A method for screening of genes associated with growth and disease resistance traits and microorganisms

Abstract

The present application belongs to the field of genetic resources and breeding, and particularly relates to a screening method of related genes and microorganisms balancing growth traits and disease resistance traits. The present application finds that the microbial population in gill mucus changes significantly during the attack, and finds the microorganisms resisting the infection of pathogenic bacteria by using the finding, and further finds the genes balancing growth traits and the infection of pathogenic bacteria by combining with whole genome association analysis. The present application first discloses the role of gill mucus microorganisms and their associated host genes in balancing growth and disease resistance traits of fish, provides candidate genes for molecular marker assisted breeding of fish, and provides a new idea for improving the disease resistance of fish by microbial regulation.

Description

Technical Field

[0001] This invention belongs to the field of genetic resources and breeding, and specifically relates to a method for screening genes and microorganisms related to balancing growth traits and disease resistance traits. Background Technology

[0002] With global population growth and the depletion of wild fishery resources, aquaculture is seen as a key way to ensure food security. In fish farming, pathogen infections cause enormous mortality and economic losses. For example, tilapia, a major aquaculture species globally, is severely threatened by Streptococcus agalactiae infection. Genetically modified tilapia (GIFT), derived from Nile tilapia, is one of the most successful tilapia breeding programs; however, GIFT is highly susceptible to Streptococcus agalactiae infection, with a mortality rate as high as 90%. Zhuangluo No. 1 (ZL) is a disease-resistant strain successfully bred from GIFT through family selection. Its average mortality rate against Streptococcus agalactiae is as low as 15%, and its absolute growth rate is as high as 2.50 grams per day, higher than the 1.89 grams per day of its F0 generation GIFT, achieving a balance between growth and disease resistance.

[0003] Typically, due to physiological limitations or energy resource management, organisms must make trade-offs between immune function and growth, making it difficult to obtain breeding lines that combine both growth and disease resistance. In poultry, compromises in immune function have been commonly observed after selective growth. However, under certain special circumstances, organisms may acquire pathogen resistance through exomicrobiota, resulting in an attractive model that integrates immune function and growth traits into a single breeding line. Almost energy-efficient resistance based on exomicrobiota may be an ideal solution and is effective against a variety of pathogens.

[0004] Currently, research on the role of fish gill mucus microbiota in resisting pathogen infection, and how host genes regulate the microbiota to achieve a balance between growth and disease resistance, is insufficient. Therefore, there is an urgent need to develop a marker-based screening method based on host-microbe interactions to achieve precise and synergistic improvement of fish growth and disease resistance traits. Summary of the Invention

[0005] This invention provides a method for screening genes and microorganisms that balance growth traits and disease resistance traits, aiming to solve the technical problem of the inability to simultaneously achieve immune function and growth traits in fish selective breeding, and to provide a new direction for fish selective breeding.

[0006] The technical solution of this invention is a method for screening genes and microorganisms related to balancing growth traits and disease resistance traits, comprising the following steps: Microbial screening: S1. Offspring fish with balanced growth traits and resistance to bacterial infection in the family and first-generation parent fish in the family were selected for a challenge experiment. At the beginning of the challenge experiment, during the peak mortality period, during the mortality decline period, and during the mortality quiescence period, gill mucus, gill tissue and intestinal contents of the offspring fish and first-generation parent fish were collected respectively. S2, Microbial species composition analysis was performed on gill mucus from offspring and primary parent fish during the challenge period, where significant changes in OTU numbers were observed. The microbial species composition of the gill mucus from both offspring and primary parent fish during the challenge period was obtained. Based on the microbial species composition of the gill mucus from the offspring fish during the challenge period, the class and genera of microbial species whose abundance showed an increasing trend during the challenge period were extracted. Based on linear discriminant analysis of effect size, genera whose abundance in the gill mucus of the offspring fish during the challenge period showed an increasing trend but significantly different abundance from that of the primary parent fish were selected as microbial genera balancing growth and disease resistance traits. Gene screening: S3, perform association analysis on the differentially expressed genes of the microbial genera with balanced growth traits and disease resistance traits selected in step S2 with the offspring fish and primary parent fish during the challenge period, and exclude differentially expressed genes of microbial genera with balanced growth traits and disease resistance traits that are positively correlated only in the primary parent fish, and obtain differentially expressed genes of microbial genera with positively correlated with balanced growth traits and disease resistance traits. S4. Perform whole-genome resequencing on naturally surviving, disease-resistant offspring fish and first-generation parent fish. Compare with the reference genome to extract SNP molecular markers and genes associated with SNP molecular markers that are significantly associated with disease resistance. Select genes from the genes associated with SNP molecular markers that are positively correlated with differentially expressed genes in the same microbial genera as balanced growth and disease resistance traits, and use them as genes for balanced growth and disease resistance traits.

