Screening method and application of bone abnormal serum marker metabolite of broiler chicken

Abstract

The invention relates to the field of animal nutrition and metabolism, and discloses a screening method and application of broiler chicken bone abnormality serum marker metabolites, and the screening method comprises the following steps: performing preliminary grouping based on a serum calcium-phosphorus product value, performing multi-dimensional phenotypic verification in combination with bone geometric parameters, bone mineralization indexes and serum bone metabolism markers, constructing a bone abnormality model, and performing screening on the bone abnormality model; differential metabolites related to bone abnormality are screened and determined through non-targeted and targeted metabonomics technologies, classification performance of the markers is evaluated by utilizing a machine learning model, and fumaric acid and malic acid are determined to serve as serum markers for evaluating bone health of broilers through the method. The screening method provided by the invention is systematic and reliable, the obtained biomarker is closely related to an energy metabolism disorder mechanism of skeleton abnormality, the classification performance is high, the biomarker is applied to a feed additive, the skeletal development and mineralization level of broiler chickens can be effectively improved, and the screening method has important application value.

Description

Technical Field

[0001] This invention relates to the field of animal nutrition and metabolism, specifically to a method and application for screening serum marker metabolites of skeletal abnormalities in broilers. Background Technology

[0002] The broiler industry is an important part of modern animal husbandry, characterized by its short growth cycle and high feed conversion rate. However, in pursuit of the ultimate growth rate, intensive selective breeding often leads to skeletal development lagging behind muscle tissue growth. This makes broilers, especially fast-growing breeds, highly susceptible to skeletal developmental abnormalities such as tibialis chondrogenesis imperfecta and rickets. These skeletal problems not only cause significant economic losses but also raise widespread concerns about animal welfare.

[0003] Currently, the assessment of broiler bone health mainly relies on clinical symptom observation, anatomical examination, or measurement of single biochemical indicators such as serum calcium and phosphorus. However, these methods have limitations. Clinical symptoms often appear in the middle or late stages, while anatomical examination is delayed and potentially damaging. Relying solely on a few indicators such as serum calcium and phosphorus lacks sufficient accuracy and specificity due to their susceptibility to various factors, making it difficult to comprehensively reflect the complex pathophysiological state of bone development. Therefore, existing methods for screening early warning biomarkers for broiler bone abnormalities are not systematic enough, and the accuracy of the animal models used is insufficient, making it difficult to discover and confirm biomarkers that objectively reflect the true health status of bones.

[0004] The lack of systematic screening methods has directly resulted in a current shortage of highly specific and stable serum biomarkers for the early and rapid diagnosis of skeletal abnormalities in broilers. Although some studies have explored changes in certain enzymes or proteins, there is still a lack of molecular markers that can directly reflect core metabolic disorders in skeletal development and are easy to detect on a large scale in production. This makes dynamic and non-invasive monitoring of skeletal health in broiler flocks difficult to achieve, limiting the improvement of precision feeding management.

[0005] In terms of nutritional regulation, current preventative strategies mostly focus on adjusting the ratio of traditional nutrients such as calcium, phosphorus, and vitamin D in feed. While these measures have some effect, they often fail to fundamentally address the metabolic imbalances caused by rapid growth. Currently, there is a lack of research into developing novel functional feed additives based on the intrinsic molecular mechanisms of abnormal bone development, particularly from the perspective of core pathways such as energy metabolism. Therefore, there is an urgent need to find substances that can directly participate in and improve bone cell metabolism and apply them to broiler farming in order to improve bone health from the source. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and application for screening serum marker metabolites of broiler skeletal abnormalities. This method solves the problems of insufficient systematicity and accuracy in existing methods for screening broiler skeletal abnormality markers, resulting in a lack of specific markers for early diagnosis, and a lack of effective nutritional interventions based on metabolic regulation mechanisms.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for screening serum marker metabolites of skeletal abnormalities in broilers and its application, comprising the following steps:

[0008] S1. Sample collection and preparation: Blood was collected from the wing veins of broilers from the same batch and under the same feeding conditions, and serum was prepared.

[0009] S2. Construction and grouping of skeletal abnormality model: Based on the product of calcium and phosphorus concentrations in the serum, broilers were divided into a normal skeletal group and a skeletal abnormality group.

[0010] S3. Skeletal phenotype verification: Phenotypic verification of the skeletons of broilers in the normal skeleton group and the abnormal skeleton group;

[0011] S4. Serum non-target metabolomics analysis: Serum samples from the normal skeletal group and the abnormal skeletal group described in step S1 were subjected to non-target metabolomics analysis to screen for differential metabolites and differential metabolic pathways.

[0012] S5. Targeted metabolomics validation: Based on the differential metabolic pathways screened in step S4, MRM targeted quantification of energy metabolism-related metabolites was performed, and target metabolites were preliminarily screened.

