Product for predicting subacute rumen acidosis and application thereof

Abstract

The invention relates to the technical field of biological detection, in particular to a product for predicting subacute rumen acidosis and application thereof. The biomarker is selected from maltotriitol and / or pyrophosphate. The subacute rumen acidosis is predicted by adopting the biomarker maltotriitol and / or pyrophosphate, so that the prediction accuracy is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of biological detection, and in particular to a product for predicting subacute rumen acidosis and application thereof. Background Art

[0002] Subacute rumen acidosis (SARA) is a common nutritional metabolic disease in ruminants, which changes the gastrointestinal microflora and host metabolism. SARA can lead to rumen metabolic disorders, disrupt the homeostasis of the rumen environment, and induce diseases such as rumen epithelial inflammation, which ultimately seriously endangers the health of animals and reduces their production performance, causing huge economic losses to ruminant farming.

[0003] In actual production, breeders often reduce the incidence of SARA by adjusting the formula, adding buffers such as sodium bicarbonate, or microecological preparations such as yeast culture. However, despite this, the incidence of SARA is still relatively high during the breeding process. In order to detect and take timely treatment measures as soon as possible, it is usually judged whether rumen acidosis has occurred in combination with food intake and the symptoms of the animal. In "Diagnosis and Prevention of Rumen Acidosis", the mild type is characterized by a slow onset, with visible food loss, reduced rumen motility, decreased lactation in maternal animals, black feces, and no change in body temperature; the more severe type is characterized by a large amount of feed intake. After 12-24 hours, symptoms of poisoning such as indigestion, loss of appetite, a sharp increase in milk production, staggering gait, and mental depression are manifested, with slow reactions, kicking the abdomen, groaning on the ground, muscle tremors, loose feces, undigested substances, and a rancid odor. Sometimes the feces contain blood or intestinal mucosa, and the eyes are congested. The rumen is congested, the conjunctiva is diffusely light red, the breathing is weak, the body temperature is 37-37.5℃, the rumen is percussed to produce a solid gong sound, the rumen motility is reduced by auscultation, the reticulum and omasum motility sounds are weakened or disappeared, and the pulse is 90-100 times / minute; the severe type quickly shows the above mild and more severe symptoms and then falls into a coma. The sick animal is paralyzed, the head and neck are bent toward the waist, the pulse is 120-140 times / minute, the body temperature is below 37℃, the eyeballs are sunken, the nose is dry, and there are severe dehydration symptoms. Failure to rescue in time may lead to death. Although the above methods can be used to determine the occurrence of rumen acidosis, there is still the problem of inaccurate judgment. For example, when making a judgment, it is necessary to distinguish it from the following cases: (1) acute gastroenteritis with elevated body temperature, (2) postpartum paralysis of the mother animal without systemic symptoms such as bloating of acidosis, and (3) simple rumen bloating with rapid onset, significant bloating of the abdominal circumference, and tympanic sound when percussed.

[0004] In the prior art, there is also a method of collecting rumen fluid orally to determine whether rumen acidosis has occurred, but this method is difficult to collect and is easily affected by saliva and food intake, and there is also the phenomenon of inaccurate judgment. Therefore, it is necessary to find an accurate prediction method to provide a theoretical basis for in-depth research on the early diagnosis and pathogenesis of SARA. Summary of the invention

[0005] In view of the problem that the methods for predicting subacute ruminal acidosis in the prior art have low accuracy, the present invention provides a product for predicting subacute ruminal acidosis and its application.

[0006] To achieve the above technical objectives, the present invention provides a use of a biomarker in predicting products of subacute rumen acidosis, wherein the biomarker is selected from maltotriose and / or pyrophosphate.

[0007] Furthermore, the product includes a kit.

[0008] Furthermore, the kit comprises reagent 1, and the reagent 1 is an organic solvent; preferably, the organic solvent is methanol.

[0009] Furthermore, the prediction is to detect the level of biomarkers in the feces or urine of the test animal to be tested. If the level of any of the above biomarkers in the feces or urine of the test animal is higher than the upper limit of the reference value, the test animal is at risk of developing subacute rumen acidosis.

[0010] Furthermore, the reference value is the level of the biomarker in the feces or urine of normally fed animals.

[0011] Furthermore, based on 100 parts by weight of the feed, the feed normally fed to animals is a diet with a mass ratio of concentrated feed to roughage of 30-50:50-70, and the concentrated feed or roughage is a conventional concentrated feed or roughage in the art; preferably, the roughage is alfalfa hay.

[0012] Furthermore, when the maltotriose level in the feces of the test animal reaches more than 6 times that of the normally fed animal, the test animal is at risk of developing subacute rumen acidosis.

[0013] Furthermore, when the level of pyrophosphate in the urine of the test animal reaches more than 9,000 times that of the normally fed animal, the test animal is at risk of developing subacute rumen acidosis.

[0014] Furthermore, the levels of biomarkers were determined by liquid chromatography-mass spectrometry.

[0015] Furthermore, the biomarker is selected from one or more of lactic acid, lipopolysaccharide, and Actinobacteria;

[0016] Preferably, when the biomarker level in the rumen fluid of the test animal meets one or more of the following AC, the test animal is at risk of developing subacute rumen acidosis;

[0017] A. The activity of the lipopolysaccharide is ≥ 20000EU / mL;

[0018] B. The lactic acid content is ≥2.00mmol / L;

[0019] C. The relative abundance of the Actinobacteria is ≥ 1.6%.

[0020] The present invention also provides a use of the above biomarker in screening drugs for preventing or treating subacute rumen acidosis.

[0021] The technical solution of the present invention has the following beneficial effects:

[0022] (1) The present invention provides a use of a biomarker in predicting subacute ruminal acidosis, wherein the biomarker is selected from maltotriose and / or pyrophosphate. By using the above biomarker to predict subacute ruminal acidosis, the accuracy of the prediction is greatly improved.

[0023] (2) The use of a biomarker provided by the present invention in predicting a product of subacute ruminal acidosis, wherein the biomarker is also selected from at least one of lactic acid, lipopolysaccharide, and actinomycetes. Based on the biomarkers maltotriose and / or pyrophosphate, combined with other biomarkers, the accuracy of prediction is further improved.

