Application of histone modification markers in the preparation of sepsis diagnostic products and kits

Abstract

The present invention provides the use of histone modification markers in the preparation of sepsis diagnostic products and a kit thereof, belonging to the field of biomarkers. By obtaining peripheral blood mononuclear cells, histone extraction, Western blot detection, and data analysis, the present invention demonstrates that the acetylation level of histone H3 lysine 18 residue (H3K18ac) can be used as a sepsis assessment marker. The reagent used to detect H3K18ac can be used to prepare a kit for distinguishing severe infections from non-severe infections, assessing sepsis severity, or predicting sepsis prognosis.

Description

Technical Field

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

[0002] Sepsis is a systemic inflammatory response caused by infection. Septic shock is a severe form of sepsis, characterized by persistent hypotension and tissue hypoperfusion. Despite increasing medical understanding, the complexity and diverse clinical manifestations of sepsis and septic shock make early identification and accurate diagnosis difficult. The immune response to sepsis is a dynamic process involving multiple cell types and molecular mechanisms. Epigenetics plays a key role in the heterogeneous response to sepsis. Gene reprogramming processes such as DNA methylation, altered histone modifications, and regulation of noncoding RNA transcription may partially explain the differences in prognosis between patients.

[0003] Biomarkers are specific substances produced by the body that reflect the onset and progression of disease. Sepsis biomarkers have the potential to be used to diagnose pathogenic infection, the severity of the disease, and the effectiveness of drug treatment. Ideal sepsis biomarkers should rapidly and specifically distinguish sepsis from other non-infectious inflammatory reactions. Currently, a variety of proteins have been reported to be useful for the diagnosis of sepsis, including procalcitonin, C-reactive protein, interleukin-6, and soluble receptors such as triggering receptor 1 expressed on myeloid cells. However, these markers have limitations and lack specificity, and no single biomarker has yet been found to be independently useful for the diagnosis of sepsis.

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

[0005] Serum lactate levels are used as an important indicator for assessing the severity and prognosis of sepsis. However, clinical management strategies for shock patients often prioritize early volume resuscitation, resulting in rapid clearance of serum lactate. This makes it difficult to obtain accurate serum lactate values ​​during critical illness. Therefore, the exploration of biomarkers that are less susceptible to early clinical interventions (such as fluid resuscitation and vasoactive medications) is particularly important. These biomarkers should not only provide a wider detection window but also, due to their deep connection to pathophysiological processes, provide more reliable clinical assessment tools.

[0006] 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 lactylation; a reagent for detecting histone H3K18 lactylation; a reagent for detecting cytokines; and a reagent for detecting arginase-1 messenger RNA levels. A disadvantage of this technology is its poor diagnostic accuracy.

[0007] Zhang Hong et al. (Zhang Hong, Ni Zheng, Zhang Liying, et al. The significance of rapid PCT testing in the prognosis assessment of sepsis in the emergency department [J]. Journal of Clinical Emergency Medicine, 2015) found that as PCT values ​​increase, mortality rates in patients with sepsis tend to increase and hospital stays are prolonged. For the prognosis assessment of patients with sepsis, the initial PCT value upon admission and dynamic PCT monitoring have comparable sensitivity, but dynamic PCT monitoring has greater specificity. Rapid PCT testing results in the emergency department provide guidance for the diagnosis and prognosis of sepsis; dynamic monitoring of changes in these indicators is even more valuable for short-term prognosis assessment. However, the disadvantage of this technique 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 susceptible to early clinical intervention (such as fluid resuscitation, vasoactive drug application, etc.). These markers should not only provide a wider detection time window, but also provide a more reliable disease assessment tool for the clinic due to their deep connection with pathophysiological processes. Based on this, the present invention provides the application of histone modification markers in the preparation of sepsis diagnostic products and a kit thereof. The present invention proves that the acetylation modification level of the 18th lysine residue of histone H3 (H3K18ac) can be used as a sepsis assessment marker by obtaining peripheral blood mononuclear cells, histone extraction, Western blot detection and data analysis; the reagent for detecting H3K18ac can be used to prepare a kit for distinguishing severe infection from non-infectious severe illness, sepsis severity assessment or sepsis prognosis prediction, which is not susceptible to early clinical intervention and has high sensitivity and specificity.

