Application of pseudomonas as marker in evaluation of bull semen quality

Abstract

The invention discloses application of pseudomonas as a marker in evaluation of bull semen quality, and belongs to the technical field of animal breeding and microbiomics. The relative abundance of Pseudomonas in a bull rumen fluid with high semen quality is significantly higher than the relative abundance in a bull rumen fluid with low semen quality. By detecting the relative abundance, the bull semen quality can be evaluated, and the reproductive performance can be reliably judged on the premise of not depending on a semen sample. Evaluation can be carried out before semen collection for the first time or in a breeding candidate period, potential low-fecundity individuals are screened out in advance, and the breeding risk is reduced.

Description

TECHNICAL FIELD

[0001] The application relates to the fields of animal reproduction and microbiome technology, and particularly relates to application of pseudomonas as a marker in evaluation of bull semen quality. BACKGROUND

[0002] Bull semen quality is one of the core indexes in genetic improvement of dairy cows, and is directly related to the success rate of artificial insemination and genetic potential of offspring. At present, the mainstream semen quality evaluation method relies on computer-aided semen analysis system (CASA) to detect sperm density, motility, morphology and other parameters, but its evaluation system is mainly based on terminal phenotype, lacks explanation of the molecular mechanism behind reproductive performance, and has limitations in prediction accuracy and application scenarios.

[0003] In recent years, rumen microorganisms have begun to attract attention as an important factor in regulating the health and metabolic performance of ruminants. Studies have shown that changes in the structure of rumen microbial community are closely related to milk yield, methane emission and metabolic disease risk of dairy cows. However, there are still few studies on the relationship between rumen flora and bull fertility, especially the establishment of operable, heritable and predictable microbial markers. SUMMARY

[0004] The purpose of the present application is to provide the application of pseudomonas as a marker in evaluation of bull semen quality, to provide a microbial dimension index for evaluation of bull semen quality, to make a reliable judgment on reproductive performance without relying on semen samples, and to further assist in breeding selection and semen quality control.

[0005] The technical solution of the present application is described in detail as follows: In a first aspect, the present application provides the application of pseudomonas as a marker in evaluation of bull semen quality, wherein the relative abundance of pseudomonas in rumen fluid of a bull with high semen quality is significantly higher than that in rumen fluid of a bull with low semen quality; and the semen quality includes sperm density and sperm motility.

[0006] The relative abundance refers to the relative expression abundance of V3-V4 region 16S rRNA gene fragment of pseudomonas.

[0007] Optionally or preferably, in the above application, the evaluation method comprises: collecting rumen fluid sample of the to-be-tested bull, amplifying V3-V4 region 16S rRNA gene fragment after extracting DNA, detecting the relative abundance of pseudomonas, and determining that the bull has high semen quality if the relative abundance exceeds a preset value.

[0008] Secondly, the present invention also provides the application of a reagent for detecting Pseudomonas spp. in the preparation of products for evaluating the quality of bull semen, wherein the relative abundance of Pseudomonas spp. in the rumen fluid of bulls with high semen quality is significantly higher than that in the rumen fluid of bulls with low semen quality; wherein the semen quality includes sperm density and sperm motility.

[0009] Thirdly, the present invention also provides the application of Pseudomonas spp. as a marker in constructing an evaluation model for bull semen quality, wherein the input variable of the evaluation model is the relative abundance of Pseudomonas spp.; the relative abundance of Pseudomonas spp. in the rumen fluid of bulls with high semen quality is significantly higher than that in the rumen fluid of bulls with low semen quality; the semen quality includes sperm density and sperm motility.

[0010] Compared with the prior art, the present invention has the following beneficial effects: Pseudomonas spp. ( Pseudomonas Pseudomonas is a widely distributed facultative anaerobic bacterium, with some strains possessing antioxidant, detoxification, and nitrogen cycling promotion functions. In the rumen environment, Pseudomonas (…) Pseudomonas Through its metabolic activity, it can participate in the degradation process of various compounds (such as fatty acids, nitro compounds, etc.), help maintain the stability of the rumen microecology and improve energy metabolism, thereby potentially indirectly improving the sperm function and overall reproductive performance of bulls.

