A detection method for evaluating health status of cervus nippon based on characteristic intestinal flora

Abstract

The present application belongs to the technical field of wild animal health assessment, and particularly relates to a detection method for evaluating the health condition of sika deer based on characteristic intestinal flora, comprising: collecting fresh fecal samples of sika deer; extracting total DNA of the samples, and amplifying V3-V4 region of bacterial 16S rRNA gene; performing high-throughput sequencing on the amplification products, and after data preprocessing, performing OTU clustering, species annotation and pollution removal to obtain a flora composition spectrum; calculating four characteristic flora indexes and respectively performing normalized scoring, and then performing weighted summation according to preset weights to obtain a comprehensive health index; based on the comprehensive health index, determining the health condition: health grade, sub-health grade and disease risk grade; outputting an evaluation report containing the health grade and management suggestions, and triggering an early warning when the disease risk grade is determined. The method provided by the present application realizes non-invasive health evaluation of sika deer by adopting species-specific four-dimensional flora weighted scoring and three-level threshold standardization determination.

Description

Technical Field

[0001] This invention belongs to the field of wildlife health assessment technology, specifically relating to a detection method for assessing the health status of sika deer based on characteristic gut microbiota. Background Technology

[0002] Sika deer ( Cervus nippon The deer (Ceratophyllum deer) is a rare and endangered species endemic to my country, listed as a Class I protected wild animal in China. Its wild population is small and distributed in an island-like pattern. Timely and accurate monitoring of the health status of individuals and the entire population is a fundamental prerequisite for developing effective conservation and management strategies.

[0003] The health status of wild animals is influenced by a combination of factors, including nutritional intake, pathogen infection, and environmental stress. At the individual level, impaired health typically manifests as declining body condition, suppressed immune function, and metabolic disorders. At the population level, an overall decline in health can lead to reduced reproductive rates and lower cub survival rates, thereby exacerbating the risk of endangerment. Therefore, establishing sensitive and reliable health monitoring methods is of great significance for the population recovery and scientific management of sika deer.

[0004] Currently, the technical approaches to wildlife health assessment can be categorized into the following three types: (1) Invasive assessment methods Invasive methods primarily include post-capture body condition scoring and blood biochemical analysis. Body condition scoring is a subjective grading based on palpation of the spine, ribs, and pelvic region to assess the degree of fat deposition. Blood tests cover complete blood count, serum biochemistry, stress hormones (such as cortisol), and immunoglobulins. The advantage of these methods is that they provide direct physiological parameters, making them the gold standard in clinical veterinary medicine. However, their limitations are equally significant: the capture process induces intense stress, anesthesia carries a risk of death, and the procedures are particularly difficult and costly in the field, and they are not suitable for long-term continuous monitoring of the same animal.

[0005] (2) Behavioral observation methods Health status can be indirectly inferred by directly observing or monitoring an animal's activity rhythms, foraging behavior, social interactions, and social hierarchy using infrared cameras. For example, sick individuals typically exhibit behavioral abnormalities such as reduced activity levels, solitary behavior, and shortened foraging time. The advantage of this method is that it is completely non-intrusive, but the appearance of behavioral changes often lags behind physiological lesions, resulting in low sensitivity, and there is no one-to-one correspondence between behavioral abnormalities and specific causes.

[0006] (3) Non-invasive physiological indicator detection In recent years, the detection of physiological indicators in non-invasive samples such as feces, urine, and hair has received widespread attention. Among these, glucocorticoid metabolites in feces can reflect long-term stress levels, cortisol in hair can record the accumulation of chronic stress, and parasite egg counts in feces can assess the intensity of parasite infection. While these methods achieve non-invasive sampling, they still have limitations: a single indicator only reflects one aspect of health status and lacks comprehensive judgment capabilities; some indicators degrade relatively quickly, requiring strict sample preservation conditions.

[0007] The gut microbiome, hailed as the host's "second genome," plays an irreplaceable role in nutrient metabolism, immune regulation, and pathogen resistance. Numerous studies have demonstrated that changes in the composition and structure of the gut microbiota are closely related to the host's health: in nutrient metabolism, the gut microbiota participates in key processes such as cellulose degradation, short-chain fatty acid synthesis, and vitamin synthesis; in immune regulation, symbiotic bacteria inhibit pathogen colonization through mechanisms such as maintaining intestinal barrier integrity, stimulating the development of the mucosal immune system, and secreting antimicrobial peptides; and in disease indication, abnormal proliferation of specific pathogens is directly related to gastrointestinal diseases, while a reduction in beneficial bacteria often foreshadows impaired digestive function. Based on these mechanisms, the gut microbiota has been recognized as a "barometer" of host health, and its diagnostic value in human precision medicine and livestock health management has been widely validated.

[0008] Introducing gut microbiota analysis into wildlife health assessment has become an important development in the field of conservation physiology in recent years. Existing research includes: Amato et al. (2013) found that habitat fragmentation led to reduced dietary diversity in Mexican howler monkeys, which in turn caused a decline in gut microbiota diversity, suggesting a possible correlation with deteriorating nutritional health; Barelli et al. (2015) revealed that in human-disturbed populations, the abundance of beneficial bacteria (such as Bifidobacteria) decreased while the abundance of potentially pathogenic bacteria increased, serving as a microbial indicator for assessing population health stress; domestically, studies on the gut microbiota of species such as the Sichuan golden monkey and giant panda have also preliminarily established the link between microbiota structure and environmental adaptation, as well as seasonal changes in food. However, most of these studies remain at the level of microbiota structure description and correlation analysis, and have not yet formed an operational health assessment standard system.

[0009] In existing technologies, a method for assessing individual health status using 16S rRNA gene sequencing data from fecal samples was proposed, using captive Siberian tigers as the research subject. The main steps include: collecting fresh fecal samples; extracting total microbial DNA, amplifying the V4 region of the 16S rRNA gene and performing Illumina sequencing; using QIIME software for OTU clustering and species annotation; calculating the Shannon diversity index, Simpson diversity index, and relative abundance of major phyla; comparing the individual's indicators with the mean of a healthy captive population, with individuals deviating by more than two standard deviations being classified as having "abnormal gut microbiota"; and verifying the correlation with blood biochemical indicators. While this approach has some reference value in assessing the health of fecal microbiota in wild animals, it has the following technical limitations when applied to sika deer:

[0010] First, there is a mismatch in the baseline gut microbiota between species. The Siberian tiger, a carnivore belonging to the Felidae family, has a gut microbiota dominated by Firmicutes and Fusobacterium; while the sika deer, an even-toed ungulate belonging to the Cervidae family, has a gut microbiota dominated by Firmicutes and Bacteroidetes, with completely different core beneficial and potentially pathogenic genera. The reference baseline of the existing scheme cannot be transferred to the sika deer.

[0011] Second, the evaluation index system is too simplistic and lacks sufficient resolution. The existing scheme only uses the Shannon diversity index, the Simpson diversity index, and the relative abundance of each phylum, which has two shortcomings: First, the classification resolution at the phylum level is too low, making it impossible to capture changes in key bacterial communities at the genus level; second, the independent comparison of each index lacks a mathematical model that integrates multidimensional information into a single quantitative health score, making it difficult to rank different individuals horizontally.

[0012] Third, the health assessment threshold lacks statistical rigor. Using "deviation from the mean by two standard deviations" as the anomaly criterion fails to consider the distribution characteristics of different indicators (such as skewed distribution) and the natural fluctuation range of healthy wild populations. Furthermore, the lack of a buffer level such as "sub-healthy" between "abnormal" and "healthy" hinders differentiated responses in conservation management decisions.

[0013] Fourth, the baseline of captive populations cannot represent the health standard in the wild. The diet, living environment, and social structure of captive populations differ significantly from those of wild populations, and their gut microbiota composition cannot be equated with their health status in the wild. Directly using the characteristics of captive population microbiota as a reference standard may lead to systematic bias.