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

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

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

[0012] Preferably, the quantitative or semi-quantitative method comprises any one or more of ELISA detection, Dot blot detection, Western blot detection and immunohistochemistry detection.

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

[0014] Preferably, when the application is judged based on the test results, the diagnostic critical value is 0.930-0.950; when H3K18ac is less than or equal to the critical value, it is judged as an infectious severe disease; when H3K18ac is greater than the critical value, it is judged as a non-infectious severe disease.

[0015] Preferably, the infectious severe illness includes sepsis or septic shock, and the non-infectious severe illness refers to a severe illness in which infection is not the main cause.

[0016] In another aspect, the present invention provides a kit for diagnosing, assessing the condition, or evaluating the prognosis of sepsis, wherein the kit comprises a reagent for quantitative or semi-quantitative detection of H3K18ac.

[0017] Preferably, the kit comprises the following steps when used for detection:

[0018] Sample collection, sample pretreatment, PBMC isolation, histone extraction, quantitative or semi-quantitative determination of H3K18ac, and result interpretation.

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

[0020] Preferably, the quantitative or semi-quantitative detection reagents include any one or more of electrophoresis gel, loading buffer, prestained protein, electrophoresis buffer, electrotransfer buffer, histone H3K18 acetylation antibody, histone H3 antibody, secondary antibody, blocking agent, TBST buffer and SuperECLPlus supersensitive luminescent solution.

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

[0022] 1. The present invention discloses that the acetylation modification level of the lysine 18 residue of histone H3 (H3K18ac) can be used as a marker for evaluating sepsis. The reagent for detecting H3K18ac can be used to prepare a kit for distinguishing infectious severe illness from non-infectious severe illness, assessing the severity of sepsis, or predicting the prognosis of sepsis.

[0023] 2. The H3K18ac indicator used in the present invention showed higher efficacy than H3K18la in judging the severity of infection. The area under the receiver operating characteristic (ROC) curve (AUC) of H3K18ac reached 0.865, which is close to the diagnostic ability of procalcitonin, which is widely used clinically. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0026] Figure 3 Correlation analysis between H3K18ac levels and infection-related laboratory indicators was performed using Spearman correlation analysis, and simple linear regression analysis was further performed for indicators with significant correlation.

[0027] Figure 4 Receiver operating characteristic (ROC) curves of H3K18la and H3K18ac for septic shock in a critically ill cohort with infection.

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

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

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

[0031] Figure 8 Correlation analysis between H3K18ac and serum inflammatory factor expression was performed using Spearman correlation analysis, and simple linear regression analysis was further performed for indicators with significant correlation.

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

[0033] Unless otherwise specified, the raw materials used in the present invention are all common commercially available products, and their sources are not specifically limited.

[0034] 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 critical care settings. Overall, patients with sepsis and septic shock experience a dramatic decrease in health-related quality of life while in the ICU and increased mortality in 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, with the diagnosis often only documented after clinical deterioration during hospitalization. Differential diagnosis is also challenging in patients with circulatory shock, particularly when accompanied by other conditions such as cardiac injury, hypovolemia, and trauma, or when classic signs of infection are absent, as in infants, the elderly, and immunocompromised individuals. Sepsis triggers a complex immune response, resulting in a destabilizing balance between pro- and anti-inflammatory pathways, leading to vastly different outcomes among patients with similar injuries. Genetic regulation may play a central role in this process. Indeed, extensive genetic reprogramming, such as DNA methylation, histone modifications, and transcriptional regulation of noncoding 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 relationships with each other and with DNA. Attenuation of local and systemic proinflammatory cytokines, protection from distant organ damage, enhanced bacterial clearance and phagocytosis, and suppression of immune cell apoptosis have been associated with improved survival.

