Histone modified marker combination for sepsis as well as application and kit of histone modified marker combination

Abstract

The invention provides a histone modified marker combination for sepsis as well as application and a kit thereof, and belongs to the field of biomarkers. Through acquisition of peripheral blood mononuclear cells, histone extraction, Western blot detection and data analysis, it is proved that the ratio (H3K18la / ac) of the lactylation modification level (H3K18la) to the acetylation modification level (H3K18ac) of the 18th lysine residue of histone H3 can be used as a sepsis assessment marker and used for preparing a kit, and the kit has the advantages of being high in specificity, high in sensitivity and good in application prospect. The kit can be used for distinguishing severe sepsis infection and severe sepsis non-infection, severity evaluation of sepsis and prognosis prediction of sepsis.

Description

Technical Field

[0001] The present invention belongs to the field of biomarkers, relates to sepsis biomarkers, and specifically relates to a combination of histone modification biomarkers for sepsis, their applications, and kits. Background Art

[0002] Sepsis is a systemic inflammatory response caused by infection and is currently considered an immune imbalance response of the body to infection. It may further evolve into septic shock and life-threatening multiple organ failure. Septic shock is a severe form of sepsis, manifested as persistent hypotension and insufficient tissue perfusion. Despite the continuous in-depth understanding in the medical field, the complexity of sepsis and septic shock and the diversity of clinical manifestations still make early identification and accurate diagnosis difficult. The pathophysiological mechanisms involve ischemia-hypoxia, inflammatory response, and metabolic disorders. Among them, the release of inflammatory mediators such as cytokines and chemokines leads to abnormal vasomotor function and further affects microcirculation.

[0003] 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 partially explain the prognostic differences among different patients. Post-translational modifications (PTMs) of histones, such as lactylation and acetylation, regulate gene expression and affect cell function by remodeling chromatin structure. Lactylation modification is an important post-translational modification in proteins. Lysine lactylation modification (Kla) can occur on all core histones and shares the most common modification sites with histone lysine acetylation. Lactylation modification involves the process of transferring the lactyl group from lactyl-CoA and removing the lactyl group. Acetylation modification is a key step in regulating gene activity. Histone acetyltransferases (HATs) and deacetylases (HDACs) finely regulate and maintain the rapid acetylation and deacetylation cycles at the genomic level, forming an "acetylation homeostasis". Imbalance of acetylation can lead to the occurrence of various diseases, including sepsis. Research shows that changes in acetylation epigenetics play a crucial role in the stress responses of various traumas and shocks.

[0004] Lactylation and acetylation at the histone H3K18 site are important mechanisms for regulating gene expression. The ratio of H3K18 lactylation to acetylation (H3K18 la / ac) reflects the relative levels of these two modifications, thereby affecting the transcriptional activity of specific genes. In cellular homeostasis, lactylation and acetylation at the histone H3K18 site maintain gene expression through a dynamic balance. Cells dynamically adjust the modification levels at the H3K18 site by regulating the activities of HATs, HDACs, and lactylation-related enzymes, which is crucial for maintaining normal cell metabolism and gene expression.

[0005] In the ICU environment, the rapid and accurate identification of infections poses a major challenge, mainly due to the multiple complex factors encountered in the diagnostic process. The prevalence of Systemic Inflammatory Response Syndrome (SIRS) does not always indicate infection, as a significant portion of SIRS cases may stem from non-infectious factors such as surgery, trauma, or acute pancreatitis. Additionally, the similar SIRS clinical manifestations caused by infectious and non-infectious factors make it difficult to determine the cause solely based on clinical symptoms. The complex conditions of ICU patients, including multiple complications, treatment interventions, and the limitations of diagnostic tests themselves, such as the delay in blood culture results and the non-specific responses of biomarkers, further increase the difficulty of diagnosis. Moreover, patients may have received antibiotic treatment before blood culture, which not only affects the results of microbiological tests but also makes the diagnosis of infection more complex. These factors together exacerbate the problem of antibiotic use, including unnecessary antibiotic use and the consequent issue of antibiotic resistance. To address these challenges, in addition to refined empirical antimicrobial application based on pathogen epidemiology, the development of new biomarkers and diagnostic tools that combine multiple markers is particularly necessary. These tools and markers need to be highly sensitive and specific to assist doctors in making accurate judgments in complex clinical situations. And the combined use of multiple biomarkers may further improve the diagnostic accuracy, contribute to the early detection of infections, and reduce the mortality and complication risks of ICU patients.