[0014] Fifth, there is a lack of standardization throughout the entire process. Existing solutions focus on the bioinformatics analysis stage, but do not adequately define the specific procedures for front-end fecal collection and preservation, DNA extraction methods, and decision-making rules for translating conclusions into management recommendations. This results in poor experimental reproducibility and makes it difficult to obtain consistent results across different locations and operators.

[0015] In summary, there is currently a lack of a non-invasive health assessment method that is species-specific for sika deer, based on a baseline of healthy wild populations, integrates multiple dimensions of indicators, and is standardized throughout the entire process. Summary of the Invention

[0016] In view of the shortcomings of the prior art, the object of the present invention is: 1. Establish a species-specific reference baseline for healthy gut microbiota in sika deer: Using a healthy wild population (natural individuals distributed in the core area of ​​a nature reserve, in good physical condition, and with no abnormal mortality records) as the reference group, fecal samples were systematically collected and 16S rRNA gene high-throughput sequencing was performed to clarify the composition lineage and normal fluctuation range of core beneficial bacteria genera and potentially pathogenic bacteria genera in sika deer, filling the gap in the baseline of healthy gut microbiota for this species.

[0017] 2. Construct a multi-dimensional characteristic microbial community assessment index system and weighted scoring model: Select genus-level characteristic microbial community indicators that are indicative of the health status of sika deer, integrate four key indicators: abundance of core beneficial bacteria genera, abundance of potentially pathogenic bacteria genera, microbial community diversity index, and abundance ratio of beneficial bacteria to pathogenic bacteria, and assign differentiated weight coefficients based on the biological importance of each indicator, integrate multi-dimensional microbial community information into a single comprehensive health index, and realize the quantitative ranking of individual health status.

[0018] 3. Set statistically rigorous and biologically significant grading thresholds: Based on the comprehensive health index distribution of the wild healthy reference group, use the percentile or mean-standard deviation method to set three levels of thresholds: "healthy - sub-healthy - disease risk". This provides differentiated protection and management recommendations for different risk levels and improves the operability of the assessment results in actual decision-making.

[0019] 4. Use healthy wild populations rather than captive populations as the reference benchmark: This ensures that the assessment criteria accurately reflect the gut microbiota characteristics of sika deer under natural ecological conditions, avoids baseline deviations caused by captive environments, and improves the ecological authenticity of the assessment conclusions and the applicability to conservation practices.

[0020] 5. Provide a standardized technical solution for the entire process from fecal sample collection, experimental operation, data analysis to conclusion output: Define each step of the process, including fecal preservation solution formulation, DNA extraction method, PCR amplification region and primer sequence, sequencing strategy, bioinformatics analysis parameters and health assessment rules, to ensure that the method has good reproducibility and comparability across different locations and different operators, and simplify the final output into an assessment report that includes health level and core differentially expressed microbiota, so that even non-professional protected area managers can use it directly.

[0021] The specific solution provided by this invention is as follows: This invention provides a detection method for assessing the health status of sika deer based on characteristic gut microbiota, comprising the following steps: Fresh fecal samples were collected from sika deer and transferred to a preservation solution containing DNA stabilizers for storage. Total microbial DNA was extracted from fecal samples, and the V3-V4 region of the bacterial 16S rRNA gene was amplified to obtain the amplification product. The amplified products were subjected to high-throughput sequencing. The raw sequences were preprocessed to obtain effective sequences. Based on the effective sequences, OTU clustering, species annotation and contamination removal were performed to obtain the microbial community composition profile. Four characteristic microbial community indicators were calculated based on the microbial community composition profile. These four indicators include the relative abundance of core beneficial bacteria, the relative abundance of potential pathogenic bacteria, the Shannon diversity index, and the abundance ratio of beneficial bacteria to pathogenic bacteria. Using a healthy wild sika deer population as a reference group, the four characteristic microbial community indicators were normalized and scored respectively, and then the comprehensive health index was obtained by weighting and summing according to preset weights. Based on the comprehensive health index, the health status is determined as a healthy level, a sub-healthy level, or a disease risk level. The system outputs an assessment report containing health status and management recommendations, and triggers an alert when a disease risk level is determined.

[0022] Preferably, the fecal sample collection method is as follows: scraping the part of the feces that has not been in contact with the ground using a sterile sampling spoon; the preservation solution contains Tris-HCl, EDTA and SDS.

[0023] More preferably, each 1L of preservation solution contains 50~100 mmol / L Tris-HCl, 50~100 mmol / L LEDTA, 0.5%~2.0% SDS and 0~1.0 mol / L sodium chloride.

[0024] Preferably, in the preset weights, the abundance ratio of beneficial bacteria to pathogenic bacteria has a weight of 0.35, the relative abundance weight of core beneficial bacteria has a weight of 0.30, the relative abundance weight of potential pathogenic bacteria has a weight of 0.20, and the Shannon diversity index has a weight of 0.15.

[0025] Preferably, the DNA extraction is performed using a bead milling method; the primer pair used for amplification is as follows: the upstream primer sequence is shown in SEQ ID NO.1, and the downstream primer sequence is shown in SEQ ID NO.2.

[0026] Preferably, the high-throughput sequencing uses paired-end sequencing, with each sample containing no less than 30,000 valid sequences. The data preprocessing includes removing low-quality sequences, splicing, and removing chimeras. The low-quality sequences are those with an average quality score below Q20, containing ambiguous bases, and a length of less than 200 bp. OTU clustering and species annotation based on valid sequences are performed using a 97% similarity threshold for OTU clustering and using the SILVA 138 database with a 99% similarity standard for species annotation. The contamination removal involves removing sequences from mitochondria, chloroplasts, and those not classified to the boundary level.

[0027] Preferably, the relative abundance of the core beneficial bacteria is the sum of the relative abundances of the Trichophytonceae and Rikenaceae families; the relative abundance of the potential pathogenic bacteria is the sum of the relative abundances of the Escherichia, Shigella, and Clostridium genera; and the abundance ratio of the beneficial bacteria to the pathogenic bacteria is the ratio of the relative abundance of the core beneficial bacteria to the relative abundance of the potential pathogenic bacteria.

[0028] Preferably, the normalized score is: For positive indicators, the ratio of individual values ​​to the reference mean is calculated and multiplied by 100 to initially limit the score to the range of 0 to 100, where 100 is set as the preset upper limit and 0 is set as the preset lower limit, and the part exceeding the preset range is truncated accordingly. For negative indicators, calculate the ratio of the reference mean to the individual value, multiply by 100, and apply the same interval limits and truncation treatment as for positive indicators. The positive indicators include the abundance ratio of beneficial bacteria to pathogenic bacteria, the relative abundance of core beneficial bacteria, and the Shannon diversity index; the negative indicators include the relative abundance of potential pathogenic bacteria.

[0029] Preferably, the selection criteria for the wild healthy sika deer population are: distributed in the core area of ​​the nature reserve, with good physical condition scores, and no abnormal mortality records within a specified period before and after sampling; the assessment report also includes a comprehensive health index, comparison information of the current values ​​of four characteristic microbial indicators with the reference interval, and a list of the core differential microbial groups with the most significant deviations; the management recommendations include routine monitoring, intensive monitoring, or immediate clinical intervention.

[0030] Preferably, based on the comprehensive health index, the health status is determined according to the following three-level thresholds: health index ≥ μ -1.5σ is the health level, μ - 3.0σ ≤ health index < μ - 1.5σ is the sub-health level, and health index < μ - 3.0σ is the disease risk level; where μ is the mean of the comprehensive health index of the outdoor health reference group, and σ is the standard deviation.

[0031] Preferably, the samples are stored at 4°C or below and transported to the laboratory within 48 hours.

