Fecal flora marker related to lignified pectoral muscle of broiler chicken, product and application of fecal flora marker

Abstract

The invention belongs to the technical field of biomedicine, and particularly relates to a fecal flora marker related to lignified pectoral muscles of broilers, a product and application of the fecal flora marker. The fecal flora marker comprises Lactobacillus crispatus (Lactobacillus crispatus) and / or Lactobacillus gallinarum (Lactobacillus gallinarum), and the fecal flora marker comprises Lactobacillus crispatus (Lactobacillus crispatus) and / or Lactobacillus gallinarum (Lactobacillus gallinarum). Based on the fecal flora markers, the invention also develops products for screening and diagnosing lignified pectoral muscles of broilers. The product can realize in-vitro noninvasive prediction or diagnosis of the lignified pectoralis muscle of the broiler chicken, has high feasibility and accuracy, can effectively evaluate the onset risk of the lignified pectoralis muscle of the broiler chicken, provides a brand new tool for diagnosis, and is expected to play an important role in health management and disease prevention and control of the broiler chicken.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedical technology, and particularly relates to a fecal bacterial flora marker related to broiler wood breast, a product and application thereof. BACKGROUND

[0002] Wood breast (WB) is an abnormal meat problem that occurs simultaneously with the rapid growth of broilers and the demand for high meat yield, and was officially proposed in 2014. The muscle hardness of wood breast is significantly increased, and the surface of severe individuals is in the form of wood texture, and even bleeding and sticky secretions occur, which seriously affects the appearance and quality of the product, leading to consumer rejection. The incidence of wood breast is extremely high, and studies have shown that the incidence of 9-week-old broilers can reach 85%, of which nearly half are severe or extremely severe, resulting in a large number of chicken carcasses being discarded due to substandard quality. At the same time, the disease is more common in broilers approaching the market weight (5-6 weeks of age and above), especially in faster-growing, larger-chest male chickens, and therefore, wood breast has become an important problem that needs to be solved in the chicken meat industry, posing a serious challenge to the sustainable development of the entire industry chain.

[0003] The current diagnostic technology for lignified breast muscle of broilers is mainly based on the following three methods: (1) Near-infrared spectroscopy: This technology is based on molecular vibration theory. By detecting the summed frequency and harmonic absorption spectra generated by chemical bonds such as CH, NH, and OH in the near-infrared band (780-2500nm), a quantitative relationship model between material components and characteristic absorption peaks is established. By utilizing the specific absorption characteristics of the functional groups of the material, non-destructive detection of the water, protein and fat content of muscle tissue can be achieved. Although it has the advantages of high instrument convenience and real-time online analysis, its local sampling method leads to a single detection site, which is prone to systematic errors in heterogeneous samples. (2) Digital image processing technology: Photoelectric sensors are used to simulate the visual function of the human eye. Through image digitization processing combined with computer pattern recognition algorithms, objective quantitative analysis of sample morphological parameters (geometric characteristics, color distribution, etc.) is achieved. This method overcomes the subjective bias of manual interpretation, but has two limitations in practical applications: first, factors such as lighting conditions, imaging system resolution and background noise significantly affect the measurement accuracy; second, it can only obtain information on apparent physical properties and cannot analyze the chemical composition characteristics of muscle tissue. (3) Hyperspectral fusion detection technology: Integrating the advantages of spectral analysis and digital imaging technology, by acquiring three-dimensional hypercube data (x, y, λ) composed of two-dimensional spatial information and continuous spectral information, the spatial distribution analysis of the sample's physical morphology and chemical components is achieved simultaneously. Although this technology has the characteristics of spectrum fusion detection and high spatial resolution, it is still difficult to meet the needs of industrial online detection due to factors such as the environmental sensitivity of the precision optical system, the complexity of high-dimensional data dimensionality reduction processing, and the volume and cost of the equipment. Therefore, although these methods have their own advantages, they all have limitations in practical applications and are difficult to fully meet the requirements of rapid, accurate, and efficient detection of lignified breast muscle in industrial production. Summary of the Invention

[0004] To address the above technical issues, the present invention provides a fecal microbiome marker, product, and application associated with lignified breast muscle in broiler chickens. Research has revealed a significant association between Lactobacillus crispatus and Lactobacillus gallinarum and lignified breast muscle in broiler chickens. Receiver-operating characteristic (ROC) curve analysis demonstrates that these two markers possess high specificity and sensitivity as detection variables. Therefore, these two bacterial species can be used as detection markers for the prediction and diagnosis of lignified breast muscle in chickens, providing a new approach and method for the diagnosis and treatment of lignified breast muscle in broiler chickens.

