Method for detecting true pregnancy of panda

Abstract

The invention relates to the technical field of panda true pregnancy detection, and discloses a panda true pregnancy detection method. The method comprises the following steps: (1) respectively collecting blood samples of pandas with anorexia (averagely 3 months) before and after mating, and extracting RNA (Ribonucleic Acid) for later use; (2) calculating relative expression quantities of the marker 1, the marker 2 and the marker 3 in the to-be-detected sample after mating by taking a panda group sample before mating as baseline data; and (3) comparing the markers with respective relative expression quantity confidence range values, and judging true pregnancy or false pregnancy. Based on quantitative expression detection of the specific blood molecular marker, true pregnancy and false pregnancy states can be accurately distinguished, and the pregnancy state can be more stably and directly reflected compared with a urine metabolite. Compared with the prior art, samples only need to be collected at the two time points before and after hybridization, comparison detection can be completed through two times of sampling, frequent urine collection every week is not needed, and the labor cost is remarkably reduced.

Description

Technical Field

[0001] This invention belongs to the field of giant panda true pregnancy detection technology, specifically a method for detecting true pregnancy in giant pandas. Background Technology

[0002] Existing literature indicates that during pregnancy, exosomes secreted by the embryo can enter the maternal bloodstream to facilitate communication between the mother and embryo, ensuring stability throughout the pregnancy. Exosomes carry important genetic information or powerful biological signals; in particular, compared to intracellular and cell-free blood, exosomes provide a protective and enriched source of microRNAs (miRNAs) for biomarker analysis. miRNAs can regulate various biological functions, including cell proliferation, apoptosis, differentiation, metabolism, organ development, embryogenesis, and implantation. In recent years, the role of exosomal miRNAs in pregnancy has attracted considerable attention in humans and some important mammals, such as cows, pigs, and sheep. For example, in humans, exosomal miRNAs in early and late pregnancy may affect the gestation period, and human embryo implantation failure is associated with different miRNA profiles in plasma and plasma exosomes during the implantation window. In dairy cows, miR-126-3p levels are significantly reduced in the pregnant group and can serve as a biomarker for early pregnancy diagnosis in dairy cows. In pigs, miR-92b-3p and miR-17-5p can be used as potential circulating biomarkers for early pregnancy diagnosis. In sheep, several exosomal miRNAs show varying abundance levels in early pregnancy, potentially serving as biomarkers for accurately assessing reproductive status. Therefore, exosomal miRNAs are considered potential molecular biomarkers for precise assessment of pregnancy status.

[0003] Giant panda fetal development begins approximately one month before birth. Because newborn cubs are underdeveloped, weighing only 90-130g, a fraction of the mother's weight, it's impossible to determine pregnancy based on appearance alone. After mating, giant pandas may experience pseudopregnancy (or failed pregnancy), and individuals with pseudopregnancy (or failed pregnancy) exhibit consistent physiological and behavioral changes with those with true pregnancy, such as decreased appetite, nesting, and breast enlargement. Regarding sex hormones, there is no difference in the duration and level of progesterone in the urine of truly pregnant and pseudopregnant (or failed pregnancy) pandas. Currently, the main methods for determining true pregnancy in giant pandas include ultrasound, molecular biology methods, and urinary metabolic products.

[0004] Using ultrasound to determine true pregnancy in giant pandas has two major limitations. First, panda fetuses are too small to be detected by ultrasound, requiring highly skilled operators and increasing the risk of misdiagnosis. Second, pandas undergoing ultrasound examinations need extensive training to cooperate with the procedure, demanding a high degree of cooperation. Therefore, performing ultrasound examinations on all giant pandas after mating is too restrictive.

[0005] Patent application CN202210794976.5 discloses a molecular method for detecting early pregnancy in giant pandas. It utilizes differentially expressed innate and adaptive immune genes between pregnant and non-pregnant pandas as molecular markers, and uses real-time quantitative PCR to quantitatively detect these markers in the sample. This patent examines pregnancy in giant pandas at the molecular biological level. While this method can detect true pregnancy in early pregnancy to some extent, it cannot distinguish between true and false pregnancies in giant pandas.

