Histone modification marker combination for sepsis and application and kit thereof

Abstract

The application provides a histone modification marker combination for sepsis and application and kit thereof, and belongs to the field of biomarkers. Through obtaining peripheral blood mononuclear cells, histone extraction, Western blot detection and data analysis, it is proved that the ratio (H3K18la / ac) of the acetylation modification level (H3K18ac) and the lysine residue lactylation modification level (H3K18la) of the 18th lysine residue of histone H3 can be used as a sepsis evaluation marker, and the kit can be used for distinguishing sepsis infection severe cases from non-infection severe cases, sepsis severity evaluation and sepsis prognosis prediction.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomarkers, and relates to a sepsis marker, in particular to a histone modification marker combination for sepsis and application and kit thereof. BACKGROUND

[0002] Sepsis is a systemic inflammatory response caused by infection, which is currently considered as an immune imbalance response of the body to infection, and it can further evolve into septic shock and life-threatening multiple organ failure. Sepsis shock is a severe form of sepsis, which is characterized by persistent hypotension and insufficient tissue perfusion. Despite the deepening understanding of the medical community, the complexity and diversity of clinical manifestations of sepsis and sepsis shock make it difficult to identify and accurately diagnose early. The pathophysiological mechanism involves ischemia and hypoxia, inflammatory response and metabolic disorder, among which the release of inflammatory mediators such as cytokines and chemokines leads to abnormal vasomotor function, further affecting microcirculation.

[0003] Epigenetics plays a key role in the heterogeneous response of sepsis. Genetic reprogramming processes such as DNA methylation, histone modification changes and non-coding RNA transcription regulation may partially explain the differences in prognosis between different patients. Post-translational modification (PTM) of histone, such as lactylation and acetylation, regulates gene expression by remodeling chromatin structure and affects cell function. Lactylation modification is an important post-translational modification in proteins. Lysine lactylation modification (Kla) can occur on all core histones, sharing the most common modification site with histone lysine acetylation. Lactylation modification involves the transfer of lactyl groups from lactyl coenzyme A and the removal of lactyl groups. Acetylation modification is a key step in regulating gene activity. Histone acetyltransferase (HAT) and deacetylase (HDAC) finely regulate and maintain the rapid acetylation and deacetylation cycle at the genome level, forming an "acetylation homeostasis". Imbalance of acetylation can lead to the occurrence of various diseases, including sepsis. Studies have shown that changes in acetylation epigenetics play a crucial role in the stress response of various trauma and shock.

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

[0005] In the ICU setting, rapid and accurate identification of infection is a major challenge, mainly due to the multiple complex factors encountered during the diagnostic process. The prevalence of Systemic Inflammatory Response Syndrome (SIRS) does not always indicate infection, as a significant proportion of SIRS cases can be attributed to non-infectious factors such as surgery, trauma, or acute pancreatitis. Furthermore, similar SIRS clinical manifestations induced by infectious and non-infectious factors make it difficult to determine the etiology based on clinical symptoms alone. The complexity of ICU patients, including multiple comorbidities, therapeutic interventions, and limitations of diagnostic tests themselves, such as the delay in blood culture results and the non-specific response of biomarkers, further increase the difficulty of diagnosis. And patients may have received antibiotic treatment before blood culture, which not only affects the results of microbiological detection, but also makes it more complex to diagnose infection. These factors collectively exacerbate the challenges of antibiotic use, including unnecessary antibiotic use and the resulting problem of antibiotic resistance. To address these challenges, in addition to pathogen epidemiology-based, refined empirical antibiotic application, the development of new biomarkers and diagnostic tools combining multiple markers is particularly necessary. These tools and markers need to have high sensitivity and specificity to assist doctors in making accurate judgments in complex clinical situations. And the combination of multiple biomarkers can further improve the accuracy of diagnosis, help early detection of infection, and reduce the mortality and complication risk of ICU patients.

[0006] The prior art CN114113631B discloses a sepsis detection kit for the prognosis of sepsis, comprising at least one of the following reagents: a reagent for detecting total protein lactic acid level; a reagent for detecting histone H3K18 lactic acid level; 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 marker is single, and the diagnostic accuracy is poor.

[0007] Zhang Hong et al. (Zhang Hong, Ni Jian, Zhang Liying, et al. The significance of rapid PCT detection in emergency department for the prognosis of sepsis [J]. Journal of Clinical Emergency Medicine, 2015.) found that as the PCT value increased, the mortality rate of sepsis patients showed a trend of increasing, and the hospitalization time was prolonged. For the evaluation of the prognosis of sepsis patients, the first PCT value at admission was compared with PCT dynamic monitoring, and the sensitivity was comparable, but the specificity of PCT dynamic monitoring was stronger. The results of rapid PCT detection in the emergency department have certain guiding significance for the judgment of sepsis and the evaluation of prognosis; dynamic monitoring of the changes of the index is more meaningful for short-term prognosis evaluation. The disadvantage of this technology is that it is susceptible to early clinical intervention, and the sensitivity and specificity are poor. SUMMARY

[0008] In view of the problems 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 condition assessment tool for the clinic due to their deep connection with the pathophysiological process. Based on this, the present application provides a histone modification marker combination for sepsis and its application and kit. Through obtaining peripheral blood mononuclear cells, histone extraction, Western blot detection and data analysis, the present application proves that the ratio (H3K18 la / ac) of the level of lactylation modification (H3K18 la) and the level of acetylation modification (H3K18 ac) of the 18th lysine residue of histone H3 can be used as a sepsis evaluation marker for distinguishing between infection and non-infection, evaluating the severity of sepsis and predicting the prognosis of sepsis, and is not easily affected by early clinical intervention, has high sensitivity and specificity.