[0007] The antibacterial infection described in this invention refers to anti-streptococcal infection.

[0008] Step S1, the offspring fish with balanced growth traits and resistance to bacterial infection refers to Zhuangluo No. 1 ZL, and the first-generation parent fish in the family refers to the genetically improved tilapia GIFT.

[0009] Step S1, the challenge experiment refers to the Streptococcus agalactiae infection experiment: Offspring fish with balanced growth traits and resistance to infection from adult males and first-generation parent fish from the family lineage were selected, and each fish was intraperitoneally injected with 0.2 mL of a 5.8 × 10⁻⁶ solution. 8 A challenge experiment was conducted using the CFU / mL Streptococcus agalactiae GD001 strain.

[0010] Step S2, the method for selecting gill mucus from which the number of OTUs changed significantly between offspring and primary parent fish during the challenge period includes: performing 16S rRNA sequencing and OTU analysis on gill mucus and intestinal contents collected from the offspring and primary parent fish respectively during the challenge period; comparing the changes in the number of OTUs in the gill mucus and intestinal contents of the offspring and primary parent fish during the challenge period; and selecting gill mucus from which the number of OTUs changed significantly between the offspring and primary parent fish during the challenge period.

[0011] The primers used for the 16S rRNA sequencing include 5'-ACTCCTACGGGAGGCAGCAG-3' and 5'-GGACTACHVGGGTWTCTAAT-3', which amplify the V3 and V4 hypervariable regions, with amplified fragment sizes of 350 to 400 bp.

[0012] The amplified library generated by the 16S rRNA sequencing was sequenced on the Illumina MiSeq platform to generate raw reads. The adapter sequences were removed using Cutadapt software, the paired-end sequences were merged using PEAR, and quality control was performed using PRINSEQ.

[0013] Step S2, the method for obtaining the microbial species composition in the gill mucus of offspring fish and primary parent fish during the challenge period is as follows: the microbial species composition in the gill mucus of offspring fish and primary parent fish during the challenge period is identified by the ribosome database project classifier, then α diversity is assessed by calculating the Shannon Wiener index, Pielou evenness and Simpson diversity index, then β diversity is assessed by partial least squares discriminant analysis and nonmetric multidimensional scaling analysis, and finally linear discriminant analysis effect size analysis is performed.

[0014] Step S2, the genera of microorganisms that balance growth traits and disease resistance traits include, but are not limited to, Flavobacterium and / or Hydrogenophaga.

[0015] Step S3, the method for obtaining differentially expressed genes between offspring and primary parent fish during the challenge period is as follows: RNA sequencing is performed on gill tissues of the offspring and primary parent fish collected during the challenge period. Genes from the offspring fish whose expression was significantly higher or lower than that at the start of the challenge experiment during the peak mortality period, the decline in mortality period, and the quiescent mortality period are selected as differentially expressed genes between the offspring and primary parent fish at the corresponding time points. Further, genes from the offspring fish whose expression was significantly higher or lower than that at the start of the challenge experiment during the peak mortality period, the decline in mortality period, and the quiescent mortality period are selected as differentially expressed genes between the offspring and primary parent fish at the corresponding time points. Even further, genes from the offspring fish whose expression was significantly higher than that at the start of the challenge experiment during the peak mortality period, the decline in mortality period, and the quiescent mortality period are selected as differentially expressed genes between the offspring and primary parent fish at the corresponding time points. 633 differentially expressed genes were identified.

[0016] The RNA sequencing was performed on the Illumina HiSeq×Ten platform. Cutadapt was used to remove sequencing adapters and low-quality reads. STAR was used to align clean reads to the reference genome (GIFT strain tilapia genome: Genbank accession number GCA_922820385.1) to verify sequencing data quality. Gene expression levels were calculated using featureCounts and normalized to FPKM.

[0017] Step S3: Perform Pearson correlation analysis on the differentially expressed genes of the microbial genera with balanced growth traits and disease resistance traits selected in Step S2 and the offspring and primary parent fish during the challenge period. Use correlation coefficient > 0.7 and t-test P < 0.05 as the criterion to obtain differentially expressed genes of microbial genera that are positively correlated with balanced growth traits and disease resistance traits. Use correlation coefficient > 0.7 as the criterion to exclude differentially expressed genes of microbial genera that are positively correlated with balanced growth traits and disease resistance traits that exist only in the primary parent fish. Obtain differentially expressed genes of microbial genera that are positively correlated with balanced growth traits and disease resistance traits.

