Application of intestinal flora metabolite salicylic acid in prediction marker of Escherichia coli calf diarrhea

Abstract

The invention discloses an application of intestinal flora metabolite salicylic acid as a predictive marker of Escherichia coli calf diarrhea. The obviously up-regulated metabolite salicylic acid is screened out from the diarrhea calf feces through a non-targeted metabonomics technology, and in a group of new independent samples, it is verified through an ELISA test that the salicylic acid concentration of the diarrhea calf feces is obviously higher than that of a healthy group. ROC curve analysis shows that the area under curve (AUC) of SA for predicting escherichia coli calf diarrhea is 0.8148, and the method has good prediction capability. The invention provides a non-invasive, cheap, rapid and high-specificity prediction method, can be used for early warning and auxiliary diagnosis of Escherichia coli calf diarrhea, and has important significance for guiding clinical precise medication and reducing the death rate of calves.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biomedical detection, and relates to a new application of a biomarker, in particular to application of intestinal flora metabolite salicylic acid to an E. coli calf diarrhea prediction marker. BACKGROUND

[0002] Cattle breeding is an important part of animal husbandry. Calf diarrhea is a common acute infectious disease in cattle breeding, which not only hinders the early development of calves and affects the later reproduction and lactation, but also causes anorexia, ataxia and even death due to dehydration and acidosis, thereby causing huge economic losses and seriously threatening the sustainable development of cattle breeding.

[0003] Calf diarrhea is a typical multifactorial disease with complex causes, mainly involving single or mixed infections of various pathogenic microorganisms such as viruses, bacteria and parasites. Among the viral pathogens, rotavirus and coronavirus are the most common, the former mainly causes small intestinal absorption dysfunction in 1-2-week-old calves, leading to watery diarrhea, and the latter has stronger pathogenicity and can invade the small intestine and colon, causing more serious intestinal damage; bovine viral diarrhea virus often causes systemic symptoms including high fever, oral erosion and diarrhea. In terms of bacterial pathogens, pathogenic Escherichia coli is the primary bacterial cause of acute diarrhea and sepsis in newborn calves, especially within one week of age; in addition, Salmonella can cause septic diarrhea in calves, with feces often containing blood and a foul odor, and a high mortality rate; Clostridium perfringens often causes the most acute hemorrhagic enteritis due to its production of potent exotoxins, with a short course and extremely high mortality rate. Parasitic infection is also an important predisposing factor for calf diarrhea, among which the damage caused by Cryptosporidium is the most significant, which can cause persistent watery diarrhea in 1-4-week-old calves and is not sensitive to conventional antibiotic treatment, causing persistent dehydration and growth retardation in calves; coccidian infection is more common in older calves, and the typical symptoms are mucous bloody stool and tenesmus.

[0004] Early diagnosis and intervention of calf diarrhea can greatly improve the cure rate and reduce losses. Currently, the clinical diagnosis of calf diarrhea relies on conventional methods such as age estimation and fecal character observation, or laboratory techniques such as pathogen culture and PCR detection. However, due to the overlap of diarrhea symptoms caused by different pathogen infections and the frequent mixed occurrence, it is difficult to determine the cause, which easily delays treatment and leads to antibiotic misuse. Therefore, developing a new diagnostic technique that can quickly and accurately distinguish and specifically warn of pathogenic pathogens such as E. coli is of great significance for achieving early and precise intervention of calf diarrhea, reducing mortality and reducing economic losses. SUMMARY