[0024] (3) The present invention provides a biomarker for use in a product for predicting subacute rumen acidosis. The present invention detects the level of maltotriose in the feces of the test animal and the level of pyrophosphate in the urine. Compared with conventional detection methods, the sampling or detection process is convenient and does not require surgery. It is not only harmless to the animal, but also improves the detection speed, reduces manpower and material resources, expands the scope of application, and saves social resources to a great extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 This is a graph showing the changing trend of the pH value of rumen fluid at different time points in step S2 of Example 1.

[0027] Figure 2 This is a diagram showing the dynamic changes in fermentation parameters in rumen fluid in step S2 of Example 1.

[0028] Figure 3 This is a diagram showing the dynamic changes of LPS (lipopolysaccharide), lactic acid and histamine in rumen fluid and blood in step S2 of Example 1.

[0029] Figure 4 This is a graph showing the changing trend of the acute phase reactant protein content in the blood at different time points in step S2 of Example 1.

[0030] Figure 5 This is a graph showing the changes in the Shannon index of cattle feces samples in the study of the gastrointestinal bacterial community structure in step S3 of Example 1.

[0031] Figure 6 This is a comparison chart of the relative content of bacteria in fecal samples based on the phylum level in the study of the gastrointestinal bacterial community structure in step S3 of Example 1.

[0032] Figure 7 This is a differential analysis diagram of the top 10 species in relative abundance in the CON group, AP group, and CG group at the phylum level in the study of the gastrointestinal bacterial community structure in step S3 of Example 1.

[0033] Figure 8 This is a comparative analysis diagram of cattle feces samples based on relative contents greater than 1% (sequence proportion) at the genus level in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0034] Fig. 9 This is a comparative analysis chart of the top 20 bacterial genera in the cattle feces samples based on the relative content at the genus level in the study of the gastrointestinal bacterial community structure in step S3 of Example 1.

[0035] Fig.10 These are the results of ANOSIM analysis between and within groups at the OTU level in cattle feces samples in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0036] Fig.11 This is the PLS-DA analysis result at the OTU level of cattle feces samples in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0037] Fig.12 This is the result of the Venn diagram analysis of cattle feces samples among the groups in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0038] Fig.13 This is a graph of the bacterial diversity index in bovine rumen fluid samples in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0039] Fig.14 This is a graph of the aroma index of bovine rumen fluid samples in the study of the gastrointestinal bacterial community structure in step S3 of Example 1.

[0040] Fig.15 This is the relative content of bacteria in rumen fluid samples based on the phylum level in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0041] Fig.16 This is a differential analysis diagram of the top 10 species in relative abundance among the CON group, AP group, and CG group at the phylum level in rumen fluid samples in the study of the gastrointestinal bacterial community structure in step S3 of Example 1.

[0042] Fig.17 This is a comparative analysis of rumen fluid samples based on a relative content greater than 1% (sequence proportion) at the genus level in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0043] Fig.18 This is a comparative analysis of the top 15 bacterial genera in rumen fluid samples based on the relative content at the genus level in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0044] Fig.19 This is the PLS-DA analysis result at the OTU level of rumen fluid samples in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0045] Fig. 20 This is the Venn diagram analysis result of rumen fluid samples among the groups in the study of gastrointestinal bacterial community structure in step S3 of Example 1.

[0046] Fig.21 This is the total ion current diagram of cattle feces GC-TOF MS detection in the study of gastrointestinal microorganisms in step S4 of Example 1, wherein from top to bottom are the detection results of C1-C8, B1-B8, and D1-D8.

[0047] Fig. 22 This is a scatter plot of the scores of CON, AP (Tre1) and CG (Tre2) groups in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0048] Fig.23 This is the OPLS-DA score plot and permutation test results of the CON group and AP group (Tre1) in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0049] Fig.24 This is the OPLS-DA score plot and permutation test results of the CON group and CG group (Tre2) in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0050] Fig.25 This is the OPLS-DA score graph and permutation test results of the AP group (Tre1) and the CG group (Tre2) in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0051] Fig.26 This is a hierarchical clustering heat map of metabolites between the CON group and the AP group (Tre1) in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0052] Fig. 27 This is a hierarchical clustering heat map of metabolites between the CON group and the CG group (Tre2) in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0053] Fig.28 This is a hierarchical clustering heat map of metabolites between the AP group (Tre1) and the CG group (Tre2) in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0054] Fig.29 This is a diagram of the metabolite pathway analysis between the CON group and the AP group in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0055] Fig.30 This is a diagram of the metabolite pathway analysis between the CON group and the CG group in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0056] Fig.31 This is a diagram of the metabolite pathway analysis between the AP group and the CG group in the study of gastrointestinal microorganisms in step S4 of Example 1.

[0057] Fig.32 This is a scatter plot of the CON, AP (Tre1) and CG (Tre2) group scores in the study of the effect of step S4 on host metabolism in Example 1.

[0058] Fig.33 This is the OPLS-DA score plot and permutation test results of the CON group and AP group (Tre1) in the study of host metabolism in step S4 of Example 1.

[0059] Fig.34 This is the OPLS-DA score plot and permutation test results of the CON group and CG group (Tre2) in the study of host metabolism in step S4 of Example 1.

[0060] Fig.35 This is the OPLS-DA score graph and permutation test results of the AP group (Tre1) and the CG group (Tre2) in the study of host metabolism in step S4 of Example 1.

[0061] Fig.36 This is a hierarchical clustering heat map of metabolites between the CON group and the AP group (Tre1) in the study of host metabolism in step S4 of Example 1.

[0062] Fig.37Hierarchical clustering heatmap of metabolites between the CON group and the CG group (Tre2) in the study of host metabolism in step S4 of Example 1.

[0063] Fig.38 Hierarchical clustering heatmap of metabolites between the AP group (Tre1) and the CG group (Tre2) in the study of host metabolism in step S4 of Example 1.