[0018] Step S4: Collect the tail fins of naturally surviving, disease-resistant offspring fish and first-generation parent fish for whole-genome resequencing, with an average sequencing depth of 5.9×.

[0019] Step S4: Genes balancing growth traits and disease resistance traits include, but are not limited to, SRP19, PPIB, FOXL1, LPAR4, ARPC3, FABP4A, PHF5A, SLC6A15, TRIM69, APOO, TRX1, NDUFA1, NDUFA3, NDUFA6, NDUFA10, NDUFA13, COX6B1, COX6C, TIMM50, PRDX3, LTV1, AK2, HER6, TUBa2, SCFD1, CHST3a, NAGK, GPD1c, NAGS, ARG1, UCK2b, AK2, ATP5MF, IL10, PSME2, ​​TXN, CH25h, DENR, MMP13, TUBA, and MAMDC4.

[0020] The screening method of this invention initially identified several differentially expressed genes associated with balanced growth and disease resistance traits in fish. Combined with genome-wide association analysis, these genes were confirmed as MMP13, TUBA, and MAMDC4 genes influencing resistance to Streptococcus agalactiae infection and balanced growth traits in fish. Simultaneously, Flavobacterium and Hydrogenophaga were identified as microbial genera associated with balanced growth traits and resistance to pathogenic infections.

[0021] This invention also provides the application of genes and / or microorganisms that balance growth morphology and resistance to pathogen infection in fish breeding. The genes include at least one of the following: SRP19, PPIB, FOXL1, LPAR4, ARPC3, FABP4A, PHF5A, SLC6A15, TRIM69, APOO, TRX1, NDUFA1, NDUFA3, NDUFA6, NDUFA10, NDUFA13, COX6B1, COX6C, TIMM50, PRDX3, LTV1, AK2, HER6, TUBa2, SCFD1, CHST3a, NAGK, GPD1c, NAGS, ARG1, UCK2b, AK2, ATP5MF, IL10, PSME2, ​​TXN, CH25h, DENR, MMP13, TUBA, and MAMDC4. The microorganisms are derived from the genera *Flavobacterium* and / or *Hydrogenophaga*.

[0022] Beneficial effects of this invention: This invention, through challenge experiments on offspring fish exhibiting balanced growth and disease resistance traits and their primary parents, combined with 16S rRNA sequencing of gill mucus microorganisms and gill tissue transcriptome sequencing, systematically screened for the first time key microbial genera (Flavobacterium and Hydrogenobacterium) associated with disease resistance and highly related host genes (MMP13, TUBA, MAMDC4, etc.). The challenge experiments showed that the mortality rate of the offspring fish was significantly lower than that of the primary parents, while the offspring fish maintained a higher growth rate. Furthermore, by comparing the changes in the gut microbiota and gill mucus microbiota during challenge, this invention found that the number of operational taxa in the gill mucus microbiota changed more significantly during challenge, while the gut microbiota remained relatively stable, thus confirming that the gill mucus microbiota is a key target for disease resistance screening. This invention not only reveals the mechanism by which the host regulates the gill mucus microenvironment through gene expression, thereby influencing the colonization of beneficial microorganisms to resist pathogens, providing new molecular markers (genes and SNPs) for disease-resistant fish breeding, but also directly screens out microorganisms with potential probiotic functions, providing a novel strategy for balancing fish growth and disease resistance traits through microbial intervention. Attached Figure Description

[0023] Figure 1 Images of adult male GIFT and ZL (Zhuangluo 1) fish and sparse curves showing the relationship between mortality and sequencing depth during Streptococcus agalactiae challenge. In the images, A represents adult male GIFT and ZL fish, B represents the mortality rate of GIFT and ZL fish during Streptococcus agalactiae challenge, and C represents the sparse curves showing the relationship between the number of OTUs in intestinal and gill mucus microorganisms collected at various time points during Streptococcus agalactiae challenge and sequencing depth (G represents GIFT, g represents gill mucus, and i represents intestinal contents).

[0024] Figure 2 The results show the α and β diversity analysis of gill mucus microorganisms during Streptococcus agalactiae challenge. A is a box plot of the α diversity indices (Shannon index, Shannon evenness index, and Simpson index) of gill mucus microorganisms in two tilapia strains at 0, 12, 24, and 48 hours post-challenge; B is the nonmetric multidimensional scaling (NMDS) ordination plot; and C is the partial least squares discriminant analysis (PLS-DA) score plot.