[0006] The prior art CN114113631B discloses a detection kit for sepsis prognosis, including 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 biomarker is single and the diagnostic accuracy is poor.

[0007] Zhang Hong et al. (Zhang Hong, Ni Zheng, Zhang Liying, et al. 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 evaluation 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 evaluation; dynamically monitoring the changes of its indicators is more significant for short-term prognosis evaluation. The disadvantage of this technology is that it is susceptible 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 disease assessment due to their deep connection with the pathophysiological process. Based on this, the present invention provides a combination of histone modification biomarkers for sepsis, their applications, and a kit. The present invention proves that the ratio of the lactylation modification level (H3K18la) to the acetylation modification level (H3K18ac) of lysine residue 18 of histone H3 (H3K18 la / ac) can be used as a sepsis assessment biomarker through obtaining peripheral blood mononuclear cells, histone extraction, Western blot detection, and data analysis, which is used for distinguishing severe infection from non-severe infection, assessing the severity of sepsis, and predicting the prognosis of sepsis, is not easily affected by early clinical interventions, 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 a combination of histone modification biomarkers for the diagnosis, disease assessment, or prognosis assessment of sepsis, including H3K18la and H3K18ac; the H3K18ac is the acetylation level of lysine residue 18 of histone H3, and the H3K18la is the lactylation level of lysine residue 18 of histone H3.

[0011] On the other hand, the present invention provides the application of the detection reagent of the above histone modification biomarker composition in the preparation of a kit for the diagnosis, disease assessment, or prognosis assessment of sepsis.

[0012] Preferably, the detection reagent is used for the quantification or semi-quantification of the combination of histone modification biomarkers.

[0013] Preferably, the quantification or semi-quantification method includes any one or more of ELISA detection, Dot blot detection, Western blot detection, and immunohistochemical detection.

[0014] Preferably, the diagnosis, disease assessment, or prognosis assessment of sepsis is achieved through the interpretation of H3K18 la / ac, and the H3K18 la / ac is the quantification or semi-quantification ratio of H3K18la to H3K18ac.

[0015] Preferably, the application includes differentiating severe infectious diseases from severe non-infectious diseases, with a diagnostic threshold of 1.300 - 1.500; when H3K18 la / ac is greater than or equal to the threshold, it is judged as severe infectious disease; when H3K18 la / ac is less than the threshold, it is judged as severe non-infectious disease; the severe infectious diseases include sepsis and septic shock, and the severe non-infectious diseases refer to those that are severe but infection is not the main cause.

[0016] Preferably, when the kit is used for detection, it includes the following steps:

[0017] Sample collection, sample pretreatment, separation of PBMC, extraction of histone, determination of H3K18la and H3K18ac by quantitative or semi-quantitative method, calculation of the ratio, and result interpretation.

[0018] On the other hand, the present invention provides a kit for the diagnosis, condition assessment or prognosis assessment of sepsis, and the kit includes all reagents for quantitatively or semi-quantitatively detecting H3K18ac and H3K18la.

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

[0020] Preferably, the quantitative or semi-quantitative detection reagents include any one or more of electrophoresis gels, loading buffers, pre-stained proteins, electrophoresis buffers, electrotransfer buffers, histone H3K18 acetylation antibodies, histone H3K18 lactylation antibodies, histone H3 antibodies, secondary antibodies, blocking agents, TBST buffers, and SuperECLPlus hypersensitive luminescent solutions.

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

[0022] 1. The present invention discloses that the ratio (H3K18 la / ac) of the lactylation modification level (H3K18la) and acetylation modification level (H3K18ac) of lysine residue at position 18 of histone H3 can be used as a sepsis assessment marker for differentiating severe infectious diseases from severe non-infectious diseases, assessing the severity of sepsis, and predicting the prognosis of sepsis, and is not easily affected by early clinical intervention.