[0064] Fig.39 Analysis diagram of metabolite pathways between the CON group and the AP group in the study of host metabolism in step S4 of Example 1.

[0065] Fig.40 Analysis diagram of metabolite pathways between the CON group and the CG group in the study of host metabolism in step S4 of Example 1.

[0066] Fig.41 Analysis diagram of metabolite pathways between the AP group and the CG group in the study of host metabolism in step S4 of Example 1.

[0067] Among them, * indicates 0.01 < P ≤ 0.05, ** indicates 0.001 < P ≤ 0.01, and *** indicates P ≤ 0.001. Detailed implementation mode

[0068] The following embodiments are provided to better further understand the present invention. They are not limited to the described best implementation mode and do not constitute limitations on the content and protection scope of the present invention. Any product that is the same as or similar to the present invention obtained by anyone under the inspiration of the present invention or by combining the features of the present invention with other existing technologies falls within the protection scope of the present invention.

[0069] For those not specifying specific experimental steps or conditions in the embodiments, the operations or conditions of the conventional experimental steps described in the literature in this field can be followed. For the reagents or instruments not specifying the manufacturer, they are all conventional reagent products that can be obtained through commercial purchase. Concentrate feed (Cargill Feed Co., Ltd., ruminant feed).

[0070] Example 1

[0071] This example provides the use of a biomarker in a product for predicting subacute ruminal acidosis, including the following steps:

[0072] S1. Eight Jinjiang cattle (four of which were equipped with permanent rumen fistulas) were selected and randomly divided into two groups (two fistula cattle in each group). A cross-over experiment was used. The cattle were fed a control diet with a ratio of concentrate to roughage of 40:60 in the first, second and sixth weeks of each experiment. Alfalfa hay was used as roughage, and the samples collected in the second week were used as the control group (CON). In the third to fifth weeks, one of the groups replaced the "total mixed diet" in the control diet with "corn grains"; the other group replaced the "alfalfa hay" in the control diet with "crushed alfalfa pellets". SARA induction models of corn-induced group (CG) and alfalfa pellet-induced group (AP) were constructed.

[0073] S2. The fermentation parameters and changes of abnormal metabolites of Jinjiang Yellow Cattle under different SARA induction modes were studied.

[0074] Sampling and testing: On Monday and Sunday of the 2nd and 5th weeks of each trial, rumen fluid and blood were collected before morning feeding (08:00) and 2 hours (10:00), 4 hours (12:00), 6 hours (14:00), 8 hours (16:00) and 12 hours (20:00) after feeding, and the pH value, volatile fatty acids (VFA) and ammonia nitrogen (NH3-N) content in the rumen fluid were immediately determined; the contents of lipopolysaccharide (Nanjing Jiancheng Bioengineering Institute, A054-1-1), lactic acid (Nanjing Jiancheng Bioengineering Institute, A020-1-1) and histamine (Nanjing Jiancheng Bioengineering Institute, H171-1-2) in rumen fluid and blood samples were determined by kits.

[0075] Results and analysis: A. Effects of different SARA induction modes on the pH value of rumen fluid. Inducing rumen acidosis in Jinjiang cattle with corn grains and crushed alfalfa particles had a significant effect on the pH of rumen fluid. Figure 1 It can be seen that the pH value of the rumen fluid of the two induced groups was lower than 5.8 between 4h and 8h after feeding. Generally, the occurrence of acidosis is determined by the time when the pH value of the rumen fluid is lower than 5.8 for more than 180 minutes per day. Therefore, the two induction methods in the present invention successfully induced the occurrence of SARA in Jinjiang Yellow Cattle. The mean pH values ​​of the rumen fluid of the control group (CON), corn grain induced group (CG) and alfalfa pellet induced group (AP) after feeding were 6.51, 5.75 and 5.84, respectively, and the differences among the groups were significant (p<0.05). From the pH dynamic change diagram at different time points among the groups ( Figure 1) showed that the pH value of rumen fluid decreased significantly after feeding, among which the pH value of CON group was the lowest 4 hours after feeding, while the pH values ​​of CG group and AP group were the lowest 6 hours after feeding, and then the pH value increased with time, and the pH decrease of CG induced group was greater than that of AP induced group, indicating that CG group had stronger ability to produce acid by fermentation. This may be because the content of non-structural carbohydrates was increased after feeding corn grains and crushed alfalfa particles, which increased the content of VFA and lactic acid in rumen fluid, and the acid production capacity of corn grains after degradation and utilization by the body was stronger, causing the pH value of rumen fluid to decrease. At the same time, with the effect of the animal's own buffering capacity, the pH value gradually increased after 6 hours.

[0076] B. Effects of different SARA induction modes on rumen fermentation. The effects of different SARA induction modes on fermentation parameters and ammonia nitrogen in the rumen of Jinjiang cattle are shown in Table 1. As shown in Table 1, SARA induction by corn grains and crushed alfalfa particles can significantly change the content of total VFA, acetic acid, propionic acid, butyric acid, isobutyric acid, and valeric acid in rumen fluid. Among them, the content of total VFA, propionic acid, and butyric acid increased significantly (p<0.05), while acetic acid, isobutyric acid, and acetic acid / propionic acid ratio (A:P) decreased significantly (p<0.05), and isovaleric acid did not change significantly (p>0.05). From the acetic acid / propionic acid ratio, it can be found that SARA induction by easily fermented corn grains and crushed alfalfa particles significantly changed the rumen fermentation type of Jinjiang cattle, and A:P was significantly reduced from 3.26 in the CON group to 2.38 in the CG group and 2.25 in the AP group.

[0077] Table 1 Determination results of components in rumen fluid of CON group, CG group and AP group

[0078]

[0079] Note: In a, b, c, and ab, different letters indicate significant differences; the same letters indicate no significant differences.