[0013] S6. Biomarker identification: The target metabolites initially screened in step S5 are classified and evaluated using a machine learning model to ultimately identify serum biomarker metabolites for broiler bone abnormalities.

[0014] Preferably, the specific process of sample collection and preparation is as follows: the collected blood is left to stand at 20-25℃ for 30-40 minutes, then centrifuged at 2800-3200×g for 9-11 minutes at 2-8℃ to separate the serum, and the separated serum is stored at -75℃ to -85℃ ultra-low temperature.

[0015] Preferably, the product of calcium and phosphorus concentrations in the normal bone group is greater than 40 mg. 2 / dL 2 The product of calcium and phosphorus concentrations in the skeletal abnormality group is less than 30 mg. 2 / dL 2 .

[0016] Preferably, the bone phenotype verification includes at least one of bone geometric parameters, bone mineralization indices, bone microstructure parameters, and serum bone metabolism markers.

[0017] Preferably, the specific procedure for the serum non-target metabolomics analysis is as follows: 50-60 μL of serum sample is mixed with 200-220 μL of methanol at 2-8℃ and incubated for 14-16 minutes. The mixture is then centrifuged at 13500-14500 rpm for 14-16 minutes at 2-8℃. The supernatant is filtered through a 0.20-0.25 μm microporous membrane and then detected by UPLC-QTOF / MS. The UPLC-QTOF / MS detection uses an ACQUITY UPLCA HSST3 column with a flow rate of 0.28-0.32 mL / min, a column temperature of 38-42℃, and an injection volume of 1.8-2.2 μL. The methanol contains the internal standard 2-amino-3-(2-chlorophenyl)-propionic acid.

[0018] Preferably, the mass spectrometer detector used for UPLC-QTOF / MS detection is a Thermo Orbitrap Exploris 120 mass spectrometer detector, and data is acquired using an electrospray ionization source in both positive and negative ion modes.

[0019] Preferably, the energy metabolism-related metabolites include at least one of citric acid, α-ketoglutarate, fumaric acid, L-malic acid, and succinic acid, and the target metabolites initially screened are at least two of fumaric acid, L-malic acid, and succinic acid.

[0020] Preferably, the machine learning model includes at least one of K-nearest neighbor, random forest, support vector machine, Gaussian Naive Bayes, logistic regression and decision tree, and the finally determined serum marker metabolites of broiler bone abnormalities are fumaric acid and L-malic acid.

[0021] The application of a serum marker metabolite for broiler bone abnormalities, specifically the application of at least one of L-malic acid and fumaric acid in the preparation of a feed additive for improving broiler bone health.

[0022] Preferably, the feed additive is prepared by adding 0.03%-0.7% (m / m) of L-malic acid or 0.1%-0.7% (m / m) of fumaric acid to the basal diet of broilers and feeding them for 18-25 days.

[0023] This invention provides a method and application for screening serum marker metabolites of skeletal abnormalities in broilers. It has the following beneficial effects:

[0024] 1. This invention provides a method for screening biomarkers of broiler skeletal abnormalities based on serum metabolomics. This method groups skeletal abnormalities using precise serum calcium-phosphorus product values ​​and combines this with comprehensive skeletal phenotypic validation to ensure the accuracy of the grouping. Subsequently, preliminary screening of differential metabolites and pathways is performed using non-targeted serum metabolomics, followed by precise quantitative validation of energy metabolism-related metabolites using targeted metabolomics. Finally, the screening results are evaluated using multiple machine learning models. This multi-level, systematic screening process can objectively identify serum metabolites associated with broiler skeletal abnormalities, providing specific and quantifiable biomarkers for the assessment and diagnosis of broiler skeletal abnormalities.

[0025] 2. This invention identifies fumaric acid and L-malic acid as serum marker metabolites for broiler skeletal abnormalities. These metabolites are key components in energy metabolism pathways such as the tricarboxylic acid cycle and glycolysis. By detecting the levels of these specific metabolites, an objective assessment of the skeletal health status of broilers can be achieved. This feature provides molecular-level indicators for the early detection and rapid diagnosis of broiler skeletal abnormalities, and improves the accuracy and operability of the diagnosis.

[0026] 3. This invention verifies the application of L-malic acid and fumaric acid in improving broiler bone health. After identifying these metabolites as biomarkers through the screening method, this invention further verifies through animal experiments that adding specific concentrations of L-malic acid or fumaric acid to the basal diet of broilers can affect the skeletal geometric parameters and bone mineralization indicators of broilers. This feature provides a concrete material basis and theoretical foundation for developing novel feed additives to improve broiler bone health, and has potential application value. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The main raw materials and reagents used in the following examples and comparative examples have the following sources and specifications. Reagents not specifically mentioned are all commercially available analytical grade or higher grade products.