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

[0010] On the one hand, the present application provides a histone modification marker combination for sepsis diagnosis, condition assessment or prognosis evaluation, comprising H3K18 la and H3K18 ac; the H3K18 ac is the acetylation level of the 18th lysine residue of histone H3, and the H3K18 la is the lactylation level of the 18th lysine residue of histone H3.

[0011] On the other hand, the present application provides the use of a detection reagent of the above-mentioned histone modification marker combination in the preparation of a kit for sepsis diagnosis, condition assessment or prognosis evaluation.

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

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

[0014] Preferably, the sepsis diagnosis, condition assessment or prognosis evaluation is achieved by interpreting H3K18 la / ac, which is the quantitative or semi-quantitative ratio of H3K18 la and H3K18 ac.

[0015] Preferably, the application includes distinguishing between infected critical and non-infected critical, and the diagnosis threshold is 1.300-1.500; when H3K18 la / ac is greater than or equal to the threshold, it is judged as infected critical; when H3K18 la / ac is less than the threshold, it is judged as non-infected critical; the infected critical includes sepsis and septic shock, and the non-infected critical refers to critical illness but infection is not the main cause.

[0016] Preferably, the kit for detection includes the following steps:

[0017] Sample collection, sample pretreatment, PBMC separation, histone extraction, quantitative or semi-quantitative determination of H3K18 la and H3K18 ac, calculation of the ratio, and interpretation of the results.

[0018] In another aspect, the present application provides a kit for sepsis diagnosis, disease assessment or prognosis assessment, which comprises all reagents for quantitative or semi-quantitative detection of H3K18 ac and H3K18 la.

[0019] Preferably, the kit comprises 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 gel, loading buffer, pre-stained 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 luminous liquid.

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

[0022] 1. The present application discloses that the ratio of the level of histone H3 18th lysine residue lactylation modification (H3K18 la) and the level of acetylation modification (H3K18 ac) (H3K18 la / ac) can be used as a sepsis evaluation marker for distinguishing between infected critical and non-infected critical, sepsis severity assessment and sepsis prognosis prediction, and is not susceptible to early clinical intervention.

[0023] 2, The H3K18 la / ac ratio index is first developed and applied in the application, which shows higher efficiency than H3K18 la alone or H3K18 ac alone in judging severe infection, and the area under the ROC curve (AUC) reaches 0.915, close to the diagnostic ability of procalcitonin widely used in clinic. Further mechanism analysis also shows that the change of H3K18 la / ac may reflect the change of intracellular metabolic state and stress response, which may be related to the occurrence and development of various diseases, not just limited to infectious diseases. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 Western blotting detection of H3K18 la, H3K18 ac and histone H3 in serum samples of severe patients and healthy subjects; wherein 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 H3K18 la level and infection-related laboratory indicators; Spearman correlation analysis was used, and simple linear regression analysis was further used for indicators with significant correlation; wherein CRP: C-reactive protein, PCT: procalcitonin.

[0026] Figure 3 Correlation analysis of H3K18 ac level and infection-related laboratory indicators; Spearman correlation analysis was used, and simple linear regression analysis was further used for indicators with significant correlation.

[0027] Figure 4 Correlation analysis of H3K18 la / ac level and infection-related laboratory indicators; Spearman correlation analysis was used, and simple linear regression analysis was further used for indicators with significant correlation.

[0028] Figure 5 ROC curve of H3K18 la, H3K18 ac, and H3K18 la / ac for determining septic shock from the infectious critical illness cohort.

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

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

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

[0032] Figure 9Correlation analysis between H3K18la and serum inflammatory factor expression; Spearman correlation analysis was used, and simple linear regression analysis was further used for indexes with significant correlation; TNF-α: tumor necrosis factor alpha, IL: interleukin, IFN-α: interferon alpha.

[0033] Figure 10 Correlation analysis between H3K18ac and serum inflammatory factor expression; Spearman correlation analysis was used, and simple linear regression analysis was further used for indexes with significant correlation.

[0034] Figure 11 Correlation analysis between H3K18la / ac and serum inflammatory factor expression; Spearman correlation analysis was used, and simple linear regression analysis was further used for indexes with significant correlation.

[0035] Figure 12 Correlation analysis between H3K18la, H3K18ac, H3K18 la / ac and downstream M2 type macrophage polarization related marker (ARG1) expression; Spearman correlation analysis was used, and simple linear regression analysis was further used for indexes with significant correlation. DETAILED DESCRIPTION

[0036] Except for special description, the raw materials used in the present application are all ordinary commercially available products, and their sources are not specifically limited.