[0005] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a fecal flora marker associated with lignified breast muscle of broilers, wherein the fecal flora marker includes Lactobacillus crispatus ( Lactobacillus crispatus ) and / or Lactobacillus gallinarum ( Lactobacillus gallinarum ).

[0006] Preferably, the fecal microbiota marker is Lactobacillus crispatus and Lactobacillus gallinaceus.

[0007] In a second aspect, the present application also provides a use of a reagent for detecting the above-mentioned fecal microbiota marker in the preparation of a diagnostic or screening product for broiler chicken xanthomatous breast muscle.

[0008] In a third aspect, the present application also provides a kit comprising a reagent for detecting the above-mentioned fecal microbiota marker.

[0009] Preferably, the kit comprises primers specific to the above-mentioned fecal microbiota marker.

[0010] In a fourth aspect, the present application also provides a use of the above-mentioned kit in the preparation of a diagnostic or screening product for broiler chicken xanthomatous breast muscle.

[0011] In a fifth aspect, the present application also provides a computer program product related to broiler chicken xanthomatous breast muscle, which is used to diagnose the risk of a broiler chicken having xanthomatous breast muscle, comprising the following steps: S1: obtaining the relative abundance values of Lactobacillus crispatus and / or Lactobacillus gallinaceus in the feces of the broiler chicken to be tested; S2: calculating the first probability value Y of the broiler chicken to be tested according to a binary logistic regression equation; S3: calculating the probability P value of the broiler chicken to be tested being a normal broiler chicken according to Y, wherein P is the probability value of the broiler chicken to be tested being a normal broiler chicken, e Y is the natural exponential function of Y; S4: diagnosing or predicting whether the broiler chicken to be tested has xanthomatous breast muscle or has the risk of having xanthomatous breast muscle according to the comparison result of the probability value P and a reference value.

[0012] Preferably, S1 is obtaining the relative abundance values of Lactobacillus crispatus and Lactobacillus gallinaceus in the feces of the broiler chicken to be tested; and the formula of the binary logistic regression equation in S2 is: Y = A + B1X 1+ B2X2; wherein A is the intercept term, B1 and B2 are the regression coefficients of the independent variables; and X1 and X2 are the relative abundance values of Lactobacillus crispatus and Lactobacillus gallinaceus.

[0013] More preferably, A is 2.1312, B1 is -16.5027, and B2 is -34.8801.

[0014] ​Preferably, when the probability P value in S4 is greater than or equal to 0.8, it is judged that the broiler chicken to be tested does not have or has a low risk of having a wooden breast; when the probability P value is less than or equal to 0.1, it is judged that the broiler chicken to be tested has or has a high risk of having a wooden breast.

[0015] Advantages of the present application: The present application finds that Lactobacillus crispatus and Lactobacillus gallinaceus are associated with broiler chicken wooden breast as biomarkers, and when a single strain is predicted, Lactobacillus crispatus has the best single strain prediction effect on broiler chicken wooden breast, and Lactobacillus gallinaceus has the second best single strain prediction effect, but the combined prediction effect of the two bacteria is the best. Through ROC curve analysis, the above two markers have high specificity and sensitivity as detection variables, therefore, Lactobacillus crispatus and Lactobacillus gallinaceus, especially the combination of the two, can be used as effective biomarkers for predicting and diagnosing broiler chicken wooden breast. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 Figure for linear discriminant analysis result of fecal flora in Example 1; Figure 2 Box scatter plot of fecal flora markers in Example 1; Figure 3 ROC curve in Example 2. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application more clear, the following will further describe the present application in combination with specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

[0019] Example 1 In order to evaluate whether the composition of fecal flora can be used as a predictor of broiler chicken wooden breast, the present application collects samples of broiler chickens with wooden breast and normal broiler chickens, performs metagenomic sequencing and uses bioinformatics to statistically analyze the sequencing data, finds fecal flora associated with wooden breast, integrates the fecal flora with disease information, and maximizes the prediction of the occurrence of broiler chicken wooden breast.