[0080] This example analyzes the variation of various rumen fermentation parameters over time. Figure 2 It can be seen that the total VFA and butyrate contents reached a peak 2 hours after feeding and then gradually decreased. The total VFA content at 12 hours was basically equivalent to the level at 0 hours. In each group, acetate reached a peak 2 hours after feeding and then decreased, while propionic acid reached a peak 4 hours after feeding in the induction group and then gradually decreased with time, while the CON group reached a peak after 2 hours and then decreased. Isobutyric acid showed a trend of first increasing, then decreasing, and then increasing, and the change trends among the three groups were consistent. The A:P ratio generally showed a trend of first decreasing and then increasing, but the A:P ratio of the CG group and the AP group was significantly reduced, so the fermentation type changed from acetic acid fermentation in the CON group to propionic acid fermentation in SARA.

[0081] C. Effects of different SARA induction modes on LPS, lactic acid and histamine in rumen fluid and blood. The effects of different SARA induction modes on LPS, lactic acid and histamine in rumen fluid and blood of Jinjiang cattle are shown in Table 2. The concentrations of LPS in rumen fluid of CON group, CG group and AP group were 7856EU / mL, 38414EU / mL and 34423EU / mL, respectively. The two different SARA induction modes significantly increased the LPS content in rumen fluid, and both increased the content by more than 4 times. Among them, the LPS content in CG group was significantly higher than that in AP group. The results showed that corn grains and alfalfa pellets could significantly increase the LPS content in rumen fluid, mainly due to high grain diets and increased fermentable carbohydrates. Under acidosis, the lactic acid content in rumen fluid increased. The lactic acid content in CG group and AP group was 2.46 and 2.69mmol / L, respectively, which were significantly higher than that in CON group. Lactic acid is an intermediate product of carbohydrate metabolism in rumen fluid. It can rapidly proliferate and produce lactic acid after animals eat various carbohydrates such as sugars and starch. The histamine concentration in rumen fluid increased significantly after acidosis occurred, and the CG and AP groups were significantly higher than the CON group. This indicates that after SARA occurs, the pH of the rumen fluid decreases and the histidine decarboxylation process leads to the formation of more histamine. Secondly, the formation of histamine in rumen fluid is the main reason for the increase in histamine content in the blood.

[0082] From the results of LPS, lactic acid and histamine in the blood, it can be seen that when SARA occurs, the LPS content in the rumen fluid is increased, but LPS is not detected in the blood. However, the histamine content in the blood has increased significantly, while the blood lactic acid AP group has decreased significantly. The above results show that the histamine produced in the rumen enters the blood through the rumen wall, but LPS does not enter the blood through the rumen wall. This may be because the rumen wall of the digestive tract has not been damaged, resulting in LPS being unable to enter the blood through the digestive tract barrier.

[0083] Table 2 Results of determination of LPS, lactic acid and histamine in rumen fluid and blood in CON, CG and AP groups

[0084]

[0085] Note: In a, b, and c, different letters indicate significant differences; the same letters indicate no significant differences.

[0086] From the perspective of time changes ( Figure 3), the LPS content in the rumen fluid of the CON group was very low, while the induction group reached a peak after 4 hours of feeding, and the CG group was higher than the AP group, and then gradually decreased; lactic acid and histamine in the rumen fluid showed a trend of first increasing and then decreasing after SARA occurred, and both reached a peak at 4 hours after feeding, but the lactic acid in the rumen fluid showed a trend of first decreasing, then increasing, and then decreasing. The lactic acid in the blood showed a downward trend as a whole, but the histamine content in the blood reached a peak at 4 hours and then decreased.

[0087] D. Effect of different SARA induction modes on acute phase reaction proteins in blood. This embodiment studies the effect of different SARA induction modes on acute phase reaction proteins in the blood of Jinjiang cattle, and finds that under two induction modes, LPS binding protein (LBP), amyloid protein A (SAA) and haptoglobin (Hp) all significantly increase, which indicates that SARA induces inflammation in the animal body, which may be caused by the entry of compounds such as intestinal mucosal damage or immunogens caused by the diet, such as LPS. However, LPS is not detected in the blood in this embodiment, so the increase in the concentration of acute phase proteins SAA and Hp in the blood cannot be proved to be caused by the translocation of LPS, and it is speculated that the increase in the concentration of acute phase reaction proteins is caused by other inflammatory stimuli.

[0088] From the results at each time point, we can see that ( Figure 4 ), LBP in the induction group showed a pattern of first increasing and then decreasing, and LBP, SAA and Hp in the induction group were higher than those in the CON group. The Hp content in the induction group remained relatively stable at all times without major fluctuations, while the acute phase reaction protein content in the CON group remained at a low level at all time points.

[0089] Table 3 Results of determination of LPS binding protein (LPB), serum amyloid A (SAA), and haptoglobin (Hp) in CON, CG, and AP groups

[0090]

[0091] Note: In a, b, and c, different letters indicate significant differences; the same letters indicate no significant differences.

[0092] The above results show that corn grains and crushed alfalfa particles can successfully induce the occurrence of SARA in Jinjiang cattle, and can significantly change the type of rumen fermentation from acetic acid to propionic acid fermentation; at the same time, the occurrence of SARA can significantly increase the content of LPS, lactic acid and histamine in rumen fluid and cause inflammation in the body, but LPS does not translocate into the blood, and various fermentation parameters and abnormal metabolites show a certain degree of dynamic change.

[0093] S3. The diversity of gastrointestinal bacterial community structure of Jinjiang Yellow Cattle under different SARA induction modes was studied.

[0094] Sampling and detection: Based on the SARA induction model of Jinjiang cattle, rumen fluid and feces samples (rectal feces) were collected from each animal before feeding on Monday and Sunday mornings of the second and fifth weeks of each trial, and total DNA was extracted. According to the conservative sequence of the V3-V4 region of the bacterial 16SrDNA gene, universal primers were designed: 338F: 5'-ACTCCTACGGGAGGCAGCAG-3', 806R: 5'-GGACTACHVGGGTWTCTAAT-3', and amplified and purified, and a sequencing library was constructed, and the MiSeq sequencer was used for on-machine analysis. The obtained sequences were first subjected to quality control analysis, and then the high-quality sequences were processed by bioinformatics. According to the principle of 97% sequence similarity, the sequences were classified into multiple operational classification units, and then microbial diversity analysis, abundance analysis and cluster analysis were performed. At the same time, similarity analysis was used to compare the spatial relationship of microorganisms in the gastrointestinal tract of the CON group and different SARA-induced groups. The data obtained by high-throughput sequencing were analyzed with the help of the partial least squares discriminant model (PLS-DA) in a supervised mode to further screen the key bacterial community factors that differed in the microbial community structure between the CON group and the SARA-induced group. The fecal samples of the CON group, AP group, and CG group were a1-a8, b1-b8, and c1-c8, respectively; the rumen fluid samples of the CON group, AP group, and CG group were A1-A8, B1-B8, and C1-C8, respectively.