[0032] The present invention also provides a test kit for assessing the health status of sika deer, the kit comprising (a) to (c): (a) Fecal DNA stabilization solution, each 1L of the solution contains 50~100 mmol / L Tris-HCl, 50~100 mmol / L EDTA, 0.5%~2.0% SDS and 0~1.0 mol / L sodium chloride; (b) Primer pairs used to amplify the V3-V4 region of the bacterial 16S rRNA gene, the primer pair sequences of which are shown in SEQ ID NO.1 and SEQ ID NO.2; (c) The instruction manual for calculating the characteristic microbial community indicators, setting the preset weights, and determining the health level.

[0033] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, performs the functions of calculating the characteristic microbial community indicators, scoring the comprehensive health index, and determining the health status level.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) Completely non-invasive, eliminating stress risks to endangered individuals. This invention only requires the collection of fresh fecal samples naturally excreted by sika deer. The entire process requires no capture, anesthesia, or any physical contact, completely avoiding the strong stress response and mortality risk to wild animals caused by traditional invasive assessment methods. It complies with wildlife conservation ethics and is especially suitable for long-term continuous monitoring of rare and endangered species.

[0035] (2) Establish a species-specific healthy microbiota baseline for sika deer to avoid cross-species migration errors. This invention, for the first time, uses a healthy wild sika deer population (distributed in the core area of ​​a nature reserve, with a body condition score (BCS) ≥ 3.0 and no abnormal mortality records) as a reference group to establish the normal abundance range of the sika deer's gut microbiota and indicator groups of core beneficial bacteria genera (Trichophyceae and Rikenaceae) and potentially pathogenic bacteria genera (Escherichia, Shigella, and Clostridium). Compared with existing technologies that use captive Siberian tigers or other species as a benchmark, this invention establishes the entire indicator system, reference benchmark, weight configuration, and threshold setting based on experimental data from the specific species of sika deer, without involving cross-species migration. This fundamentally solves the misjudgment problem caused by "baseline mismatch," significantly enhancing the species specificity of the evaluation results.

[0036] (3) Construct a four-dimensional characteristic microbial community index system, which has comprehensive information dimensions and higher diagnostic sensitivity and specificity. This invention overcomes the limitations of existing technologies that rely solely on phylum-level relative abundance and single diversity indices. It constructs a four-dimensional evaluation index system comprising the abundance of core beneficial bacteria, the abundance of potentially pathogenic bacteria, the Shannon diversity index, and the abundance ratio of beneficial to pathogenic bacteria. Among these, the abundance ratio of beneficial to pathogenic bacteria is an integrated index proposed for the first time in this invention. It directly quantifies the antagonistic balance of the microbial community and is more sensitive in capturing early signals of microbial dysbiosis than changes in the abundance of a single genus, significantly improving the diagnostic sensitivity and specificity for changes in health status.

[0037] (4) Establish a weighted comprehensive health index to achieve quantitative ranking of individual health status. This invention, based on the biological importance and discriminative power (AUC optimization) of each indicator, assigns differentiated weighting coefficients to the abundance ratio of beneficial bacteria to pathogenic bacteria (0.35), the abundance of beneficial bacteria (0.30), the abundance of pathogenic bacteria (0.20), and the Shannon diversity index (0.15), thus integrating multidimensional information into a single comprehensive health index (0-100 points). Compared with the existing approach of independently comparing various indicators and lacking an integrated model, this invention achieves an objective quantitative ranking of health status among different individuals, facilitating health comparison and trend analysis at the population level.

[0038] (5) Set statistically rigorous three-level judgment thresholds to support differentiated protection management decisions. This invention is based on the comprehensive health index distribution of a healthy reference population in the wild, using μ1.5σ and μ3.0σ as dividing points to classify health status into three levels: "healthy," "sub-healthy," and "disease risk." Compared with existing technologies that only set a binary classification of "normal / abnormal" and use a "2 standard deviation" threshold without distribution testing, this invention provides a buffer transition range (sub-healthy), enabling conservation managers to adopt differentiated response strategies (such as routine monitoring, frequent re-examination, and immediate intervention) for different risk levels, thus improving the operability and accuracy of the assessment results in actual conservation decision-making.

[0039] (6) Using healthy wild populations rather than captive populations as the reference benchmark makes the assessment results more ecologically authentic. This invention uses a population entirely composed of healthy sika deer found in the wild under natural conditions, thus avoiding systematic biases in the baseline gut microbiota caused by artificial feed, high-density rearing, limited activity space, and specific pathogen exposure profiles in captive populations. Therefore, the evaluation criteria of this invention can accurately reflect the gut microbiota characteristics of sika deer in their natural ecological state, effectively preventing the misjudgment of normal seasonal food fluctuations in wild individuals as abnormalities, or masking the decline in gut microbiota function in captive individuals.

[0040] (7) Standardized design of the entire process to ensure method reproducibility and cross-study comparability. This invention specifies every step, from fecal sample collection and preservation (using a specific DNA stabilizer formula: 100mM TrisHCl, 100mM EDTA, 2% SDS), DNA extraction method (PowerSoil Pro kit), 16S rRNA gene amplification region (V3V4 region, primers 338F / 806R), sequencing depth (≥30,000 valid sequences), bioinformatics analysis parameters (QIIME2, 97% OTU clustering, SILVA 138 database), to health determination rules. Compared to the shortcomings of existing technologies, such as non-standardized key steps and incomparable results, this invention ensures a high degree of consistency and reproducibility of results across different locations, operators, and batches, meeting the stringent requirements for data standardization in long-term wildlife monitoring.

[0041] (8) The output results are concise and intuitive, and can be used directly by non-professionals. The final output of this invention includes a comprehensive health index, health level, a comparative chart of characteristic microbiota, a list of core differentially expressed microbiota, and corresponding management recommendations (such as "routine monitoring," "intensified monitoring," and "immediate clinical intervention"). This structured output encapsulates complex bioinformatics analysis results into content that protection managers can directly understand and use, significantly lowering the technical threshold and solving the practical bottleneck of existing technologies that rely on professional interpretation.

[0042] (9) Provide reagent kits and analysis software with industrial application value. This invention also provides a sika deer intestinal health assessment kit that integrates the aforementioned key reagents (fecal preservation solution, amplification primers, etc.) and judgment rules instructions, as well as a computer-readable storage medium for storing index calculations, health scores, and grade determination procedures. This kit is convenient for promotion and application in wildlife conservation agencies, nature reserves, and research institutions, and has good prospects for industrial transformation.

[0043] In summary, this invention is significantly superior to existing technologies in terms of non-invasiveness, species specificity, multi-dimensional integration, quantitative scoring, grading, ecological authenticity, standardization, and ease of use, providing a sensitive, reliable, and standardized technical tool for the health monitoring and conservation management of sika deer and other endangered deer species. Attached Figure Description

[0044] Figure 1 The PCoA graph is drawn based on the Bray-Curtis distance.

[0045] Figure 2 This is a flowchart of the detection method for assessing the health status of sika deer based on characteristic gut microbiota provided by the present invention.

[0046] Figure 3It is the ratio of the abundance of beneficial bacteria to pathogenic bacteria. Detailed Implementation

[0047] Example 1 The detection method for assessing the health status of sika deer based on characteristic gut microbiota provided by this invention forms a complete closed loop from sample collection to management recommendation output, such as... Figure 2 As shown. The core of this method lies in using the microbial community characteristics of healthy wild sika deer populations as a reference benchmark, overcoming the limitations of single indicators through multi-dimensional weighted integration, overcoming information loss from binary judgments through hierarchical thresholds, and overcoming the incomparability between experiments through full-process standardization. The specific process is as follows:

[0048] Step S1: Non-invasive collection and standardized preservation of fecal samples This step aims to ensure the representativeness of the sample and the stability of the microbial community composition from the source.

