0015] Preferably, the infectious severe cases include sepsis or septic shock, and the non - infectious severe cases refer to severe cases where infection is not the main cause.

[0016] On the other hand, the present invention provides a kit for the diagnosis, condition assessment, or prognosis assessment of sepsis, and the kit includes a reagent for quantitatively or semi - quantitatively detecting H3K18ac.

[0017] Preferably, when the kit is used for detection, it includes the following steps:

[0018] Sample collection, sample pretreatment, isolation of PBMC, extraction of histones, quantitatively or semi - quantitatively determining H3K18ac, and interpreting the results.

[0019] Preferably, the kit includes a sample extraction reagent, a sample preservation reagent, a sample pretreatment reagent, a PBMC isolation reagent, a histone extraction reagent, and a quantitative or semi - quantitative detection reagent.

[0020] Preferably, the quantitative or semi - quantitative detection reagent includes any one or more of an electrophoresis gel, a loading buffer, a prestained protein, an electrophoresis buffer, a electrotransfer buffer, a histone H3K18 acetylation antibody, a histone H3 antibody, a secondary antibody, a blocking agent, a TBST buffer, and a SuperECL Plus hypersensitive luminescence solution.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention discloses that the acetylation modification level of lysine residue at position 18 of histone H3 (H3K18ac) can be used as a sepsis evaluation marker, and the reagent for detecting H3K18ac can be applied to the preparation of a kit for differentiating infectious severe diseases from non-infectious severe diseases, evaluating the severity of sepsis, or predicting the prognosis of sepsis.

[0023] 2. The H3K18ac index applied in the present invention shows higher efficacy than H3K18la in judging severe infections. The area under the ROC curve (AUC) of H3K18ac reaches 0.865, approaching the diagnostic ability of procalcitonin widely used clinically. Description of the Drawings

[0024] Figure 1 Western blotting detection developed images of H3K18la, H3K18ac, and histone H3 in serum samples of severe patients and healthy subjects; where SS: septic shock, S: sepsis, ICI: infectious critical illness (including septic shock and sepsis), NICI: non-infectious critical illness.

[0025] Figure 2 Correlation analysis of H3K18la level and infection-related laboratory indicators; Spearman correlation analysis was used, and for the indicators with significant correlation, simple linear regression analysis was further performed; where CRP: C-reactive protein, PCT: procalcitonin.

[0026] Figure 3 Correlation analysis of H3K18ac level and infection-related laboratory indicators; Spearman correlation analysis was used, and for the indicators with significant correlation, simple linear regression analysis was further performed.

[0027] Figure 4 ROC curves of H3K18la and H3K18ac for determining septic shock in the infectious severe disease cohort.

[0028] Figure 5Correlation analysis of H3K18la with disease severity scores and common prognostic indicators; Spearman correlation analysis was used, and for indicators with significant correlations, simple linear regression analysis was further performed; among them, APACHE II: acute physiology and chronic health evaluation II score; SOFA: sequential organ failure assessment score.

[0029] Figure 6 Correlation analysis of H3K18ac with disease severity scores and common prognostic indicators; Spearman correlation analysis was used, and for indicators with significant correlations, simple linear regression analysis was further performed; among them, APACHE II: acute physiology and chronic health evaluation II score; SOFA: sequential organ failure assessment score.

[0030] Figure 7 Correlation analysis of H3K18la with the expression of serum inflammatory factors; Spearman correlation analysis was used, and for indicators with significant correlations, simple linear regression analysis was further performed; among them, TNF-α: tumor necrosis factor α, IL: interleukin, IFN-α: interferon α.

[0031] Figure 8 Correlation analysis of H3K18ac with the expression of serum inflammatory factors; Spearman correlation analysis was used, and for indicators with significant correlations, simple linear regression analysis was further performed.

[0032] Figure 9 Correlation analysis of H3K18la and H3K18ac with the expression of downstream M2 macrophage polarization-related marker (ARG1); Spearman correlation analysis was used, and for indicators with significant correlations, simple linear regression analysis was further performed. Detailed implementation manners

[0033] Unless otherwise specified, the raw materials used in the present invention are all ordinary commercially available products, and no specific limitation is imposed on their sources.