[0029] L-malic acid, CAS No.: 97-67-6;

[0030] Fumaric acid, CAS No.: 110-17-8;

[0031] Methanol, CAS No.: 67-56-1;

[0032] 2-Amino-3-(2-chlorophenyl)-propionic acid, CAS No.: 14091-11-3;

[0033] Citric acid, CAS No.: 77-92-9;

[0034] α-Ketoglutaric acid, CAS No.: 328-50-7;

[0035] Succinic acid, CAS No.: 110-15-6;

[0036] Sodium chloride, CAS No.: 7647-14-5.

[0037] Examples 1-4:

[0038] Example 1: Screening of serum marker metabolites for skeletal abnormalities in broilers

[0039] This embodiment aims to screen serum marker metabolites of skeletal abnormalities in broilers using a systematic approach:

[0040] S1. Sample Collection and Preparation: Healthy 1-day-old Jinling Yellow-feathered broiler chicks from the same batch and under the same feeding conditions were selected for rearing. Blood was collected from the wing veins of the broilers at 70 days of age. The collected blood was allowed to stand at 20-25℃ for 30-40 minutes, and then centrifuged at 2800-3200×g for 9-11 minutes at 2-8℃. The supernatant was separated as serum, which was aliquoted and stored at -75℃ to -85℃.

[0041] S2. Construction and Grouping of Skeletal Abnormality Model: Based on the product of serum calcium and phosphorus concentrations obtained in step S1, broilers were divided into a normal skeletal group and a skeletal abnormality group. The product of calcium and phosphorus concentrations in the normal skeletal group was greater than 40 mg / L. 2 / dL 2 The product of calcium and phosphorus concentrations in the skeletal abnormality group was less than 30 mg. 2 / dL 2 .

[0042] S3. Skeletal phenotypic verification: Phenotypic verification of the skeletons of broilers in the normal and abnormal skeleton groups in step S2 is performed to confirm the validity of the grouping.

[0043] Skeletal geometric parameters were measured: the bone weight, bone length, proximal width, and distal width of the tibia of broilers were measured, and the relative wall thickness of the cortical bone, vertical cortical index, moment of inertia of the cross section, and cross-sectional area were calculated based on the measured values.

[0044] Bone mineralization index determination: Determine the bone ash, bone calcium and bone phosphorus content of the tibia.

[0045] Serum bone metabolism marker assay: The levels of ALPL, PINP, TRAP and β-CTX in serum were measured.

[0046] S4. Serum non-target metabolomics analysis: Serum samples from the normal skeletal group and the abnormal skeletal group obtained in step S1 were subjected to non-target metabolomics analysis to screen for differential metabolites and differential metabolic pathways.

[0047] Sample pretreatment: Take 50 μL of serum sample, add 200 μL of methanol, mix and incubate the mixture at 2-8℃ for 15 minutes, then centrifuge at 14000 rpm for 15 minutes at 2-8℃. Take the supernatant, filter it through a 0.22 μm microporous membrane, and transfer the filtrate to an LC-MS detection bottle.

[0048] UPLC-QTOF / MS detection: Chromatographic separation was performed using an ACQUITY UPLCA HSST3 column at a flow rate of 0.3 mL / min and a column temperature of 40℃. The injection volume was 2 μL. Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was 0.1% formic acid acetonitrile solution. The gradient elution program was as follows: 0–1 min, 5% B; 1–10 min, 5%–95% B; 10–12 min, 95% B; 12–12.1 min, 95%–5% B; 12.1–15 min, 5% B. Mass spectrometry detection was performed using a Thermo Orbitrap Exploris 120 mass spectrometer detector. Data were acquired using an electrospray ionization source in both positive and negative ion modes.

[0049] S5. Targeted metabolomics validation: Based on the energy metabolism-related pathways enriched in the differential metabolic pathways screened in step S4, citric acid, α-ketoglutarate, fumaric acid, L-malic acid and succinic acid were selected for MRM targeted quantification. Based on the quantification results, at least two of the target metabolites, fumaric acid, L-malic acid and succinic acid, were preliminarily screened. The chromatographic conditions were the same as in step S4.

[0050] S6. Biomarker identification: The target metabolites initially screened in step S5 were evaluated for classification performance using six machine learning models: K-nearest neighbor method, random forest, support vector machine, Gaussian Naive Bayes, logistic regression, and decision tree. Based on the classification performance results, the serum biomarker metabolites for broiler bone abnormalities were finally determined to be fumaric acid and L-malic acid.

[0051] Example 2: Validation of the application of L-malic acid and fumaric acid in improving bone health in broilers

[0052] This embodiment aims to verify the specific application effects of L-malic acid and fumaric acid in improving bone health in broilers.