[0023] 2. The present invention first developed and applied the H3K18 la / ac ratio index, which shows higher efficacy than either H3K18la alone or H3K18ac alone in judging severe infections. The area under the ROC curve (AUC) of this index reaches 0.915, approaching the diagnostic ability of procalcitonin widely used clinically. Further mechanism analysis also shows that the change of H3K18 la / ac may reflect the changes in intracellular metabolic state and stress response, which may be related to the occurrence and development of various diseases, not limited to infectious diseases. Description of the Drawings

[0024] Figure 1 Western blotting detection and development images of H3K18la, H3K18ac, and histone H3 in serum samples of critically ill 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 simple linear regression analysis was further performed on the indicators with significant correlation; 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 simple linear regression analysis was further performed on the indicators with significant correlation.

[0027] Figure 4 Correlation analysis of H3K18la / ac level and infection-related laboratory indicators; Spearman correlation analysis was used, and simple linear regression analysis was further performed on the indicators with significant correlation.

[0028] Figure 5 ROC curves of H3K18la, H3K18ac, and H3K18la / ac for determining septic shock from the infectious critical illness cohort.

[0029] Figure 6Correlation analysis of H3K18la with disease severity scores and common prognostic indicators; Spearman correlation analysis was used, and simple linear regression analysis was further performed on the indicators with significant correlations; among them, APACHE II: acute physiology and chronic health evaluation II score; SOFA: sequential organ failure assessment score; ICU stay: number of days in the ICU; hospital stay: total length of hospital stay; mechanical ventilation time: number of days of mechanical ventilation.

[0030] Figure 7 Correlation analysis of H3K18ac with disease severity scores and common prognostic indicators; Spearman correlation analysis was used, and simple linear regression analysis was further performed on the indicators with significant correlations; among them, APACHE II: acute physiology and chronic health evaluation II score; SOFA: sequential organ failure assessment score; ICU stay: number of days in the ICU; hospital stay: total length of hospital stay; mechanical ventilation time: number of days of mechanical ventilation.

[0031] Figure 8 Correlation analysis of H3K18la / ac with disease severity scores and common prognostic indicators; Spearman correlation analysis was used, and simple linear regression analysis was further performed on the indicators with significant correlations; among them, APACHE II: acute physiology and chronic health evaluation II score; SOFA: sequential organ failure assessment score; ICU stay: number of days in the ICU; hospital stay: total length of hospital stay; mechanical ventilation time: number of days of mechanical ventilation.

[0032] Figure 9Analysis of the correlation between H3K18la and the expression of serum inflammatory factors; Spearman correlation analysis was used, and for the indicators with significant correlation, simple linear regression analysis was further performed; among them, TNF-α: tumor necrosis factor α, IL: interleukin, IFN-α: interferon α.

[0033] Figure 10 Analysis of the correlation between H3K18ac and the expression of serum inflammatory factors; Spearman correlation analysis was used, and for the indicators with significant correlation, simple linear regression analysis was further performed.

[0034] Figure 11 Analysis of the correlation between H3K18la / ac and the expression of serum inflammatory factors; Spearman correlation analysis was used, and for the indicators with significant correlation, simple linear regression analysis was further performed.

[0035] Figure 12 Analysis of the correlation between H3K18la, H3K18ac, H3K18 la / ac and the expression of downstream M2 macrophage polarization-related marker (ARG1); Spearman correlation analysis was used, and for the indicators with significant correlation, simple linear regression analysis was further performed. Specific implementation manner

[0036] Unless otherwise specified, the raw materials used in the present invention are all ordinary commercially available products, and their sources are not specifically limited.

[0037] 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. Overall, 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 presentations, and is usually only diagnosed 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 individuals. 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, enhancement of bacterial clearance and phagocytosis, and inhibition of immune cell apoptosis are associated with improved survival.

[0038] Different sepsis patients are faced with similar injury strikes, but their prognoses may vary greatly. Extensive gene recoding, including DNA methylation, histone modification changes, and non-coding RNA transcriptional regulation, etc., may partly explain this difference. As the most important 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.