[0034] Sepsis is a systemic inflammatory response caused by infection, which can progress to septic shock and even life-threatening organ dysfunction. Sepsis is a major problem in intensive care. Generally speaking, the health-related quality of life of patients with sepsis and septic shock drops sharply during their stay in the ICU, and the mortality rate increases during long-term care. Despite significant improvements in the diagnosis and management of sepsis, it remains a challenging clinical entity due to its diverse etiologies and manifestations, and is usually only documented after clinical deterioration during hospitalization. Differential diagnosis is also difficult when the patient is in circulatory shock, especially when accompanied by other conditions such as cardiac injury, hypovolemia, and trauma, or when there are no typical signs of infection in infants, the elderly, and immunocompromised patients. Sepsis triggers complex immune responses, leading to a dysregulation of the homeostatic balance between pro-inflammatory and anti-inflammatory responses, such that patients with similar injuries may have very different prognoses. Genetic regulation may play a central role here. In fact, extensive gene reprogramming, such as DNA methylation, histone modification, and transcriptional regulation of non-coding RNAs, can lead to cell cycle disruption, endothelial dysfunction, mitochondrial damage, metabolic disorders, immune failure, and cardiovascular failure. Histones can undergo a variety of covalent modifications, including methylation, citrullination, acetylation, and phosphorylation, which alter their relationship with each other and with DNA. The attenuation of local and systemic pro-inflammatory cytokines, protection against distant organ injury, enhanced bacterial clearance and phagocytosis, and inhibition of immune cell apoptosis are associated with improved survival.

[0035] Different sepsis patients are faced with similar injury insults, but their prognoses may vary greatly. Extensive gene recoding, including DNA methylation, histone modification alterations, and non-coding RNA transcriptional regulation, etc., may partly explain this difference. As the main proteins in the cell nucleus, which is the control center of cell metabolism, growth, and differentiation, the N-terminal tails of histones can be reshaped to regulate transcription, replication, and DNA repair by various post-translational modifications (PTMs). Some metabolites, including propionyl-CoA, butyryl-CoA, crotonyl-CoA, lactyl-CoA, 2-hydroxyisobutyryl-CoA, β-hydroxybutyryl-CoA, succinyl-CoA, benzoyl-CoA, malonyl-CoA, and glutaryl-CoA, have recently been identified as substrates for relevant modifications, which can promote histone PTM and thus affect gene expression. Through the above mechanisms, the functions of different genes and proteins can be rapidly regulated.

[0036] The only amino acid in proteins that contains an ε-amino side chain is lysine, which has the characteristics and reactivity of a primary amine. Therefore, it is one of the most diverse amino acids in terms of post-translational modifications in living organisms. In 2019, the presence of lysine lactylation modification (Kla) in histones in several human cell lines and mouse bone marrow-derived macrophages was reported for the first time. Kla can occur on all core histones and shares the most common modification sites with histone lysine acetylation (Kac). Similar to many PTMs, Kla involves the transfer of lactyl groups from lactyl-CoA and the removal of lactyl groups. P300, also known as lysine acetyltransferase (KAT3B), has been shown to be a lactyltransferase and can promote lactylation modification. In terms of non-histone proteins, it has been demonstrated that macrophages can uptake extracellular lactate through monocarboxylate transporters (MCTs), and then promote the lactylation of HMGB1 through a p300 / CBP-dependent mechanism, and stimulate the acetylation of HMGB1 by inhibiting the deacetylase SIRT1 and recruiting the acetyltransferase p300 / CBP to the nucleus.

[0037] The acetylation and deacetylation processes of core histones are a key step in regulating gene activity. Histone acetyltransferases (HATs) and HDACs finely regulate and maintain a rapid acetylation and deacetylation cycle at the genomic level, forming an "acetylation homeostasis". Not only histones, but also a large number of key proteins involved in other cellular functions have a balance between acetylation and deacetylation, playing important roles in aspects such as the cell cycle, stress response, cytoskeleton and motility, signal transduction, damage repair and remodeling, and proliferation. Imbalance of acetylation can lead to the occurrence of various diseases.

[0038] Baseline characteristics of the study subjects and statistical analysis

[0039] In this invention, the research cohort consisted of 98 people, including critically ill patients in the Department of Critical Care Medicine of Beijing Hospital and healthy volunteers prospectively collected from August 22, 2018, to October 7, 2022. Among them, there were 37 people in the septic shock group, 13 people in the sepsis group, 36 people in the non-infectious severe group, and 12 people in the healthy control group. All the above diagnoses were based on the clinical manifestations and auxiliary examination information on the day of specimen collection (basically the day of admission to the ICU). The diagnoses of septic shock and sepsis were in line with the relevant definitions of Sepsis 3.0. The following baseline information of the sample subjects in this invention was collected: age, gender, underlying diseases, Sequential Organ Failure Assessment (SOFA, from the 1st day to the 3rd day of ICU admission), Acute Physiology and Chronic Health Evaluation II (APACHE II) within 24 hours, mechanical ventilation time, ICU length of stay, total hospital length of stay, 28-day mortality rate, and laboratory indicators on the same collection date: serum lactate, white blood cell count (WBC), neutrophil count, neutrophil percentage, lymphocyte count, lymphocyte percentage, monocyte count, monocyte percentage, procalcitonin level (PCT), and C-reactive protein (CRP).