[0035] Despite similar traumatic consequences, different patients with sepsis may experience vastly different outcomes. Extensive genetic reprogramming, including changes in DNA methylation, histone modifications, and regulation of noncoding RNA transcription, may partially explain this variability. As the primary proteins in the nucleus that control cellular metabolism, growth, and differentiation, histone N-terminal tails undergo various post-translational modifications (PTMs) that reshape the local chromatin landscape to regulate transcription, replication, and DNA repair. Several metabolites, including propionyl-CoA, butyl-CoA, crotonyl-CoA, lactyl-CoA, 2-hydroxyisobutyl-CoA, β-hydroxybutyryl-CoA, succinyl-CoA, benzoyl-CoA, malonyl-CoA, and glutaryl-CoA, have recently been identified as substrates for these modifications, promoting histone PTMs and thereby influencing gene expression. Through these mechanisms, the functions of diverse genes and proteins can be rapidly modulated.

[0036] Lysine is the only amino acid in proteins that contains an ε-amino side chain. Its characteristic and reactive nature as a primary amine makes it the most diverse amino acid subject to post-translational modifications in vivo. In 2019, the presence of lysine lactylation (Kla) on histones was first reported in several human cell lines and mouse bone marrow-derived macrophages. Kla can be found on all core histones and shares the most common modification site with histone lysine acetylation (Kac). Similar to many PTMs, Kla is involved in the transfer and removal of lactyl groups from lactyl-CoA. P300, also known as lysine acetyltransferase (KAT3B), has been shown to be a lactyltransferase that promotes lactylation. Regarding non-histone proteins, studies have demonstrated that macrophages can take up extracellular lactate through monocarboxylate transporters (MCTs), which in turn promotes HMGB1 lactylation through a p300 / CBP-dependent mechanism. This stimulation of HMGB1 acetylation occurs by inhibiting the deacetylase SIRT1 and recruiting the acetyltransferase p300 / CBP to the nucleus.

[0037] The acetylation and deacetylation of core histones is 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." A balance of acetylation and deacetylation exists not only for histones but also for a wide range of other proteins critical to cellular function. These proteins play important roles in the cell cycle, stress response, cytoskeleton and motility, signal transduction, damage repair and remodeling, and proliferation. Imbalances in acetylation can lead to the development of a variety of diseases.

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

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

[0040] Data Analysis:

[0041] Normally distributed data were compared using Student's t-test or one-way ANOVA, and the results were expressed as mean ± SD. Correlation analysis was performed using the Pearson correlation test. Non-normally distributed data were analyzed using the nonparametric Mann-Whitney U test, and the results were expressed as median and interquartile range (IQR). Correlation analysis was performed using the Spearman correlation test. Categorical variables were compared using the chi-square or Fisher's exact test, and the results were expressed as numbers and percentages. P < 0.05 was considered statistically significant.

[0042] Diagnostic performance was determined using receiver operating characteristic (ROC) curve analysis. The true positive rate (sensitivity) was plotted against the false positive rate (specificity) at various classification thresholds. The area under the ROC curve (AUC) provides an indicator of classification performance, with higher AUC values ​​corresponding to better model prediction. A P value < 0.05 was considered statistically significant.

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

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

[0045] Table 1 Sample baseline information

[0046]

[0047]

[0048] As shown in Table 1, in terms of age, the average age of all subjects was 65.60 years old, including 67.49 years old in the septic shock group, 65.31 years old in the sepsis group, 65.72 years old in the non-infectious severe group, and 59.75 years old in the healthy control group. There was no statistical difference between the first three disease groups and all four groups (P = 0.880, 0.561). In terms of gender ratio, males accounted for 61.22% of the entire cohort. There was no statistical difference between the first three disease groups and 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, and 0.022); in terms of vital signs, only mean arterial pressure was statistically different among the first three disease groups (P<0.001); in addition, the worst blood lactate value on the day of specimen collection and two disease severity scores (APACHE) were significantly different among the first three disease groups. There were statistically significant differences in the ICU II score and SOFA score) and the proportion of invasive mechanical ventilation (all P values ​​were less than 0.001), with higher values ​​in the septic shock group, which was in line with general expectations. Regarding prognostic indicators, there were no statistically significant differences in ICU length of stay and total hospitalization length of stay among the first three disease groups (P = 0.128, 0.285). Fifteen patients in the first three disease groups died within 28 days of ICU admission (15.30% of the entire cohort and 17.4% of the patient cohort alone), including 12 patients with septic shock (32.43% of the group), 2 patients with sepsis (15.38%), and 1 patient with non-infectious severe illness (2.78%). There were statistically significant differences among the three groups (P = 0.004).