[0011] This invention uses microbiome sequencing, differential analysis, and machine learning models to screen and validate the genus *Pseudomonas* (…). Pseudomonas The key value of semen in bull semen quality assessment suggests that it can serve as a biomarker. By detecting its relative abundance, bull semen quality can be evaluated, allowing for a reliable assessment of reproductive performance without relying on semen samples. Through high-throughput sequencing combined with machine learning models, it is hoped that early prediction of bull semen quality can be achieved. Assessments can be conducted before the first semen collection or during the breeding candidate period, allowing for the early screening of potentially low-fertility individuals and reducing breeding risks. Attached Figure Description

[0012] Figure 1 This is a schematic diagram (RSQH and RSQL) of grouping breeding bulls based on semen quality (sperm density and sperm motility) in Example 1.

[0013] Figure 2 The results of the α-diversity analysis of the rumen microbial community between the RSQH and RSQL groups in Example 1 are shown.

[0014] Figure 3 The results of the β-diversity analysis of the rumen microbial community between the RSQH and RSQL groups in Example 1 are shown.

[0015] Figure 4Comparison results of relative abundance of Pseudomonas in RSQH and RSQL groups in Example 1. Pseudomonas DETAILED DESCRIPTION

[0016] In order to enable persons skilled in the art to better understand the present application, the present application will be described clearly and completely below in conjunction with examples and drawings. Obviously, the described examples are only a part of the embodiments of the present application, rather than all the embodiments. Based on the examples in the present application, all other examples obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application. The instruments and reagents used in the examples are all from commercial channels, unless otherwise specified.

[0017] Example 1 Screening and identification of rumen microbial markers 1.1 Semen quality grouping In this study, 48 healthy Holstein bulls aged 4-6 years were selected from the same bull station group, which were fed with uniform total mixed ration, and the semen collection period and environmental conditions were consistent. Semen was collected twice a week routinely, and comprehensive records were made during the entire sampling period. The parameters measured included ejaculate volume (mL), sperm motility (%), sperm density (×10 8 / mL), abnormality rate (%) and post-thaw motility of frozen semen (%). According to sperm motility and sperm density, the bulls were divided into 24 high semen quality group (RSQH) and 24 low semen quality group (RSQL), and the specific grouping was shown in Table 1. Figure 1

[0018] Table 1 Semen quality of two groups of Holstein bulls 1.2 Rumen sample collection and DNA extraction Twelve hours before semen collection, about 50 mL of rumen contents was extracted from the central part of the rumen using a stomach tube sampling device, and the middle section liquid was collected, filtered through three layers of sterile gauze, quickly frozen in liquid nitrogen and stored at -80℃. The CTAB (cetyltrimethylammonium bromide) method was used to extract microbial genomic DNA, the purity (A260 / 280 = 1.8-2.0) was determined using Nanodrop (ultraviolet spectrophotometer), and the DNA integrity was evaluated by agarose gel electrophoresis.

[0019] 1.3 16S sequencing and data processing ​​The V3-V4 region of the 16S rRNA gene was amplified using primers 341F and 806R, and after library construction, 2x250 bp paired-end sequencing was performed on the Illumina NovaSeq6000 platform. The raw data was analyzed using the QIIME2 platform, including DADA2 denoising, ASV construction, classification annotation based on the SILVA 138 database (a comprehensive database containing the rRNA gene sequences of three-domain microorganisms, bacteria, archaea, and eukaryotes), and generation of a standardized feature abundance matrix for subsequent analysis.

[0020] 341F: 5'-CCTAYGGGRBGCASCAG-3' (SEQ ID NO: 1), 806R: 5'-GGACTACNNGGGTATCTAAT-3' (SEQ ID NO: 2).

[0021] Illumina NovaSeq 6000 platform: a high-throughput gene sequencing system developed by Illumina.

[0022] QIIME2 platform: a tool for microbial community data analysis.

[0023] DADA2: an algorithm for correcting errors in amplicon sequencing data.

[0024] ASV: Amplicon Sequence Variant, refers to the microbial DNA fragments with precise sequence differences obtained by high-throughput sequencing.

[0025] SILVA 138 database: a comprehensive database containing the rRNA gene sequences of three-domain microorganisms (bacteria, archaea, and eukaryotes).

[0026] 1.4 Microbial diversity and community structure analysis Chao1 and Shannon index were used to evaluate alpha diversity; beta diversity matrix was constructed based on Bray-Curtis distance, and PCoA and NMDS dimensionality reduction analysis were performed. Community composition analysis included determination of dominant groups at the phylum and genus levels to characterize the rumen core microbial structure.

[0027] Alpha diversity: describes the richness and evenness of microbial species in a single sample (or population), reflecting the internal diversity level.