[0095] Results and analysis: A. Research results on the diversity of bacterial community structure in cattle feces of Jinjiang Yellow Cattle SARA.

[0096] (1) Bacterial richness and diversity. After Illumina-Miseq high-throughput sequencing, 864,818 high-quality sequences were obtained after quality control for the 24 fecal samples in this example, with an average of 36,025 sequences produced per sample, and an average sequence length of 435 bp. In order to study the effect of different SARA induction modes on the diversity of bacterial flora structure in feces, this example uses the Chao and Ace indexes to calculate the species richness of the community, and the Shannon and Simpson indexes to calculate the diversity of the community structure. The sequencing coverage depth of the sample at a 97% similarity level is calculated using Good'coverage. The number of sequences and diversity index of the 24 fecal samples at a 97% similarity level are shown in Table 4. The library coverage (Good'scoverage) of each group is above 0.99, indicating that the sequencing amount of each sample is reasonable and can well reflect the diversity of bacterial flora species and structure in feces.

[0097] Table 4 Richness and diversity index results of 24 fecal samples at 97% similarity level

[0098]

[0099] The Shannon diversity curve can reflect the biodiversity of the library at the sequencing amount and 97% similarity OTU level. Figure 5 It can be seen that the number of OTUs increases with the increase of sequencing amount. When the sequencing amount reaches a certain level, the increase trend of OTU becomes slow. When the sequencing amount exceeds 4000, the diversity index has basically reached saturation. The above results show that it is still possible to discover new bacterial species by increasing the sequencing amount, but the sequencing amount in this experiment can basically reflect the diversity of bacteria in cattle feces.

[0100] (2) Bacterial composition and community structure. In this example, RDP and BLAST homology sequence comparison methods were used to identify the sequences of all samples. The results are shown in Figure 6 As shown in the figure, at the phylum level, 10 phyla were identified with a relative abundance greater than 1%, including Firmicutes, Bacteroidetes, Actinobacteria, Spirochaetae, Verrucomicrobia, Proteobacteria, Tenericutes, Cyanobacteria, Saccharibacteria, and Fibrobacteres. Firmicutes and Bacteroidetes were the dominant phyla in the CON, AP, and CG groups, with Firmicutes having the highest content. In the CON group, the contents of Firmicutes and Bacteroidetes were 62% and 23.5%, respectively, in the CG group, 69.8% and 16.7%, respectively, and in the AP group, 57.7% and 32.1%, respectively.

[0101] In this example, the Kruskal-Wallis test was used to compare the differences of the top 10 bacterial phyla in relative content in the three groups of samples. The results are as follows: Figure 7As shown. There was no significant difference in the relative content of Firmicutes and Bacteroidetes among the three groups, but when compared pairwise, it was found that the induction mode did not change the abundance of Firmicutes and Bacteroidetes in feces, but there were significant differences between different induction methods, among which Firmicutes in the AP group was significantly lower than that in the CG group, while Bacteroidetes was significantly higher than that in the CG group. At the same time, the bacterial phyla with significant differences among the three groups were Actinobacteria (P<0.05), Verrucomicrobia (P<0.05) and Proteobacteria (P<0.05).

[0102] In this example, samples from the CON, AP, and CG groups were processed by mean value. At the genus level, 31 bacterial genera with a relative abundance greater than 1% were identified. The specific distribution is as follows: Figure 8 The Kruskal-Wallis test was used to compare the top 20 bacterial genera in the three groups of samples. Fig. 9 As shown. Fig. 9 It can be seen that among the top 20 genera with relative content, except for 6 genera with no significant difference, the other 14 genera showed significant differences after SARA occurred. Among the genera with higher relative content greater than 8%, they included Turicibacter, ruminococcaceae_UCG-005, unclassifier_f_peptostreptococcaceae, Clostridium_sensu_stricto_1 and Bifidobacterium.

[0103] (3) Similarity analysis among CON, AP and CG groups. Similarity analysis among samples of each group was performed, and ANOSIM analysis and PLS-DA analysis were performed at the OTU level. The results are shown in the figure. Fig.10 and Fig.11 As shown. Fig.10 It can be seen that the median of the inter-group box plot is higher than the median values ​​of the CON, CG and AP groups, indicating that the difference between groups is greater than the difference within groups, and there are obvious differences among the treatment groups. The PLS-DA analysis more intuitively found that the similarity of samples within each group is very high, while the distance between sample groups is far, and there are significant differences.

[0104] In this example, the shared and unique OTU bacterial abundances in the bacterial community were used to analyze the CON group, AP group, and CG group. The results are shown in Fig.12At the 97% similarity level, the numbers of OTUs in the CON, CG and AP groups were 1224, 1146 and 1187, respectively. The three groups shared 1004 OTUs, accounting for 82%, 88% and 85% of the total OTU numbers in each group, respectively. There were only 32, 32 and 29 unique OTUs in the CON, CG and AP groups. It can be seen that the number and types of OTUs among the AP, CG and CON groups did not change significantly.

[0105] Different SARA induction modes will affect the bacterial community structure in cattle feces to a certain extent. The main bacterial phyla Firmicutes and Bacteroidetes in the AP group, CG group and CON group did not change significantly, but there were significant differences in the dominant bacterial phyla and genera (Turicibacter and Clostridium_sensu_stricto_1) between different induction methods, which can be used as a microbial marker for diagnosing the type of rumen acidosis in the future.

[0106] B. The results of SARA research on the diversity of bacterial community structure in rumen fluid of Jinjiang Yellow Cattle.