[0049] Fresh, moist fecal samples were collected in the Taohongling Sika Deer National Nature Reserve in Jiangxi Province and the Qingliangfeng National Nature Reserve in Zhejiang Province. Approximately 5 g of the internal, non-contact portion of the feces was scraped using a sterile sampling spoon to avoid cross-contamination by soil microorganisms. This design was based on the following technical considerations: the surface layer and the portion in contact with the ground of the feces have been exposed to environmental microorganisms, and their microbial composition deviates from the in situ gut microbiota; sampling the internal core portion can best restore the true gut microbiota signal.

[0050] Fecal samples were immediately transferred to a preservation solution containing DNA stabilizers. 1000 mL of the preservation solution contained: 100 mM Tris-HCl (pH 8.0), 100 mM EDTA (pH 8.0), and 2% SDS. The formulation was designed based on: (1) high concentrations of EDTA chelate metal ions, inhibiting DNase activity and preventing microbial DNA degradation; (2) SDS lyses microbial cell membranes, releasing DNA and inactivating endogenous nucleases; and (3) Tris-HCl maintains a neutral pH, preventing DNA depurination and breakage due to an acidic environment. Samples were stored at temperatures below 4°C and transported to the laboratory within 48 hours. Samples exceeding this time limit were discarded to ensure that the bacterial community composition did not undergo post-in vitro proliferative shift.

[0051] The sample preservation solution provided by this invention offers a cost advantage of 60% (actually 73.3%) compared to commercially available general fecal preservation solutions. Furthermore, it effectively protects the microbial DNA in sika deer feces from degradation even under the challenging 48-hour ambient temperature transportation conditions, maintaining the stability of the original intestinal flora structure and ensuring the accuracy of subsequent characteristic flora assessments of health indicators. The specific experimental comparison process is as follows:

[0052] 1. Experimental Materials and Methods The preservation solution formulation of this invention (per 1000 mL): Tris-HCl (pH 8.0) 100 mmol / L, EDTA (pH 8.0) 50 mmol / L, SDS 1.0% (w / v), with the remainder being deionized water. The dispensing volume per tube is 5 mL, and the cost of consumables and reagents per tube is 3.2 yuan / sample.

[0053] Commercially available control preservation solution: A commonly used general-purpose fecal DNA preservation solution (purchased from SIMGEN, product number: 4103100) was selected. The cost per use was RMB 12.0 per sample (cost reduction of approximately 73.3%, meeting the requirement of a reduction of more than 60%). Experimental design: Fresh feces were collected from 3 healthy sika deer and divided into 3 portions each: Control group (Fresh group): Immediately after collection, the samples were stored at -80°C. The present invention group (Test-48h group): Mixed in the preservation solution of the present invention, and placed at room temperature (25°C) for 48 hours before DNA extraction; Commercially available preservation solution group (Com-48h group): Mix well in commercially available preservation solution, and extract DNA after standing at room temperature (25°C) for 48 hours.

[0054] 2. Cost comparison data The raw materials, consumables, and processing costs of the preservation solution of this invention and commercially available preservation solutions were calculated and compared, as shown in Table 1.

[0055] Table 1. Comparison of Single Sample Preservation Solution Costs (RMB / sample) 3. Results of the 48-hour ambient temperature transport challenge experiment 3.1 Comparison of DNA extraction quality Total DNA was extracted from each group of samples, and its concentration and purity were determined using Nanodrop. The results are shown in Table 2. The results indicate that after 48 hours of storage at room temperature, the concentration and purity (A260 / A280 ratio) of the extracted DNA in the preservation solution of this invention were not significantly different from those in the Fresh group and the commercially available group, meeting the requirements for subsequent high-throughput sequencing.

[0056] Table 2. Comparison of DNA extraction quality among different groups of samples 3.2 Correlation analysis of microbial community composition and Beta diversity analysis (PCoA) 16S rRNA gene sequencing was performed on samples from each group. Based on the relative abundance of OTUs, the Spearman correlation coefficient of the genus-level population structure with the Fresh group (0 h) was calculated after 48 hours of storage at room temperature. And perform PCoA (principal coordinate analysis) and PERMANOVA based on Bray-Curtis distance to test the significance of differences.

[0057] Microbial community structure correlation: This invention group (Test-48h) vs. Fresh group: Attribute-level correlation coefficient R 2 =0.962±0.015; Commercially available group (Com-48h) vs. Fresh group: Horizontal correlation coefficient R 2 =0.968±0.011.

[0058] Experiments have shown that the preservation solution of this invention is as effective as commercially available preservation solutions in maintaining the consistency of bacterial species abundance.

[0059] PCoA plots were drawn based on Bray-Curtis distance. The results show ( Figure 1 ): Samples from the same sika deer (whether in the Fresh, Test-48h, or Com-48h group) cluster closely together on the PCoA graph, while samples from different individuals are far apart.

[0060] By calculating the Bray-Curtis distance of the same individual before and after 48 hours of storage at room temperature (Table 3), the deviation distance of the present invention group is: The levels were extremely low (generally considered to be <0.20 within acceptable ranges of variation within the same host) and showed no significant difference from the marketed group. ).

[0061] PERMANOVA analysis showed that the type of preservation solution and preservation time had no significant effect on the microbial community structure of the two groups of samples. ).

[0062] Table 3. Comparison of Bray-Curtis distance between samples stored at room temperature for 48 hours and the Fresh group. *Note: The P-value was obtained by t-test with Com-48h as a reference. P>0.05 indicates that there is no significant difference between the preservation solution of the present invention and the commercially available preservation solution.

[0063] Step S2: Extraction of total microbial DNA and amplification of the V3-V4 region of the 16S rRNA gene This step aims to obtain high-quality and representative microbial genomic DNA fragments.

[0064] Extraction was performed using the PowerSoil Pro DNA Extraction Kit, following standard operating procedures. This kit was chosen because its bead milling process effectively breaks down the thick cell walls of Gram-positive bacteria, avoiding distortion of bacterial composition due to differences in lysis efficiency—a key variable affecting the accuracy of fecal microbiota quantification.

[0065] The hypervariable region of bacterial 16S rRNA gene V3-V4 was used as the amplification target, and the primer sequence was as follows: Upstream primer 338F: 5'-CCTACGGGNGGCWGCAG-3', SEQ ID NO.1; Downstream primer 806R: 5'-GACTACHVGGGTATCTAATCC-3', SEQ ID NO.2.

[0066] The V3-V4 region was chosen because: (1) this region is approximately 450 bp in length, which can achieve complete coverage and high-quality splicing under Illumina's paired-end 250 bp sequencing strategy, avoiding splicing errors caused by long fragments; (2) compared with a single segment of the V4 region, the V3-V4 region has stronger classification resolution and can achieve more accurate species annotation at the genus level; (3) this primer pair has good universality, balanced amplification efficiency for most bacterial groups, and low bias. The number of PCR amplification cycles was limited to 30 to avoid chimeras and amplification bias introduced by over-amplification.

[0067] The above limitations ensure that comparable amplification products are obtained between different batches and different operators, laying the foundation for subsequent quantitative comparisons.

[0068] Step S3: High-throughput sequencing and data preprocessing This step aims to obtain high-quality information on the composition of the microbial community from the raw sequencing data.

[0069] We employed an Illumina paired-end 250 bp sequencing strategy, aiming for a sequencing depth of at least 30,000 valid sequences per sample. This depth was determined based on sparse curve analysis. When the sequencing depth reached 30,000 sequences, the Shannon diversity index of the sika deer fecal samples tended to saturate, and the detection rate of new OTUs plateaued, which was sufficient to capture the microbial diversity within the samples.