[0028] Beta diversity: measures the difference in microbial composition between different samples (or populations), used to compare the diversity differences between populations.

[0029] Chao1 index: Chao1 richness, also known as Chao1 richness index or Chao1 diversity index, is an index used to measure the species diversity of a community.

[0030] Shannon index: Shannon-Wiener index, also known as Shannon-Wiener index, is used to evaluate the diversity and heterogeneity of species within a community.

[0031] Bray-Curtis: A method used to measure the similarity or difference of species composition within a sample, mainly reflecting the richness and uniformity of species in a single sample.

[0032] PCoA: Principal Coordinates Analysis, a method of mapping high-dimensional data into low-dimensional space, thereby observing the similarity or difference between data intuitively.

[0033] NMDS: Non-metric Multidimensional Scaling, a data dimensionality reduction technique that simplifies high-dimensional data into low-dimensional space (usually two or three dimensions) while preserving the original relationship between data points.

[0034] ACE index: An index that estimates species diversity using rare species, with a higher value representing a more diverse community.

[0035] Simpson index: An index used to quantify species diversity, which calculates the probability of individual distribution of species in the community, reflecting the richness and uniformity of species diversity.

[0036] PD_whole_tree index: Phylogenetic Diversity whole tree, based on the evolutionary history or phylogenetic relationship of species to assess biodiversity.

[0037] 1.5 Differential flora screening and statistical analysis Apply the zero-inflated Gaussian model (ZIG) in the metagenomeSeq package for differential analysis at the genus level, with the screening conditions of p<0.01 and log2FC≥1. Significant differential genera are visualized by volcano plot, heatmap and boxplot.

[0038] 2 Experimental results 2.1 α and β diversity analysis Please refer to Figure 2 and Figure 3Alpha diversity analysis (ACE, Chao1, Shannon, Simpson, PD_whole_tree) showed no significant differences between the two groups; however, β diversity analysis showed significant community structure separation in PCoA and NMDS plots, suggesting systematic differences in the rumen microbial community between the two groups.

[0039] 2.2 Composition of dominant rumen microbiota The dominant phyla in the rumen microbiota include Bacteroides, Firmicutes, and Proteobacteria, with a combined abundance exceeding 85%. Among these, the abundance of Bacteroides ranged from 33.6% to 81%, with a median of 63.6%. The dominant genera mainly include Prevotella (…). Prevotella (Median 19.5%), Riken Bacteriaceae RC9 intestinal group ( Rikenellaceae_RC9_gut_group The rumen core functional microbiota consists of the genus F082 (median 16.8%) and the unclassified genus F082 (median 6.8%).

[0040] 2.3 Differential genus identification and Pseudomonas filter Differential abundance analysis identified 107 microbial genera with significantly different abundances between RSQH and RSQL. Table 2 lists the relative abundance values ​​of the top 10 genera and the fold differences in abundance between the two groups, ranked according to their relative abundance in the RSQH group.

[0041] Table 2. Microbial genera with significant differences in abundance between the RSQH and RSQL groups. Note: Subscript ① indicates that the relative abundance of the RSQL group is higher than that of the RSQH group by a multiple.

[0042] Among them, Pseudomonas spp. ( Pseudomonas Among the significantly differentially expressed bacterial genera, this genera had the highest relative abundance, at 78.23% in the RSQH group and 13.47% in the RSQL group, with a fold change of approximately 5.8 (log2Fold Change = 2.54, p = 4.1e-05). It was a dominant bacterial genera that was significantly enriched in the high semen quality group and has the potential function of a reproductive biomarker.

[0043] Example 2: Validation of Pseudomonas spp. based on machine learning model ( Pseudomonas Predictive ability of bull semen quality 1 Experimental Methods 1.1 Model Construction Based on the genus-level microbial relative abundance data obtained in Example 1, two supervised classification models, Random Forest and Extreme Gradient Boosting (XGBoost), were constructed to predict bull semen quality groupings (RSQH or RSQL). Model training was performed using the R language platform, with the caret package used to set up three-fold cross-validation. The evaluation metric was AUC (area under the ROC curve), and automatic hyperparameter tuning was enabled to improve model performance.

[0044] 1.2 Model Evaluation The sample data was divided into training and testing sets in a 7:3 ratio. The model's accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were comprehensively evaluated during both the training and prediction phases. Feature importance was extracted based on the Gini index from random forest and the gain value from XGBoost, with a focus on *Pseudomonas* spp. (…). Pseudomonas The ranking position in the two models.