[0107] (1) Bacterial richness and diversity. After Illumina-Miseq high-throughput sequencing, 892,242 high-quality sequences were obtained from 24 rumen fluid samples after quality control. Each sample produced an average of 37,176 sequences with an average sequence length of 434 bp. The number of sequences and diversity index of the 24 samples at a similarity level of 97% are shown in Table 5. The richness indexes Chao and Ace of the CG group were significantly higher than those of the CON group, and the Simpson index of the CG group was also significantly higher than that of the CON group. There were no significant differences among the other groups. Fig.13 , Fig.14 .

[0108] Table 5 Richness and diversity index results of 24 rumen fluid samples at 97% similarity level

[0109]

[0110]

[0111] (2) Bacterial composition and community structure. After sequencing of rumen fluid, a total of 25 phyla, 40 classes, 89 orders, 163 families, 360 genera, and 1,691 OTUs were identified. Fig.15It can be seen that at the phylum level, 10 bacterial phyla with relative abundance greater than 1% were identified, namely Firmicutes, Bacteroidetes, Proteobacteria, Cyanobacteria, patescibacteria, Actinobacteria, Elusimicrobia, Tenericutes, Kiritimatiellaeota, and Spirochaetae. Firmicutes and Bacteroidetes were the dominant phyla in the CON, AP, and CG groups, among which Firmicutes had the highest content. In the CON group, the contents of Firmicutes and Bacteroidetes were 51.8% and 24.8%, respectively, in the CG group, the contents of Firmicutes and Bacteroidetes were 52.0% and 30.9%, respectively, and in the AP group, the contents of Firmicutes and Bacteroidetes were 60.3% and 23.8%, respectively.

[0112] The top 10 bacterial phyla in relative content in the three groups of samples were compared and the results were as follows: Fig.16 As shown. The relative contents of Firmicutes and Bacteroidetes, which had the highest relative contents, were not significantly different among the three groups. The only bacterial phyla with significant differences included Cyanobacteria and Actinobacteria. Therefore, the occurrence of acidosis can significantly reduce the relative abundance of cyanobacteria. The relative abundance of cyanobacteria in the CON group was 10.67%, and the relative abundance of Actinobacteria was 0.32%; the relative abundance of cyanobacteria in the AP group was 2.64%, and the relative abundance of Actinobacteria was 2.69%; the relative abundance of cyanobacteria in the CG group was 0.81%, and the relative abundance of Actinobacteria was 2.50%.

[0113] At the genus level, 32 genera with relative abundance greater than 1% were identified. The specific distribution is as follows: Fig.17 The top 15 bacterial genera in relative content in the three groups of samples were compared and the results are shown in Fig.18 The relative abundance of the top three ruminococcus_2, prevotella_1 and ruminococcus_1 was not significantly different among the groups.

[0114] (3) Results of similarity analysis among CON, AP and CG groups. Similarity analysis was performed among samples from each group, and PLS-DA analysis was performed at the OTU level. The results are shown in Figure 2. Fig.19 As shown, the samples within each group are very similar, while the samples between groups are far apart and have significant differences.

[0115] At the 97% similarity level, the numbers of OTUs in the CON, CG and AP groups were 1347, 1371 and 1515, respectively. The three groups shared 1056 OTUs, accounting for 78%, 77% and 70% of the total OTU numbers in each group, respectively. The numbers of unique OTUs in the CON, CG and AP groups were only 39, 90 and 76, respectively. It can be seen that compared with the CON group, the AP group and the CG group both had unique OTUs. Fig. 20 .

[0116] The rumen fluid bacteria were mainly Firmicutes and Bacteroidetes. Acidosis significantly reduced the relative abundance of cyanobacteria in rumen fluid and increased the relative abundance of Actinobacteria. Unique OTUs were produced when SARA was induced in different modes.

[0117] S4. Study the gastrointestinal microbiome and host metabolomics of Jinjiang Yellow Cattle SARA.

[0118] Sampling and detection: Based on the SARA induction model of Jinjiang cattle, urine and feces samples were collected from each cattle before morning feeding on Sundays of the second and fifth weeks of each trial and stored at -80°C. During analysis, the samples were thawed and methanol was added to precipitate proteins. The supernatant was then taken through oscillation, centrifugation and filtration, and analyzed by ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry. The spectral results were first phase adjusted, baseline corrected, noise filtered, peak matched, standardized and normalized, and then the metabolites detected during the experiment were identified by combining the compound attribution table and the existing metabolite database. PCA and PLS-DA were used for data dimensionality reduction, and the metabolites with significant differences between the CON group, AP group, and CG group were compared to find potential metabolic markers during SARA in Jinjiang cattle.

[0119] A. Results of SARA study on gastrointestinal microbial metabolism in Jinjiang cattle. Typical total ion currents (TIC) of GC-MS analysis of fecal samples in AP, CG and CON groups with different induction modes are shown in Figure 2. Fig.21 , with the help of LECO's Chroma TOF4.3X and LECO-Fiehn Rtx 5 databases, the data were processed for raw peak recognition, noise filtering, baseline correction, peak alignment, deconvolution analysis of spectra, peak qualitative and quantitative analysis, etc. In this experiment, a total of 807 peaks were detected, of which 94 substances (similarity ≥ 700) were identified. The results are shown in Fig.21 In addition, the analysis of basic data included Student's t-test in univariate statistical analysis, principal component analysis (PCA) in multivariate statistical analysis, orthogonal partial least squares-discriminant analysis (OPLS-DA) and screening and identification of differentially expressed compounds.

[0120] (1) Principal component analysis: Principal component analysis (PCA) is a statistical method that transforms a set of possibly correlated variables into linearly uncorrelated variables (i.e., principal components) through orthogonal transformation. SIMCA software (V14.1, MKS Data Analytics Solutions, Umea, Sweden) was used to perform logarithmic (LOG) transformation and centering (CTR) formatting on the data, and then automatic modeling analysis was performed. Fig. 22 From the PCA score graph results, we can see that most of the samples are within the 95% confidence interval (Hotelling's T-squared ellipse), and the CG group can be well separated from the CON and AP groups, with obvious differences. The distances between samples in the CON and AP groups are close, and there is a certain overlap between the two groups.