[0006] Another authorized patent document, CN119534872B, discloses a method for predicting true pregnancy in giant pandas. Based on the physiological characteristic that the mother's immunity gradually decreases as pregnancy progresses under reproductive stress, this method predicts true pregnancy in female giant pandas. The method provides a method for predicting true pregnancy based on the concentrations of progesterone, neopterin, and creatinine in urine. N1 is the average value of neopterin (based on creatinine correction) from the moment the progesterone level in the female giant panda's urine first exceeds 100 ng / mL Cr until the progesterone level reaches its peak. N2 is the average value of neopterin (based on creatinine correction) during the period when the progesterone level first drops from its peak to below 200 ng / mL Cr. When the N2 / N1 ratio is between 1.39 and 2.08, the mated giant panda is considered to be truly pregnant; otherwise, it is considered a false pregnancy. This invention aims to predict true pregnancy by examining pregnancy metabolites. While neopterin concentration can predict true pregnancy in giant pandas to some extent, urinary metabolites only indirectly reflect pregnancy status and are subject to numerous interfering factors. Furthermore, the environment in giant panda enclosures is often polluted, making urine collection susceptible to environmental contamination. Additionally, the frequency of urine sampling is only once a week, which is relatively frequent and still requires considerable manpower. Summary of the Invention

[0007] The purpose of this invention is to provide a method for detecting true pregnancy in giant pandas, addressing the problems mentioned in the background section. This invention, based on the quantitative expression detection of specific molecular markers, can accurately distinguish between true and false pregnancy states, providing a more stable and direct reflection of pregnancy status than urinary metabolites. Compared to existing technologies, this invention only requires sample collection at two time points, before and after mating, allowing for comparative detection through two samplings, eliminating the need for frequent weekly urine collection and significantly reducing labor costs. Furthermore, when mating samples are unavailable, this invention employs a "population reference" strategy for effective discrimination. That is, the detection results of each post-mating sample are compared with a standardized baseline of an unmated population, thereby identifying statistically significant differential expressions and solving the technical challenge of comparison due to insufficient control samples. Additionally, this invention utilizes a multi-marker joint detection strategy, providing more comprehensive detection and improving the accuracy of result prediction compared to the use of a single marker.

[0008] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0009] A method for detecting true pregnancy in giant pandas includes the following steps:

[0010] (1) Collect blood samples from giant pandas before and after mating when their appetite began to decrease, and extract RNA for later use;

[0011] (2) Using the pre-mating giant panda population sample as baseline data, 2 ^-△△Ct The method involves calculating the relative expression levels of marker 1, marker 2, and marker 3 in the test sample after mating; the sequences of marker 1, marker 2, and marker 3 are shown in SEQ ID NO.1, SEQ ID NO.4, and SEQ ID NO.7, respectively.

[0012] (3) Compare the markers with their respective relative expression level confidence ranges. If all three markers are below the lower limit of the relative expression level confidence range, it is a false pregnancy. If at least two of the three markers are above the lower limit of the relative expression level confidence range, it is a true pregnancy.

[0013] Furthermore, giant pandas typically experience a decrease in appetite three months after mating.

[0014] Furthermore, the relative expression levels of marker 1 had a confidence range of 1.299–2.122 (95% CI); the relative expression levels of marker 2 had a confidence range of 1.185–1.894 (95% CI); and the relative expression levels of marker 3 had a confidence range of 1.276–1.895 (95% CI).

[0015] Furthermore, the primer sequences for amplification marker 1 are shown in SEQ ID NO. 2, 3 and 10; the primer sequences for amplification marker 2 are shown in SEQ ID NO. 5, 6 and 10; and the primer sequences for amplification marker 3 are shown in SEQ ID NO. 8, 9 and 10.