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

[0050] The present invention uses a sepsis detection kit for distinguishing severe infections from non-severe infections, assessing sepsis severity, or predicting sepsis prognosis. The kit includes at least one of the following reagents: a reagent for detecting histone H3K18 lactylation, a reagent for detecting histone H3K18 acetylation, a reagent for detecting arginase-1 messenger RNA levels, and a reagent for detecting cytokines. Furthermore, the detection kit may also include a BCA protein concentration assay kit, a histone extraction kit, and an SDS-PAGE gel preparation kit.

[0051] Specifically, a blood sample is first collected, and then the collected blood sample is subjected to peripheral blood mononuclear cell separation and serum separation. Blood samples (5-10 ml) are collected using ethylenediaminetetraacetic acid (EDTA)-containing blood collection tubes and serum separation tubes, and peripheral blood mononuclear cell separation and serum separation are performed respectively. For isolation of peripheral blood mononuclear cells, the sample was diluted 1:1 with phosphate-buffered saline (PBS) at pH 7.2. The diluted blood sample was placed in 15 ml of lymphocyte separation medium (STEMCELL Technologies Cat#07851) and centrifuged at 500 x g and 20°C for 20 minutes. The upper layer was mostly aspirated, leaving the white, light-yellow hairs (mononuclear cells) in the interphase. The mononuclear cells were separated and filled with phosphate-buffered saline. After mixing, the mixture was centrifuged at 500 x g and 20°C for 7 minutes. The supernatant was completely removed. If there was red impurities in the sediment at the bottom of the tube, red blood cell buffer (Solarbio Cat#R1010) was added for 5 minutes. A sufficient amount of phosphate-buffered saline was added and the tube was centrifuged at 500 x g and 20°C for 7 minutes. After removing the supernatant, the peripheral blood mononuclear cells were collected and resuspended in 2 ml of cryoprotectant (fetal bovine serum:dimethyl sulfoxide = 9:1). The obtained peripheral blood mononuclear cells were stored at -80°C. Serum separation can be carried out under sterile conditions by centrifuging the blood sample at 3000 rpm at 4°C for 10 minutes; the supernatant is aspirated and stored at low temperature, and the obtained serum is stored at -80°C.

[0052] Example 2: Cytokine Level Detection

[0053] The reagent for detecting cytokines of 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, and TNF-α. The reagent for detecting cytokines of the present invention can include a capture microsphere antibody, a detection antibody, SA-PE, and a washing solution.

[0054] The present invention uses flow cytometry to detect cytokines in 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 a calibration tube, and 25 μl of serum sample buffer is added to matrix B. The sample is thoroughly mixed with 25 μl of capture microsphere antibody; 25 μl of detection antibody is added to all test tubes, and the cells are incubated at room temperature, protected from light, and shaken at 400-500 rpm. After 2 hours, 25 μl of SA-PE is added to all test tubes, and the cells are incubated at room temperature, protected from light, and shaken at 400-5500 rpm. After half an hour, 500 μl of 1× wash buffer is added, the cells are spun for several seconds, and then centrifuged at 500 x g for 5 minutes. After removing the supernatant, 300 μl of 1× wash buffer is added to the test tubes, and the cells are spun for several seconds. Flow cytometry was used to analyze the samples, which can detect 12 cytokines, 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 data accuracy, at least 1,100 microspheres were collected for each sample.

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

[0056] Total RNA in the examples of the present invention was extracted using the guanidine isothiocyanate-phenol-chloroform method (RNAiso Plus, TaKaRaBio Code No. 9109). RNA yield was determined using a Thermo nanodrop 2000C (A260 / A280). RNA quality was determined by agarose gel electrophoresis.