[0045] Gini index: The Gini index reflects the influence of a feature on the classification result.

[0046] Gain value: Indicates the degree to which a feature contributes to performance improvement in the model.

[0047] 2. Experimental Results 2.1 Classification Model Performance The model performs well on both the training and test sets: The training set AUC values ​​were 1.000 (Random Forest) and 0.997 (XGBoost). The AUC values ​​on the test set were 0.953 (Random Forest) and 0.875 (XGBoost).

[0048] The above results indicate that rumen microbial community information can be effectively used to distinguish different semen quality levels, demonstrating good biopredictive potential. Among them, *Pseudomonas* spp. (…) Pseudomonas Among the features that rank in the top 10 in both models, it demonstrates its key role in identifying bull reproductive capacity.

[0049] 2.2 Pseudomonas spp. ( Pseudomonas Abundance and trait association Pseudomonas spp. Pseudomonas This genus was significantly enriched in the RSQH group, and its relative abundance was positively correlated with two key traits, sperm motility and sperm density, with a correlation coefficient of 0.71 and a p-value of 0.0021. This genus showed good stability in bulls with high semen quality and possesses potential breeding indicator value.

[0050] 2.3 Pseudomonas spp. ( PseudomonasFunctional verification as a marker Ten healthy Holstein bulls aged 4-6 years from another breed were randomly selected. Semen was collected twice routinely, and sperm motility and density were recorded. Twelve hours before the first semen collection, mid-rumen fluid was collected as a sample. The 16S rRNA gene V3-V4 region was amplified using primers 341F and 806R. The product was sequenced, and *Pseudomonas* spp. were detected. Pseudomonas Relative expression abundance.

[0051] Table 3. Relative expression abundance of Pseudomonas spp. in the validation group and semen quality. The results showed that 7 of the bulls were infected with Pseudomonas spp. ( Pseudomonas The relative expression abundance of *Pseudomonas* was relatively high, all greater than 70%, and the expression abundance of *Pseudomonas* spp. was also high in the other three bulls. Pseudomonas The relative expression abundance was low, all less than 20%, and the corresponding sperm density and sperm motility also showed significant differences.

[0052] 2.4 Comprehensive Analysis Pseudomonas spp. Pseudomonas As a widely distributed conditionally beneficial bacterium, *Pseudomonas* possesses multiple functions, including antioxidant activity, detoxification, and regulation of energy metabolism. This study validated the role of *Pseudomonas* genus through microbiome sequencing, differential analysis, and machine learning models. Pseudomonas Its key value in assessing the quality of bull semen can be developed and applied as a microbial biomarker for assisting breeding and regulating the microecology.

[0053] This article uses specific examples to illustrate the inventive concept in detail. The description of the above embodiments is only for the purpose of helping to understand the core idea of ​​the present invention. It should be noted that any obvious modifications, equivalent substitutions or other improvements made by those skilled in the art without departing from the inventive concept should be included within the protection scope of the present invention.

Claims

1. The application of *Pseudomonas* spp. as a biomarker in assessing bull semen quality, characterized in that, The relative abundance of the *Pseudomonas* genus in the rumen fluid of bulls with high semen quality was significantly higher than that in the rumen fluid of bulls with low semen quality; the semen quality includes sperm density and sperm motility.

2. The application according to claim 1, characterized in that, The evaluation method includes: collecting rumen fluid samples from the bulls to be tested, extracting DNA and amplifying the 16S rRNA gene fragment in the V3-V4 region, detecting the relative abundance of Pseudomonas spp., and determining that the bulls have high semen quality if the abundance exceeds the preset value.

3. The application of reagents for detecting Pseudomonas spp. in the preparation of products for evaluating the quality of bull semen, characterized in that, The relative abundance of the *Pseudomonas* genus in the rumen fluid of bulls with high semen quality was significantly higher than that in the rumen fluid of bulls with low semen quality; the semen quality includes sperm density and sperm motility.

4. The application of *Pseudomonas* spp. as a biomarker in constructing an assessment model for bull semen quality, characterized in that... The input variable of the evaluation model is the relative abundance of the genus *Pseudomonas*. The relative abundance of *Pseudomonas* in the rumen fluid of bulls with high semen quality is significantly higher than that in the rumen fluid of bulls with low semen quality. The semen quality includes sperm density and sperm motility.

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