[0040] Data analysis:

[0041] In this invention, for data with normal distribution, Student's t-test or one-way analysis of variance (One way ANOVA) was used for comparison, and the results were expressed as mean ± SD; Pearson correlation test was used for correlation analysis. For data with non-normal distribution, non-parametric Mann-Whitney U test was used for analysis, and the results were expressed as median and interquartile range (IQR); Spearman correlation test was used for correlation analysis. For the comparison of categorical variables, chi-square or Fisher's exact test was used, and the results were expressed as numbers and percentages. P < 0.05 was considered statistically significant.

[0042] The diagnostic value was determined by receiver operating characteristic (ROC) curve analysis. The relationship between the true positive rate (sensitivity) and the false positive rate (specificity) was statistically analyzed and plotted at different classification thresholds. The area under the ROC curve (AUC) gave an index of classification performance, and a higher value of AUC corresponded to a good prediction of the model. P < 0.05 was considered statistically significant.

[0043] All statistical analyses in this invention were performed using IBM SPSS 24.0, Prism 9.0, and Stata 17 software.

[0044] After statistical analysis, the sample baseline information is shown in Table 1.

[0045] Table 1 Sample baseline information

[0046]

[0047]

[0048] As shown in Table 1, in terms of age, the average age of all the research subjects was 65.60 years old. Among them, the average age of the septic shock group was 67.49 years old, that of the sepsis group was 65.31 years old, that of the non-infectious severe group was 65.72 years old, and that of the healthy control group was 59.75 years old. There were no statistical differences among the first three disease groups and among all four groups (P = 0.880, 0.561); in terms of gender ratio, the male ratio in the whole cohort was 61.22%, and there were no statistical differences among the first three disease groups and among all four groups (P = 0.241, 0.386); in terms of common comorbidities, there were no statistical differences in all common comorbidities among the first three disease groups, but there were statistical differences in chronic heart disease, hypertension, and diabetes among the four groups including the healthy control group (P = 0.002, 0.015, 0.022); in terms of vital signs, there was only a statistical difference in the mean arterial pressure among the first three disease groups (P < 0.001); in addition, there were statistical differences in the worst value of blood lactate on the day of specimen collection, two disease severity scores (APACHE II score and SOFA score), and the proportion of patients using invasive mechanical ventilation among the first three disease groups (all P values were less than 0.001), and all showed higher values in the septic shock group, which was in line with general expectations; in terms of prognostic indicators, there were no statistical differences in the ICU length of stay and total length of stay among the first three disease groups (P = 0.128, 0.285). Among the first three disease groups, 15 people died within 28 days after admission to the ICU (accounting for 15.30% of the whole cohort and 17.4% of the patient cohort only). Among them, there were 12 people with septic shock (accounting for 32.43% within the group), 2 people with sepsis (15.38%), and 1 person with non-infectious severe disease (2.78%). There was a statistical difference among the three groups (P = 0.004).

[0049] Example 1: Blood collection, isolation of peripheral blood mononuclear cells from blood samples, and serum separation

[0050] The present invention uses a detection kit for sepsis to distinguish between infectious severe cases and non-infectious severe cases, evaluate the severity of sepsis, or predict the prognosis of sepsis, including at least one of the following reagents: a reagent for detecting the level of histone H3K18 lactylation, a reagent for detecting the level of histone H3K18 acetylation, a reagent for detecting the messenger ribonucleic acid level of arginase-1, and a reagent for detecting cytokines. In addition, the detection kit in the present invention may further include a BCA protein concentration assay kit, a histone extraction kit, and an SDS-PAGE gel preparation kit.