[0057] The present invention uses reagents for detecting arginase-1 messenger RNA levels, including reverse transcription reagents, DNA enzymes, primers, and a real-time fluorescence quantitative PCR mixture. The real-time fluorescence quantitative PCR mixture includes 5 microliters of 2× PCR mixture, 0.5 microliters of primer F (10 μM), 0.5 microliters of primer R (10 μM), 1 microliter of template, and 3 microliters of double-distilled water. The primers include the following:

[0058] 5'-AAGAGTGTGATGTGAAGGATTATGG-3' / 5'-TTCTTCTTGACTTCTGCCACCTT-3'(Arginase-1),

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

[0060] Qualified RNA samples were denatured at 65°C for 5 minutes and reverse transcribed using a reverse transcription reagent. Residual genomic DNA was removed using DNase. Primers were designed using Primer 5.0 to assemble upstream and downstream regions of the target gene. cDNA and primers were added to a qRT-PCR system (Tip Green qPCR SuperMix, AQ141-02; Thermo Fisher Scientific). The real-time quantitative PCR mixture contained 5 μl of 2× PCR mix, 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, in a final volume of 10 μl. Reactions were performed in a LightCycler 480 II (Roche Diagnostics) using the following conditions: 95°C for 5 minutes, denaturation at 95°C for 10 seconds, annealing at 60°C for 30 seconds, and 72°C for 10 seconds, for a total of 45 PCR cycles. Melting curve analysis consisted of 95°C for 5 seconds, 65°C for 1 minute, and 97°C for 1 second. Finally, the cells were cooled at 4°C for 30 seconds and fluorescence was measured. GAPDH was used as an internal reference. Expression data were normalized to GAPDH using the delta-delta CT method.

[0061] Example 4: Histone extraction, H3K18 lactylation level and H3K18 acetylation level detection

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

[0063] In this example, the isolated peripheral blood mononuclear cells were centrifuged at 1000 rpm for 5 minutes at 4°C and then 7 Resuspend cells in diluted 1× prelysis buffer. Keep the tube on ice for 10 minutes, gently stir, and centrifuge at 10,000 rpm for 1 minute at 4°C. After removing the supernatant, resuspend the cells in 3 times the volume (approximately 200 μl / 10 7 The cells were incubated in lysate on ice for 30 minutes. After centrifugation at 12000 rpm for 5 minutes at 4°C, 0.3 times the volume of equilibrated-DTT buffer was added to the supernatant (containing acid-soluble proteins) to determine the protein concentration. The isolated histones were stored at -80°C. This disclosure uses a BCA protein assay kit (Thermo Scientific TM Pierce TMBCA Protein Assay Kit, Cat#23227) was used to detect the concentration of histones extracted from peripheral blood mononuclear cells. According to the results of the protein concentration, 5 μl of 5× loading buffer and 2% sodium dodecyl sulfate were added to an equal amount of 15 μg of histones to make a final volume of 20 μl. 5 microliters of prestained protein and 20 μl of sample were not added to the 5% chromatography gel + 15% separation gel. 80V / gel was used to make the sample appear as a line, and 120V / gel was used until the sample touched the bottom. Wet transfer was performed and the protein was transferred to a 0.2 μm PVDF membrane (ImmobilonTM-PSQ membrane) at 300 mA for 3 hours. In this example, the level of H3K18 acetylation was measured by Western blotting. The primary antibody used was a rabbit mAb against acetyl histone H3 (Lys18) ((PTM BioLab, Inc., Cat#PTM-114RM; 1:1000 diluted in Life Technologies TM Antibody dilution reagent solution, cat#003218) and anti-histone H3 antibody (Abcam, ab1791, diluted 1:1000 in 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. no. ab6721), diluted 1:3000 in TBS-T buffer containing 5% blocking milk, and incubated at room temperature for 2 hours. The detected bands were quantified using a chemiluminescence imaging system (VILBER Fusion Solo S) to obtain the level of histone H3K18 lactylation modification.

[0064] The detection of H3K18 lactylation modification level was similar to the above-mentioned acetylation level detection method, except that the primary antibody anti-acetyl histone H3 (Lys18) rabbit mAb was replaced with anti-lactate histone H3 (Lys18) rabbit mAb (PTM BioLab, Inc., Cat# PTM-1406RM; Life Technologies TM Antibody dilution reagent solution (1:1000 dilution).