[0037] Sepsis is a systemic inflammatory response to infection that can progress to septic shock and even life-threatening organ dysfunction. Sepsis is a major problem in intensive care, and overall, patients with sepsis and septic shock have a dramatic decrease in health-related quality of life during their ICU stay and an increased mortality rate in long-term care. Despite significant improvements in the diagnosis and management of sepsis, it remains a challenging clinical entity due to its diverse etiology and presentation, often only documented after clinical deterioration during hospitalization. The differential diagnosis is also difficult when patients are in circulatory shock, especially when accompanied by other conditions such as cardiac injury, hypovolemia, and trauma, or in the absence of typical signs of infection in infants, the elderly, and immunocompromised individuals. Sepsis triggers a complex immune response, leading to an imbalance in the homeostatic equilibrium between pro- and anti-inflammatory, resulting in patients with similar injuries having very different outcomes. Genetic regulation can play a central role in this. Indeed, extensive genetic reprogramming, such as DNA methylation, histone modification, and transcriptional regulation of non-coding RNAs, can lead to cell cycle disruption, endothelial dysfunction, mitochondrial injury, metabolic derangement, immune failure, and cardiovascular collapse. Histones undergo various covalent modifications, including methylation, citrullination, acetylation, and phosphorylation, which alter their relationships with each other and with DNA. Attenuation of local and systemic pro-inflammatory cytokines, protection from distant organ injury, enhancement of bacterial clearance and phagocytosis, and inhibition of immune cell apoptosis are associated with improved survival.

[0038] Different sepsis patients face similar injury strikes, and their outcomes can be very different, and extensive genetic reprogramming, including DNA methylation, histone modification changes, and non-coding RNA transcriptional regulation, or can partially explain this difference. As the most important protein in the nucleus, which controls cell metabolism, growth, and differentiation, the N-terminal tail of histone can be reshaped after various post-translational modifications (PTM) to regulate transcription, replication, and DNA repair. Some metabolites, including propionyl coenzyme A, butyl coenzyme A, crotonyl coenzyme A, lactyl coenzyme A, 2-hydroxyisobutyl coenzyme A, beta-hydroxybutyryl coenzyme A, succinyl coenzyme A, benzoyl coenzyme A, malonyl coenzyme A, and glutaryl coenzyme A, have recently been identified as substrates for relevant modifications that can promote histone PTM and 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 epsilon-amino side chain is lysine, which has the characteristics and reactivity of a primary amine, so it is the most diverse post-translational modification in organisms. In 2019, it was first reported that lysine lactylation modification (Kla) existed in histones in several human cell lines and mouse bone marrow-derived macrophages. Kla can occur on all core histones, sharing the most common modification sites with histone lysine acetylation (Kac). Like many PTMs, Kla involves transferring lactyl groups from lactyl coenzyme A and removing lactyl groups. P300, also known as lysine acetyltransferase (KAT3B), has been shown to be a lactyltransferase as well, which can promote lactylation modification. In non-histone aspects, studies have shown that macrophages can uptake extracellular lactate through monocarboxylate transporters (MCTs), and then promote HMGB1 lactylation through a p300 / CBP-dependent mechanism, and stimulate HMGB1 acetylation by inhibiting deacetylase SIRT1 and recruiting 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 rapid acetylation and deacetylation cycles at the genome level, forming an "acetylation homeostasis". Not only histones, but also a large number of key proteins of other cell functions exist in a balance of acetylation and deacetylation, which plays an important role in cell cycle, stress response, cytoskeleton and movement, signal transduction, damage repair and remodeling, and proliferation, and the imbalance of acetylation can lead to the occurrence of various diseases.

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

[0042] When cellular metabolism is normal and energy supply is sufficient, HATs activity is enhanced, leading to increased H3K18 acetylation levels, thus promoting gene expression. In the case of cellular stress or energy metabolism disorders, such as hypoxia or inflammatory states, 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 metabolic status and stress response within cells, which may be related to the occurrence and development of diseases. Cells dynamically adjust the acetylation and lactylation levels of H3K18 sites by regulating the activities of HATs and histone deacetylases (HDACs) and lactylation-related enzymes. This balance is crucial for maintaining normal cellular metabolism and gene expression. By monitoring the H3K18 la / ac ratio, we can better understand the metabolic and gene expression regulation mechanisms of cells under different physiological and pathological conditions.