[0005] The present application aims to overcome the deficiencies of the prior art and provide a new application of intestinal flora metabolite salicylic acid, i.e. as a predictive marker of E. coli calf scours. The application can achieve early, rapid and low-cost prediction of specific pathogenic diarrhea. To achieve the above-mentioned purpose, the present application completes the whole process from marker discovery to clinical verification through systematic experiments. First, through pathogenic bacteria isolation, biochemical identification, 16S rRNA sequencing and mouse pathogenicity test, it is confirmed that the calf diarrhea studied is caused by pathogenic E. coli, and the strain has multiple drug resistance characteristics. Subsequently, by analyzing the feces of diarrhea and healthy calves through non-targeted metabolomics technology, combined with multivariate statistical analysis, the intestinal flora metabolite salicylic acid which is extremely significantly up-regulated in the diarrhea group is screened out, and it is confirmed that the metabolite is related to a variety of intestinal flora differences. Finally, in newly collected independent samples, the ELISA method is used to quantitatively verify that the salicylic acid concentration in the feces of the diarrhea group is significantly higher than that in the healthy group, and the ROC curve analysis confirms that its prediction value is excellent (AUC = 0.8148). In summary, the present application confirms that salicylic acid can be used as a specific biomarker for predicting E. coli calf diarrhea.

[0006] On this basis, the present application provides a reagent for detecting intestinal flora metabolite salicylic acid for preparing an E. coli calf diarrhea prediction or auxiliary diagnosis instrument.

[0007] Preferably, the reagent includes the salicylic acid standard and antibodies required for enzyme-linked immunosorbent assay, and the instrument is an ELISA kit.

[0008] Preferably, the use method of the ELISA kit includes the following steps: 1) Collecting the fecal sample of the calf to be tested; 2) Extracting salicylic acid from the sample; 3) Detecting salicylic acid using enzyme-linked immunosorbent assay; 4) Comparing the detection result with that of a healthy control calf, and if the salicylic acid concentration is significantly increased, it indicates that the calf has E. coli calf diarrhea or has a risk of getting sick.

[0009] Compared with the prior art, the present application has the following remarkable beneficial effects: The present application firstly discovers and verifies that the concentration change of salicylic acid has a specific correlation with calf diarrhea caused by pathogenic E. coli infection, which can effectively distinguish diarrhea caused by other pathogens. As a metabolite, the concentration change of salicylic acid is earlier than the obvious clinical symptoms, which can realize early warning of the disease and win valuable time for intervention treatment. The present application uses mature ELISA technology for detection, which is simple to operate, fast to detect and low in cost, and is very suitable for popularization and use in basic-level farms. By specifically predicting E. coli infection, it can guide farmers to carry out precise antibacterial treatment and avoid antibiotic abuse, which has important significance for containing the development of bacterial drug resistance. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 Figure 1 is a colony morphology diagram of the isolated strain on a MacConkey culture medium.

[0011] Figure 2 Figure 4 is a PCR detection diagram of 16S rRNA of the isolated strain, in which M is DL2000 DNA Marker.

[0012] Figure 3 Figure 5 is a survival curve of mice in the control group (control) and the four E. coli groups (F1, F2, F3, F4).

[0013] Figure 4 Figure 6 is a PCA analysis diagram of the fecal samples of the diarrhea group (F) and the healthy group (J) in positive and negative ion modes.

[0014] Figure 5 Figure 7 is an OPLS-DA score diagram established in positive and negative ion modes of the fecal samples of the diarrhea group (F) and the healthy group (J).

[0015] Figure 6 Figure 8 is a volcano plot of the differential metabolites of the diarrhea group (F) and the healthy group (J).

[0016] Figure 7 Figure 9 is a quantitative identification comparison diagram of the metabolite salicylic acid (SA) of the diarrhea group (F) and the healthy group (J).

[0017] Figure 8 Figure 10 is a comparison diagram of the concentration of the metabolite salicylic acid (SA) in the feces of the diarrhea group (F) and the healthy group (J) detected by ELISA.

[0018] Figure 9 Figure 11 is a ROC curve of the prediction model constructed by the metabolite salicylic acid. DETAILED DESCRIPTION

[0019] The technical solutions of the present application will be further described in detail below in combination with specific embodiments.

[0020] Example 1: Isolation and identification of pathogenic E. coli To identify the pathogen of diarrhea in calves, 6 fresh fecal samples were collected from 2-month-old calves with acute diarrhea in a large-scale breeding farm in Xundian Hui and Yi Autonomous County, Kunming, Yunnan Province.