[0051] Specifically, first, a blood sample is collected, and then the collected blood sample is separately subjected to peripheral blood mononuclear cell separation and serum separation. The blood sample (5 - 10 ml) is collected using an ethylenediaminetetraacetic acid (EDTA) blood collection tube and a serum separation blood collection tube, and peripheral blood mononuclear cell separation and serum separation are respectively performed. Among them, the peripheral blood mononuclear cell separation can be diluted 1:1 with a phosphate buffered saline solution at pH 7.2; the diluted blood sample is placed on 15 ml of lymphocyte separation medium (STEMCELL Technologies Cat#07851) and centrifuged at 500 g and 20 °C for 20 minutes; most of the upper layer is aspirated, leaving a white - light yellow tuft (mononuclear cells) in the interphase; the mononuclear cells are separated and filled with phosphate buffered saline solution, mixed and centrifuged at 500 g at 20 °C for 7 minutes; the supernatant is completely removed. If there are red impurities in the precipitate at the bottom of the tube, add erythrocyte buffer (Solarbio Cat#R1010) for 5 minutes; add a sufficient amount of phosphate buffered saline solution and centrifuge at 20 °C and 500 xg for 7 minutes; after removing the supernatant, collect the peripheral blood mononuclear cells and resuspend them with 2 ml of cryoprotectant (fetal bovine serum: dimethyl sulfoxide = 9:1), and the obtained peripheral blood mononuclear cells are stored at - 80 °C. For serum separation, under sterile conditions, the blood specimen is centrifuged at 3000 rpm at 4 °C for 10 minutes; the supernatant is aspirated and stored at low temperature, and the obtained serum is stored at - 80 °C.

[0052] Example 2: Detection of cytokine levels

[0053] The reagent for detecting cytokines in the present invention can detect one or more of IL - 1β, IL - 2, IL - 4, IL - 5, IL - 6, IL - 8, IL - 10, IL - 12p70, IL - 17, IFN - α, IFN - γ, TNF - α. The reagent for detecting cytokines in the present invention may include capture microsphere antibodies, detection antibodies, SA - PE, and washing solution.

[0054] The present invention uses flow cytometry to detect cytokines in the isolated serum, including: IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, IFN-α, IFN-γ and TNF-α. 25 μl of the calibration product sample is added to the calibration tube, and 25 μl of the serum sample buffer is added to Matrix B. The sample is thoroughly mixed with 25 μl of the capture microsphere antibody; 25 μl of the detection antibody is added to all test tubes, and they are incubated at room temperature in the dark with shaking at 400 - 500 r / min. After 2 hours, 25 μl of SA-PE is added to all test tubes, and they are incubated in a shaker at 400 - 5500 r / min at room temperature in the dark. After half an hour, 500 μl of 1× wash solution is added, rotated for a few seconds, and centrifuged at 500 xg for 5 minutes. After removing the supernatant, 300 μl of 1× wash solution is added to the test tube and rotated for a few seconds. The specimens are detected using a flow cytometer, and 12 cytokines can be detected, including IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, IFN-α, IFN-γ and TNF-α. To ensure the accuracy of the data, at least 1100 microspheres are collected for each sample.

[0055] Example 3: RNA Extraction and qRT-PCR

[0056] The total RNA of the examples of the present invention is extracted by the guanidine isothiocyanate-phenol-chloroform method (RNAiso Plus, TaKaRa Bio Code No. 9109). The RNA yield is measured using a Thermo nanodrop 2000C (A260 / A280). The quality of the RNA is detected by agarose gel electrophoresis.

[0057] The reagents used in the present invention for detecting the messenger ribonucleic acid level of arginase-1 include: reverse transcription reagents, DNase, primers, and a real-time fluorescence quantitative PCR mixture. Among them, the real-time fluorescence quantitative PCR mixture includes: 5 μl of 2× PCR mixture, 0.5 μl of primer F (10 uM), 0.5 μl of primer R (10 uM), 1 μl of template, and 3 μl of double-distilled water. The primers include the following primers:

[0058] 5’-AAGAGTGTGATGTGAAGGATTATGG-3’ / 5’-TTCTTCTTGACTTCTGCCACCTT-3’ (arginase-1),

[0059] 5’-TGACTTCAACAGCGACACCCA-3’ / 5’-CACCCTGTTGCTGTAGCCAAA3’ (GAPDH).

[0060] The qualified RNA sample was denatured at 65 °C for 5 minutes and reverse-transcribed using reverse transcription reagents. Residual genomic DNA was removed using DNase. Primers were designed using Primer5.0 to assemble the upstream and downstream regions of the target gene. The cDNA and primers were added to the qRT-PCR system (Tip Green qPCR SuperMix, AQ141-02; Thermo Fisher Scientific). The real-time fluorescence quantitative PCR mixture contained 5 μl of 2× PCR mixture, 0.5 μl of primer F (10 μM), 0.5 μl of primer R (10 μM), 1 μl of template, and 3 μl of ddH2O, with a final volume of 10 μl. The reaction was carried out in a LightCycler480 II (Roche Diagnostics) under the following conditions: 95 °C for 5 minutes, 95 °C denaturation for 10 seconds, 60 °C annealing for 30 seconds, and 72 °C for 10 seconds for a total of 45 PCR amplification cycles; the melting curve included 95 °C for 5 seconds, 65 °C for 1 minute, and 97 °C for 1 second. Finally, it was cooled at 4 °C for 30 seconds and fluorescence was measured. GAPDH was used as an internal reference. The expression data was normalized to GAPDH using the delta-delta CT method.