[0065] In this example, an equal amount of 15 μg of protein was added to each lane, so the WB results between samples were comparable.

[0066] Effect Example 1: Detection and Analysis

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

[0068] (1) Distribution and differences between the levels of lactylation modification (H3K18la) and acetylation modification (H3K18ac) at histone H3K18 sites

[0069] Figure 1 The results of Western blotting after incubation with anti-histone H3 antibodies, specific anti-histone H3K18 acetylation (H3K18ac) antibodies, and specific anti-histone H3K18 lactylation (H3K18la) antibodies were shown in Tables 2 and 3. The statistical analysis showed that the H3K18ac modification level in the septic shock group (0.706 [0.476, 0.804]) was lower than that in the sepsis group (0.850 [0.688, 0.851]) and the healthy control group (0.992 [0.805, 1.141]). The differences between the two groups were statistically significant (P < 0.001), indicating that H3K18ac is closely related to the disease. For H3K18la levels, the trend was 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]), and the difference between the groups was statistically significant (P = 0.002), showing a positive correlation between H3K18la and disease severity.

[0070] In addition, compared with the non-severe infection group (H3K18ac: 1.225 [0.858, 1.488]; H3K18la: 1.262 [0.683, 1.740]), the severe infection group including septic shock and sepsis (H3K18ac: 0.711 [0.482, 0.849]; H3K18la: 1.640 [1.248, 1.955]) had decreased H3K18ac levels and increased H3K18la levels. The differences between the groups were statistically significant (H3K18ac: P < 0.001; H3K18la: P = 0.006), indicating the potential role of the two modifications in identifying severe infection.

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

[0072]

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

[0074]

[0075] Note: The nonparametric Mann-Whitney U test was used to analyze the differences between different groups. The results are expressed as median and interquartile range; P < 0.05 indicates a significant difference.

[0076] (3) The diagnostic efficacy of H3K18la, H3K18ac, and corresponding reference indicators in identifying critically ill patients with infection from the ICU critically ill cohort

[0077] Further analysis of H3K18la and H3K18ac using receiver operating characteristic (ROC) curves is as follows: In terms of identifying patients with severe infection from the ICU critically ill cohort, as shown in Table 4, the AUCs for H3K18la, H3K18ac, and traditional infection-related laboratory indicators were 0.677 (0.547, 0.808) for H3K18la, 0.865 (0.774, 0.956) for H3K18ac, 0.932 (0.875, 0.989) for PCT, and 0.734 (0.613, 0.854) for CRP. As can be seen, the AUC value of 0.865 for H3K18ac was much higher than that of 0.677 for H3K18la, close to that of 0.932 for PCT, and higher than that of 0.734 for CRP, demonstrating the diagnostic efficacy of H3K18ac in identifying patients with severe infection from the ICU critically ill cohort.

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

[0079]

[0080]

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

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

[0083] Next, we will further demonstrate the association of the three modifiers with severe infection (including septic shock and sepsis) by correlating them with common infection-related laboratory indicators. The results are as follows:

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

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

[0086] As previously noted, the changing trends in H3K18la and H3K18ac levels in septic shock, sepsis, and healthy controls were evident. This grouping exhibited a distinct disease severity gradient, further analyzed using receiver operating characteristic (ROC) curves as follows:

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

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

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

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

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

[0092] Correlation analysis was used to further analyze the correlation between the modification index and disease severity (represented by APACHE II score and SOFA score), and to explore its possibility as a predictive indicator of prognosis (represented by ICU stay, total hospital stay and mechanical ventilation time).

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

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

[0095] (7) Correlation between H3K18la, H3K18ac and serum inflammatory factor expression

[0096] This section explored the correlation between H3K18la and H3K18ac and the expression of serum inflammatory factors, including 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), and other cytokines that promote inflammatory responses, as well as interleukin-10 (IL-10) and interleukin-4 (IL-4), which inhibit inflammatory responses.