[0070] Data preprocessing parameters: Standardization is performed using the QIIME2 platform: Quality control: Remove sequences with an average quality score below Q20, containing ambiguous bases, or with a length less than 200 bp; splicing: minimum overlap length 20 bp, maximum mismatch number 1; Chimera removal: using the uchime-denovo method; OTU clustering: 97% similarity threshold; Species annotation: SILVA 138 database, 99% similarity standard; Contamination removal: Removal of mitochondrial, chloroplast, and unclassified sequences.

[0071] The fixation of the above parameters is crucial to ensuring comparability across studies. In particular, the choice of 97% for the OTU clustering threshold, rather than the more popular 100% ASV method, is based on the following considerations: the gut microbiota of sika deer contains a large number of uncultured novel strains, and their 16S rRNA genes exhibit natural variations within the species. A 100% similarity threshold might lead to the same species being over-segmented into multiple ASVs, thereby overestimating diversity and reducing the power of statistical tests.

[0072] Step S4: Calculation of characteristic microbial community indicators The purpose of this step is to construct a four-dimensional characteristic microbial community indicator system specific to sika deer. This four-dimensional indicator system characterizes the health status of the microbial community from four dimensions: "beneficial forces, pathogenic forces, overall complexity, and antagonistic balance," and its information completeness is significantly better than the single-dimensional or two-dimensional indicators of existing schemes.

[0073] I. Determination of Core Beneficial Bacteria and Their Health Benchmark Values 1. Sample Source The invention team collected 375 fresh fecal samples from wild sika deer that were determined to be healthy based on clinical observation and the degree of fecal formation in the Taohongling Sika Deer National Nature Reserve in Jiangxi Province and the Qingliangfeng National Nature Reserve in Zhejiang Province. The samples were then mixed in a 5-in-1 manner according to the sampling season and region, resulting in 75 mixed samples. The samples covered all four seasons and different geographical populations (sample numbers: ZJ1-15, Spring1-15, Summer1-15, Autumn1-15, Winter1-15), and were highly representative.

[0074] 2. Method Sequencing analysis: Total DNA was extracted from fecal samples, and an amplicon library was constructed using V3-V4 region-specific primers (SEQ ID NO.1 and SEQ ID NO.2). The original sequences were obtained using a high-throughput sequencing platform.

[0075] Bioinformatics analysis: The QIIME2 pipeline was used for noise reduction and clustering, and the relative abundance of microorganisms at the family level was obtained by comparing with the SILVA 138 classification database.

[0076] Quantitative analysis of core microbiota: The average abundance, detection rate, and coefficient of variation of each bacterial family were statistically analyzed in 75 healthy samples.

[0077] 3. Screening of core beneficial bacteria Based on species abundance statistics and known functional association analysis, this invention focuses on two core symbiotic families: Lachnospiraceae and Rikenellaceae.

[0078] Lachnospiraceae was detected in 100% of 75 healthy field samples, with an average relative abundance of [missing information]. Functional gene prediction showed that it was enriched in butyrate synthesis pathways (such as...). buk and ptb (Genes) produce butyric acid, which provides energy to the intestinal epithelium of sika deer and strengthens the physical barrier.

[0079] The detection rate of Rikenellaceae in the whole sample was 100%, with an average relative abundance of [missing information]. .

[0080] Given that sika deer consume large amounts of crude fiber plants in the wild, the RIKEN family of fungi, as the main hemicellulose-degrading bacteria, is key to their acquisition of energy metabolites.

[0081] 4. Establishment of the health baseline range (17%~24%) The team of this invention summed the "relative abundance of Trichophyceae" and "relative abundance of Riken Bacteriaceae" in each sample and defined it as the core beneficial bacteria index (Apro).

[0082] Data calculation: A normality test (Shapiro-Wilk test) was performed on the Apro values ​​of 75 samples. The results showed that the indicator exhibited a significant normal distribution in the healthy population.

[0083] Population Mean: 21.0% Standard deviation (SD): 1.8% Threshold setting: Based on statistical principles, a one-sided 95% confidence interval for the healthy population distribution was used, plus a margin for fluctuation. Further adjustments were made based on seasonal fluctuations in the data (the value was found to fluctuate between 18% and 23% in winter and summer). Calculation results: , .

[0084] To improve the robustness and sensitivity of the evaluation indicators in clinical applications, this invention ultimately establishes that when the sum of the abundance of Trichophyceae and Rikenaceae is stable within the range of 17% to 24%, the sika deer's intestines are considered to be in a healthy state with efficient nutrient conversion and stable immune defense.

[0085] 5. Experimental verification The relative abundance of microorganisms at the family level obtained from the above bioinformatics analysis indicates that the Zhejiang Qingliangfeng samples (ZJ1-ZJ15) have... The mean is Although the percentage of individuals in the Taohongling sample from Jiangxi Province (Spring / Summer / Autumn / Winter) showed slight variations across different seasons, over 90% fell within the range of 17.0% to 23.8%. This stable statistical pattern confirms the scientific validity and feasibility of this indicator as a benchmark for assessing the health of sika deer.

[0086] II. Screening of Indicator Genus of Potential Pathogenic Bacteria and Determination of Health Standards 1. Sample Source The genus-level bacterial composition of 75 healthy wild sika deer (Healthy Group) from Taohongling Nature Reserve in Jiangxi and Qingliangfeng Nature Reserve in Zhejiang was analyzed in depth and compared with that of individuals in the Poor Condition Group (12 samples, sample numbers: FX1-12) collected at the same time, in order to establish a pathogenic bacteria distribution benchmark with preventive and surveillance significance.

[0087] 2. Functions and screening of indicator bacteria genera Following the same bioinformatics analysis as the aforementioned core beneficial bacteria, genus-level abundance was obtained. Through genus-level differential significance analysis (LEfSe, LDA Score > 3.5), this invention identified the following two key bacterial genera that abnormally increased in anomalous samples: *Escherichia* (…). Escherichia ), Shigella genus ( Shigella ) and Clostridium ( Clostridium ):

[0088] Escherichia coli ( Escherichia ) and Shigella genus ( Shigella This genus belongs to the phylum Proteobacteria, family Enterobacteriaceae. Its abundance is extremely low in healthy individuals. However, its proportion increases rapidly during intestinal stress or when invaded by environmental pathogens. It contains potentially pathogenic bacteria such as enterotoxigenic Escherichia coli and Shigella. Abnormally high abundance can lead to intestinal secretion disorders, causing acute diarrhea and intestinal mucosal inflammation.

[0089] Clostridium ( Clostridium This genus belongs to the phylum Firmicutes, family Clostridium. Healthy individuals contain only very low abundances of symbiotic Clostridium species in their intestines, but in immunocompromised individuals, certain virulent strains (such as Clostridium perfringens and similar species) can abnormally occupy niches. Some members of this genus can produce potent exotoxins that disrupt intestinal wall integrity.

[0090] 3. Statistical analysis based on data from 75 healthy field samples. Based on the obtained relative abundance at the genus level, the present invention summarizes the sum of the abundances of the above two types of potentially pathogenic genera ( System calculations were performed: Health benchmark data statistics: Sample size: n=75 (covering two geographical populations in Jiangxi and Zhejiang, spanning all four seasons).

[0091] Calculation method: .

[0092] Statistical results: Among the 75 healthy individuals in the wild, most individuals had this indicator at a level that was within acceptable limits. Fluctuations between.

[0093] Mean: Standard deviation (SD): .

[0094] The basis for establishing the health threshold of "4.5%" is as follows: This invention employs the statistical upper limit discriminant method, taking... This serves as the critical safety value. The calculated theoretical value is... To facilitate clinical interpretation and provide a certain degree of dynamic tolerance redundancy in practical applications, this invention ultimately established it as 4.5%.

[0095] 4. Experimental verification Among 75 healthy samples, using 4.5% as the threshold, the accuracy rate was as high as 96% (only a very few spring samples experienced brief fluctuations at the critical point due to environmental changes).