[0039] 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, so it is one of the most diverse amino acids for post-translational modification in 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 coenzyme A and the removal of lactyl groups. P300, also known as lysine acetyltransferase (KAT3B), has been shown to also be a lactyltransferase, which can promote lactylation modification. In terms of non-histones, it has been confirmed that macrophages can take up 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.

[0040] 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 also have a balance between acetylation and deacetylation, playing important roles in cell cycle, stress response, cytoskeleton and motility, signal transduction, damage repair and remodeling, and proliferation. The imbalance of acetylation can lead to the occurrence of various diseases.

[0041] Lactylation and acetylation at the histone H3K18 site are two important post-translational modifications, which play key roles in regulating gene expression and affecting cell functions within cells. The ratio of the levels of these two modifications, that is, the ratio of the levels of H3K18 lactylation to acetylation (H3K18 la / ac), is of great significance in cell homeostasis and disease development. Lactylation and acetylation at the histone H3K18 site are important mechanisms for regulating gene expression. Acetylation is usually associated with gene activation, while lactylation is associated with gene repression in some cases. The ratio of H3K18 la / ac reflects the relative levels of these two modifications, thus affecting the transcriptional activity of specific genes. The ratio of H3K18 la / ac can be used as an indicator of the cell metabolic state and energy balance. In cell homeostasis, the lactylation and acetylation at the histone H3K18 site maintain balance through the following mechanism. Catalyzed by histone acetyltransferases (HATs), an acetyl group is added to the 18th lysine residue of histone H3. This modification is usually associated with chromatin relaxation and gene activation, promoting the binding of transcription factors and the activity of RNA polymerase.

[0042] When cellular metabolism is normal and energy supply is sufficient, the activity of HATs is enhanced, leading to an increase in the acetylation level of H3K18, thereby promoting gene expression. During cellular stress or energy metabolism disorders, such as under hypoxia or inflammatory conditions, lactylation modification may increase. Lactylation modification may inhibit gene expression by hindering the binding of certain transcription factors or altering chromatin structure. The ratio change of H3K18 la / ac reflects the changes in intracellular metabolic status and stress response, and may be related to the occurrence and development of diseases. Cells dynamically adjust the acetylation and lactylation levels at the H3K18 site by regulating the activities of HATs, deacetylases (HDACs), and lactylation-related enzymes. This balance is crucial for maintaining normal cell metabolism and gene expression. By monitoring the H3K18 la / ac ratio, the metabolic and gene expression regulation mechanisms of cells under different physiological and pathological conditions can be better understood.

[0043] Baseline Characteristics and Statistical Analysis of the Research Subjects

[0044] In this invention, the research cohort consisted of 98 people, including critically ill patients in the intensive care unit 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-infected 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 both conformed to 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 score (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 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).

[0045] Data Analysis:

[0046] For data following a normal distribution, the 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 not following a 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 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.

[0047] The diagnostic value was determined by receiver operating characteristic (ROC) curve analysis. The relationship between true positive rate (sensitivity) and 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 better prediction of the model. P < 0.05 was considered statistically significant.

[0048] All statistical analyses of the present invention were performed using IBM SPSS 24.0, Prism 9.0, and Stata 17 software.

[0049] After statistical analysis, the sample baseline information is shown in Table 1.

[0050] Table 1 Sample baseline information

[0051]

[0052]

[0053] 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 or 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 or 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 prognosis 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 (2.78%). There was a statistical difference among the three groups (P = 0.004).

[0054] Example 1: Blood collection, isolation of peripheral blood mononuclear cells from blood samples, and serum separation

[0055] The present invention uses a detection kit for sepsis, which is used to distinguish infectious severe cases from non-infectious severe cases, evaluate the severity of sepsis, or predict the prognosis of sepsis, and includes at least one of the following reagents: a reagent for detecting the lactylation level of histone H3K18, a reagent for detecting the acetylation level of histone H3K18, 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 also include a BCA protein concentration assay kit, a histone extraction kit, and an SDS-PAGE gel preparation kit.

[0056] 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 carried out. 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 out 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 solution (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, 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. Serum separation can be carried out under sterile conditions, centrifuge the blood specimen at 3000 rpm at 4 °C for 10 minutes; aspirate the supernatant and store it at low temperature, and store the obtained serum at -80 °C.