[0016] Furthermore, when all three markers are above the lower limit of the relative expression level confidence range, it is determined to be a true pregnancy.

[0017] A marker combination for detecting true pregnancy in giant pandas, comprising at least two of markers 1, 2 and 3 as shown in SEQ ID NO.1, SEQ ID NO.4 and SEQ ID NO.7.

[0018] Furthermore, it includes marker 1, marker 2 and marker 3 as shown in SEQ ID NO.1, SEQ ID NO.4 and SEQ ID NO.7.

[0019] A primer set for amplifying the above-mentioned marker combination, comprising primer sequences as shown in SEQ ID NO.2, SEQ ID NO.3, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.8, SEQ ID NO.9 and SEQ ID NO.10;

[0020] The primer sequences for amplification marker 1 are shown in SEQ ID NO. 2, 3 and 10; the primer sequences for amplification marker 2 are shown in SEQ ID NO. 5, 6 and 10; and the primer sequences for amplification marker 3 are shown in SEQ ID NO. 8, 9 and 10.

[0021] The above-mentioned marker combinations, or primer sets, are used in predicting true or false pregnancies in giant pandas.

[0022] The present invention has the following beneficial effects:

[0023] This invention, based on the quantitative expression detection of specific molecular markers, can accurately distinguish between true and false pregnancies, reflecting pregnancy status more stably and directly than urinary metabolites. Furthermore, this invention employs a multi-marker combined detection strategy, providing more comprehensive detection and improving accuracy compared to using a single marker. Compared to existing technologies, this invention only requires sample collection at two time points, before and after mating, allowing for comparative testing through two samplings, eliminating the need for frequent weekly urine collection and significantly reducing labor costs.

[0024] Furthermore, in situations where paired samples are unavailable, this invention employs a "population reference" strategy to achieve effective discrimination. That is, the detection results of each post-mating test sample are compared with a standardized unmated population baseline, thereby identifying statistically significant differential expressions and solving the technical challenge of comparisons due to insufficient control samples. Attached Figure Description

[0025] Figure 1 The figure shows the results of detecting the difference in expression levels of marker 1 in truly pregnant and falsely pregnant giant pandas; in the figure, ns indicates a corrected p-value > 0.05, and ** indicates a corrected p-value < 0.01;

[0026] Figure 2 The figure shows the results of detecting the difference in expression levels of marker 2 in truly pregnant and falsely pregnant giant pandas; in the figure, ns indicates a corrected p-value > 0.05, and * indicates a corrected p-value < 0.05;

[0027] Figure 3 The figure shows the detection results of the difference in expression level of marker 3 in true and false pregnant giant pandas; in the figure, ns indicates a corrected p-value > 0.05, and ** indicates a corrected p-value < 0.01. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0029] Example 1: Development of Tags

[0030] Based on previous transcriptome sequencing analysis of plasma exosomes from three groups (non-pregnant, true pregnancy, and false pregnancy), a total of 267 expressed exosomal miRNAs were obtained. Using the non-pregnant group as a control, three significantly differentially expressed exosomal miRNA markers were screened between the true pregnancy and false pregnancy groups, and named Marker 1, Marker 2, and Marker 3, respectively. Their specific sequences and amplification primers are as follows:

[0031] Marker 1 (mir-3925-3p): 5'-gcuccaguggccugugacugac-3'; (SEQ ID NO.1)

[0032] Label 1-F: 5'-CGCTCCAGTGGCCTGTG-3'; (SEQ ID NO.2)

[0033] Mark 1-RT: 5’-GTCGTATCGACTGCAGGGTCCGAGGTATTCGCAGTCGATACGACGTCAGT-3’; (SEQ ID NO.3)

[0034] Marker 2 (mir-153-5p): 5’-gucauuuuugugaucugcagcua-3’; (SEQ ID NO.4)