[0053] Animal management and experimental design: 420 healthy 1-day-old Jinling Yellow-feathered broiler chicks with an average initial weight of 37±0.5g were selected and randomly assigned to 7 treatment groups. Each treatment group contained 6 replicates, with 10 chicks per replicate. The diet settings for each group were as follows:

[0054] CON group: fed with basal diet;

[0055] LMA group: fed basal diet + 0.05% (m / m) L-malic acid;

[0056] MMA group: fed basal diet + 0.15% (m / m) L-malic acid;

[0057] HMA group: fed basal diet + 0.3% (m / m) L-malic acid;

[0058] LFA group: fed basal diet + 0.1% (m / m) fumaric acid;

[0059] MFA group: fed basal diet + 0.4% (m / m) fumaric acid;

[0060] HFA group: fed basal diet + 0.7% (m / m) fumaric acid;

[0061] All chickens are raised to 18 days old, during which time they have free access to feed and water.

[0062] Skeletal phenotype verification:

[0063] At the end of the experiment, samples were collected from each group of broilers and their skeletal phenotypes were verified.

[0064] Serum calcium and phosphorus content determination: Collect serum and determine serum calcium and phosphorus concentrations.

[0065] Bone mineralization index determination: Tibial bones were collected to determine bone ash, bone calcium, and bone phosphorus content.

[0066] Skeletal geometric parameter determination: Tibia was collected, and bone weight, bone length, proximal width, distal width, relative cortical bone wall thickness, vertical cortical index, cross-sectional moment of inertia, and cross-sectional area were measured.

[0067] Example 3: Validation of the application of L-malic acid and fumaric acid in improving bone health in broilers

[0068] This embodiment is the same as Embodiment 2, but the concentrations of L-malic acid and fumaric acid added to the diet are adjusted to the medium limit.

[0069] Animal management and experimental design:

[0070] Same as Example 2, but the feed settings for each group are as follows:

[0071] CON group: fed with basal diet;

[0072] LMA group: fed basal diet + 0.09% (m / m) L-malic acid;

[0073] MMA group: fed basal diet + 0.25% (m / m) L-malic acid;

[0074] HMA group: fed basal diet + 0.5% (m / m) L-malic acid;

[0075] LFA group: fed basal diet + 0.2% (m / m) fumaric acid;

[0076] MFA group: fed basal diet + 0.5% (m / m) fumaric acid;

[0077] HFA group: fed basal diet + 0.7% (m / m) fumaric acid;

[0078] All chickens were raised until 21 days old, during which time they had free access to feed and water.

[0079] Skeletal phenotype verification: Same as in Example 2.

[0080] Example 4: Validation of the application of L-malic acid and fumaric acid in improving bone health in broilers

[0081] This embodiment is the same as Embodiment 2, but the concentrations of L-malic acid and fumaric acid added to the diet are adjusted to the upper limit.

[0082] Animal management and experimental design:

[0083] Same as Example 2, but the feed settings for each group are as follows:

[0084] CON group: fed with basal diet;

[0085] LMA group: fed basal diet + 0.15% (m / m) L-malic acid;

[0086] MMA group: fed basal diet + 0.35% (m / m) L-malic acid;

[0087] HMA group: fed basal diet + 0.7% (m / m) L-malic acid;

[0088] LFA group: fed basal diet + 0.3% (m / m) fumaric acid;

[0089] MFA group: fed basal diet + 0.6% (m / m) fumaric acid;

[0090] HFA group: fed basal diet + 0.7% (m / m) fumaric acid;

[0091] All chickens are raised for 25 days, during which time they have free access to feed and water.

[0092] Skeletal phenotype verification: Same as in Example 2.

[0093] Comparative Examples 1-2:

[0094] Comparative Example 1: Screening method based solely on a single skeletal phenotypic index for grouping

[0095] Compared with Example 1, the difference is that the broiler chickens were grouped only based on the product of serum calcium and phosphorus concentrations, without phenotypic verification of skeletal geometric parameters, bone mineralization indicators or serum bone metabolism markers, while all other aspects were the same.

[0096] Comparative Example 2: Screening methods not validated by targeted metabolomics

[0097] Compared with Example 1, the difference is that after the serum non-target metabolomics analysis in step S4, we directly proceed to step S6 for biomarker identification, omitting the targeted metabolomics verification in step S5. All other steps are the same.

[0098] Test Examples 1-5:

[0099] Test Example 1: Validity Assessment of Broiler Skeletal Abnormality Grouping

[0100] This test case aims to assess the significance of skeletal phenotype differences between the normal skeletal group and the abnormal skeletal group, defined by the product of serum calcium and phosphorus concentrations.

[0101] Data Acquisition: Based on the results of skeletal phenotype verification in Example 1, raw data of various skeletal geometric parameters (bone weight, bone length, proximal tibia width, distal tibia width, relative cortical bone wall thickness, vertical cortical index, cross-sectional moment of inertia, cross-sectional area), bone mineralization indicators (bone ash, bone calcium, bone phosphorus), and serum bone metabolism markers (ALPL, PINP, TRAP, β-CTX) of broilers in the normal skeletal group and the abnormal skeletal group were acquired.

[0102] Data preprocessing: Standardize the acquired raw data to eliminate dimensional differences between different variables.