[0057] Example 2: Detection of cytokine levels

[0058] The reagent for detecting cytokines used 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.

[0059] 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 the 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 the 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 several seconds, and centrifuged at 500 xg for 5 minutes. After removing the supernatant, 300 μl of 1× wash solution is added to the test tubes and rotated for a few seconds. The specimens are detected by flow cytometry, 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.

[0060] Example 3: RNA Extraction and qRT-PCR

[0061] 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 determined using a Thermo nanodrop 2000C (A260 / A280). The quality of the RNA is detected by agarose gel electrophoresis.

[0062] The reagents used in the present invention to detect 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:

[0063] 5’-AAGAGTGTGATGTGAAGGATTATGG-3’ / 5’-TTCTTCTTGACTTCTGCCACCTT-3’ (arginase-1),

[0064] 5’-TGACTTCAACAGCGACACCCA-3’ / 5’-CACCCTGTTGCTGTAGCCAAA3’ (GAPDH).

[0065] 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.

[0066] Example 4: Histone extraction, detection of H3K18 lactylation level and H3K18 acetylation level

[0067] The reagents used in this example for detecting the histone H3K18 lactylation level include: histone extraction kit, sodium dodecyl sulfate polyacrylamide gel, loading buffer, sodium dodecyl sulfate, prestained protein, electrophoresis buffer, electrotransfer buffer, histone H3K18 lactylation antibody, histone H3 antibody, goat anti-rabbit secondary antibody, blocking milk, TBST buffer, and SuperECL Plus hypersensitive luminescent solution.

[0068] 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-lysis 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 equilibration-DTT buffer was added to the supernatant part (containing acid-soluble proteins) to measure the protein concentration. The isolated histones were stored at -80°C. This disclosure uses 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. The voltage was set at 80 V / gel until the sample showed as a single line, and then at 120 V / gel until the sample reached the bottom of the gel. Wet transfer was performed to transfer the protein onto a 0.2 μm PVDF membrane (ImmobilonTM-PSQ membrane) at 300 mA for 3 hours. In this example, the level of H3K18 lactylation was detected by Western blotting. The primary antibodies used were rabbit mAb against lactylated histone H3 (Lys18) (PTM BioLab, Inc., Cat# PTM-1406RM; diluted 1:1000 in the antibody dilution reagent solution, cat# 003218 from Life Technologies) and anti-histone H3 antibody (Abcam, ab1791, diluted 1:1000 in the antibody dilution reagent solution from Life Technologies). 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, and incubated 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 level of histone H3K18 lactylation modification. TM Antibody dilution reagent solution, cat# 003218 from Life Technologies) and anti-histone H3 antibody (Abcam, ab1791, diluted 1:1000 in the antibody dilution reagent solution from Life Technologies). TM 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, and incubated 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 level of histone H3K18 lactylation modification.

[0069] The detection of H3K18 acetylation modification level was carried out with reference to the above lactylation level detection method, except that the anti-lactylated histone H3 in the primary antibody was replaced with rabbit mAb against acetylated histone H3 (Lys18) ((PTM BioLab, Inc., Cat# PTM-114RM; diluted 1:1000 in the antibody dilution reagent solution from Life Technologies). TM 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, and incubated 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 level of histone H3K18 lactylation modification.

[0070] In this example, 15 μg of protein in equal amounts was added to each lane. Therefore, the WB results between samples were comparable.

[0071] Effect Example 1: Detection and Analysis

[0072] 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.

[0073] (1) Distribution and differences between histone H3K18 site lactylation modification levels (H3K18la) and acetylation modification levels (H3K18ac)

[0074] 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.

[0075] 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.

[0076] (2) Inter-group distribution and differences in histone H3K18 site lactylation / acetylation (H3K18 la / ac) levels

[0077] As can be seen from Table 2, there was a statistically significant difference in H3K18 la / ac between the severe infection group (2.383 [1.613, 3.596]) and the non-severe infection group (0.890 [0.669, 1.222]) (P < 0.001). As can be seen from Table 3, for the ratio index of H3K18 la / ac, the septic shock group (2.724 [2.038, 4.132]) > the sepsis group (1.554 [1.487, 2.308]) > the healthy control group (0.968 [0.804, 1.144]), and the inter-group differences were also statistically significant (P < 0.001), showing a positive correlation between the H3K18 la / ac ratio and the disease severity.