[0035] Marker 2-F: 5’-CGTCATTTTTGTGATCTGCAGCTA-3’; (SEQ ID NO.5)

[0036] Marker 2-RT: 5’-GTCGTATCGACTGCAGGGTCCGAGGTATTCGCAGTCGATACGACTAGCTG-3’; (SEQ ID NO.6)

[0037] Marker 3 (miR-599-5p): 5’-uuugauaagcugacaugggac-3’; (SEQ ID NO.7)

[0038] Marker 3-F: 5’-CGTTTGATAAGCTGACATGGGAC-3’; (SEQ ID NO.8)

[0039] Marker 3-RT: 5’-GTCGTATCGACTGCAGGGTCCGAGGTATTCGCAGTCGATACGACGTCCCA-3’; (SEQ ID NO.9)

[0040] Universal R: 5’-ACTGCAGGGTCCGAGGTATT-3’; (SEQ ID NO.10)

[0041] Reference gene U6

[0042] F: 5’-CTCGCTTCGGCAGCACA-3’; (SEQ ID NO.11)

[0043] R: 5’-AACGCTTCACGAATTTGCGT-3’. (SEQ ID NO.12)

[0044] The three markers mentioned above were significantly upregulated in the true pregnancy group of giant pandas (corrected p < 0.05), while their upregulation was not significant or absent in the false pregnancy group (corrected p > 0.05). Validation by real-time quantitative PCR showed that marker 1 (corrected p = 0.0072), marker 2 (corrected p = 0.0119), and marker 3 (corrected p = 0.0057) showed a statistically significant increase in expression levels in the true pregnancy group compared to the non-pregnant group.

[0045] In the pseudopregnancy group, the expression levels of markers 1 (corrected p-value = 0.0581), 2 (corrected p-value = 0.0851), and 3 (corrected p-value = 0.3217) were slightly higher than the average expression levels in the nonpregnant group, but the differences were not statistically significant. Therefore, the experiment demonstrates that the markers used in this invention are consistent with their actual expression in transcriptome sequencing.

[0046] Example 2: Constructing a method for predicting true pregnancy

[0047] Based on the markers developed in Example 1, a method for detecting true pregnancy in giant pandas was constructed, as follows:

[0048] (1) Collect 2 mL of blood samples from the upper limbs of giant pandas before and after mating when their appetite begins to decrease (appearing on average 3 months later);

[0049] (2) Centrifuge at 1500×g for 10 min at 4℃ to obtain 1 mL of supernatant plasma. Then centrifuge the separated plasma at 3000×g for 15 min at 4℃ to remove cell debris.

[0050] (3) Exosomes were isolated and total RNA was extracted according to the method described in the instructions for the Qiagen exoRNeasy Midi Kit Cat. No. 77144;

[0051] (4) The extracted RNA was reverse transcribed into cDNA. Reverse transcription amplification was performed using the SynScript® Ⅲ RTSuperMix for qPCR kit, with samples added according to the components listed in Table 1 below:

[0052] Table 1 Reverse transcription system

[0053]

[0054] After mixing, incubate at 42°C for 2 min, then at 60°C for 5 min. Quickly cool on ice, and after brief centrifugation, add the components listed in Table 2 below:

[0055] Table 2 Reverse Transcription System

[0056]

[0057] After mixing, incubate at 25°C for 10 min, 50°C for 30 min, and 85°C for 5 min. Place the reverse transcription product on ice or refrigerate for later use.

[0058] (5) The cDNA product obtained by reverse transcription was used as a qPCR template, and ArtiCan was used to PCR the cDNA product. CEO The SYBR qPCR Mix was used for amplification, and the components of the amplification system are shown in Table 3.