[0061] Example 4: Histone Extraction, Detection of H3K18 Lactylation Level and H3K18 Acetylation Level

[0062] The reagents used in this example for detecting the acetylation level of histone H3K18 included: a histone extraction kit, sodium dodecyl sulfate polyacrylamide gel, loading buffer, sodium dodecyl sulfate, prestained protein, electrophoresis buffer, electrotransfer buffer, histone H3K18 acetylation antibody, histone H3 antibody, goat anti-rabbit secondary antibody, blocking milk, TBST buffer, and SuperECLPlus hypersensitive luminescence solution.

[0063] In this example, the isolated peripheral blood mononuclear cells were centrifuged at 1000 rpm for 5 minutes at 4 °C, and then resuspended at 10 7 cells / ml in diluted 1× pre-dissolved buffer. The tube was kept on ice for 10 minutes, gently stirred, and centrifuged at 10000 rpm for 1 minute at 4 °C. After removing the supernatant, the cells were resuspended in 3 volumes (about 200 μl / 10 7 cells) of lysis buffer and incubated on ice for 30 minutes. After centrifuging at 12000 rpm for 5 minutes at 4 °C, 0.3 volumes of balanced-DTT buffer were added to the supernatant part (containing acid-soluble proteins) to measure the protein concentration. The isolated histones were stored at -80 °C. This disclosure used a BCA protein detection kit (Thermo Scientific TM Pierce TMThe concentration of histone extracted from peripheral blood mononuclear cells was detected using a BCA Protein Assay Kit (Cat#23227). According to the protein concentration results, 5 μl of 5× loading buffer and 2% sodium dodecyl sulfate were added to 15 μg of histone in equal amounts to make the final volume 20 μl. 5 μl of prestained protein and 20 μl of the sample were loaded onto a 5% stacking gel + 15% separating gel. At 80 V / gel, the sample was run until it showed as a single band, and then at 120 V / gel until the sample reached the bottom. Wet transfer was performed, and the protein was transferred onto a 0.2 μm PVDF membrane (ImmobilonTM-PSQ membrane) at 300 mA for 3 hours. In this example, the acetylation level of H3K18 was detected by Western blotting. The primary antibodies used were rabbit mAb against acetylated histone H3 (Lys18) ((PTM BioLab, Inc., Cat#PTM-114RM; diluted 1:1000 in Life Technologies TM antibody dilution reagent solution, cat#003218) and anti-histone H3 antibody (Abcam, ab1791, diluted 1:1000 in Life Technologies TM antibody dilution reagent solution), and the reaction was carried out overnight at 4°C. The secondary antibody was goat anti-rabbit IgG H&L (HRP, purchased from Abcam, cat#ab6721), diluted 1:3000 in TBS-T buffer containing 5% blocking milk, for 2 hours at room temperature. A chemiluminescence imaging system (VILBER Fusion Solo S) was used to quantitatively analyze the detected bands to obtain the lactylation modification level of histone H3K18.

[0064] The detection of the lactylation modification level of H3K18 was carried out with reference to the above-mentioned acetylation level detection method, except that the rabbit mAb against acetylated histone H3 (Lys18) as the primary antibody was replaced with a rabbit mAb against lactylated histone H3 (Lys18) (PTM BioLab, Inc., Cat#PTM-1406RM; diluted 1:1000 in Life Technologies TM antibody dilution reagent solution).

[0065] In this example, 15 μg of protein in equal amounts was added to each lane. Therefore, the WB results between samples are comparable.

[0066] Effect Example 1: Detection and Analysis

[0067] In this example, relevant indicators of the research cohort (cytokine levels, messenger ribonucleic acid levels of arginase-1, histone H3K18 site lactylation modification levels (H3K18la), acetylation modification levels (H3K18ac), and relevant items in the serum of the subjects) were measured and analyzed, and the results and analysis are as follows.