[0078] Table 2 Comparison of H3K18la, H3K18ac, and H3K18la / ac between severe infection and non-severe infection

[0079]

[0080] Table 3 Comparison of H3K18la, H3K18ac, and H3K18la / ac among septic shock, sepsis, and healthy subjects

[0081]

[0082]

[0083] Note: Nonparametric 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.

[0084] (3) Diagnostic efficacy of H3K18la, H3K18ac, H3K18 la / ac and corresponding reference indicators in identifying severe infection patients from the ICU severe cohort

[0085] The three parameters of H3K18la, H3K18ac, and H3K18 la / ac were further analyzed using the ROC curve as follows: In identifying severe infections in the ICU critical care cohort, as shown in Table 4, the AUCs of the three parameters of H3K18la, H3K18ac, H3K18 la / ac, and traditional infection-related laboratory indicators were H3K18la 0.677 (0.547, 0.808), H3K18ac 0.865 (0.774, 0.956), H3K18 la / ac 0.915 (0.842, 0.989), PCT 0.932 (0.875, 0.989), and CRP 0.734 (0.613, 0.854). And only the AUCs of H3K18 la / ac and PCT had no statistical difference (P = 0.807, 0.362, DeLong test), that is, H3K18 la / ac has approached the ability of PCT, which has been widely verified clinically, to identify infections. The AUCs of the remaining indicators and PCT had statistical differences, that is, they could not reach the ability of PCT to identify infections. In addition, when using WBC, NEUT, and NEUTP to identify severe infections in the ICU critical care cohort, their discrimination efficacy was not significant (the AUCs were 0.577 [0.444, 0.710], P = 0.272; 0.590 [0.458, 0.723], P = 0.198; 0.610 [0.479, 0.741], P = 0.117), equivalent to random guessing. It can be seen that they are not specific indicators of infection, which is consistent with clinical experience. Table 4 Diagnostic efficacy of H3K18la, H3K18ac, H3K18 la / ac, and corresponding reference indicators in identifying severe infection patients from the ICU critical care cohort

[0086]

[0087]

[0088] 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.

[0089] (4) Correlation between H3K18la, H3K18ac, H3K18 la / ac and infection-related laboratory indicators

[0090] Next, the correlation between the three modified indicators and common infection-related laboratory indicators will be used to further corroborate their association with severe infections (including septic shock and sepsis), and the results are as follows:

[0091] 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). From Figure 4 it can be seen that H3K18la / ac was positively correlated with CRP and PCT (Spearman ρ = 0.2612, P = 0.0201; Spearman ρ = 0.4615, P < 0.0001).

[0092] (5) Efficacy of H3K18la, H3K18ac, and H3K18la / ac in identifying septic shock from healthy controls and severe infection cohorts

[0093] As previously pointed out, the changing trends of the levels of H3K18la, H3K18ac, and H3K18la / ac in septic shock, sepsis, and healthy control groups were presented. And this grouping had an obvious disease severity gradient, suggesting that the three modification indexes might be used for disease severity assessment. Thus, further analysis was performed through the ROC curve as follows:

[0094] As shown in Table 5 and Figure 5 as shown, in terms of the efficacy of identifying septic shock from healthy controls and severe infection cohorts, the descending order of the AUCs of H3K18la, H3K18ac, and H3K18la / ac was H3K18la / ac 0.859 (0.759, 0.960), H3K18la 0.807 (0.700, 0.914), and H3K18ac 0.730 (0.603, 0.856). The newly constructed modification ratio index H3K18la / ac of the present invention showed the best performance.

[0095] Table 5 Efficacy of H3K18la, H3K18ac, and H3K18la / ac in identifying septic shock from severe infection cohorts

[0096] 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 H3K18la / ac 0.859 0.759,0.960 <0.001 1.604 0.692 0.892 0.800

[0097] Note: # represents that the null hypothesis corresponding to P is that the area under the ROC curve is equal to 0.5.