[0043] Baseline characteristics of study subjects and statistical analysis

[0044] In the present application, the research cohort consisted of 98 people, including 37 patients with septic shock, 13 patients with sepsis, 36 patients with non-infectious critical illness, and 12 healthy volunteers, prospectively collected from August 22, 2018 to October 7, 2022 in the intensive care unit of Beijing Hospital. The above diagnoses were based on clinical manifestations and auxiliary examination information on the day of sample collection (mostly on the day of ICU admission), and the diagnoses of septic shock and sepsis were consistent with the related definitions of sepsis 3.0. The following baseline information of the sample subjects was collected in the present application: age, gender, underlying disease, sequential organ failure estimation score (SOFA, ICU admission day 1 to day 3), acute physiology and chronic health evaluation score II (APACHE II) within 24 hours, mechanical ventilation time, ICU hospitalization time, total hospitalization time, 28-day mortality, 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] The data of normal distribution were compared by Student's t test or one-way ANOVA, and the results were expressed as mean ± SD; the correlation analysis used Pearson correlation test. The data of non-normal distribution were analyzed by non-parametric Mann-Whitney U test, and the results were expressed as median and interquartile range (IQR); the correlation analysis used Spearman correlation test. The comparison of categorical variables used chi-square or Fisher's exact test, 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) at different classification thresholds was statistically analyzed and plotted. The area under the ROC curve (AUC) gives an indicator of classification performance, and a higher value of AUC corresponds to a good prediction of the model. P<0.05 was considered statistically significant.

[0048] All statistical analyses of the present application used 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 the whole study object was 65.60 years old, among which the septic shock group was 67.49 years old, the sepsis group was 65.31 years old, the non-infectious severe group was 65.72 years old, and the healthy control group was 59.75 years old, and there was no statistical difference among the first three disease groups and all four groups (P=0.880, 0.561); in terms of gender ratio, the male accounted for 61.22% in the whole cohort, and there was no statistical difference among the first three disease groups and all four groups (P=0.241, 0.386); in terms of common comorbidities, there was no statistical difference among the first three disease groups, and there was statistical difference among the four groups including the chronic heart disease, hypertension and diabetes of the healthy control group (P=0.002, 0.015, 0.022); in terms of vital signs, there was statistical difference in mean arterial pressure among the first three disease groups (P<0.001); in addition, the worst value of blood lactic acid on the day of sample collection, two disease severity scores (APACHE II score and SOFA score) and the proportion of application of invasive mechanical ventilation all had statistical difference among the first three disease groups (all P<0.001), all of which showed higher values in the septic shock group, which was consistent with the general expectation; in terms of prognosis indicators, there was no statistical difference in ICU stay and total hospital stay among the first three disease groups (P=0.128, 0.285), and 15 people in the first three disease groups died within 28 days of ICU admission (accounting for 15.30% of the whole cohort and 17.4% of the patient cohort), among which 12 people in the septic shock group (accounting for 32.43% of the group), 2 people in the sepsis group (15.38%) and 1 person in the non-infectious severe group (2.78%) had statistical difference among the three groups (P=0.004).

[0054] Example 1: Blood collection, peripheral blood mononuclear cell separation and serum separation of blood sample

[0055] The present application uses a sepsis detection kit for distinguishing between infectious severe and non-infectious severe, sepsis severity assessment or sepsis prognosis prediction, which comprises at least one of the following reagents: a reagent for detecting the level of histone H3K18 acetylation, a reagent for detecting the level of histone H3K18 acetylation, a reagent for detecting the level of messenger ribonucleic acid of arginase-1 and a reagent for detecting cytokines. In addition, the detection kit in the present application can also include a BCA protein concentration determination kit, a histone extraction kit and a SDS-PAGE gel preparation kit.

[0056] Specifically, first, a blood sample is collected, and then the collected blood sample is subjected to peripheral blood mononuclear cell separation and serum separation, respectively. The blood sample (5-10 ml) is collected by using an ethylenediaminetetraacetic acid (EDTA) containing blood collection tube and a serum separation blood collection tube, and subjected to peripheral blood mononuclear cell separation and serum separation, respectively. In the peripheral blood mononuclear cell separation, the blood sample is diluted with a phosphate buffered saline solution at pH 7.2 at a ratio of 1:1; the diluted blood sample is placed on a 15 ml lymphocyte separation medium (STEMCELL Technologies, Cat# 07851), and centrifuged at 500g and 20°C for 20 minutes; most of the upper layer is sucked out, and a white and light yellow fluff (mononuclear cells) is left in the interval; the mononuclear cells are separated and filled with a phosphate buffered saline solution, mixed, and centrifuged at 500g at 20°C for 7 minutes; the supernatant is completely removed, and if there is red impurity in the bottom of the tube, red blood cell buffer (Solarbio, Cat# R1010) is added for 5 minutes; a sufficient amount of phosphate buffered saline solution is added, and centrifuged at 20°C and 500xg for 7 minutes; after the supernatant is removed, the peripheral blood mononuclear cells are collected, resuspended with 2 ml of cryoprotectant (fetal bovine serum: dimethyl sulfoxide = 9:1), and the obtained peripheral blood mononuclear cells are stored at -80°C. In the serum separation, the blood sample is centrifuged at 3000 rpm at 4°C for 10 minutes under sterile conditions; the supernatant is sucked and stored at low temperature, and the obtained serum is stored at -80°C.

[0057] Example 2: Detection of cytokine levels

[0058] The reagent for detecting cytokines of the present application 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, and TNF-α. The reagent for detecting cytokines of the present application can include a capture microsphere antibody, a detection antibody, SA-PE, and a washing solution.