[0068] (1) Distribution and differences between histone H3K18 site lactylation modification levels (H3K18la) and acetylation modification levels (H3K18ac)

[0069] Figure 1 The results of Western blotting after incubation with anti-histone H3 antibody, specific anti-histone H3K18 site acetylation (H3K18ac) antibody, and specific anti-histone H3K18 site lactylation (H3K18la) antibody. Combining with the statistical analysis results in Table 2 and Table 3, it can be seen that in terms of the H3K18ac modification level alone, the septic shock group (0.706 [0.476, 0.804]) < the sepsis group (0.850 [0.688, 0.851]) < the healthy control group (0.992 [0.805, 1.141]), and the inter-group difference is statistically significant (P < 0.001), showing a negative correlation between H3K18ac and disease severity; for the H3K18la level, the trend is the septic shock group (1.851 [1.318, 1.966]) > the sepsis group (1.375 [1.177, 1.147]) > the healthy control group (1.006 [0.738, 1.186]), and the inter-group difference is statistically significant (P = 0.002), showing a positive correlation between H3K18la and disease severity.

[0070] In addition, compared with the non-infected severe group (H3K18ac: 1.225 [0.858, 1.488]; H3K18la: 1.262 [0.683, 1.740]), the infected severe group including septic shock and sepsis (H3K18ac: 0.711 [0.482, 0.849]; H3K18la: 1.640 [1.248, 1.955]) has a lower H3K18ac level and a higher H3K18la level, and the inter-group difference is statistically significant (H3K18ac: P < 0.001; H3K18la: P = 0.006), indicating the potential role of the two modifications in identifying infected severe cases.

[0071] Table 2 Comparison of H3K18la and H3K18ac between infected severe cases and non-infected severe cases

[0072]

[0073] Table 3 Comparison of H3K18la and H3K18ac among septic shock, sepsis and healthy subjects

[0074]

[0075] Note: Non-parametric Mann-Whitney U test was used for analysis between different groups, and the results were expressed as median and interquartile range; P<0.05 indicated significant difference.

[0076] (3) Diagnostic efficacy of H3K18la, H3K18ac and corresponding reference indicators in identifying critically ill infected patients from the ICU critical care cohort

[0077] H3K18la and H3K18ac were further analyzed using the ROC curve as follows: In identifying critically ill infected patients from the ICU critical care cohort, as shown in Table 4, the AUCs of H3K18la, H3K18ac and traditional infection-related laboratory indicators were 0.677 (0.547, 0.808) for H3K18la, 0.865 (0.774, 0.956) for H3K18ac, 0.932 (0.875, 0.989) for PCT, and 0.734 (0.613, 0.854) for CRP. It can be seen that the AUC value of H3K18ac, 0.865, is much higher than that of H3K18la, 0.677, close to 0.932 of PCT, and higher than 0.734 of CRP, indicating the diagnostic efficacy of H3K18ac in identifying critically ill infected patients from the ICU critical care cohort.

[0078] Table 4 Diagnostic efficacy of H3K18la, H3K18ac and corresponding reference indicators in identifying critically ill infected patients from the ICU critical care cohort

[0079]

[0080]

[0081] Note: # represents that the null hypothesis corresponding to P1 is that the area under the ROC curve is equal to 0.5; * represents that the null hypothesis corresponding to P2 is that there is no difference in the area under the ROC curve between this indicator and PCT.

[0082] (4) Correlation between H3K18la, H3K18ac and infection-related laboratory indicators

[0083] Next, the correlation between the three modified indicators and common infection-related laboratory indicators was used to further corroborate their association with critically ill infections (including septic shock and sepsis), and the results were as follows:

[0084] As Figure 2 shown, H3K18la had no significant correlation with various infection-related laboratory indicators. AsFigure 3 As shown, H3K18ac was negatively correlated with CRP and PCT (Spearman ρ = -0.3956, P = 0.0003; Spearman ρ = -0.4935, P < 0.0001), while it was positively correlated with monocyte count and lymphocyte count (Spearman ρ = 0.2531, P = 0.0187; Spearman ρ = 0.2162, P = 0.0455).

[0085] (5) Efficacy of H3K18la and H3K18ac in identifying septic shock from healthy controls and severe infection cohorts

[0086] As previously pointed out, the changing trends of the levels of H3K18la and H3K18ac in septic shock, sepsis, and healthy control groups were analyzed. Since this grouping had an obvious disease severity gradient, further analysis was performed using the ROC curve as follows:

[0087] As shown in Table 5 and Figure 4 As shown, in terms of the efficacy of identifying septic shock from healthy controls and severe infection cohorts, the descending order of H3K18la and H3K18ac was H3K18la 0.807 (0.700, 0.914) and H3K18ac 0.730 (0.603, 0.856).

[0088] Table 5 Efficacy of H3K18la and H3K18ac in identifying septic shock from severe infection cohorts

[0089] Type AUC 95% CI <![CDATA[P # > Cut-off value Youden index Sensitivity Specificity H3K18la 0.807 0.700，0.914 <0.001 1.607 0.569 0.649 0.920 H3K18ac 0.730 0.603，0.856 0.002 0.734 0.449 0.649 0.800

[0090] Note: # represents that the null hypothesis corresponding to P is that the area under the ROC curve is equal to 0.5.