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

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

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

[0100] Based on the PBMC samples collected from clinical patients in this study cohort, RNA was extracted and the expression level of ARG1 (Arginase 1), a marker of M2 macrophage polarization, was detected by qRT-PCR with GAPDH as the internal reference gene, and its relationship with H3K18la and H3K18ac was analyzed. Figure 9 As shown in the Figure 3, H3K18la was positively correlated with the mRNA expression level of ARG1 (Spearman ρ = 0.330, P = 0.001), and H3K18ac was negatively correlated with ARG1 expression (Spearman ρ = -0.257, P = 0.011).

[0101] Comparative Example 1: Detection and Analysis of 2-Hydroxyisobutyrylation Modification Levels of H3K18

[0102] In addition to lactylation and acetylation, histone H3 is also modified by methylation, citrullination, butyrylation, propionylation, crotonylation, and 2-hydroxyisobutyrylation.

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

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

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

[0106] The above results also show that H3K18ac is correlated with disease severity and can be used to assess disease severity. The correlation between H3K18ac modification indicators and ICU length of stay, total hospital stay, and mechanical ventilation time suggests its application as a prognostic predictor.

[0107] Verification Example 1: Classification of severe infectious and non-severe infectious diseases

[0108] In this validation case, the research cohort consisted of 20 critically ill patients in the Department of Critical Care Medicine of Beijing Hospital prospectively collected from November 1, 2022 to December 31, 2022, including 10 patients with infectious severe illness (including 5 in the septic shock group and 5 in the sepsis group) and 10 in the non-infectious severe illness group. All of the above diagnoses are based on the clinical manifestations and auxiliary examination information on the day of specimen collection (basically the day of admission to the ICU), and the diagnoses of septic shock and sepsis are in line with the relevant definitions of Sepsis 3.0. According to the method of Example 1-4, the H3K18ac levels of the above 20 patients were detected and analyzed. The results are shown in Table 6. It can be seen that the H3K18ac provided by the present invention was used as a marker for judgment. Within the scope of the validation sample, the accuracy of dividing infectious severe patients and non-infectious severe patients reached 90% and 80%, respectively, and H3K18ac had a significant effect as a marker.

[0109] Table 6 Detection and analysis of H3K18ac levels in 20 patients

[0110]

[0111]

[0112] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. The use of histone modification markers in the preparation of products for etiology diagnosis, condition assessment or prognosis assessment of severe sepsis, characterized in that: The histone modification marker includes H3K18ac; the H3K18ac is the acetylation level of the lysine residue at position 18 of histone H3; the application includes determining infectious severe illness and non-infectious severe illness based on the quantitative or semi-quantitative detection results of H3K18ac; the infectious severe illness includes sepsis or septic shock, and the non-infectious severe illness refers to a severe illness but infection is not the main cause.

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

3. The use according to claim 2, characterized in that The quantitative or semi-quantitative method includes any one or more of ELISA detection, Dot blot detection, Western blot detection and immunohistochemistry detection.

4. The use according to claim 1, characterized in that When the application is judged based on the test results, the diagnostic critical value is 0.930-0.950; when H3K18ac is less than or equal to the critical value, it is judged as an infectious severe disease; when H3K18ac is greater than the critical value, it is judged as a non-infectious severe disease.

5. The use according to claim 2, characterized in that The kit includes reagents for quantitative or semi-quantitative detection of H3K18ac.

6. The use according to claim 5, characterized in that The kit comprises the following steps when used for detection: Sample collection, sample pretreatment, PBMC isolation, histone extraction, quantitative or semi-quantitative determination of H3K18ac, and result interpretation.

7. The use according to claim 6, characterized in that The kit includes a sample extraction reagent, a sample preservation reagent, a sample pre-treatment reagent, a PBMC separation reagent, a histone extraction reagent, and a quantitative or semi-quantitative detection reagent.

8. The use according to claim 7, characterized in that The quantitative or semi-quantitative detection reagents include any one or more of electrophoresis gel, loading buffer, prestained protein, electrophoresis buffer, electrotransfer buffer, histone H3K18 acetylation antibody, histone H3 antibody, secondary antibody, blocking agent, TBST buffer and SuperECLPlus supersensitive luminescent solution.

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