[0059] Table 3 Amplification System

[0060]

[0061] (6) The above amplification system was amplified and melted according to the amplification procedure in Table 4 below:

[0062] Table 4 Amplification Procedure

[0063]

[0064] (7) Samples taken before mating and samples taken after mating were processed three times according to step (5) to obtain three technical replicates of Ct values. All samples from giant pandas before mating were used as baseline data for the non-pregnant group. 2 ^-△△Ct Methods: Using U6 as an internal reference gene for calibration, the relative expression levels of each marker gene (marker 1, marker 2, and marker 3) in the post-mating test samples in the non-pregnant group were calculated, as follows:

[0065] A. Obtain the ΔCt of marker 1 in the post-mating samples and non-pregnant groups by subtracting the Ct values ​​of the three technical replicates of the internal reference gene U6 from the Ct values ​​of the three technical replicates of marker 1. Calculate the average ΔCt of marker 1 in the non-pregnant group samples. Subtract the ΔCt of marker 1 in the post-mating samples to obtain the -ΔCt of marker 1 in the post-mating samples. Then, obtain the relative expression level of marker 1 in the post-mating samples based on -ΔCt. ^-△△Ct The average relative expression level was calculated repeatedly for the three technologies. ^-△△Ct Finally, the average relative expression level of marker 1 in the samples after mating was obtained. ^-△△Ct value.

[0066] B. Calculate the ΔCt values ​​of the three technical replicates of marker 2 in the samples after mating and in the non-pregnant group, and subtract the ΔCt values ​​of the three technical replicates of the internal reference gene U6 to obtain the ΔCt of marker 2 in the samples after mating and in the non-pregnant group. Calculate the average ΔCt of marker 2 in the non-pregnant group samples. Subtract the ΔCt of marker 2 in the samples after mating from the average ΔCt of marker 2 in the non-pregnant group samples to obtain the -ΔCt of marker 2 in the samples after mating. Then, obtain the relative expression level of marker 2 in the samples after mating based on -ΔCt. ^-△△Ct The average relative expression level was calculated repeatedly for the three technologies. ^-△△Ct Finally, the average relative expression level of marker 2 in the samples after mating was obtained. ^-△△Ct value.

[0067] C. Subtract the Ct values ​​of the three technical replicates of marker 3 from the three technical replicates of the internal reference gene U6 in the samples after mating and the non-pregnant group to obtain the ΔCt of marker 3 in the samples after mating and the non-pregnant group. Calculate the average ΔCt of marker 3 in the non-pregnant group samples. Subtract the ΔCt of marker 3 in the samples after mating from the average ΔCt of marker 3 in the non-pregnant group samples to obtain the -ΔCt of marker 3 in the samples after mating. Then, obtain the relative expression level of marker 3 in the samples after mating based on -ΔCt. ^-△△Ct The average relative expression level was calculated repeatedly for the three technologies. ^-△△Ct Finally, the average relative expression level of marker 3 in the samples after mating was obtained. ^-△△Ct value.

[0068] (8) Then compare each marker in the sample after mating with its respective relative expression level confidence range. If all of them are lower than the lower limit of the relative expression level confidence range, it is predicted to be a false pregnancy. If at least two of the three markers are higher than the lower limit of the relative expression level confidence range, it is predicted to be a true pregnancy.

[0069] The relative expression levels of marker 1 had a confidence range of 1.299–2.122 (95% CI); marker 2 had a confidence range of 1.185–1.894 (95% CI); and marker 3 had a confidence range of 1.276–1.895 (95% CI).

[0070] Example 3

[0071] Case 1

[0072] The blood sample of Qiaoqiao taken on June 22, 2025 (approximately 3 months after mating, when her appetite began to decline) was analyzed and processed according to the implementation scheme of this invention. The specific process is as follows:

[0073] Use 2^ -△△CtThe method, using U6 as an internal reference gene for calibration, calculated the average relative expression level of marker 1 in the test sample in the non-pregnant group. -△△Ct The value was 1.609, which is higher than the lower limit of the relative expression confidence range of 1.299–2.122 (95% CI) for marker 1.