[0025] Figure 3 The composition and relative abundance of the microbial community in gill mucus are shown. Here, A represents the relative abundance at the class level identified in gill mucus, and B represents the relative abundance at the genus level identified in gill mucus.

[0026] Figure 4A Circos visualization of differentially expressed microbial communities identified by LEfSe analysis. Concentric circles represent taxa (phylum to genus), and lines connect taxa to their relative abundance within each group. The legend on the right lists taxa showing significant differences.

[0027] Figure 5 A flowchart for analyzing genes expressing different values ​​at 12, 24, or 48 hours post-infection.

[0028] Figure 6 The results show the temporal expression profiles and functional enrichment analysis of infection response genes. In this diagram, A represents the gene temporal expression patterns clustered using the Mfuzz algorithm (divided into 7 clusters), and B represents the KEGG pathway enrichment analysis results from DEG.

[0029] Figure 7 Heatmap of 633 ZL-specific DEGs identified during challenge with Streptococcus agalactiae (I).

[0030] Figure 8 Heatmap of 633 ZL-specific DEGs identified during challenge with Streptococcus agalactiae (II).

[0031] Figure 9 The results of genome-wide association analysis (GWAS) on resistance to Streptococcus agalactiae in tilapia are shown. In the figure, A represents the population structure analysis diagram, and C represents the Manhattan plot.

[0032] Figure 10 The results of genome-wide association analysis (GWAS) on resistance to Streptococcus agalactiae in tilapia are shown. In the figure, B is the principal component analysis plot, and D is the quantile-quantile plot. Detailed Implementation

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0034] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0035] The materials and methods used in the experiments of this invention are as follows: 1. Challenge experiment and sample collection Ninety adult male tilapia (GIFT) and ninety offspring fish (Zhuangluo No. 1, ZL) with balanced growth traits and resistance to pathogen infection from the family were selected. The absolute growth rate of GIFT was 1.89 g / day, and that of ZL was 2.50 g / day. The *Streptococcus agalactiae* strain GD001 was provided by the Fish Disease Control Laboratory of the Guangxi Zhuang Autonomous Region Fisheries Research Institute. Its complete 16S rRNA sequence was 100% identical to that of *Streptococcus agalactiae* strain SA20-06, and its median lethal concentration (LC50) was 5.83 × 10⁻⁶. 8 CFU / mL. 0.2 mL of a concentration of 5.8 × 10⁻⁶ CFU / mL was injected intraperitoneally into each fish. 8 The GD001 strain was tested at CFU / mL. Samples were collected at four time points post-infection: 0 hours (at the start of challenge), 12 hours (peak mortality), 24 hours (mortality decline), and 48 hours (mortality quiescence). Ten fish were collected from each group at each time point. Gill mucus was collected using sterile swabs, and gill tissue was excised. Intestinal contents (hindgut) were also collected. All mixed samples had at least three biological replicates.

[0036] 2. Microbial 16S rRNA Sequencing and Analysis 2.1 DNA extraction and PCR amplification Total microbial DNA was extracted from gill mucus and intestinal contents according to the kit instructions. The V3-V4 hypervariable region of 16S rRNA was amplified by PCR using universal primers 5'-ACTCCTACGGGAGGCAGCAG-3' and 5'-GGACTACHVGGGTWTCTAAT-3', with amplified fragment sizes ranging from 350 to 400 bp. Samples with DNA content less than 200 ng were excluded from subsequent analyses.

[0037] 2.2 Library Construction and Sequencing Libraries with an average insert size of 350 to 400 bp were constructed and sequenced on the Illumina MiSeq platform to generate raw reads. Adapter sequences were removed using Cutadapt software, paired-end sequences were merged using PEAR, and quality control was performed using PRINSEQ.

[0038] 2.3 Microbial sequencing and OTU clustering of intestinal contents and gill mucus 16S rRNA sequencing was performed on intestinal contents and gill mucus samples collected at corresponding time points during the challenge period. After quality control, the raw reads generated by sequencing were clustered into operational taxonomic units (OTUs) at a similarity threshold of 97% to obtain the number of OTUs in the intestinal and gill mucus microbiota.