[0121] (2) Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA): In order to reduce the influence of related variables when using PCA model or PLS model analysis, the difference variables will be dispersed to more principal components, which cannot be better visualized and analyzed later. In this embodiment, the statistical method of Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) is used to analyze the results. By filtering out the orthogonal variables that are not related to the classification variables in the metabolites, the non-orthogonal variables and orthogonal variables are analyzed separately to further obtain more reliable information on the correlation between the inter-group differences of metabolites and the experimental groups. Fig.23 , Fig.24 , Fig.25 It can be seen that the horizontal axis t[1]P in the figure represents the predicted principal component score of the first principal component, and the vertical axis t[1]O represents the orthogonal principal component score. It can be clearly seen that the samples between the groups are very significantly distinguished, and the samples are basically within the 95% confidence interval (Hotelling's T-squared ellipse).

[0122] (3) Analysis of differential metabolites: Previously, irrelevant orthogonal signals were filtered out through OPLS-DA analysis. This analysis used the chi-square standard recognized by the academic community, that is, the P value of the Student's t-test was less than 0.05, and the variable importance in the projection (VIP) of the first principal component of the OPLS-DA model was greater than 1 to find differential metabolites.

[0123] In this example, a total of 45 differential metabolites were found between the CON group and the AP group, such as Fig.26 As shown. The expression of significantly different metabolites was analyzed by hierarchical clustering using Ward clustering and Pearson distance calculation in MetaboAnalyst software, thus showing the overview of all metabolites detected in feces, and intuitively showing the relationship between samples in different groups, evaluating the rationality of candidate metabolites, and assisting in screening of iconic metabolites. The results show that there are significant differences in metabolites between the CON group and the AP group, with 42 metabolites being downregulated and only 3 metabolites being upregulated, namely ribitol, maltotriitol, and tagatose.

[0124] Between CON and CG groups, a total of 28 differential metabolites were found in this example (e.g. Fig. 27 There were significant differences between the different groups. After SARA induction with corn grains, 6 metabolites were downregulated, while 22 metabolites were upregulated. Among them, the upregulation folds were significantly increased including proline, sulfaalanine, maltotriose, maltotriose, and thymine. Maltotriose was significantly upregulated in both SARA induction modes, but the maltotriose content in the two induction modes was similar. Therefore, maltotriose can be used as an important metabolic marker when acidosis occurs. The maltotriose content in the AP group was 6.96 times that in the CON group, and the maltotriose content in the CG group was 6.94 times that in the CON group; the maltotriose content in the AP group was 1 times that in the CG group.

[0125] Between AP and CG groups, a total of 30 differential metabolites were found in this example (e.g. Fig.28 There were significant differences among the different groups, indicating that different SARA induction methods would also change the gastrointestinal metabolism of animals. Compared with the alfalfa pellet-induced group, 28 differential metabolites were upregulated after SARA was induced by corn grains, and only glutamine and ribitol were downregulated.

[0126] In order to find the key pathway with the highest correlation with the metabolite difference, this example maps the KEGG and PubChem authoritative metabolite databases through the differential metabolites. After obtaining the matching information of the differential metabolites, the pathway database of the corresponding species Gallus gallus is searched and the metabolic pathway is analyzed. The results are displayed in a bubble plot, see Fig.29 , Fig.30 , Fig.31 Each bubble in the bubble chart represents a metabolic pathway. The horizontal axis of the bubble and the size of the bubble indicate the size of the influencing factor of the pathway in the topological analysis. The larger the bubble, the greater the influencing factor. The vertical axis of the bubble and the color of the bubble indicate the P value of the enrichment analysis (negative natural logarithm, i.e. -lnP-value). The darker the color, the smaller the P value and the more significant the enrichment.

[0127] In the CON and AP groups, 11 metabolic pathways were screened in this embodiment, among which linoleic acid metabolism and arachidonic acid metabolism had the greatest impact on the metabolic pathways. In the CON and CG groups, 5 metabolic pathways were screened, among which linoleic acid metabolism had the greatest impact on the metabolic pathways. In the AP and CG groups, 5 metabolic pathways were screened, among which alanine, aspartic acid and glutamate metabolism had the greatest impact on the metabolic pathways. It can be seen that whether it is corn grain induction or crushed alfalfa particles induction of SARA, it will have a significant impact on linoleic acid metabolism.

[0128] Inducing SARA in Jinjiang cattle in different ways will significantly change the metabolic function in the gastrointestinal tract, and maltotriose can be used as a metabolic marker for acidosis in animals. Regardless of the induction method, it will have a significant effect on linoleic acid metabolism.

[0129] B. The results of the study on host metabolism of Jinjiang cattle SARA. In urine samples, the typical total ion current of GC-MS analysis of AP, CG and CON groups with different induction modes was detected. The data were processed by original peak recognition, noise filtering, baseline correction, peak alignment, deconvolution analysis of spectra, peak qualitative and quantitative analysis with the help of two databases of LECO, Chroma TOF 4.3X and LECO-Fiehn Rtx 5. In addition, the analysis of basic data included Student's t-test in univariate statistical analysis, principal component analysis (PCA) in multivariate statistical analysis, orthogonal partial least squares-discriminant analysis (OPLS-DA) and screening and identification of differential compounds.

[0130] (1) Principal component analysis: Fig.32 From the results of the PCA score graph, it can be seen that most of the samples are within the 95% confidence interval (Hotelling's T-squared ellipse), the 8 samples in the CON group and the AP group can be well clustered together, and the CG group is relatively separated from the CON and AP groups. Further in-depth analysis of the differential metabolites between the groups can be performed.

[0131] (2) Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA): Fig.33 , Fig.34 , Fig.35 It can be seen that the samples are all within the 95% confidence interval (Hotelling's T-squared ellipse). The R2Y values ​​in the OPLS-DA graphs between the CON-AP group, the CON-CG group, and the AP-CG group are 0.94, 0.86, and 0.84, respectively. The R2Y values ​​corresponding to the three groups are all greater than 0.8, indicating that the OPLS-DA analysis model can be effectively used for comparison between the two treatment groups.