[0074] Calculate the mean 2^2 of the relative expression level of marker 2 in the test sample in the non-pregnant group. -△△Ct The value was 1.479, which is higher than the lower limit of the relative expression confidence range of 1.185–1.894 (95% CI).

[0075] Calculate the mean relative expression level of marker 3 in the post-mating test sample compared to the non-pregnant group sample. -△△Ct The value was 1.524, which is higher than the lower limit of the relative expression confidence range of 1.276–1.895 (95% CI).

[0076] After mating with Qiaoqiao, the tested sample showed that all three markers were above the lower limit of the relative expression confidence range, indicating a true pregnancy. Qiaoqiao mated on March 16, 2025, and gave birth to a cub on August 3, 2025, consistent with the marker prediction results. The specific results are shown in Table 5.

[0077] Table 5 Label Detection

[0078]

[0079]

[0080] Case 2

[0081] According to the implementation scheme of this invention, blood samples from rabbits on June 22, 2025 (who began to show decreased appetite 3 months after mating) were analyzed and processed. The specific process is as follows:

[0082] Use 2 ^-△△Ct Methods: Using U6 as an internal reference gene for calibration, the average relative expression level of marker 1 in the test sample was calculated in the non-pregnant group sample. ^-△△Ct The value was 1.758, which is higher than the lower limit of the relative expression confidence range of 1.299–2.122 (95% CI) for marker 1.

[0083] Calculate the mean relative expression level of marker 2 in the test sample in the non-pregnant group sample. ^-△△Ct The value was 1.576, which is higher than the lower limit of the relative expression confidence range of 1.185–1.894 (95% CI).

[0084] Calculate the mean relative expression level of marker 3 in the post-mating test sample compared to the non-pregnant group sample.2 ^-△△CtThe value was 1.587, which is higher than the lower limit of the relative expression confidence range of 1.276–1.895 (95% CI).

[0085] After mating with the rabbits, the tested samples all met the lower limit of the relative expression confidence range for the three markers, indicating a true pregnancy. The rabbits mated on March 22, 2025, and gave birth to a litter on July 17, 2025, consistent with the marker prediction results. The specific results are shown in Table 6.

[0086] Table 6 Label Detection

[0087]

[0088]

[0089] Case 3

[0090] According to the implementation scheme of this invention, the blood sample of Nan Xiaoyue on June 25, 2025 (approximately 3 months after mating, when her appetite began to decline) was analyzed and processed. The specific process is as follows:

[0091] Use 2 ^-△△Ct Methods: Using U6 as an internal reference gene for calibration, the average relative expression level of marker 1 in the test sample was calculated in the non-pregnant group sample. ^-△△Ct The value was 1.365, which is higher than the lower limit of the relative expression confidence range of 1.299–2.122 (95% CI) for marker 1.

[0092] Calculate the mean relative expression level of marker 2 in the test sample in the non-pregnant group sample. ^-△△Ct The value was 1.234, which is higher than the lower limit of the relative expression confidence range of 1.185–1.894 (95% CI).

[0093] Calculate the mean relative expression level of marker 3 in the post-mating test sample compared to the non-pregnant group sample.2 ^-△△Ct The value was 1.338, which is higher than the lower limit of the relative expression confidence range of 1.276-1.895 (95% CI).

[0094] After mating with *Lysimachia nummularia*, the tested samples all met the lower limit of the relative expression confidence range for all three markers, indicating a true pregnancy. *Lysimachia nummularia* mated on March 16, 2025, and gave birth to two pups on July 30, 2025, consistent with the marker prediction results. Specific results are shown in Table 7.

[0095] Table 7 Label Detection

[0096]

[0097]

[0098] Case 4

[0099] The blood sample of Linglang taken on June 25, 2024 (three months after mating, when her appetite began to decline) was analyzed and processed according to the implementation scheme of this invention. The specific process is as follows:

[0100] Use 2 ^-△△Ct Methods: Using U6 as an internal reference gene for calibration, the average relative expression level of marker 1 in the test sample was calculated in the non-pregnant group sample. ^-△△Ct The value was 1.126, which is below the lower limit of the relative expression confidence range of 1.299–2.122 (95% CI) for marker 1.