[0096] When the test results show that the abundance of core beneficial bacteria (Trichophyceae + Rikenaceae) is above 17% and the abundance of potentially pathogenic bacteria (Escherichiae + Shigella + Clostridium) is below 4.5%, the sika deer's intestinal flora is considered to be in a robust state. Once the sum of the abundance of potentially pathogenic bacteria genera exceeds 4.5%, even if the individual has not yet shown clinical symptoms of diarrhea, it indicates that its intestinal barrier has been damaged, and timely environmental intervention or feed supplementation is required.

[0097] III. Determination of the abundance ratio of beneficial bacteria to pathogenic bacteria 1. Literature Support and Existing Technological Background B / E value ( Bifidobacterium / Enterobacteriaceae In human clinical microbiology, the ratio of Bifidobacteria (beneficial bacteria) to Enterobacteriaceae (pathogenic bacteria) is a recognized indicator of intestinal colonization resistance. Studies have shown that a decrease in this ratio is highly correlated with impaired intestinal barrier function and endotoxemia.

[0098] According to the existing technology "Wang, ZT, et al. (2006). Assessment of intestinal microbiota in patients with severe acute pancreatitis. Chinese MedicalJournal, 119(24), 2097-2101", "Pinzone, MR, et al. (2012). Gut dysbiosis in HIV infection: unique challenge and research opportunities. Frontiers inMicrobiology, 3, 280", "Nurmi, E., & Rantala, M. (1973). New aspects of Salmonella infection in broiler production. Nature, 241(5386), 210-211.", "Kamada, N., et al. (2013). Role of the gut microbiota in host defense against gastrointestinal pathogens. Cold Spring Harbor Perspectives in Biology, 5(6), a013102.","Maltz, MA, et al. According to the record "(2015). Competitive exclusion of transient pathogens by the resident gut microbiota. Microbiology Spectrum,3(3), 10.1128 / microbiolspec.", the B / E ratio (Bifidobacterium / Enterobacterium ratio) is the core indicator for assessing intestinal colonization resistance. A value less than 1 indicates that the competitive exclusion of pathogens by beneficial bacteria has failed, and the intestinal barrier is severely damaged. Existing technologies mostly use the B / E ratio as an indicator for assessing intestinal colonization resistance in humans or livestock (pigs, cattle). However, this invention is the first to use a specific bacterial group combination of (Trichophyceae + Rikenaceae) / (Escherichia + Shigella + Clostridium) as an alternative assessment indicator for wild sika deer, and for the first time establishes "1" as the critical threshold for its intestinal barrier function.Furthermore, this invention, for the first time, demonstrates using data from all four seasons that this ratio can automatically offset false positives caused by seasonal feeding (such as fluctuations in the microbial community due to spring grazing on green fodder). The specific verification process is as follows:

[0099] 2. Indicator Construction and Calculation Formula To improve diagnostic sensitivity, this invention constructs a beneficial-to-pathogenic (VBP) ratio: .

[0100] Statistical robustness analysis (refuting the ambiguity of a single indicator) This invention compares data from Jiangxi populations in spring (when food and water are plentiful) and winter (when they feed on dry bark / fallen leaves): Single indicator observation: The abundance of RIKEN fungi in healthy individuals in spring (average 15%) was significantly lower than in winter (average 22%). If this indicator is considered alone, it may be mistakenly concluded that the digestive capacity of individuals is reduced in spring.

[0101] Integrated indicator observation: Although the values ​​of individual bacterial genera fluctuated with the seasons, the VBP values ​​of the healthy group (n=75) remained between 4.2 and 85.0 throughout the four seasons. This demonstrates that beneficial bacteria consistently hold an overwhelming dominant position in a healthy gut, and the ratio method can effectively filter out seasonal "background noise".

[0102] Determining the critical value "1": Box plot analysis of VBP distribution in 75 healthy individuals and 12 individuals with declining physical condition (diarrhea) revealed a mean VBP of 18.5 in the healthy group. 98% of these individuals had a VBP > 3.0. The VBP distribution in the declining physical condition group... Beneficial bacteria shrink due to insufficient nutrient intake, while The pathogens broke out due to weakened immunity. Calculations showed that the VBP values ​​in this group dropped sharply, concentrated in the range of 0.2 to 0.9.

[0103] VBP = 1 is the "lifeline" for gut microbiota balance. When VBP < 1, it means that the strength of pathogenic bacteria has exceeded the sum of the two core beneficial bacteria, and the original intestinal defense barrier has failed. Figure 3 .

[0104] This invention utilizes receiver operating characteristic (ROC) curves to evaluate the diagnostic value of various indicators for health abnormalities in sika deer: RIKEN's single indicator for Mycology: AUC (Area Under the Curve) = 0.72, indicating a moderate accuracy rate in identification.

[0105] The single index for Escherichia coli and Shigella spp. was AUC = 0.81, indicating a certain rate of underreporting.

[0106] VBP ratio metrics: AUC = 0.96, sensitivity 91.7%, specificity 98.6%.

[0107] The results demonstrate that the VBP ratio is significantly optimal in evaluating the gut health of sika deer, and can capture signals of gut microbiota dysbiosis earlier and more accurately.

[0108] IV. Weighting of the Health Index (HGI) for Sika Deer 1. Definition of evaluation indicators and extraction of basic data Relevant data were extracted from all 75 healthy field samples and 12 diseased / diarrhea samples from the genus-level abundance table, and the following four sub-indicators were calculated and defined: The ratio of the abundance of beneficial bacteria to pathogenic bacteria (VBP ratio). Relative abundance of core beneficial bacteria genera (sum of relative abundance of Trichophyceae and RIKEN Trichophyceae) Relative abundance of potentially pathogenic bacteria genera (sum of relative abundance of Escherichia, Shigella, and Clostridium). Shannon Index Calculations based on the relative abundance distribution of each genera at the genus level: Pi is the relative abundance of the i-th species.

[0109] 2. Data Normalization Score Calculation Rules For positive indicators, the ratio of individual values ​​to the reference mean is calculated and multiplied by 100 to initially limit the score to the range of 0 to 100, where 100 is set as the preset upper limit and 0 is set as the preset lower limit, and the part exceeding the preset range is truncated accordingly. For negative indicators, calculate the ratio of the reference mean to the individual value, multiply by 100, and apply the same interval limits and truncation treatment as for positive indicators. The positive indicators include individual values ​​such as the abundance ratio of beneficial bacteria to pathogenic bacteria, the relative abundance of core beneficial bacteria, and the Shannon diversity index; the negative indicators include individual values ​​of the relative abundance of potential pathogenic bacteria. The specific calculation formulas are as follows:

[0110] Positive indicators (abundance ratio of beneficial bacteria to pathogenic bacteria) Core beneficial bacteria Shannon index ): Negative indicators (relative abundance of potentially pathogenic genera) ): 3. Construction of Comprehensive Health Index and Optimization of Grid Search Constructing a linearly weighted comprehensive gut health index (HGI): To determine the optimal weight allocation (W1, W2, W3, W4), For step size, in Perform a four-dimensional mesh parameter search within the specified range: Positive (healthy) sample input (Label = 1): Import data from 75 field background samples. The inclusion criteria were: (1) distributed in the core area of ​​the nature reserve with minimal human interference; (2) good body condition score (BCS≥3.0); (3) no abnormal mortality records within 3 months before and after sampling. The setting of these three criteria ensures that the reference population truly represents the ideal health status of sika deer under natural ecological conditions and avoids the possible baseline bias of the microbiome in captive populations.

[0111] Negative (illness) sample input (Label = 0): Import data from 12 individuals with diarrhea (FX1-FX12).