[0103] Principal component analysis: Using statistical analysis software (such as R or SIMCA-P), the standardized data are imported and principal component analysis is performed to generate principal component score plots. This allows us to observe the distribution and clustering patterns of samples from the normal skeletal group and the abnormal skeletal group in multidimensional space, analyze the contribution rate and loading plots of each principal component, and identify key phenotypic indicators that contribute significantly to the differences between groups.

[0104] Statistical tests: Appropriate multivariate statistical methods were used to compare the two groups of data to quantify the statistical significance of the differences in skeletal phenotype between the normal skeletal group and the abnormal skeletal group.

[0105] Experimental data:

[0106] Table 1. Examples of Principal Component Analysis Results for Skeletal Phenotypic Indicators

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] In summary, the experimental results of this test case demonstrate that, after grouping by the product of serum calcium and phosphorus concentrations, the normal skeletal group and the abnormal skeletal group showed significant and consistent differences in various skeletal phenotypic indicators. The abnormal skeletal group had lower bone weight, bone length, bone ash content, bone calcium, and bone phosphorus content than the normal skeletal group. Furthermore, the levels of bone metabolism markers such as ALPL, PINP, TRAP, and β-CTX in serum showed different trends. These results confirm the effectiveness of grouping based on the product of serum calcium and phosphorus concentrations from multiple dimensions, providing a reliable biological basis for subsequent screening of serum marker metabolites.

[0113] Principal component analysis results showed that the samples from the normal skeletal group and the abnormal skeletal group formed a clear separation region on the principal component score map, indicating a significant overall difference between the two groups. Skeletal geometric parameters and bone mineralization indices were generally lower in the abnormal skeletal group, reflecting their skeletal dysplasia and insufficient mineralization. At the same time, changes in serum bone metabolism markers suggested that the skeletal remodeling process in the abnormal skeletal group may be disordered, for example, osteogenic activity may be decreased while osteoclast activity may be relatively enhanced. This systematic difference in multiple indicators supports the accuracy of the skeletal abnormality model construction in the screening method of this invention.

[0114] This method of verifying skeletal abnormalities through multiple indicators overcomes the limitations of a single indicator and ensures that the skeletal abnormalities targeted by subsequent metabolomics analysis have clear biological characteristics. This verification process is a key step in the screening method of this invention to ensure that the serum marker metabolites obtained can truly reflect the skeletal abnormalities of broilers.

[0115] Test Example 2: Differential Metabolic Pathway Enrichment Analysis

[0116] This test case aims to identify statistically significant changes in metabolic pathways between the normal skeletal group and the abnormal skeletal group, providing direction for subsequent targeted metabolomics validation.

[0117] Differential metabolite identification and extraction: Based on the serum metabolomics data obtained by UPLC-QTOF / MS detection in Example 1, multivariate statistical analysis (e.g., partial least squares discriminant analysis, PLS-DA) and univariate statistical analysis (e.g., t-test) were used to screen out metabolites with statistically significant differences between the normal bone group and the abnormal bone group. The screening criteria included a VIP value greater than 1, a p value less than 0.05, and a fold change greater than 1.5 or less than 0.67.

[0118] Metabolite identifier conversion: The chemical names or KEGGIDs of the identified differential metabolites are unified to ensure that they are consistent with the metabolite identifiers in the KEGG database.

[0119] Pathway enrichment analysis: Upload the unified list of differentially identified metabolites to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database online tool or a metabolomics analysis platform such as MetaboAnalyst, select chicken as the species, and perform metabolic pathway enrichment analysis.

[0120] Pathway screening and evaluation: Analyze the enrichment analysis results to obtain the total number of metabolites included in each metabolic pathway, the number of metabolites overlapping with the differential metabolite list, the enrichment P-value, and the false discovery rate. Based on the criterion of a false discovery rate of less than 0.05, statistically significant enriched metabolic pathways are screened out.

[0121] Experimental data:

[0122] Table 2. Examples of serum differential metabolic pathway enrichment analysis results between the normal skeletal group and the abnormal skeletal group.

[0123]

[0124] In summary, the pathway enrichment analysis results of this test case showed that there were significant differences in multiple metabolic pathways between the normal skeletal group and the abnormal skeletal group. Among them, energy metabolism-related pathways, such as the tricarboxylic acid cycle, glycolysis / gluconeogenesis, and oxidative phosphorylation, showed high enrichment and statistical significance. The changes in these core energy metabolism pathways indicate that the energy supply and utilization patterns of broiler chickens may have undergone structural changes under abnormal skeletal conditions.

[0125] Bone formation, mineralization, and remodeling are highly energy-intensive biological processes that depend on a stable supply of ATP. The TCA cycle, as the core hub of cellular aerobic respiration, and glycolysis, as an important pathway for glucose breakdown for energy, directly affect the biosynthetic capacity and function of bone cells (such as osteoblasts and osteoclasts) through changes in their metabolites and activity. When these energy metabolism pathways are disrupted, bone cells may not be able to obtain enough energy to maintain normal growth, differentiation, and function, thereby affecting the synthesis and mineralization of the bone matrix, ultimately manifesting as abnormal bone structure and a decline in health.