[0020] 1. Sample collection Normal and wooden breast (WB) broilers (age 40-42 d) were selected from a large broiler farm in China. The broilers were first evaluated for WB by palpation of both sides of the breast muscle and by observing the broiler's stance and wing flapping. Broilers with no increased firmness in the breast region were considered normal, and those with localized or extensive areas of palpable firmness were considered to have WB. Based on these criteria, suspected WB and normal broilers were selected and labeled, and their weights were recorded. The broilers were then bled via the jugular vein and killed, and their feces were collected from the cloaca. Fresh feces were immediately transferred to sterile 5 mL centrifuge tubes and stored at -80°C for later use.

[0021] Subsequently, the macroscopic lesion characteristics of the right pectoral muscle of all broilers were observed by naked eye, and the degree of hardness (WB) was assessed subjectively based on tactile evaluation according to the criteria of Tijare et al. (2015), as follows: 0 = the whole breast muscle is always flexible (normal), 1 = the breast muscle is mainly hardened in the head region, but is flexible in other parts (mild); 2 = the breast muscle is generally hardened, but is flexible in the middle to tail region (moderate); 3 = the breast muscle is extremely hard and stiff from the head region to the tail tip (severe). Normal broiler groups (no obvious lesions or hardened parts) and wooden breast broiler groups (the whole breast muscle is hard to the touch, accompanied by the appearance of blood clots, viscous liquid and white lines, etc.) were further determined by evaluation. The feces of broilers with a wooden breast muscle degree of 0 were labeled as the normal broiler group (CON), and the feces of broilers with a wooden breast muscle degree of 2 or 3 were labeled as the wooden breast broiler group (WB).

[0022] 2. DNA extraction, library construction and sequencing Total microbial DNA was extracted from fecal samples using the FastDNA Spin for Stool Kit (MP Biomedicals, California, USA) according to the manufacturer’s instructions. The quality and concentration of the extracted DNA were evaluated by agarose gel electrophoresis (1.5%) and spectrophotometry. All extracted DNA was stored at -20 °C for further applications. A total of 1 μg of DNA was used for each sample as input material for library preparation. Sequencing libraries were generated using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (NEB, USA) following the manufacturer’s recommendations and adding index codes to help identify each sample. Briefly, DNA samples were fragmented by sonication to a size of 350 bp, then DNA fragments were end- polished, A-tailed, and ligated to full-length adapters for Illumina sequencing and further PCR amplification. After PCR product purification by the AMPure XP system, library size distribution was analyzed using an Agilent 2100 Bioanalyzer. Macrobiome sequencing was performed using the Illumina NovaSeq 6000 platform. One sample containing sterile water was used as a control for macrobiome library preparation and sequencing. After sequencing, low-quality raw data (phred score lower than 30, containing ambiguous bases, and sequence length shorter than 150 bp) was removed using the NGS QC Toolkit, and raw data was generated for further bioinformatics analysis.

[0023] 3. LEfSe analysis to screen biomarkers In the samples of Example 1, as shown in Table 1, 80% of the samples were randomly selected as the training set, and the remaining 20% of the samples were used as the validation set.

[0024] Table 1 Sample information table

[0025] The gene set was aligned with the database using BLASTP (BLAST Version 2.8.1) (BLAST alignment parameter settings e-value 1e-5), and species annotation information at each taxonomic level of Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species was obtained, and then the abundance of the species was calculated using the sum of the gene abundance corresponding to the species. The abundance data of each sample in the training set was analyzed using the LEfSe software, and the default setting of the LDA Score screening value was 3, and the results are shown in Table 2. Figure 1

[0026] ​Three microorganisms were screened out from the feces of wood breast chicken, which were Lactobacillus crispatus, Lactobacillus gallinaceus and Lactobacillus sp930989465. Lactobacillus sp930989465 Lactobacillus sp930989465 belongs to a microorganism not included in the database, so it is not used as a biomarker for research. Box scatter plots were used to compare the relative abundance of Lactobacillus crispatus and Lactobacillus gallinaceus in the feces of normal and wood breast chicken, as shown in Figure 2 It can be seen that the relative abundance of Lactobacillus crispatus and Lactobacillus gallinaceus in the feces of wood breast chicken is significantly higher than that of normal chicken, and the relative abundance of the two bacteria in the two types of chicken is significantly different.

[0027] Example 2 Construction and verification of binary regression equation 1. Construction of binary regression equation Based on the above-mentioned biomarkers, the binary logistic regression algorithm in the SPSS software was used to calculate the probability of each sample predicting that the chicken had wood breast or was normal, which facilitated the subsequent evaluation of the performance of the binary regression model of each bacterium.