[0059] The present application adopts flow cytometry to detect the cytokines in the separated 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 calibration product sample is added into the calibration tube, and 25 μl of serum sample buffer is added into the matrix B. The sample is fully mixed with 25 μl of capture microsphere antibody; 25 μl of detection antibody is added into all the test tubes, and incubated at room temperature in the dark, with shaking at 400-500 r / min. After 2 hours, 25 μl of SA-PE is added into all the test tubes, and incubated at room temperature in the dark, with shaking at 400-5500 r / min. After half an hour, 500 μl of 1× washing solution is added, and centrifuged at 500xg for 5 minutes after rotating for several seconds. After removing the supernatant, 300 μl of 1× washing solution is added into the test tube, and rotated for several seconds. The specimen is detected by using a flow cytometer, and 12 kinds of 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. In order 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 embodiment of the present application is extracted by using guanidine isothiocyanate-phenol-chloroform method (RNAiso Plus, TaKaRaBio Code No. 9109). The RNA yield is determined by using Thermo nanodrop 2000C (A260 / A280). The quality of the RNA is detected by using agarose gel electrophoresis.

[0062] The reagent for detecting the messenger ribonucleic acid level of arginase-1 adopted by the present application includes: reverse transcription reagent, DNA enzyme, primer and real-time fluorescent quantitative PCR mixture. The real-time fluorescent quantitative PCR mixture includes: 5 microliters of 2×PCR mixture, 0.5 microliters of primer F (10 uM), 0.5 microliters of primer R (10 uM), 1 microliter of template and 3 microliters of double distilled water. The primer includes the following primers:

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

[0064] 5'-TGACTTCAACAGCGACACCCA-3' / 5'-CACCCTGTTGCTGTAGCCAAA 3' (GAPDH).

[0065] The qualified RNA sample was denatured at 65°C for 5 minutes, and reverse transcription was performed with reverse transcription reagent. DNAse was used to remove residual genomic DNA. Primers were designed using Primer 5.0 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 fluorescent quantitative PCR mixture contained 5 μl of 2x 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 ddH2O, with a final volume of 10 μl. The reaction was performed in a LightCycler480 II (Roche Diagnostics) with the following conditions: 95°C for 5 minutes, 95°C for 10 seconds, 60°C for 30 seconds, 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, cooling at 4°C for 30 seconds and fluorescence measurement. 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, H3K18 lactylation level and H3K18 acetylation level detection

[0067] The reagents used in this example to detect the level of histone H3K18 lactylation include: a histone extraction kit, a sodium dodecyl sulfate polyacrylamide gel, a loading buffer, sodium dodecyl sulfate, a pre-stained protein, an electrophoresis buffer, an electrotransfer buffer, a histone H3K18 lactylation antibody, a histone H3 antibody, a goat anti-rabbit secondary antibody, blocking milk, a TBST buffer, and a SuperECLPlus hypersensitive luminescent solution.

[0068] In this example, the isolated peripheral blood mononuclear cells were centrifuged at 1000 rpm for 5 minutes at 4°C, then resuspended in diluted 1x pre-dissolved buffer at 10 7 cells per milliliter. The test 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 microliters / 10 7 cells) of lysis solution and incubated on ice for 30 minutes. After centrifugation at 12000 rpm for 5 minutes at 4°C, 0.3 volumes of equilibrated-DTT buffer were added to the supernatant fraction (containing acid-soluble proteins) to determine the protein concentration. The isolated histones were stored at -80°C. The BCA protein detection kit (Thermo Scientific TM Pierce TMBCA Protein Assay Kit, Cat#23227) and according to the results of the protein concentration, 5 μΐ of 5x loading buffer and 2% sodium dodecyl sulfate were added to an equivalent of 15 μg of histones, making the final volume 20 μΐ. Five microliters of pre-stained protein and 20 μΐ of sample were not added to 5% Tris-acetate-EDTA (TAE) gel + 15% Bis-Tris gel. 80 V / gel, until the sample was shown as a line, 120 V / gel, until the sample was reached to the bottom. Wet transfer was performed, transferring the protein to a 0.2 μιη PVDF membrane (Immobilon™-PSQ membrane) at 300 mA for 3 hours. This example measures the level of H3K18 lactylation by Western blotting, using a primary antibody rabbit mAb against Lactic Histone H3 (Lys18) (PTM BioLab, Inc., Cat# PTM-1406RM; diluted 1:1000 in Life Technologies TM Abberior Star 488 goat anti-rabbit IgG (H&L) (ab204610, diluted 1:1000 in Life Technologies TM Abberior Star 488 goat anti-rabbit IgG (H&L) (ab204610, diluted 1:1000 in Life Technologies

[0069] The level of H3K18 acetylation was detected according to the method described above for lactylation, with the difference that the primary antibody against Lactic Histone H3 was replaced by a rabbit mAb against Acetylated Histone H3 (Lys18) (PTM BioLab, Inc., Cat# PTM-114RM; diluted 1:1000 in Life Technologies TM Abberior Star 488 goat anti-rabbit IgG (H&L) (ab204610, diluted 1:1000 in Life Technologies

[0070] In this example, an equivalent of 15 μg of protein was added to each lane, so the results of the WB between samples are comparable.