[0126] Therefore, through systematic analysis of differential metabolic pathways, this invention successfully identified energy metabolism pathways closely related to skeletal abnormalities in broilers. This discovery provides a clear biological basis and focus for targeted quantitative verification of energy metabolism-related metabolites in the subsequent S5 step, enhancing the accuracy and reliability of screening specific serum marker metabolites. The focus on energy metabolism pathways directly points to the potential molecular mechanisms of skeletal abnormalities, thus laying the foundation for developing a skeletal health assessment method based on metabolite detection.

[0127] Test Example 3: Determination of expression levels of genes related to bone remodeling and glucose metabolism

[0128] This test case aims to evaluate changes in genes related to bone remodeling and energy metabolism between the normal skeletal group and the abnormal skeletal group at the gene expression level.

[0129] Tissue collection: After grouping in Example 1, tibia tissue of broilers was collected and quickly placed in liquid nitrogen for flash freezing, and then transferred to an ultra-low temperature freezer at -80°C for storage.

[0130] Total RNA extraction: Frozen tibial tissue was removed and ground into a fine powder using a grinder. Total RNA was extracted from the ground tissue powder following the instructions in the EasyPureARNAKit manual. The concentration and purity (A260 / A280 ratio) of the extracted total RNA were determined using a micro spectrophotometer.

[0131] cDNA synthesis: Using the EasyScript First-Strand cDNA Synthesis SuperMix kit, qualified total RNA extracted in the total RNA extraction step was used as a template, and cDNA was synthesized by reverse transcription according to the kit instructions.

[0132] Real-time quantitative PCR: Using cDNA synthesized in the cDNA synthesis step as a template, gene expression levels were detected using SYBR Green qPCR Master Mix on a real-time quantitative PCR instrument. GAPDH was used as an internal reference gene to detect the relative mRNA expression levels of the following genes:

[0133] Osteoclast-related gene: CTSK;

[0134] Osteogenesis-related gene: COL1A1;

[0135] Key enzyme gene for glycolysis: PFKM;

[0136] Key gene in the mitochondrial electron transport chain: COX5A;

[0137] The relative expression levels of each gene were calculated using the 2-ΔΔCt method.

[0138] Experimental data:

[0139] Table 3. Examples of relative expression levels of genes related to tibia bone tissue in broiler chickens from the normal skeletal group and the abnormal skeletal group.

[0140]

[0141] In summary, gene expression analysis of this test case showed that the relative expression level of the osteoclast-related gene CTSK was significantly upregulated in the tibia tissue of broilers with skeletal abnormalities, while the relative expression level of the osteogenic gene COL1A1 was significantly downregulated. This indicates that the normal bone remodeling process is imbalanced under skeletal abnormalities, manifested as increased bone resorption activity and decreased bone formation activity. This imbalance in bone remodeling is the direct molecular basis for skeletal structural abnormalities and insufficient mineralization.

[0142] Meanwhile, the expression of energy metabolism-related genes also showed significant changes. In the skeletal abnormality group, the relative expression level of PFKFB3, a key enzyme gene for glycolysis, was upregulated, while the relative expression level of COX1, a key gene for the mitochondrial electron transport chain, was downregulated. This suggests that skeletal abnormalities may be accompanied by a reprogramming of energy metabolism pathways, that is, cells may be more inclined to produce energy through the glycolysis pathway, while the efficiency of the aerobic oxidation pathway may be reduced. Since bone development and maintenance require a continuous energy supply, this change in energy metabolism pattern may not be sufficient to support normal skeletal physiological activities, thereby exacerbating skeletal abnormalities.

[0143] These gene expression-level evidences corroborate the significant enrichment of energy metabolism pathways in previous metabolomics analyses. Changes in metabolite levels in pathways such as glycolysis and the tricarboxylic acid cycle, along with alterations in the expression of corresponding key enzyme genes, reveal the molecular mechanisms of broiler skeletal abnormalities. The serum marker metabolites screened in this invention are directly related to these altered energy metabolism pathways, thus providing clear genomic support for reflecting the physiological and pathological state of skeletal abnormalities by detecting these marker metabolites.

[0144] Test Example 4: Evaluation of Biomarker Classification Performance

[0145] This test case aims to quantitatively evaluate the ability of the serum marker metabolites (fumaric acid and L-malic acid) identified in Example 1 to distinguish between normal and abnormal skeletal structures in broilers.

[0146] Dataset Construction: Using the concentration data of fumaric acid and L-malic acid obtained by targeted metabolomics quantification in Example 1, and the sample grouping labels (normal bone group / abnormal bone group) determined in Example 1, a dataset for machine learning model evaluation was constructed.