[0039] 2.4 Diversity analysis of gill mucus microbiota The gill mucus microbiota was analyzed as follows: the taxonomy of the microbiota was identified by searching the Ribosome Database Project (RDP) classifier (http: / / rdp.cme.msu.edu). Alpha diversity was assessed by calculating the Shannon Wiener index, Pielou evenness, and Simpson diversity index, and sparsity analysis was performed using Mothur software. Beta diversity analysis was used to assess inter-sample microbiota differences, including partial least squares discriminant analysis (PLS-DA) using the "mixOmics" package in R and nonmetric multidimensional scaling (NMDS) using the "vegan" package. Linear discriminant analysis of effect size (LEfSE) was performed using the "microeco" package.

[0040] 3. Gill tissue transcriptome sequencing and analysis Total RNA was extracted from gill tissue using TRIzol reagent. RNA sequencing was performed on the Illumina HiSeq×Ten platform. Sequencing adapters and low-quality reads were removed using Cutadapt, and clean reads were aligned to the GIFT strain tilapia genome (Genbank accession number GCA_922820385.1) using STAR. Gene expression levels were calculated using featureCounts and normalized to FPKM. Differentially expressed genes were screened using the R package "DEGseq" with the criteria of P < 0.05 and |Log2 fold change| > 1. Time-series cluster analysis of gene expression was performed using the R package "Mfuzz", and KEGG enrichment analysis was performed using the R package "clusterProfiler".

[0041] 4. Whole-genome resequencing and association analysis Caudal fin samples were collected from 141 ZL tilapia surviving a natural outbreak of Streptococcus agalactiae and 150 common GIFT tilapia as controls. Genomic DNA was extracted and whole-genome resequencing was performed at an average depth of 5.9×. Reads were aligned to the tilapia reference genome (GIFT strain tilapia genome, Genbank accession number GCA_922820385.1) using BWA-MEM, and variant calling was performed using GATK. After quality filtering (minor allele frequency >0.05, deletion rate <0.1), genotyping was performed using Beagle. Genome-wide association analysis of survival status (binary trait) was performed using EMMAX (an efficient mixed model association analysis software), and Manhattan plots and quantile-quantile plots were generated using the R package CMplot.

[0042] 5. Data Analysis The correlation between gene expression and bacterial abundance was assessed using Pearson correlation and Student's t-test. A correlation coefficient > 0.7 and a t-test p < 0.05 were considered statistically significant positive correlations. Statistical analysis was performed using R software (v4.2.3), and network visualization was performed using Cytoscape (v3.9.1).

[0043] The experimental results are analyzed as follows: (I) Observation of mortality rate in challenge experiments Each fish was injected intraperitoneally with 0.2 mL of a solution containing 5.8 × 10⁻⁶ mg / L. 8 The GD001 strain had a concentration of CFU / mL. The first death occurred 6 hours after infection, with the mortality rate rising rapidly until approximately 12 hours later, then slowing down, with only a few deaths observed daily after 48 hours. The final mortality rate for the GIFT strain was 66.98%, and for the ZL strain it was approximately 10%. Figure 1 (B)

[0044] (II) Screening of microorganisms related to balanced growth and disease resistance traits in fish 1. Comparison of gill mucus and intestinal microbial composition during viral challenge in different strains of tilapia 16S rRNA sequencing analysis was performed on gill mucus and intestinal contents of ZL and GIFT tilapia. Figure 1 As shown in Figure C, the number of OTUs detected in gill mucus was significantly higher than that in intestinal contents, with the number of OTUs in gill mucus being approximately 3.8 times that in intestinal contents. In intestinal contents, the number of OTUs remained almost constant throughout the Streptococcus agalactiae challenge, while gill mucus showed more pronounced changes during the challenge period. Therefore, subsequent analyses focused on the gill mucus microbiota.

[0045] 2. Analysis of microbial species composition in gill mucus Results of α and β diversity analysis are as follows Figure 2 As shown, the gill mucus microbiota of the ZL strain was more stable than that of GIFT during challenge, and the community structure was significantly different. At the class level, before challenge, GIFT was dominated by Clostridium (41.90%), β-Proteobacteria (11.53%), and α-Proteobacteria (8.30%); before challenge, ZL was dominated by β-Proteobacteria (54.10%), Clostridium (24.65%), and Bacteroidetes (4.15%). 12 hours after challenge (peak mortality period), the abundance of Bacillus (including Streptococcus agalactiae) in GIFT surged to 64.37%, while the abundance of Flavobacteria in ZL increased to 39.40%. Figure 3(A). At the genus level, 12 hours after challenge, the abundance of aerobic bacteria genera such as Flavobacterium (31.24%), Hydrogenophytes (18.02%), and Vogesella (9.34%) in ZL was significantly higher than in GIFT, while the abundance of Streptococcus (including Streptococcus agalactiae) (3.27%) was significantly lower than in GIFT (64.36%). Figure 3 (B)