[0101] Calculate the mean relative expression level of marker 2 in the test sample in the non-pregnant group sample. ^-△△Ct The value was 1.105, which is below the lower limit of the relative expression confidence range of 1.185–1.894 (95% CI).

[0102] Calculate the mean relative expression level of marker 3 in the post-mating test sample compared to the non-pregnant group sample.2 ^-△△Ct The value was 1.094, which is below the lower limit of the relative expression confidence range of 1.276–1.895 (95% CI).

[0103] After mating with Linglang, the tested samples showed values ​​below the lower limit of the relative expression confidence range for three markers, indicating a pseudopregnancy. Linglang mated on March 25, 2024, but did not give birth to any offspring in 2024, consistent with the marker prediction results. Specific results are shown in Table 8.

[0104] Table 8. Label Detection

[0105]

[0106]

[0107] In summary, the marker combination constructed according to the present invention can accurately predict and distinguish the true and false pregnancy status of giant pandas after mating.

[0108] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting true pregnancy in giant pandas, characterized in that, Includes the following steps: (1) Collect blood samples from giant pandas before and after mating when their appetite began to decrease, and extract RNA for later use; (2) Using the pre-mating giant panda population sample as baseline data, 2 ^-△△Ct The method involves calculating the relative expression levels of marker 1, marker 2, and marker 3 in the test sample after mating; the sequences of marker 1, marker 2, and marker 3 are shown in SEQ ID NO. 1, SEQ ID NO. 4, and SEQ ID NO. 7, respectively. (3) Compare the markers with their respective relative expression level confidence ranges. If all three markers are below the lower limit of the relative expression level confidence range, it is a false pregnancy. If at least two of the three markers are above the lower limit of the relative expression level confidence range, it is a true pregnancy.

2. The method according to claim 1, characterized in that, The relative expression levels of marker 1 had a confidence range of 1.299–2.122 (95% CI); the relative expression levels of marker 2 had a confidence range of 1.185–1.894 (95% CI); and the relative expression levels of marker 3 had a confidence range of 1.276–1.895 (95% CI).

3. The method according to claim 1, characterized in that, The primer sequences for amplification marker 1 are shown in SEQ ID NO. 2, 3 and 10; the primer sequences for amplification marker 2 are shown in SEQ ID NO. 5, 6 and 10; and the primer sequences for amplification marker 3 are shown in SEQ ID NO. 8, 9 and 10.

4. The method according to any one of claims 1 to 3, characterized in that, When all three markers are above the lower limit of the confidence range for relative expression levels, the pregnancy is considered a true pregnancy.

5. A marker combination for detecting true pregnancy in giant pandas, characterized in that, Includes at least two of the tags 1, 2 and 3 shown in SEQ ID NO.1, SEQ ID NO.4 and SEQ ID NO.

7.

6. The marking combination according to claim 5, characterized in that, This includes marker 1, marker 2, and marker 3 as shown in SEQ ID NO.1, SEQ ID NO.4, and SEQ ID NO.

7.

7. A primer set for amplifying the marker combination of claim 5 or 6, characterized in that, The primer set includes primer sequences as shown in SEQ ID NO.2, SEQ ID NO.3, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.8, SEQ ID NO.9 and SEQ ID NO.10; The primer sequences for amplification marker 1 are shown in SEQ ID NO. 2, 3 and 10; the primer sequences for amplification marker 2 are shown in SEQ ID NO. 5, 6 and 10; and the primer sequences for amplification marker 3 are shown in SEQ ID NO. 8, 9 and 10.

8. The use of the marker combination of claim 5 or 6, or the primer set of claim 7, in predicting true or false pregnancy in giant pandas.

Images