[0091] (6) Correlation between H3K18la, H3K18ac and disease severity scores and common prognostic indicators

[0092] Through correlation analysis, the correlation between the modification indicators and disease severity (represented by APACHE II score and SOFA score) was further analyzed, and the possibility of using them as prognostic (represented by ICU length of stay, total hospital length of stay, and mechanical ventilation time) prediction indicators was explored.

[0093] As Figure 5As shown, H3K18la was positively correlated with the SOFA score (Spearman ρ = 0.3317, P = 0.0018), positively correlated with the length of ICU stay (Spearman ρ = 0.2292, P = 0.0338), and positively correlated with the duration of mechanical ventilation (Spearman ρ = 0.2742, P = 0.0111). However, no significant correlation was shown between H3K18la and the APACHE II score (Spearman ρ = 0.2139, P = 0.0508) and the length of hospital stay (Spearman ρ = -0.0034, P = 0.9751).

[0094] As Figure 6 shown, H3K18ac was negatively correlated with the APACHE II score (Spearman ρ = -0.3479, P = 0.0012), negatively correlated with the SOFA score (Spearman ρ = -0.3381, P = 0.0015), and negatively correlated with the duration of mechanical ventilation (Spearman ρ = -0.3009, P = 0.0051). However, no significant correlation was shown between H3K18ac and the length of ICU stay (Spearman ρ = -0.1715, P = 0.1144) and the number of hospital days (Spearman ρ = -0.0001, P = 0.9991).

[0095] (7) Correlation between H3K18la, H3K18ac and the expression of serum inflammatory factors

[0096] This part explored the correlation between H3K18la, H3K18ac and the expression of serum inflammatory factors. It included cytokines that promote the inflammatory response such as tumor necrosis factor α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), interferon-α (IFN-α), interferon-γ (IFN-γ), interleukin-8 (IL-8), etc., and cytokines that inhibit the inflammatory response such as interleukin-10 (IL-10), interleukin-4 (IL-4), etc.

[0097] As Figure 7 shown, H3K18la was negatively correlated with IFN-α (Spearman ρ = -0.2494, P = 0.0133), negatively correlated with IL-5 (Spearman ρ = -0.3506, P = 0.0004), and positively correlated with IL-10 (Spearman ρ = 0.2072, P = 0.0406).

[0098] As Figure 8As shown, H3K18ac was negatively correlated with IL-6 (Spearman ρ = -0.2618, P = 0.0092), negatively correlated with IL-1β (Spearman ρ = -0.2900, P = 0.0038), negatively correlated with IL-8 (Spearman ρ = -0.3083, P = 0.0020), and negatively correlated with IL-10 (Spearman ρ = -0.2008, P = 0.0474).

[0099] (8) Correlation between H3K18la, H3K18ac and the expression of downstream M2 macrophage polarization-related marker (ARG1)

[0100] Based on the PBMC samples of clinical patients collected in this study cohort, RNA was extracted, and the mRNA expression level of the M2 macrophage polarization marker ARG1 (Arginase1) was detected by qRT-PCR using GAPDH as the internal reference gene, and the relationship between it and H3K18la and H3K18ac was analyzed. As Figure 9 shown, H3K18la was positively correlated with the mRNA expression level of ARG1 (Spearman ρ = 0.330, P = 0.001), and H3K18ac was negatively correlated with the expression of ARG1 (Spearman ρ = -0.257, P = 0.011).

[0101] Comparative Example 1: Detection and analysis of the 2-hydroxyisobutyrylation modification level of H3K18

[0102] In addition to lactylation and acetylation modifications, histone H3 also has modifications such as methylation, citrullination, butyrylation, propionylation, crotonylation and 2-hydroxyisobutyrylation.

[0103] In this comparative example, according to the methods of Examples 1-4, on the basis of Effect Example 1, the 2-hydroxyisobutyrylation modification level (H3K18hib) of H3K18 was detected and analyzed.

[0104] In this comparative example, the detection of the 2-hydroxyisobutyrylation modification level (H3K18hib) of H3K18 referred to the acetylation level detection method in Example 4, except that the antibody against acetylated histone H3 (Lys18) of the primary antibody was replaced with a mouse mAb against 2-hydroxyisobutyrylated modified protein H3 (Lys18) (PTM BioLab, Inc., Cat#PTM-882; diluted 1:1000 in the LifeTechnologiesTM antibody dilution reagent solution, cat#003218).