[0046] 3. Analysis of microbial markers in gill mucus To identify key microorganisms associated with balanced growth and disease resistance in fish, this study conducted linear discriminant analysis (LEfSe) at 0, 12, 24, and 48 hours post-infection. The most significant difference in gill mucus microbial genera abundance was observed between offspring (ZL) and primary parent fish (GIFT) at 12 hours post-infection (peak mortality period), thus this time point was chosen as the key comparison node. Figure 4 As shown, 12 hours after challenge, the relative abundance of *Flavobacterium*, *Hydrogenobacterium*, *Vogesella*, *Chlorobacterium*, *Fluviicola*, *Acidobacterium*, *Acinetobacter*, and *Cetobacter* in ZL was significantly higher than that in GIFT (LDA score ≥ 3.0). Based on the criteria of "an increasing trend in abundance during challenge (0h→12h)" and "significant differences in abundance among strains 12 hours after challenge," genera of microorganisms coexisting in both criteria were extracted, identifying *Flavobacterium* and *Hydrogenobacterium* as candidate genera related to balanced growth traits and resistance to pathogen infection.

[0047] (III) Screening of genes related to balanced growth and disease resistance in fish 1. Summary of RNA sequencing data Total RNA was sequenced from gill tissues of ZL and GIFT tilapia that survived the viral challenge. A total of 55.14 Gb of clean data was obtained, with each sample yielding over 3.50 Gb of reads and a sequence quality score (Q30) greater than 90.40%. The clean data from each sample were aligned with the reference genome (GCA_922820385.1), with a matching success rate of 95%. These data indicate that the sequencing data are of high quality and suitable for further analysis.

[0048] 2. Identification and analysis of differentially expressed genes The screening process for differentially expressed genes is as follows: Figure 5 As shown, among ZL genes that showed significantly higher (or lower) expression than uninfected ZL (0 hours) at 12, 24, or 48 hours post-infection, those genes that also showed significantly higher (or lower) expression than the GIFT control group at the corresponding time points were defined as ZL DEG. Significant differential expression of all genes was determined based on a parameter of |Log² change| > 1 and P < 0.05. 633 differentially expressed genes were found between ZL and GIFT. Figure 7 and 8 Compared to GIFT, the expression patterns of differentially expressed genes in ZL showed significant differences, and time-series clustering divided them into seven clusters ( Figure 6 (Cluster A). Clusters 1, 4, and 7 showed increased gene expression at 12 and 24 hours post-infection, followed by a decrease at 48 hours.

[0049] 3. Functional enrichment analysis of differentially expressed genes KEGG functional enrichment analysis was performed on differentially expressed genes, and the results are as follows: Figure 6 As shown in Figure B, the significantly enriched pathways mainly involve the cytoskeleton and oxidative phosphorylation, with cluster 1 (28 DEGs) associated with the cytoskeleton and cluster 7 (20 DEGs) associated with oxidative phosphorylation. This indicates that amino acid biosynthesis, energy consumption, and cytoskeleton activity are enhanced in ZL gills during challenge.

[0050] 4. Results of gene-microbiome association analysis To further identify the relationship between ZL-specific gene expression and dominant bacterial genera in gill mucus, Pearson correlation analysis was performed on the 633 ZL-specific DEGs and the 27 most abundant bacterial genera in gill mucus (with a maximum abundance exceeding 0.5%, including *Flavobacterium* and *Hydrogenobacterium* identified by LEfSe analysis in "(II) Screening of Microorganisms Related to Balanced Growth and Disease Resistance in Fish"). Pearson correlation and Student's t-test were used to assess the correlation between gene expression and bacterial abundance. A correlation coefficient > 0.7 and a t-test P < 0.05 were used as criteria, and positive correlation pairs (correlation coefficient > 0.7) in the GIFT were excluded to obtain ZL-specific positively correlated gene-bacterial pairs. The results are as follows: (1) Genes positively correlated with the abundance of Flavobacterium 118 genes that were significantly positively correlated with the abundance of Flavobacterium were identified. Some representative genes and their functional categories are shown in Table 1.

[0051] Table 1. Representative genes positively correlated with the abundance of Flavobacterium. (2) Genes positively correlated with the abundance of Hydrogen-eating Bacteria 135 genes that were significantly positively correlated with the abundance of the genus *Hydrogenophytum* were identified. Some representative genes and their functional categories are shown in Table 2.