[0112] Performance evaluation index (AUC): For each weight combination (e.g. (etc.), calculate the final result for all 87 samples. Scoring. Using the receiver operating characteristic (ROC) curve, plot the true positive rate against the false positive rate and calculate the area under the curve (AUC value).

[0113] 4. Grid search results and weight configuration In thousands of different weight combinations, the discriminative effectiveness of different indicator weight combinations exhibits regular fluctuations: Sensitivity of the abundance ratio (W1) of beneficial bacteria to pathogenic bacteria: when percentage of At this point, the inflection point of the ROC curve is closest to the upper left, and the AUC increases significantly. This indicates that the integrated ratio index can greatly suppress background noise in the samples during seasonal transitions, exhibiting the highest specificity;

[0114] Sensitivity of the Shannon Index (W4): When the W4 weight is too high ( During spring and winter, the sudden change in the diet of healthy sika deer can temporarily reduce diversity, often triggering false alarms in healthy samples. Final optimal solution: When the grid search reaches When combined, the model showed the clearest distinction between the healthy group in the wild and the diseased group in captivity, with the AUC value reaching a maximum of 0.982.

[0115] Table 4 Weight Allocation 5. Evaluation of the effectiveness of the formula Substitute the final weights into the diagnostic formula: ; The score ranges from 0 to 100, with higher scores indicating that the gut microbiota is closer to that of a healthy wild population.

[0116] The calculation results show that 75 healthy samples The index is mainly concentrated in (mean 0.88), while the 12 disease samples The index then plummeted to (Mean 0.32) The two sets of data had no overlapping intervals, indicating excellent diagnostic specificity. This demonstrates that the weight allocation established through grid search is highly scientifically sound and clinically applicable in evaluating the intestinal health of sika deer.

[0117] Step S5: Determine Health Status Level This step transforms the continuous comprehensive health index into discrete health levels, facilitating protection and management decisions.

[0118] The three-level threshold setting method is as follows: Based on the comprehensive health index distribution of the field health reference group, its mean (μ) and standard deviation (σ) are calculated. Setting:

[0119] Health level: Health index ≥ μ - 1.5σ. Explanation: Balanced gut microbiota, good intestinal homeostasis. Management recommendations: Maintain the current diet program and monitor regularly.

[0120] Sub-health level: μ - 3.0σ ≤ Health Index < μ - 1.5σ. Explanation: Fluctuations in gut microbiota, with a decrease in beneficial bacteria or reduced diversity. Management recommendations: Optimize feed formulation, increase probiotic / prebiotic intake, and reduce stress.

[0121] Disease Risk Level: Health Index < μ - 3.0σ. Explanation: Severe dysbiosis with a significant increase in pathogens. Management Recommendations: Trigger alert; initiate clinical isolation and observation; provide veterinary intervention if necessary.

[0122] The reason for using 1.5σ and 3.0σ as the dividing points is that: (1) under the assumption of normal distribution, the μ±1.5σ interval contains about 86.6% of healthy individuals, and individuals outside the interval are more likely to have a real deviation; (2) the μ±3.0σ interval contains about 99.7% of individuals, and those outside this range can be highly certain to be abnormal; (3) the “sub-health” transition zone is set between the two thresholds, which provides a buffer space for “strengthening monitoring and observation” for protection managers, and avoids directly classifying individuals with slight deviations into the disease category, thus avoiding unnecessary intervention.

[0123] Step S6: Result Output and Early Warning This step transforms the aforementioned analysis results into a structured output that can be directly used by protection managers.

[0124] The individual assessment report includes: (1) the comprehensive health index and corresponding level; (2) a visual comparison of the current values ​​of the four characteristic microbiome indicators with the reference range; (3) a list of the core differential microbiomes with the most significant deviations; and (4) corresponding management recommendations ("maintain the frequency of routine monitoring", "intensify monitoring and re-examination", "immediate clinical intervention").

[0125] Warning rules: When the disease risk level is determined, an early warning is triggered, and a warning message is sent to the management personnel of the protected area. It is recommended to track and observe the individual or individuals in the area and collect subsequent samples to confirm the trend of changes in the microbial community (continuous deterioration or spontaneous recovery).

[0126] Example 2 To verify the effectiveness of the detection method for assessing the health status of sika deer based on characteristic gut microbiota established in this invention, this invention additionally collected 60 wild healthy population samples and 10 captive individuals clinically diagnosed with digestive tract diseases (disease control group) to conduct a full-process analysis, calculate and establish a health baseline reference line.

[0127] Based on this, the present invention selected two healthy individuals and two disease control groups for comparative evaluation. The following is a detailed analysis and evaluation process of practical application:

[0128] I. Establish a health assessment benchmark. Based on 60 healthy wild populations, this invention extracts four core indicators of healthy reference population characteristics from family / genus-level abundance tables and calculated diversity data: 1. Definition of the mean of the core microbiota's healthy background (mean of 60 healthy samples): Mean relative abundance of core beneficial bacteria (abundance of Trichophyceae + Rikenaceae) .

[0129] Relative abundance of potentially pathogenic bacteria (Escherichia coli) Escherichia + Shigella Shigella + Clostridium Clostridium (abundance) mean .

[0130] Shannon Diversity Index Mean .

[0131] Mean of the beneficial bacteria / pathogenic bacteria abundance ratio (VBP ratio) .

[0132] 2. Establishment of the Health Index (HGI) cutoff line: The Health Index (HGI) calculated from 60 healthy field samples was statistically analyzed using a normal distribution, yielding the following results: Mean Health Index Standard deviation .

[0133] Calculate the critical threshold for health level according to the formula in step S6 of Example 1: Health level assessment criteria: ; Sub-health level assessment criteria: (Among them, the lower limit) ); Disease risk level assessment line: .

[0134] II. Representative Sample Evaluation Methods and Calculation Process This invention extracts raw sequencing annotation data from two healthy individuals and two individuals with diarrhea, and substitutes them into the seven steps of Example 1 for calculation: 1. Normalized score calculation rules The formulas for calculating the positive indicator score, the negative indicator score, and the comprehensive health index are all shown in Example 1.

[0135] The representative sample evaluation data is shown in Table 5.

[0136] Table 5 Representative Sample Evaluation Data III. Results Analysis and Management Recommendations 1. Individual Health Analysis Report Healthy individual 1 (HGI = 100.0) and healthy individual 2 (HGI = 99.67): The intestinal barriers of both sika deer tested were extremely stable. The core beneficial bacteria were both above 20% (the upper limit of normal), the VBP ratio was in the excellent range (>20.0), and the potential pathogens were below 1%, with the Shannon index remaining high.

[0137] Management Recommendation: The patient is in good health. It is recommended to maintain the routine monitoring frequency, with quarterly checks, without requiring manual intervention.

[0138] 2. Individual Analysis Report of Disease Control Group (Analysis of Abnormal Diagnoses) Individual 1 with diarrhea (HGI = 26.17) and individual 2 with diarrhea (HGI = 20.57): Both individuals clinically diagnosed with gastrointestinal diseases had health scores below 30, far below the lower limit of health. .

[0139] Diagnostic feature analysis: The antagonistic ratio completely collapses (VBP ratio <1): The VBP ratios are 0.75 and 0.25 respectively, which means that the original "core beneficial bacteria defense line" in the intestine has been reversed and engulfed by pathogenic bacteria, and the antagonistic inhibition barrier has been lost. This can easily lead to acute enteritis and watery dehydration diarrhea.

[0140] An abnormal surge in the abundance of pathogenic bacteria: In particular, the relative abundance of potential pathogenic bacteria (Escherichia-Shigella and Clostridium) in diarrheal individual 2 was as high as 12.50%, which is more than 13 times the healthy average, and is a typical case of pathogen overload.

[0141] Diversity cliff loss: Shannon's score significantly lagged behind the health value, indicating a highly limited digestive system function and loss of cellulose hydrolysis capacity.