[0071] Example 1 : Detection and analysis

[0072] The related indicators (cytokine levels, arginase-1 messenger ribonucleic acid levels, histone H3K18 site lactylation modification levels (H3K18la), acetylation modification levels (H3K18ac), and serum-related items of the subjects) of the research cohort were determined and analyzed, and the results and analysis are as follows.

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

[0074] Figure 1 The Western blotting visualization results after incubation with anti-histone H3 antibody, specific anti-histone H3K18 site acetylation (H3K18ac) antibody and specific anti-histone H3K18 site lactylation (H3K18la) antibody. Combined with the statistical analysis results of Table 2 and Table 3, it can be seen that only the H3K18ac modification level, 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]), the difference between groups is statistically significant (P<0.001), showing a negative correlation between H3K18ac and disease severity; for the H3K18la level, the trend is septic shock group (1.851 [1.318, 1.966]) > sepsis group (1.375 [1.177, 1.147]) > healthy control group (1.006 [0.738, 1.186]), the difference between groups is statistically significant (P=0.002), showing a positive correlation between H3K18la and disease severity.

[0075] In addition, compared with the non-infectious severe group (H3K18ac: 1.225 [0.858, 1.488]; H3K18la: 1.262 [0.683, 1.740]), the H3K18ac level of the infectious severe group (H3K18ac: 0.711 [0.482, 0.849]; H3K18la: 1.640 [1.248, 1.955]) including septic shock and sepsis was reduced, and the H3K18la level was increased, the difference between groups was statistically significant (H3K18ac: P<0.001; H3K18la: P=0.006), indicating the potential role of the two modifications in identifying infectious severe cases.

[0076] (2) Distribution and difference between histone H3K18 site lactylation / acetylation (H3K18 la / ac) levels

[0077] As can be seen from Table 2, the difference in H3K18 la / ac between the infection severe group (2.383 [1.613, 3.596]) and the non-infection severe group (0.890 [0.669, 1.222]) was statistically significant (P < 0.001); as can be seen from Table 3, for the ratio index 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 difference between groups was also statistically significant (P < 0.001), showing a positive correlation between the ratio of H3K18 la / ac and the severity of the disease.

[0078] Table 2 Comparison of H3K18 la, H3K18 ac and H3K18 la / ac between infection severe and non-infection severe

[0079]

[0080] Table 3 Comparison of H3K18 la, H3K18 ac and H3K18 la / ac between septic shock, sepsis and healthy subjects

[0081]

[0082]

[0083] Note: Non-parametric Mann-Whitney U test was used for analysis between different groups, and the results were expressed as median and quartile range; P < 0.05 indicates significant difference.

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

[0085] Further analysis of the three parameters H3K18la, H3K18ac, and H3K18la / ac using ROC curves is as follows: In terms of identifying severe infections from the ICU critical care cohort, as shown in Table 4, the AUCs of the three parameters H3K18la, H3K18ac, and H3K18la / ac, as well as traditional infection-related laboratory indicators, are as follows: H3K18la 0.677 (0.547, 0.808), H3K18ac 0.865 (0.774, 0.956), H3K18la / ac 0.915 (0.842, 0.989), PCT 0.932 (0.875, 0.989), and CRP 0.734 (0.613, 0.854). Furthermore, only H3K18 la / ac showed no statistically significant difference in AUC compared to PCT (P = 0.807, 0.362, DeLong test), meaning that H3K18 la / ac is close to the clinically validated ability of PCT to identify infection. The other indicators all showed statistically significant differences in AUC compared to PCT, indicating that they did not reach the level of PCT in identifying infection. In addition, when using WBC, NEUT, and NEUTP to identify severely infected patients in the ICU cohort, their discriminatory power was not significant (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, indicating that they are not specific indicators of infection, consistent with clinical experience. Table 4. Diagnostic efficacy of H3K18la, H3K18ac, H3K18la / ac, and corresponding reference indicators in identifying critically ill infected patients in the ICU cohort.

[0086]

[0087]

[0088] Note: # represents the null hypothesis for P1 that the area under the ROC curve is equal to 0.5; * represents the null hypothesis for P2 that the area under the ROC curve of this indicator is no different from that of PCT.