[0132] (3) Analysis of differential metabolites: A total of 36 differential metabolites were found between the CON group and the AP group (eg Fig.36 There were significant differences in metabolites between the CON and AP groups. After SARA was induced with crushed alfalfa particles, 18 metabolites were down-regulated, while 18 metabolites were up-regulated. Among the up-regulated metabolites, the metabolites with significant fold changes included pyrophosphate and fructose.

[0133] Between CON and CG groups, a total of 46 differential metabolites were found in this example (e.g. Fig.37 There were significant differences between the different groups. After SARA induction with corn grains, 25 metabolites were downregulated, while 21 metabolites were upregulated. Among them, the metabolites with significantly increased upregulation times included serine, pyrophosphate, naringin and lipoic acid, all of which were upregulated by more than 10 times. Pyrophosphate was significantly upregulated in both SARA induction modes, and was upregulated by 9404 times and 80303 times in the AP and CG groups compared with the CON group, so it can be used as an important metabolic marker when acidosis occurs.

[0134] A total of 41 differential metabolites were found between the AP and CG groups (e.g. Fig.38There were significant differences among the different groups, indicating that different SARA induction methods would also change the gastrointestinal metabolism of animals. Compared with the alfalfa pellet-induced group, 13 differential metabolites were upregulated and 28 metabolites were downregulated after SARA was induced with corn grains, among which naringin, lipoic acid and cerotic acid in the CG group were significantly upregulated.

[0135] In the CON and AP groups, a total of 5 metabolic pathways were screened in this study, among which glycerolipid metabolism and tryptophan metabolism had the greatest impact on the metabolic pathways. Fig.39 In the CON and CG groups, a total of 6 metabolic pathways were screened, among which ascorbic acid and aldehyde acid metabolism, glycine, serine and threonine metabolism had the greatest impact on the metabolic pathways. The results are shown in Fig.40 In the AP and CG groups, a total of 5 metabolic pathways were screened, among which ascorbic acid and aldehyde acid metabolism, glycine, serine and threonine metabolism had the greatest impact on the metabolic pathways. The results are shown in Fig.41 It can be seen that corn grains significantly affect ascorbic acid and aldehyde acid metabolism as well as glycine, serine and threonine metabolism when SARA is induced.

[0136] Inducing SARA in Jinjiang cattle in different ways can significantly change the metabolic function in the gastrointestinal tract, and pyrophosphate can be used as a metabolic marker for acidosis in animals. Inducing SARA with corn grains can significantly change ascorbic acid and aldehyde metabolism as well as glycine, serine and threonine metabolism.

[0137] In this example, feces and urine samples of Jinjiang Yellow Cattle SARA were tested and compared with the CON group, and the results showed that the content of maltotriose in feces in the AP group and the CG group increased significantly, 6.96 times and 6.94 times that of the CON group, respectively; the content of pyrophosphate in urine increased significantly, 9404 times and 80303 times that of the CON group, respectively. Combining the changes in the content of maltotriose in feces and pyrophosphate in urine, subacute rumen acidosis can be accurately predicted.

[0138] By adopting multiple indicators for prediction, on the basis of the existing maltotriose and pyrophosphate test results, the lactic acid content in rumen fluid, the relative abundance of cyanobacteria in rumen fluid, and the relative abundance of Actinobacteria in urine were further tested. Compared with the CON group, the lipopolysaccharide activity in the AP group and the CG group was ≥20000EU / mL, the lactic acid content was ≥2mmol / L, and the relative abundance of Actinobacteria was ≥1.6%. Combining the above results for comprehensive judgment can further improve the accuracy of prediction.

[0139] Compared with the CON group, (1) when the pH value was used for prediction, the pH at different time points was fluctuating and there was an overlap in the pH values ​​between the control group and the model group, so it was impossible to accurately predict whether subacute rumen acidosis occurred; (2) when the total VFA, acetic acid, propionic acid, butyric acid, isobutyric acid, and valeric acid content was used for prediction, the values ​​at different time points overlapped, so it was impossible to accurately predict whether subacute rumen acidosis occurred.

[0140] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. Use of a biomarker in a product for predicting subacute rumen acidosis, characterized in that: The biomarker is selected from maltotriitol and / or pyrophosphate.

2. The use according to claim 1, characterized in that The products include kits.

3. The use according to claim 2, characterized in that: The kit comprises a reagent 1, and the reagent 1 is an organic solvent; preferably, the organic solvent is methanol.

4. The use according to any one of claims 1 to 3, characterized in that: The prediction is to detect the level of biomarkers in the feces or urine of the test animal to be tested. If the level of any biomarker in claim 1 in the feces or urine of the test animal is higher than the upper limit of the reference value, the test animal has a risk of developing subacute rumen acidosis.

5. The use according to claim 4, characterized in that The reference value is the level of the biomarker in the feces or urine of normally fed animals.

6. The use according to claim 5, characterized in that When the maltotriose level in the feces of the test animal reaches more than 6 times that of the normally fed animal, the test animal is at risk of developing subacute rumen acidosis.

7. The use according to claim 5, characterized in that When the level of pyrophosphate in the urine of the test animal reaches more than 9000 times that of the normally fed animal, the test animal is at risk of developing subacute rumen acidosis.

8. The use according to claim 4, characterized in that The levels of biomarkers were measured by liquid chromatography-mass spectrometry.

9. The use according to any one of claims 1 to 8, characterized in that The biomarker is also selected from one or more of lactic acid, lipopolysaccharide, and Actinobacteria; Preferably, when the biomarker level in the rumen fluid of the test animal meets one or more of the following AC, the test animal is at risk of developing subacute rumen acidosis; A. The activity of the lipopolysaccharide is ≥ 20000EU / mL; B. The lactic acid content is ≥2.00mmol / L; C. The relative abundance of the Actinobacteria is ≥ 1.6%.

10. Use of the biomarker according to claim 1 in screening drugs for preventing or treating subacute rumen acidosis.

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