[0142] Management and early warning recommendations: Immediately trigger the "Disease Risk" red alert.

[0143] "Immediate clinical intervention" is recommended: Isolate individuals 1 and 2 with diarrhea and cooperate with veterinarians to use sensitive antibiotics (targeting pathogenic Escherichia coli / Clostridium) or probiotics to rebuild the beneficial intestinal flora.

[0144] Environmental monitoring: If a batch of samples in the area show that the HGI index is tilted towards the disease range, it is necessary to investigate the current water source safety and external pathogenic factors such as food spoilage.

[0145] Example 3 Reagent kit preparation: includes reagent a, component b and component c.

[0146] (a) Preparation of fecal DNA stabilization solution (reagent a) This preservation solution is designed to rapidly lyse parts of cells and inactivate nucleases using high salt and surfactants, while maintaining the integrity of DNA fragments.

[0147] The formula and final concentration of the preservation solution for 1000mL are as follows: Tris-HCl (pH 8.0): 50~100 mmol / L (to maintain pH stability); EDTA (pH 8.0): 50~100 mmol / L (chelates metal ions and inhibits DNase activity); SDS (Sodium dodecyl sulfate): 0.5%~2.0% (w / v) (denatures proteins and lyses cells); NaCl (optional): 0.5~1.0 mol / L (increases osmotic pressure and assists in corrosion prevention); Sterilized deionized water: Add to volume.

[0148] Preservative solution preparation steps: Weigh out each chemical reagent according to the specified proportions.

[0149] Dissolve Tris, EDTA, and NaCl in deionized water and adjust the pH to 8.0 using concentrated hydrochloric acid.

[0150] Add SDS and stir gently to avoid creating too many bubbles until completely dissolved.

[0151] Use a 0.22 μm filter membrane for sterilization or autoclave (Note: If it contains SDS, autoclaving may generate bubbles. It is recommended to filter or sterilize other components first before adding SDS under aseptic conditions).

[0152] Dispense the preservation solution into 10 mL sterile sampling tubes at a rate of 5 mL per tube.

[0153] (ii) Preparation of PCR amplification primer pairs (reagent b) Specific primers were designed for the V3-V4 hypervariable region of the bacterial 16S rRNA gene, namely SEQ ID NO.1 and SEQ ID NO.2 in step S2 of Example 1.

[0154] Preparation and Packaging: The primers were synthesized using a chemical synthesis method.

[0155] After HPLC purification, it was diluted to 10 μmol / L with ultrapure water (Nuclease-free water).

[0156] Each kit contains 100-200 μL and should be stored in a sterile centrifuge tube at -20°C.

[0157] (III) Preparation of the reagent kit instructions (Component c) The instruction manual must include specific calculation formulas, preset weights, and judgment criteria.

[0158] The calculation formulas for characteristic microbial community indicators, the normalized scoring criteria, and the health level determination criteria are all the same as in Example 1.

[0159] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A detection method for assessing the health status of sika deer based on characteristic gut microbiota, characterized in that, Includes the following steps: Fresh fecal samples were collected from sika deer and transferred to a preservation solution containing DNA stabilizers for storage. Total microbial DNA was extracted from fecal samples, and the V3-V4 region of the bacterial 16S rRNA gene was amplified to obtain the amplification product. The amplified products were subjected to high-throughput sequencing. The raw sequences were preprocessed to obtain effective sequences. Based on the effective sequences, OTU clustering, species annotation and contamination removal were performed to obtain the microbial community composition profile. Four characteristic microbial community indicators were calculated based on the microbial community composition profile. These four indicators include the relative abundance of core beneficial bacteria, the relative abundance of potential pathogenic bacteria, the Shannon diversity index, and the abundance ratio of beneficial bacteria to pathogenic bacteria. Using a healthy wild sika deer population as a reference group, the four characteristic microbial community indicators were normalized and scored respectively, and then the comprehensive health index was obtained by weighted summation according to preset weights. Based on the comprehensive health index, the health status is determined as a healthy level, a sub-healthy level, or a disease risk level. The system outputs an assessment report containing health status and management recommendations, and triggers an alert when a disease risk level is determined.

2. The method according to claim 1, characterized in that, The primer pairs used for the amplification are shown in SEQ ID NO.1 and SEQ ID NO.

2.

3. The method according to claim 1, characterized in that, The high-throughput sequencing uses paired-end sequencing, with each sample containing no less than 30,000 valid sequences. The data preprocessing includes removing low-quality sequences, splicing, and removing chimeras. The low-quality sequences are those with an average quality score below Q20, containing ambiguous bases, and a length of less than 200 bp. OTU clustering and species annotation based on valid sequences are performed using a 97% similarity threshold for OTU clustering and using the SILVA138 database with a 99% similarity standard for species annotation. The contamination removal involves removing sequences from mitochondria, chloroplasts, and those not classified to the boundary level.

4. The method according to claim 1, characterized in that, The relative abundance of the core beneficial bacteria is the sum of the relative abundance of the Trichophytonceae family and the relative abundance of the Rikenaceae family; the relative abundance of the potential pathogenic bacteria is the sum of the relative abundance of the Escherichia, Shigella, and Clostridium genera; the abundance ratio of the beneficial bacteria to the pathogenic bacteria is the ratio of the relative abundance of the core beneficial bacteria to the relative abundance of the potential pathogenic bacteria.

5. The method according to claim 1, characterized in that, In the preset weights, the abundance ratio of beneficial bacteria to pathogenic bacteria has a weight of 0.35, the relative abundance weight of core beneficial bacteria has a weight of 0.30, the relative abundance weight of potential pathogenic bacteria has a weight of 0.20, and the Shannon diversity index has a weight of 0.

15.

6. The method according to claim 1, characterized in that, The normalized score is: For positive indicators, the ratio of individual values ​​to the reference mean is calculated and multiplied by 100 to initially limit the score to the range of 0 to 100, where 100 is set as the preset upper limit and 0 is set as the preset lower limit, and the part exceeding the preset range is truncated accordingly. For negative indicators, calculate the ratio of the reference mean to the individual value, multiply by 100, and apply the same interval limits and truncation treatment as for positive indicators. The positive indicators include the abundance ratio of beneficial bacteria to pathogenic bacteria, the relative abundance of core beneficial bacteria, and the Shannon diversity index; the negative indicators include the relative abundance of potential pathogenic bacteria.

7. The method according to claim 1, characterized in that, The three-level threshold is set as follows: A health index ≥ μ - 1.5σ indicates a good health level. A health index of μ - 3.0σ ≤ μ - 1.5σ indicates a sub-healthy state. A health index < μ - 3.0σ indicates a disease risk level; Where μ is the mean of the comprehensive health index of the field health reference group, and σ is the standard deviation.

8. The method according to claim 1, characterized in that, The fecal sample is the portion of the feces that has not been in contact with the ground; each 1L of preservation solution contains 50~100 mmol / L Tris-HCl, 50~100 mmol / L EDTA, 0.5%~2.0% SDS and 0~1.0 mol / L sodium chloride.

9. A test kit for assessing the health status of sika deer, characterized in that, The kit comprises (a) to (c): (a) Fecal DNA stabilization solution, each 1L of the solution contains 50~100 mmol / L Tris-HCl, 50~100 mmol / L LEDTA, 0.5%~2.0% SDS and 0~1.0 mol / L sodium chloride; (b) Primer pairs used to amplify the V3-V4 region of the bacterial 16S rRNA gene, the primer pair sequences of which are shown in SEQ ID NO.1 and SEQ ID NO.2; (c) A description containing the calculation of characteristic microbial community indicators, preset weights, and health status determination involved in the method of claim 1.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the functions of calculating characteristic microbial community indicators, scoring comprehensive health index, and determining health status level involved in the method of claim 1.

Images