[0021] 1. Bacterial isolation and culture: inoculate the appropriate amount of fecal sample into general broth medium, incubate at 37℃ for 12 hours, then streak inoculate into MacConkey medium for purification, incubate at 37℃ for 12-24 hours, and observe the typical E. coli colonies with smooth surface, round shape and pink color. Figure 1 )。

[0022] 2. Biochemical identification: pick the single colonies after purification for Gram staining, and observe under microscope as short rod-shaped Gram-negative bacteria. Then use biochemical identification tubes for identification, and the results show that the biochemical characteristics of the isolated strain are consistent with the standard of E. coli (Table 1).

[0023] Table 1: Biochemical identification results of the isolated strain

[0024] Note: + for positive, - for negative.

[0025] 3. 16S rRNA gene identification: extract bacterial genomic DNA, use it as a template, and use 16S rRNA universal primers (upstream: 5'-AGAGTTAGATCCTGGCTCAG-3', downstream: 5'-TACCGCTACCTTGTTACGACTT-3') for PCR amplification, the amplification system is TaqPCR MasterMix 12.5μL, 0.5μL of each upstream and downstream primer, 1μL of DNA template, 10.5μL of ddH2O, the reaction program is 94℃ pre-denaturation for 5min, 94℃ denaturation for 30s, 58℃ annealing for 30s, 72℃ extension for 1min, 35 cycles, 72℃ extension for 10min. The target band of about 1541bp is obtained ( Figure 2 ). The amplification product is recovered by gel recovery kit, then TA cloned, the plasmid after TA cloning is sequenced by Sanger double deoxy chain termination method, and the homologous comparison is carried out in NCBI database after sequencing, which has more than 98% homology with the reference strain of E. coli. Finally, it is confirmed that the isolated strain is E. coli.

[0026] 4. Pathogenicity verification: 4 strains of isolated E. coli (F1-F4) were injected intraperitoneally into healthy female Kunming mice with a body weight of 20-25g (dose 0.5mL, 1×10 5CFU / L bacterial dilution was administered to mice, while the control group received an equal volume of physiological saline for 7 consecutive days. Mice were observed and their condition was recorded, and survival rates were statistically analyzed using GraphpadPrism 6.0 software. Subsequently, antimicrobial susceptibility testing was performed on four strains of *E. coli* according to the standard method recommended by the National Committee on Clinical Clinical Standards (NCCLs), and the results were interpreted. Results showed that all mice in the challenge group died within 16-24 hours; necropsy revealed hemorrhage and enlargement of the liver, spleen, and intestines, and *E. coli* was re-isolated from the diseased organs. All mice in the control group survived without clinical symptoms. Figure 3 Furthermore, the four isolated pathogenic Escherichia coli strains exhibited strong resistance to gentamicin, cefazolin, chloramphenicol, ciprofloxacin, and norfloxacin, but showed moderate sensitivity to amikacin. These results indicate that the isolated Escherichia coli strains are highly pathogenic and multidrug resistant.

[0027] Example 2: Screening and discovery of salicylic acid (SA), a metabolite of gut microbiota To identify potential biomarkers associated with Escherichia coli-induced calf diarrhea, non-targeted metabolomics analysis was performed on six fecal samples from diarrheal calves (Group F) collected in Example 1 and six fecal samples from healthy calves collected during the same period (Group J).

[0028] 1. Sample Pretreatment and LC-MS Detection: Fecal samples were thawed in an ice bath. Metabolites were extracted using pre-cooled methanol solution. After vortexing, grinding, and centrifugation, the supernatant was used for ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) analysis. Data were acquired in both positive and negative ion modes. The electrospray ionization source parameters were: positive ion spray voltage 3.50 kV, negative ion spray voltage -2.50 kV, sheath gas 40 alb, and auxiliary gas 10 alb. The capillary temperature was 325℃, the first-stage full scan resolution was 70,000, and the scan range was 100~1000 m / z. Secondary fragmentation was performed using HCD with a collision energy of 30 eV and a secondary resolution of 17,500. The first 10 ions acquired were fragmented, and unnecessary MS / MS information was removed using dynamic exclusion. The raw data files generated by UPLC-MS / MS were processed using the Proteowizard software package (v3.0.8789), and peak integration and quantification were performed for each metabolite. Systematic errors are eliminated based on QC samples, and substances with an RSD > 30% in the QC samples are filtered out during the quality control and quality assurance process for subsequent data analysis.