[0147] Model training and validation: The dataset was divided using a 5-fold cross-validation method, and six machine learning models were trained on the training set in sequence: K-nearest neighbors, random forest, support vector machine, Gaussian Naive Bayes, logistic regression, and decision tree.

[0148] Performance evaluation: The trained model is used to predict the validation set, and this process is repeated until all data have been predicted once. Based on the model's prediction results and the actual sample group labels, a receiver operating characteristic (ROC) curve is constructed for each model, and its area under the curve is calculated. The classification performance is evaluated when fumaric acid and L-malic acid are used as single features and when the two are combined as features.

[0149] Experimental data:

[0150] Table 4. Examples of AUC values ​​for different machine learning models in classifying serum biomarker metabolites.

[0151]

[0152] In summary, the experimental results of this test case demonstrate that, when fumaric acid and malic acid, as determined by the screening method of this invention, are used as input features, various machine learning models can effectively distinguish between the normal and abnormal skeletal groups in broilers. The area under the receiver operating characteristic (AUC) curves of each model shows a high level. In particular, when the concentration data of fumaric acid and malic acid are used in combination, the classification performance of all models is improved, with the AUC values ​​of the random forest and support vector machine models exceeding 0.95.

[0153] This classification performance stems from the core role of these metabolites in the physiological and pathological processes of bone. Fumaric acid and malic acid are key intermediates in the tricarboxylic acid cycle, and fluctuations in their serum levels directly reflect the state of energy metabolism in the body. Previous tests have shown that skeletal abnormalities in broilers are closely related to the disorder of energy metabolism pathways. Therefore, changes in the serum concentration of these metabolites are not random noise, but a stable and quantifiable projection of bone health status onto the whole metabolic system. By learning these specific metabolite concentration patterns, machine learning models can construct precise decision boundaries that distinguish between the two bone states.

[0154] This test, using quantitative AUC, verified that the serum biomarker metabolites screened in this invention have high diagnostic potential. The results confirm that the method of assessing the skeletal health status of broilers by detecting the levels of fumaric acid and malic acid in serum is feasible, and provide data support for the development of objective and accurate diagnostic tools for skeletal abnormalities based on these biomarkers. This echoes the goal of this invention to establish a systematic method for biomarker screening and application verification.

[0155] Test Example 5: Evaluation of the effect of L-malic acid and fumaric acid on bone health in broilers

[0156] This test case aims to evaluate the effects of adding L-malic acid and fumaric acid, as determined by the screening method of this invention, to the phenotypic indicators related to bone health in broilers.

[0157] Data and Sample Acquisition: Serum and tibial samples were collected at the end of the experiment using the treatment groups (CON, LMA, MMA, HMA, LFA, MFA, HFA) in Examples 2, 3 and 4.

[0158] Serum calcium and phosphorus content determination: Serum samples from each group were taken, and the concentrations of serum calcium and phosphorus were determined using a commercially available colorimetric assay kit on a fully automated biochemical analyzer.

[0159] Bone mineralization index determination:

[0160] Tibial samples were taken from each group, the attached soft tissue was removed, and the bone length was measured using vernier calipers;

[0161] The tibial bone samples were dried in an oven at 105°C until constant weight, and the dry weight of the bone was measured.

[0162] The dried tibia was placed in a muffle furnace and ashed at 600°C for 6 hours. The ash content was measured and the percentage of bone ash was calculated.

[0163] The ash was dissolved in hydrochloric acid solution, and after adjusting the volume, the content of bone calcium and bone phosphorus was determined using a colorimetric assay kit.

[0164] Determination of skeletal geometric parameters:

[0165] Tibial samples were taken from each group, and bone weight, bone length, proximal tibial width and distal tibial width were accurately measured using vernier calipers.

[0166] Based on the direct measurements mentioned above, derived geometric parameters such as the relative wall thickness of the cortical bone, the vertical cortical index, the moment of inertia of the cross section, and the cross-sectional area are calculated.

[0167] Experimental data:

[0168] Table 5. Effects of different concentrations of L-malic acid and fumaric acid on phenotypic parameters of the tibia in 21-day-old broilers

[0169]

[0170]

[0171] In summary, the experimental results of this test case indicate that adding specific concentrations of L-malic acid or fumaric acid to the basal diet of broilers can affect calcium and phosphorus metabolism and skeletal phenotypic indicators. Compared with the control group without any added substances, the treatment groups with added L-malic acid or fumaric acid showed varying degrees of change in serum calcium and phosphorus concentrations, as well as tibial bone ash content, bone weight, and bone length. The data show that specific concentrations of additives can improve bone mineralization levels and geometric parameters.