[0098] (6) Correlation between H3K18la, H3K18ac, H3K18la / ac and disease severity scores and common prognostic indicators

[0099] Further analyze the correlations between the three modification indicators and disease severity (represented by APACHE II score and SOFA score) through correlation analysis, and explore the possibility of using them as prognostic indicators (represented by ICU length of stay, total length of stay, and mechanical ventilation time).

[0100] As Figure 6 shown, H3K18la was positively correlated with the SOFA score (Spearman ρ = 0.3317, P = 0.0018), positively correlated with the ICU length of stay (Spearman ρ = 0.2292, P = 0.0338), and positively correlated with the mechanical ventilation time (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).

[0101] As Figure 7 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 mechanical ventilation time (Spearman ρ = -0.3009, P = 0.0051). However, no significant correlation was shown between H3K18ac and the ICU length of stay (Spearman ρ = -0.1715, P = 0.1144) and the number of hospital days (Spearman ρ = -0.0001, P = 0.9991).

[0102] As Figure 8 shown, H3K18 la / ac was positively correlated with the APACHE II score (Spearman ρ = 0.4044, P < 0.001), positively correlated with the SOFA score (Spearman ρ = 0.4687, P < 0.001), positively correlated with the ICU length of stay (Spearman ρ = 0.2824, P = 0.008), and positively correlated with the mechanical ventilation time (Spearman ρ = 0.3935, P < 0.001). However, no significant correlation was shown between H3K18 la / ac and the total length of stay (Spearman ρ = 0.0258, P = 0.814).

[0103] (7) Correlation between H3K18 la / ac and serum inflammatory factor expression

[0104] This section explored the correlations between H3K18la, H3K18ac, H3K18 la / ac and the expression of serum inflammatory factors. These included cytokines that promote inflammatory responses 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 inflammatory responses such as interleukin-10 (IL-10), interleukin-4 (IL-4), etc.

[0105] As Figure 9 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).

[0106] As Figure 10 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).

[0107] As Figure 11 shown, H3K18 la / ac was positively correlated with IL-6 (Spearman ρ = 0.2149, P = 0.0345), positively correlated with IL-8 (Spearman ρ = 0.2310, P = 0.0228), positively correlated with IL-10 (Spearman ρ = 0.2721, P = 0.0070), negatively correlated with IFN-α (Spearman ρ = -0.2764, P = 0.0061), and negatively correlated with IL-5 (Spearman ρ = -0.3418, P = 0.0006).

[0108] (8) Correlations between H3K18la, H3K18ac, H3K18 la / ac and the expression of downstream M2 macrophage polarization-related marker (ARG1)

[0109] Based on the PBMC samples of clinical patients collected from this research 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 relationships among H3K18la, H3K18ac, and H3K18 la / ac were analyzed. As Figure 12 shown, H3K18la was positively correlated with the mRNA expression level of ARG1 (Spearman ρ = 0.330, P = 0.001), H3K18ac was negatively correlated with ARG1 expression (Spearman ρ = -0.257, P = 0.011), and the ratio of the former two, H3K18 la / ac, was positively correlated with ARG1 expression (Spearman ρ = 0.396, P < 0.001).

[0110] Comparative Example 1: Detection and analysis of H3 citrullination and H3K18 crotonylation levels

[0111] In addition to lactylation and acetylation modifications, histone H3 also has modifications such as methylation, citrullination, butyrylation, propionylation, and crotonylation.

[0112] In this comparative example, according to the methods of Examples 1-4, on the basis of Effect Example 1, the citrullination modification level of histone H3 (H3cit) and the crotonylation modification level of H3K18 (H3K18cr) in the research cohort were detected and analyzed.

[0113] In this comparative example, the detection of the citrullination modification level of histone H3 (H3cit) was performed using a human citrullinated histone H3 ELISA kit (purchased from Shanghai Huiying Biotechnology Co., Ltd.).