[0029] 2. Multivariate statistical analysis: Principal component analysis (PCA) was performed on the obtained metabolomics data. The results showed that the metabolite profiles of the diarrhea group and the healthy group were significantly separated in both positive and negative ion modes. Figure 4 This indicates that there are significant differences in metabolic status between the two groups.

[0030] 3. Screening for differentially expressed metabolites: Further orthogonal partial least squares discriminant analysis (OPLS-DA) was employed. In the OPLS-DA model established under both positive and negative ion modes, all samples were separated. Figure 5 This indicates a significant difference in fecal metabolites between the two groups, and the model data are stable and reliable. Using variable projection importance (VIP) > 1 and P < 0.05 as the criteria, 195 significantly different metabolites were selected. Figure 6 Based on p-values, the top 50 differentially expressed metabolites were selected. Cluster analysis showed that benzene and its substituted derivatives, carboxylic acids and their derivatives, fatty acids, glycerophospholipids, phenols, steroids and their derivatives were upregulated in the diarrhea group and downregulated in the healthy group. Volcano plot analysis revealed that salicylic acid was significantly upregulated in the diarrhea group (P<0.01). Figure 7 Based on this, SA was selected as a potential biomarker for predicting E. coli-induced calf diarrhea.

[0031] 4. Correlation analysis between differentially metabolites and differentially expressed microbiota: 16S rRNA and metabolomics correlation analysis was used to demonstrate the correlation between differentially metabolites and differentially expressed microbiota. The results showed that the metabolite salicylic acid was positively correlated with *Eubacterium* and *Enterococcaceae*. It was also positively correlated with *Bacteroides*, *Solibacillus*, *Oscillospira*, *Prevotellap*, *Parabacteroides*, *Odoribacter*, and *Rumenella*. The genera *Ruminococcus* (family Bacteriaceae), *Phascolarctobacterium*, *Butyricimonas*, *Coprococcus*, *Coprobacillus*, *Barnesiella*, *Alistipes*, *rc4-4*, and *Pseudoramibacter* (family Pseudoramibacteraceae) showed a negative correlation. Among these, *Bacteroides*, *Ruminococcus* (family Bacteriaceae), *Phascolarctobacterium*, and *Butyricimonas* are considered beneficial bacteria in the gut, while *Enterococcus* (family Enterococciaceae) are considered harmful bacteria.

[0032] Example 3: Validation of the predictive ability of salicylic acid To verify the actual predictive efficacy of salicylic acid, nine independent fecal samples were collected from the same farm, including nine samples each from diarrheal calves (identified as pathogenic Escherichia coli infection) and healthy calves.

[0033] Fecal samples were homogenized with PBS at a weight-to-volume ratio of 1:9 and centrifuged at 5000×g for 10 min at 4°C. The supernatant was collected for analysis. The bovine salicylic acid ELISA kit (enzyme immunoassay, Jiangsu, China) was used for detection, and the procedure was performed according to the manufacturer's instructions. Subsequently, the OD values ​​of each standard well and sample well were measured at 450 nm using a microplate reader, with three replicates for each well. A linear regression curve of the standards was plotted in an Excel spreadsheet, with the standard concentration on the x-axis and the corresponding average OD value on the y-axis. The salicylic acid concentration in the fecal samples was calculated using the curve equation. The salicylic acid concentrations of the two groups were statistically analyzed using GraphpadPrism 10.0 software, and the p-value between the groups was calculated using an unpaired t-test. Data are expressed as mean ± standard error (SEM).

[0034] The concentration of salicylic acid in fecal samples from two groups was measured, and it was found that the concentration in the feces of calves in the diarrhea group was significantly higher than that in the healthy group (P<0.05). Figure 8 The experimental data were analyzed using ROC curve prediction, and the area under the curve was 0.8148 ( ). Figure 9 This indicates that salicylic acid, a metabolite of gut microbiota, can serve as a predictive biomarker for Escherichia coli-induced calf diarrhea.