[0028] A binary logistic regression equation for the relative abundance of the mimic marker (two bacteria combined) was constructed, and the formula of the binary logistic regression equation was: Y=A+B1X 1+ B2X2; In the formula, A is the intercept term, B1 and B2 are the regression coefficients of the independent variables; X1 and X2 are the relative abundance values of Lactobacillus crispatus and Lactobacillus gallinaceus.

[0029] Through data fitting, the values of each variable were obtained, and the following formula was obtained: Y=2.1312-16.5027X1-34.8801X2; ; In the formula, Y is the first probability value of the tested chicken, and P is the probability value of the tested chicken being a normal chicken.

[0030] 2. Verification of binary logistic regression equation Based on the data of the verification set, the relative abundance of the mimic marker (two bacteria combined) in each sample of the wood breast chicken group and the normal chicken group was calculated, and then the first probability value Y of the tested chicken was obtained using the aforementioned binary logistic regression equation. The probability of the tested chicken being a normal chicken was calculated, and the results are shown in Table 2. The relative abundance data of the verification set marker were statistically analyzed, and the results are shown in Table 3.

[0031] Table 2 Relative data of the verification set marker

[0032] From Table 2, the binary logistic regression model uses the relative abundance of L. crispatus and L. gallinaceum as mimic markers, and has strong ability to distinguish between normal and woody breast broilers. The predicted probability value (P) of normal broiler group (label 1) is higher than 0.879, while the P value of woody breast broiler group (label 0) is lower than 0.0686, and the probability value of the two groups is clear without overlap. The prediction accuracy of 11 samples in the validation set is 100%, which confirms that the model has high reliability and application potential.

[0033] Table 3. Statistical data of relative abundance of markers in the validation set

[0034] Note: In Table 3, the mean refers to the relative abundance mean, and the standard deviation is the same Table 3 shows the relative abundance mean and standard deviation of each strain. The relative abundance mean determines the center position of the data distribution, and the standard deviation reflects the dispersion of the data relative to the mean. The q value is calculated using the formula of rank sum test. The lower the q value, the greater the difference between the woody breast broiler group and the normal broiler group. From the data in the table, it can be seen that L. crispatus and L. gallinaceum are significantly enriched in woody breast broilers, and the difference in abundance is statistically significant (q≈0.011). This confirms the reliability of the two as a powerful combined marker for distinguishing between woody breast broilers and normal broilers from a population statistics perspective.

[0035] 3. Verification results Based on the overall data, the receiver operating characteristic curve (ROC curve) analysis was performed to obtain the cutoff value (best cutoff value). The specificity and sensitivity were calculated and the ROC curve was drawn using IBM SPSS Statistics (v27) statistical software, and the results are shown in Figure 3 and Table 4. The software first calculates the threshold value of the actual measurement value, and then calculates the true positive number (TP), false positive number (FP), true negative number (TN), and false negative number (FN) corresponding to the threshold value. The specificity (true negative rate) = TN / (TN+FP), the sensitivity (true positive rate) = TP / (TP+FN), and the ROC curve can be constructed by 1-specificity and sensitivity. The integral of the ROC curve is the area under the curve (AUC). In order to calculate the specificity and sensitivity of a certain index, the first calculates the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum Youden coefficient are the specificity and sensitivity of a certain index.

[0036] Table 4. ROC diagnostic curve results

[0037] The above results show that the AUC is greater than 85%, indicating that L. crispatus and L. gallinaceum can be used as markers for meat chicken xanthic breast muscle. Among them, L. crispatus has the best single-bacterial prediction effect on xanthic breast muscle, and L. gallinaceum has the second best single-bacterial prediction effect. The AUC of the mimic marker (combination of two bacteria) for predicting meat chicken xanthic breast muscle is 90.2%, the sensitivity is 0.8519, and the specificity is 0.9655. The prediction accuracy is higher than that of a single bacterium, and it has good feasibility and accuracy. The optimal cutoff value of the mimic marker is 0.643, and the larger the P value calculated by the formula, the higher the probability of normal meat chicken.

[0038] Example 3 The present embodiment provides a method for diagnosing or predicting whether a test meat chicken has xanthic breast muscle or is at risk of having xanthic breast muscle, which comprises the following steps: 1) obtaining the relative abundance values of L. crispatus and L. gallinaceum in the test meat chicken.

[0039] 2) calculating the first probability value Y of the test meat chicken according to the binary logistic regression equation Y = 2.1312 - 16.50271X1 - 34.8801X2; wherein X1 is the relative abundance value of L. crispatus, and X2 is the relative abundance value of L. gallinaceum.