[0114] In this comparative example, the detection of the H3K18 crotonylation modification level (H3K18cr) referred to the method for detecting the lactylation level in Example 4, except that the antibody against lactylated histone H3 in the primary antibody was replaced with a mouse mAb against crotonylated histone H3 (Lys18) (PTM BioLab, Inc., Cat#PTM-540; diluted 1:1000 in the Life TechnologiesTM antibody dilution reagent solution, cat#003218).

[0115] Meanwhile, according to the analysis method of Effect Example 1, based on the data of the H3K18 lactylation modification level in Effect Example 1, the ratio of the H3K18 lactylation modification level to the H3 citrullination modification level (H3K18la / H3cit) and the ratio of the H3K18 lactylation modification level to the crotonylation modification level (H3K18 la / cr) were analyzed.

[0116] The further analysis results of applying the ROC curve to the above parameters are as follows: In terms of identifying severe infections from the ICU severe cohort (corresponding to the content of (3) in Example 1 of the effect), the AUC values of H3cit, H3K18cr, H3K18 la / H3cit, and H3K18 la / cr are H3cit 0.843 (0.728, 0.958), H3K18cr 0.872 (0.764, 0.980), H3K18 la / H3cit 0.862 (0.818, 0.906), and H3K18 la / cr 0.897 (0.836, 0.958) respectively; further analysis shows that H3cit and H3K18cr have similar AUC values compared with H3K18ac (the AUC value of H3K18ac is 0.865 (0.774, 0.956)), but the AUC values of H3K18 la / H3cit and H3K18 la / cr differ greatly from the AUC value of PCT, which is 0.932 (0.875, 0.989), and are not as good as the AUC value of H3K18 la / ac, which is 0.915 (0.842, 0.989). To a certain extent, it shows that compared with other protein modifications of the same type, the modified protein index H3K18 la / ac provided by the present invention is more reliable in identifying severe infections from the ICU severe cohort, and its effect is difficult to predict.

[0117] The above results also show that there is a significant correlation between H3K18 la / ac and the disease severity, and it can be used for the evaluation of disease severity. The correlations between the H3K18 la / ac modification index and the ICU length of stay, total length of stay, and mechanical ventilation time suggest the application direction as a prognostic prediction index.

[0118] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than limiting 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. A combination of histone modification markers for the diagnosis, condition assessment, or prognosis assessment of sepsis, characterized in that, including H3K18la and H3K18ac; the H3K18ac is the acetylation level of lysine residue at position 18 of histone H3, and the H3K18la is the lactylation level of lysine residue at position 18 of histone H3.

2. Use of the detection reagent for the histone modification marker composition according to claim 1 in the preparation of a kit for sepsis diagnosis, condition assessment or prognosis assessment.

3. The application according to claim 2, characterized in that, The detection reagent is used for the quantification or semi - quantification of the histone modification marker combination.

4. The application according to claim 3, characterized in that, The method for quantification or semi - quantification includes any one or more of ELISA detection, Dot blot detection, Western blot detection and immunohistochemical detection.

5. The application according to claim 2, wherein The sepsis diagnosis, condition assessment or prognosis assessment is achieved by the interpretation of H3K18 la / ac, and the H3K18 la / ac is the quantitative or semi - quantitative ratio of H3K18la to H3K18ac.

6. The application according to claim 5, wherein The use includes differentiating septic severe cases from non - septic severe cases, and the diagnostic cut - off value is 1.300 - 1.500; when H3K18 la / ac is greater than or equal to the cut - off value, it is judged as a septic severe case; when H3K18 la / ac is less than the cut - off value, it is judged as a non - septic severe case; the septic severe cases include sepsis and septic shock, and the non - septic severe cases refer to severe cases where infection is not the main cause.

7. The application according to any one of claims 2-6, characterized in that, When the kit is used for detection, it includes the following steps: Sample collection, sample pretreatment, isolation of PBMC, extraction of histone, determination of H3K18la and H3K18ac by quantitative or semi - quantitative method, calculation of the ratio, and interpretation of the result.

8. A kit for sepsis diagnosis, condition assessment or prognosis assessment, characterized in that, The kit includes all reagents for the quantitative or semi - quantitative detection of H3K18ac and H3K18la.

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

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

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