[0035] In summary, these results highlight that salicylic acid, a metabolite of gut microbiota, serves as a predictive biomarker for Escherichia coli-induced calf diarrhea. It can effectively detect calf diarrhea caused by pathogenic Escherichia coli, not only identifying the presence of the pathogen but also indicating its multidrug resistance. Furthermore, elevated fecal salicylic acid concentrations can simultaneously reflect gut microbiota dysbiosis symptoms often associated with diarrhea, which is of great significance for guiding clinical medication and timely intervention to reduce calf mortality and long-term growth loss.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0037] Appendix: Explanation of Key Terms: Gut microbiota: refers to the microbial community living in the intestines of animals, including bacteria, fungi, viruses, etc. They participate in a variety of physiological processes such as digestion, immune regulation, and vitamin synthesis.

[0038] Metabolites: Small molecule compounds produced during metabolism in an organism, such as amino acids, organic acids, and sugars. The types and concentrations of metabolites can reflect the physiological or disease state of an organism.

[0039] Escherichia coli-induced calf diarrhea: Calf diarrhea caused by pathogenic Escherichia coli infection, commonly seen in newborn calves. Symptoms include watery diarrhea, dehydration, and exhaustion, and can lead to death in severe cases.

[0040] Untargeted metabolomics: a technique for comprehensively analyzing all small molecule metabolites in biological samples without pre-setting target substances, used to discover potential biomarkers related to physiological or pathological states.

[0041] ROC curve: Receiver Operating Characteristic curve, used to evaluate the performance of diagnostic or predictive models.

[0042] AUC: Area Under the Curve. The closer the value is to 1, the stronger the predictive ability of the model. AUC = 0.8148 indicates that the model has good predictive accuracy.

[0043] PCA: Principal Component Analysis, used for dimensionality reduction and visualization of overall differences between samples.

[0044] OPLS-DA: Orthogonal Partial Least Squares Discriminant Analysis, focuses more on finding differential variables between groups and is often used in metabolomics to screen biomarkers.

[0045] VIP value: Variable projection importance, used to measure the contribution of a variable in the model. VIP>1 is generally considered to mean that the variable has a significant impact on grouping.

[0046] 16S rRNA gene sequencing: By sequencing the bacterial 16S rRNA gene, the species and community composition of microorganisms can be identified, and it is often used for intestinal flora analysis.

[0047] Biomarkers are biomolecules that can indicate physiological states, pathological processes, or treatment responses. In this invention, salicylic acid is used as a biomarker for predicting calf diarrhea.

Claims

1. The application of salicylic acid, a metabolite of intestinal flora, as a predictive biomarker for Escherichia coli-induced calf diarrhea, characterized by: By detecting salicylic acid in the feces of test samples, it can be determined whether the calf has E. coli-induced calf diarrhea or is at risk of developing the disease.

2. The application according to claim 1, characterized in that: Salicylic acid was detected by enzyme-linked immunosorbent assay (ELISA).

3. The application according to claim 1, characterized in that, The criterion for judgment is: if the concentration of salicylic acid in the feces of the test sample is significantly higher than that of the healthy control calf, it suggests that the calf has Escherichia coli-induced calf diarrhea or is at risk of developing the disease.

4. Application of reagents for detecting salicylic acid, a metabolite of intestinal flora, in the preparation of instruments for predicting or assisting in the diagnosis of Escherichia coli-induced calf diarrhea.

5. The application according to claim 4, characterized in that: The reagents include salicylic acid standards and antibodies required for enzyme-linked immunosorbent assay (ELISA), and the instrument is an ELISA kit.

6. The application according to claim 5, characterized in that, The method of using the ELISA kit includes the following steps: 1) Collect fecal samples from the calves to be tested; 2) Extract salicylic acid from the sample; 3) Salicylic acid was detected using an enzyme-linked immunosorbent assay (ELISA). 4) Compare the test results with those of healthy control calves. If the salicylic acid concentration is significantly increased, it indicates that the calves have E. coli-induced diarrhea or are at risk of developing the disease.

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