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

[0090] Next, we will further corroborate its association with severe infectious diseases (including septic shock and sepsis) by examining the correlation between three modified indicators and common infection-related laboratory indicators. The results are as follows:

[0091] like Figure 2 As shown, H3K18la showed no significant correlation with any infection-related laboratory indicators.Figure 3 As shown in Table 4, H3K18ac was negatively correlated with CRP, PCT (Spearman p = -0.3956, P = 0.0003; Spearman p = -0.4935, P < 0.0001), and positively correlated with monocyte count, lymphocyte count (Spearman p = 0.2531, P = 0.0187; Spearman p = 0.2162, P = 0.0455). It can be seen from Table 4 that H3K18 la / ac was positively correlated with CRP, PCT (Spearman p = 0.2612, P = 0.0201; Spearman p = 0.4615, P < 0.0001). Figure 4 As shown in Table 4, H3K18ac was negatively correlated with CRP, PCT (Spearman p = -0.3956, P = 0.0003; Spearman p = -0.4935, P < 0.0001), and positively correlated with monocyte count, lymphocyte count (Spearman p = 0.2531, P = 0.0187; Spearman p = 0.2162, P = 0.0455). It can be seen from Table 4 that H3K18 la / ac was positively correlated with CRP, PCT (Spearman p = 0.2612, P = 0.0201; Spearman p = 0.4615, P < 0.0001).

[0092] (5) The performance of H3K18la, H3K18ac and H3K18 la / ac in identifying septic shock from healthy controls and severe infection groups

[0093] As mentioned above, the change trend of H3K18la, H3K18ac and H3K18 la / ac in the levels of septic shock, sepsis and healthy control groups, and this grouping has a clear disease severity gradient, which suggests that the three modification indicators can be used for disease severity assessment, so further analysis by ROC curve is as follows:

[0094] As shown in Table 5 and Figure 5 In terms of the performance of H3K18la, H3K18ac and H3K18 la / ac in identifying septic shock from healthy controls and severe infection groups, the AUCs in descending order are H3K18 la / ac 0.859 (0.759, 0.960), H3K18la 0.807 (0.700, 0.914) and H3K18ac 0.730 (0.603, 0.856), and the newly constructed modification ratio indicator H3K18 la / ac of the present application performs best.

[0095] Table 5 The performance of H3K18la, H3K18ac and H3K18 la / ac in identifying septic shock from severe infection groups

[0096] Type AUC 95% CI P # ]] Cut-off Youden's 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) The correlation of H3K18la, H3K18ac and H3K18 la / ac with disease severity score and commonly used prognostic indicators

[0099] The correlation between the three modification indicators and the severity of the disease (represented by APACHE II score and SOFA score) was further analyzed by correlation analysis, and the possibility of their use as a prognostic indicator (represented by ICU stay time, total hospital stay time and mechanical ventilation time) was explored.

[0100] As shown in Figure 6 , H3K18 la was positively correlated with SOFA score (Spearman p = 0.3317, P = 0.0018), positively correlated with ICU stay time (Spearman p = 0.2292, P = 0.0338), and positively correlated with mechanical ventilation time (Spearman p = 0.2742, P = 0.0111). However, H3K18 la did not show significant correlation with APACHE II score (Spearman p = 0.2139, P = 0.0508) and hospital stay time (Spearman p = -0.0034, P = 0.9751).

[0101] As shown in Figure 7 , H3K18 ac was negatively correlated with APACHE II score (Spearman p = -0.3479, P = 0.0012), negatively correlated with SOFA score (Spearman p = -0.3381, P = 0.0015), and negatively correlated with mechanical ventilation time (Spearman p = -0.3009, P = 0.0051). However, H3K18 ac did not show significant correlation with ICU stay time (Spearman p = -0.1715, P = 0.1144) and hospital stay time (Spearman p = -0.0001, P = 0.9991).

[0102] As shown in Figure 8 , H3K18 la / ac was positively correlated with APACHE II score (Spearman p = 0.4044, P < 0.001), positively correlated with SOFA score (Spearman p = 0.4687, P < 0.001), positively correlated with ICU stay time (Spearman p = 0.2824, P = 0.008), and positively correlated with mechanical ventilation time (Spearman p = 0.3935, P < 0.001). However, H3K18 la / ac did not show significant correlation with total hospital stay time (Spearman p = 0.0258, P = 0.814).

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

[0104] This part explores the correlation between H3K18la, H3K18ac, H3K18 la / ac and the expression of serum inflammatory factors. Among them, including tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), alpha interferon (IFN-α), gamma interferon (IFN-γ), interleukin-8 (IL-8), etc. Cytokines that promote inflammatory response, interleukin-10 (IL-10), interleukin-4 (IL-4) and other inflammatory response inhibiting cytokines.

[0105] As shown in Figure 9 H3K18la was negatively correlated with IFN-α (Spearman ρ =-0.2494, P = 0.0133), and 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 shown in Figure 10 H3K18ac was negatively correlated with IL-6 (Spearman ρ =-0.2618, P = 0.0092), and negatively correlated with IL-1β (Spearman ρ =-0.2900, P = 0.0038), and 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 shown in Figure 11 H3K18 la / ac was positively correlated with IL-6 (Spearman ρ = 0.2149, P = 0.0345), and positively correlated with IL-8 (Spearman ρ = 0.2310, P = 0.0228), and positively correlated with IL-10 (Spearman ρ = 0.2721, P = 0.0070), and 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) Correlation between H3K18la, H3K18ac, H3K18 la / ac and downstream M2 macrophage polarization related marker (ARG1) expression