[0052] Table 2. Representative genes positively correlated with abundance of the genus *Hydrophage* These genes may collectively regulate gill mucus properties, local immune responses, and metabolic conditions, thereby affecting bacterial adhesion, growth, and community composition in gill mucus.

[0053] (iv) Validation by genome-wide association analysis To further validate the identified genes, whole-genome resequencing was performed on 141 ZL tilapia surviving a natural outbreak of Streptococcus agalactiae and 150 common GIFT tilapia as controls. Sequencing reads from each sample were aligned to the tilapia reference genome (GIFT strain tilapia genome, Genbank accession number GCA_922820385.1). After variant detection and quality filtering, a total of 4,421,935 SNPs were obtained. Population structure was analyzed using admixture software (v1.3). Figure 9 In the middle A), principal component analysis (PCA) showed that PC1 (22.35%) and PC2 (6.28%) together explained a large portion of the total genetic variance. Figure 10 (B) Of the detected SNPs, 4,421 were significantly associated with the trait (top 0.1% and P < 2.09 × 10⁻⁶). -6 After manual examination of gene function information, 112 candidate genes were located. Figure 9 (C in the middle, D in the middle). Among them: 9 and 5 significant SNPs (P<2.78×10). -7 These three SNPs were located in or near genes associated with the genus *Flavobacterium* and genes associated with the genus *Hydrogenobacterium*, respectively; three highly significant SNPs (P = 8.94 × 10⁻⁶) were also found. -6 7.24×10 -7 , and 7.74×10 -7 The region of 36.746 to 36.748 Mb on LG10 was located, precisely upstream of the MMP13 gene. MMP13 expression in ZL gill mucus was correlated with the abundance of *Flavobacterium* (R = 0.52, P = 0.004). A significant site (R = 0.77, P = 0.002) spanned the region of 14.278 to 14.280 Mb on LG16, including a variant SNP located upstream of the TUBA gene (P = 9.60 × 10⁻⁶). -7 Three other significant SNPs identified candidate regions of 31.243 to 31.247 Mb on LG7, in which the MAMDC4 gene was located.

[0054] (V) Application examples of the method of the present invention in breeding This invention was applied to the breeding of tilapia resistant to streptococcal disease. Since April 2019, with the goal of screening homozygous parents resistant to agalactolytic streptococcal disease and growth rate as a secondary trait, a combination of population selection and molecular breeding techniques was employed. During successive generations of culture, streptococci were artificially infected, surviving individuals were selected, and PCR amplification was performed on these individuals. Individuals possessing the relevant genes (MMP13, TUBA, MAMDC4) identified above were selected as parents for subsequent breeding. Selection criteria were then established based on growth performance, and three generations of selection were conducted, resulting in 3000 male and female parents and 12000 reserve parents. This invention provides a gene screening method for fish breeding that simultaneously considers growth and disease resistance traits, effectively guiding generational selection breeding.

Claims

1. A method for screening microorganisms related to balancing growth traits and disease resistance traits, characterized by the following steps: include: Microbial screening: S1. Offspring fish with balanced growth traits and resistance to bacterial infection in the family and first-generation parent fish in the family were selected for a challenge experiment. At the beginning of the challenge experiment, during the peak mortality period, during the mortality decline period, and during the mortality quiescence period, gill mucus, gill tissue and intestinal contents of the offspring fish and first-generation parent fish were collected respectively. S2, Microbial species composition analysis was performed on gill mucus from offspring and primary parent fish during the challenge period where significant changes in OTU numbers were observed. Microbial species composition was obtained from the gill mucus of both offspring and primary parent fish during the challenge period. Based on the microbial species composition in the gill mucus of offspring fish during the challenge period, the class and genera of microbial species whose abundance showed an increasing trend during the challenge period were extracted. Based on linear discriminant analysis of effect size, genera whose abundance showed an increasing trend in the gill mucus of offspring fish during the challenge period but differed significantly in abundance from those of the primary parent fish during the challenge period were selected as microbial genera balancing growth and disease resistance traits. and, Gene screening: S3, perform association analysis on the differentially expressed genes of the microbial genera with balanced growth traits and disease resistance traits selected in step S2 with the offspring fish and primary parent fish during the challenge period, and exclude differentially expressed genes of microbial genera with balanced growth traits and disease resistance traits that are positively correlated only in the primary parent fish, and obtain differentially expressed genes of microbial genera with positively correlated with balanced growth traits and disease resistance traits. S4. Perform whole-genome resequencing on naturally surviving offspring fish resistant to bacterial infection and first-generation parent fish. Compare with the reference genome to extract SNP molecular markers and genes associated with SNP molecular markers that are significantly associated with bacterial infection resistance. Select genes from the genes associated with the SNP molecular markers that are positively correlated with microbial genera that are balanced growth traits and disease resistance traits to obtain genes of microbial genera that are positively correlated with balanced growth traits and disease resistance traits. Differentially expressed genes of microbial genera with positive correlation to balanced growth traits and disease resistance traits obtained in step S3 are merged with genes of microbial genera with positive correlation to balanced growth traits and disease resistance traits obtained in step S4, and are used as genes for balanced growth traits and disease resistance traits.