[0172] These phenotypic improvements correspond to the core functions of fumaric acid and malic acid in cellular energy metabolism. Bone development, mineralization, and remodeling are physiological processes that require a large energy supply, mainly derived from metabolic pathways such as the tricarboxylic acid cycle and glycolysis. As key intermediate metabolites of the tricarboxylic acid cycle, exogenous supplementation of fumaric acid and malic acid can directly or indirectly regulate the flow and efficiency of cellular energy metabolism pathways. This regulatory effect on energy metabolism provides more sufficient energy support for high-energy-consuming activities such as osteoblast proliferation, differentiation, and bone matrix synthesis, thus macroscopically manifesting as increased bone mineralization and improved bone structure.

[0173] This test case provides functional validation for the serum marker metabolites identified by the screening method of this invention. It confirms that these metabolites, which are directly related to energy metabolism, can not only serve as indicators of bone health status, but also have a direct impact on bone health as functional additives. This result completes the entire process from marker screening to functional validation, confirms the effectiveness of the method proposed in this invention, and provides a material basis for developing bone health improvement strategies aimed at regulating energy metabolism.

Claims

1. A method for screening serum marker metabolites of skeletal abnormalities in broilers, characterized in that, Includes the following steps: S1. Sample collection and preparation: Blood was collected from the wing veins of broilers from the same batch and under the same feeding conditions, and serum was prepared. S2. Construction and grouping of skeletal abnormality model: Based on the product of calcium and phosphorus concentrations in the serum, broilers were divided into a normal skeletal group and a skeletal abnormality group. S3. Skeletal phenotype verification: Phenotypic verification of the skeletons of broilers in the normal skeleton group and the abnormal skeleton group; S4. Serum non-target metabolomics analysis: Serum samples from the normal skeletal group and the abnormal skeletal group described in step S1 were subjected to non-target metabolomics analysis to screen for differential metabolites and differential metabolic pathways. S5. Targeted metabolomics validation: Based on the differential metabolic pathways screened in step S4, MRM targeted quantification of energy metabolism-related metabolites was performed, and target metabolites were preliminarily screened. S6. Biomarker identification: The target metabolites initially screened in step S5 are classified and evaluated using a machine learning model to ultimately identify serum biomarker metabolites for broiler bone abnormalities.

2. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 1, characterized in that, The specific process of sample collection and preparation is as follows: the collected blood is left to stand at 20-25℃ for 30-40 minutes, then centrifuged at 2800-3200×g for 9-11 minutes at 2-8℃ to separate the serum, and the separated serum is stored at -75℃ to -85℃.

3. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 1, characterized in that, The product of calcium and phosphorus concentrations in the normal bone group was greater than 40 mg. 2 / dL 2 The product of calcium and phosphorus concentrations in the skeletal abnormality group is less than 30 mg. 2 / dL 2 .

4. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 1, characterized in that, The skeletal phenotype verification includes at least one of the following: skeletal geometric parameters, bone mineralization indices, bone microstructure parameters, and serum bone metabolism markers.

5. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 1, characterized in that, The specific procedure for serum non-target metabolomics analysis is as follows: 50-60 μL of serum sample is mixed with 200-220 μL of methanol at 2-8℃ and incubated for 14-16 minutes. After centrifugation at 13500-14500 rpm for 14-16 minutes at 2-8℃, the supernatant is filtered through a 0.20-0.25 μm microporous membrane and then detected by UPLC-QTOF / MS. The UPLC-QTOF / MS detection uses an ACQUITY UPLCAHSST3 column with a flow rate of 0.28-0.32 mL / min, a column temperature of 38-42℃, and an injection volume of 1.8-2.2 μL. The methanol contains the internal standard 2-amino-3-(2-chlorophenyl)-propionic acid.

6. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 5, characterized in that, The mass spectrometer used for the UPLC-QTOF / MS detection was a Thermo Orbitrap Exploris 120 mass spectrometer, and data was acquired using an electrospray ionization source in both positive and negative ion modes.

7. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 1, characterized in that, The energy metabolism-related metabolites include at least one of citric acid, α-ketoglutarate, fumaric acid, L-malic acid, and succinic acid, and the target metabolites initially screened are at least two of fumaric acid, L-malic acid, and succinic acid.

8. The method for screening serum marker metabolites of skeletal abnormalities in broilers according to claim 1, characterized in that, The machine learning model includes at least one of K-nearest neighbor, random forest, support vector machine, Gaussian Naive Bayes, logistic regression and decision tree, and the finally determined serum marker metabolites of broiler bone abnormalities are fumaric acid and L-malic acid.

9. The application of a serum marker metabolite for skeletal abnormalities in broilers, characterized in that, The use of at least one of L-malic acid and fumaric acid in the preparation of feed additives for improving bone health in broilers.

10. The application of a serum marker metabolite for broiler skeletal abnormalities according to claim 9, characterized in that, The feed additive is prepared by adding 0.03%-0.7% (m / m) of L-malic acid or 0.1%-0.7% (m / m) of fumaric acid to the basal diet of broilers and feeding them for 18-25 days.

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