[0040] 3) calculating the probability P value of the test meat chicken being a normal meat chicken according to Y, ; wherein P is the probability value of the test meat chicken being a normal meat chicken, e Y is the natural exponential function of Y.

[0041] 4) diagnosing or predicting whether the test meat chicken has xanthic breast muscle or is at risk of having xanthic breast muscle according to the comparison result of the probability value P and the reference value.

[0042] Example 4 The present embodiment provides a computer program for predicting whether a test meat chicken has xanthic breast muscle or is at risk of having xanthic breast muscle, which is used to execute the method for predicting whether a test meat chicken has xanthic breast muscle or is at risk of having xanthic breast muscle, comprising the following steps: 1) obtaining the relative abundance values of L. crispatus and L. gallinaceum in the test meat chicken.

[0043] 2) calculating the first probability value Y of the test meat chicken according to the binary logistic regression equation Y = 2.1312 - 16.50271X1 - 34.8801X2; wherein X1 is the relative abundance value of L. crispatus, and X2 is the relative abundance value of L. gallinaceum.

[0044] 3) calculating the probability P value of the test meat chicken being a normal meat chicken according to Y, ; Where, P is the probability value that the broiler chicken to be tested is a normal broiler chicken, e Y is the natural exponential function of Y.

[0045] 4) Predicting the risk of the broiler to be tested having lignified breast muscle based on a comparison of the probability P value of a normal broiler with a reference value.

[0046] In actual work, when the P value is greater than 0.8, it means that the probability of the broiler chickens being tested having lignified breast muscles is small; when the P value is less than 0.1, it means that the probability of the broiler chickens being tested having lignified breast muscles is high.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fecal flora marker associated with lignified breast muscle of broiler chickens, characterized in that: The fecal flora markers include Lactobacillus crispatus ( Lactobacillus crispatus ) and / or Lactobacillus gallinarum ( Lactobacillus gallinarum ).

2. The fecal flora marker according to claim 1, characterized in that The fecal flora markers are Lactobacillus crispatus and Lactobacillus gallinarum.

3. Use of a reagent for detecting the fecal flora marker according to claim 1 in preparing a product for diagnosing or screening lignified breast muscle of broilers.

4. A kit, characterized in that The kit comprises a reagent for detecting the fecal flora marker according to claim 1 or 2.

5. The kit according to claim 4, characterized in that The kit comprises primers specific to the fecal flora markers according to claim 1 or 2.

6. Use of the kit according to claim 4 or 5 in preparing a product for detecting or diagnosing lignified breast muscle of broilers.

7. A computer program product related to lignified breast muscle of broiler chickens, characterized in that: The computer program product is used to execute a procedure for diagnosing or predicting whether a broiler chicken to be tested has lignified breast muscle or is at risk of having lignified breast muscle, comprising the following steps: S1: Obtaining the relative abundance values ​​of Lactobacillus crispatus and / or Lactobacillus gallinarum in the feces of the broiler chickens to be tested; S2: Calculate the first probability value Y of the broiler to be tested according to the binary logistic regression equation; S3: Calculate the probability P value that the broiler chicken to be tested is a normal broiler chicken based on Y, ; Where, P is the probability value that the broiler chicken to be tested is a normal broiler chicken; e Y is the natural exponential function of Y; S4: Based on the comparison result of the probability P value and the reference value, diagnose or predict whether the broiler to be tested suffers from lignified breast muscle or is at risk of suffering from lignified breast muscle.

8. The computer program product according to claim 7, wherein: The S1 is to obtain the relative abundance values ​​of Lactobacillus crispatus and Lactobacillus gallinarum in the broiler feces to be tested; the formula of the binary logistic regression equation in the S2 is: Y=A+B1X 1+ B2X2; Where A is the intercept term, B1 and B2 are the regression coefficients of the independent variables; X1 and X2 are the relative abundance values ​​of Lactobacillus crispatus and Lactobacillus gallinarum.

9. The computer program product according to claim 8, wherein A is 2.1312, B1 is -16.5027, and B2 is -34.8801.

10. The computer program product according to claim 7, wherein In S4, when the probability P value is greater than or equal to 0.8, it is judged that the broiler to be tested does not have lignified breast muscles or has a low risk of having lignified breast muscles; when the probability P value is less than or equal to 0.1, it is judged that the broiler to be tested has lignified breast muscles or has a high risk of having lignified breast muscles.

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