[0105] According to the analysis method of Effect Example 1, the ROC curve was further applied to analyze the 2-hydroxyisobutyrylation modification level of H3K18, and the results are as follows: In terms of identifying severe infections from the ICU severe cohort (corresponding to the content of (3) in Effect Example 1), the AUC of H3K18hib was 0.572 (0.514, 0.630); the AUC of H3K18hib was lower than the AUC of H3K18ac (the AUC value of H3K18ac was 0.865 (0.774, 0.956)), which to a certain extent indicates that the modified protein index H3K18ac provided by the present invention is more reliable in identifying severe infections from the ICU severe cohort, and its effect is unexpected.

[0106] The above results also show that H3K18ac is correlated with the disease severity and can be used for disease severity assessment. The correlations between the H3K18ac modification index and the ICU length of stay, total length of stay, and mechanical ventilation time suggest its application direction as a prognostic prediction index.

[0107] Verification Example 1: Division of Severe Infectious and Non-Severe Infectious

[0108] In this verification example, the research cohort consisted of 20 people, which were critically ill patients prospectively collected from the Department of Critical Care Medicine of Beijing Hospital from November 1, 2022 to December 31, 2022. Among them, there were 10 severe infectious patients (including 5 in the septic shock group and 5 in the sepsis group) and 10 non-severe infectious patients. All the above diagnoses were based on the clinical manifestations and auxiliary examination information on the day of specimen collection (basically the day of admission to the ICU), and the diagnoses of septic shock and sepsis both met the relevant definitions of sepsis 3.0. According to the methods of Examples 1-4, the H3K18ac levels of the above 20 patients were detected and analyzed. The results are shown in Table 6. It can be seen that when using H3K18ac provided by the present invention as a marker for determination, within the verification sample range, the accuracy rates of dividing severe infectious patients and non-severe infectious patients reached 90% and 80% respectively, and H3K18ac as a marker has obvious effects.

[0109] Table 6 Detection and Analysis of H3K18ac Levels in 20 Patients

[0110]

[0111]

[0112] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than a limitation on the protection scope of the present invention. Any simple modification or equivalent replacement of the technical solution of the present invention by those of ordinary skill in the art does not depart from the essence and scope of the technical solution of the present invention.

Claims

1. Use of histone modification markers in the preparation of products for etiological diagnosis, condition assessment or prognosis assessment of severe sepsis, characterized in that, The histone modification markers described above include H3K18ac; the H3K18ac is the acetylation level of lysine residue at position 18 of histone H3.

2. The application according to claim 1, characterized in that, The application described above is to prepare a kit using an H3K18ac detection reagent; the detection reagent is used for quantitative or semi - quantitative determination of histone modification markers.

3. The application according to claim 2, wherein The methods for quantitative or semi - quantitative determination include any one or more of ELISA detection, Dot blot detection, Western blot detection, and immunohistochemical detection.

4. The application according to claim 3, characterized in that, The application described above includes determining infectious severe cases and non - infectious severe cases according to the quantitative or semi - quantitative detection results of H3K18ac.

5. The application according to claim 4, wherein When making a determination according to the detection results in the application described above, the diagnostic critical value is 0.930 - 0.950; when H3K18ac is less than or equal to the critical value, it is determined as an infectious severe case; when H3K18ac is greater than the critical value, it is determined as a non - infectious severe case.

6. The application according to claim 5, wherein The infectious severe cases include sepsis or septic shock, and the non - infectious severe cases refer to severe cases where infection is not the main cause.

7. A kit for sepsis diagnosis, condition assessment or prognosis assessment, characterized in that, The kit described above includes a reagent for quantitative or semi - quantitative detection of H3K18ac.

8. The kit according to claim 7, wherein When the kit described above is used for detection, it includes the following steps: Sample collection, sample pretreatment, isolation of PBMC, extraction of histones, determination of H3K18ac by quantitative or semi - quantitative methods, and result interpretation.

9. The kit according to claim 8, characterized in that, The kit includes a sample extraction reagent, a sample preservation reagent, a sample pretreatment reagent, a PBMC isolation reagent, a histone extraction reagent, and a quantitative or semi - quantitative detection reagent.

10. The kit according to claim 9, wherein The quantitative or semi - quantitative detection reagent includes any one or more of an electrophoresis gel, a loading buffer, a prestained protein, an electrophoresis buffer, a electrotransfer buffer, an anti - histone H3K18 acetylation antibody, an anti - histone H3 antibody, a secondary antibody, a blocking agent, a TBST buffer, and a SuperECLPlus hypersensitive luminescent solution.

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