[0109] Based on the PBMC samples collected from clinical patients in the research cohort, RNA was extracted, and the mRNA expression level of the M2 macrophage polarization marker ARG1 (Arginase 1) was detected by qRT-PCR with GAPDH as the internal reference gene, and the relationship between the mRNA expression level of ARG1 and H3K18la, H3K18ac, H3K18 la / ac was analyzed. Figure 12 It was found that the mRNA expression level of ARG1 was positively correlated with H3K18la (Spearman p = 0.330, P = 0.001), and the expression of ARG1 was negatively correlated with H3K18ac (Spearman p = -0.257, P = 0.011), and the ratio of the former two, H3K18 la / ac, was positively correlated with the expression of ARG1 (Spearman p = 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 modification, histone H3 also has methylation, citrullination, butyrylation, propionylation and crotonylation modification.

[0112] In this comparative example, the citrullination modification level of histone H3 (H3cit) was detected using a human citrullinated histone H3 Elisa kit (purchased from Shanghai Huining Biological Technology Co., Ltd.).

[0113] In this comparative example, the citrullination modification level of histone H3 (H3cit) was detected using a human citrullinated histone H3 Elisa kit (purchased from Shanghai Huining Biological Technology Co., Ltd.).

[0114] In this comparative example, the crotonylation modification level of H3K18 (H3K18cr) was detected by referring to the detection method of the lactylation level in Example 4, except that the anti-lactate histone H3 antibody in the primary antibody was replaced by the anti-crotonylated histone H3 (Lys18) mouse mAb (PTM BioLab, Inc., Cat#PTM-540; diluted 1:1000 in Life Technologies™ antibody dilution reagent solution, cat#003218).

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

[0116] Further analysis of the above parameters using ROC curves is as follows: in identifying infection severity from the ICU severe group (corresponding to the content of (3) in Example 1), the AUC of H3cit, H3K18cr, H3K18 la / H3cit and H3K18 la / cr is H3cit 0.843 (0.728, 0.958), H3K18cr 0.872 (0.764, 0.980), H3K18 la / H3cit 0.862 (0.818, 0.906), H3K18 la / cr 0.897 (0.836, 0.958), respectively. Further analysis shows that H3cit, H3K18cr and H3K18ac have similar AUC values (the AUC value of H3K18ac is 0.865 (0.774, 0.956)), but the AUC values of H3K18 la / H3cit and H3K18 la / cr are quite different from the AUC value 0.932 (0.875, 0.989) of PCT, and are less than the AUC value 0.915 (0.842, 0.989) of H3K18 la / ac, which to some extent indicates that the modified protein index H3K18 la / ac provided by the present application is more reliable in identifying infection severity from the ICU severe group than other proteins of the same type, and its effect is unpredictable.

[0117] The above results also show that H3K18 la / ac has significant correlation with disease severity and can be used for disease severity evaluation, and the correlation of the H3K18 la / ac modification index with ICU hospitalization time, total hospitalization time and mechanical ventilation time suggests its application direction as a prognosis prediction index.

[0118] Finally, it should be noted that the above content is only used to illustrate the technical solutions of the present application, and is not a limitation on the protection scope of the present application. Simple modifications or equivalent replacements of the technical solutions of the present application made by those skilled in the art do not deviate from the essence and scope of the technical solutions of the present application.

Claims

1. The application of a detection reagent of a histone modification marker composition in the preparation of a kit for the diagnosis of sepsis, characterized in that, The histone modification marker composition is H3K18la and H3K18ac; H3K18ac is the acetylation level of the lysine residue at position 18 of histone H3, and H3K18la is the lactation level of the lysine residue at position 18 of histone H3. The application distinguishes between severe infectious disease and non-severe infectious disease, 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 non-severe infectious disease. Severe infectious disease refers to sepsis, and non-severe infectious disease refers to disease that is severe but infection is not the primary cause. The H3K18la / ac ratio is the quantitative or semi-quantitative ratio of H3K18la to H3K18ac.

2. The application according to claim 1, characterized in that, Sepsis includes septic shock.

3. The application according to claim 1, characterized in that, The quantitative or semi-quantitative methods include any one or more of ELISA, Dot blot, Western blot, and immunohistochemical assays.

4. The application according to any one of claims 1-3, characterized in that, When the kit is used for detection, it includes the following steps: Sample collection, sample pretreatment, PBMC isolation, histone extraction, quantitative or semi-quantitative determination of H3K18la and H3K18ac, calculation of ratio, and interpretation of results.

5. The application according to claim 4, characterized in that, The kit includes all reagents for quantitative or semi-quantitative detection of H3K18ac and H3K18la.

6. The application according to claim 5, characterized in that, 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.

7. The application according to claim 6, characterized in that, The quantitative or semi-quantitative detection reagents include any one or more of the following: electrophoresis gel, loading buffer, pre-stained protein, electrophoresis buffer, electroporation buffer, histone H3K18 acetylated antibody, histone H3K18 lactated antibody, histone H3 antibody, secondary antibody, blocking agent, TBST buffer, and SuperECLPlus ultrasensitive luminescent solution.

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