2. The screening method according to claim 1, characterized in that, Step S2, the method for selecting gill mucus from which the number of OTUs changed significantly between offspring and primary parent fish during the challenge period includes: performing 16S rRNA sequencing and OTU analysis on gill mucus and intestinal contents collected from the offspring and primary parent fish respectively during the challenge period; comparing the changes in the number of OTUs in the gill mucus and intestinal contents of the offspring and primary parent fish during the challenge period; and selecting gill mucus from which the number of OTUs changed significantly between the offspring and primary parent fish during the challenge period.

3. The screening method according to claim 2, characterized in that, The primers used for the 16S rRNA sequencing include 5'-ACTCCTACGGGAGGCAGCAG-3' and 5'-GGACTACHVGGGTWTCTAAT-3', which amplify the V3 and V4 hypervariable regions, with amplified fragment sizes of 350 to 400 bp.

4. The screening method according to claim 1, characterized in that, Step S2, the method for obtaining the microbial species composition in the gill mucus of offspring fish and primary parent fish during the challenge period is as follows: the microbial species composition in the gill mucus of offspring fish and primary parent fish during the challenge period is identified by the ribosome database project classifier, then α diversity is assessed by calculating the Shannon-Wiener index, Pielou evenness and Simpson diversity index, then β diversity is assessed by partial least squares discriminant analysis and non-metric multidimensional scaling analysis, and finally linear discriminant analysis effect size analysis is performed.

5. The screening method according to claim 1, characterized in that, Step S2, the genera of microorganisms that balance growth traits and disease resistance traits include Flavobacterium and / or Hydrogen-eating Bacteria.

6. The screening method according to claim 1, characterized in that, Step S3, the method for obtaining differentially expressed genes between offspring fish and primary parent fish during the challenge period is as follows: RNA sequencing is performed on gill tissues of the offspring fish and primary parent fish collected during the challenge period. Among the offspring fish genes whose expression is significantly higher or lower than that at the beginning of the challenge experiment during the peak mortality period, the decline mortality period, and the quiescent mortality period, genes whose expression is also significantly higher or lower than that of the primary parent fish at the corresponding time points are selected as differentially expressed genes between offspring fish and primary parent fish during the challenge period.

7. The screening method according to claim 1, characterized in that, In step S3, Pearson correlation analysis was performed on the differentially expressed genes of the microbial genera selected in step S2 that exhibit balanced growth traits and disease resistance traits, along with those of the offspring and primary parent fish during the challenge period. Differentially expressed genes of microbial genera that were positively correlated with balanced growth traits and disease resistance traits were obtained, with a correlation coefficient > 0.7 and a t-test P < 0.05 as the criterion. Differentially expressed genes of microbial genera that were positively correlated with balanced growth traits and disease resistance traits but only existed in the primary parent fish were excluded, and differentially expressed genes of microbial genera that were positively correlated with balanced growth traits and disease resistance traits were obtained.

8. The screening method according to claim 1, characterized in that, Step S4: Collect the tail fins of naturally surviving, disease-resistant offspring fish and first-generation parent fish for whole-genome resequencing.

9. The screening method according to claim 1, characterized in that, Step S4: The genes balancing growth traits and disease resistance traits include any one or any combination of SRP19, PPIB, FOXL1, LPAR4, ARPC3, FABP4A, PHF5A, SLC6A15, TRIM69, APOO, TRX1, NDUFA1, NDUFA3, NDUFA6, NDUFA10, NDUFA13, COX6B1, COX6C, TIMM50, PRDX3, LTV1, AK2, HER6, TUBa2, SCFD1, CHST3a, NAGK, GPD1c, NAGS, ARG1, UCK2b, AK2, ATP5MF, IL10, PSME2, ​​TXN, CH25h, DENR, MMP13, TUBA, and MAMDC4.

10. The application of genes and / or microorganisms balancing growth morphology and resistance to pathogenic infection in fish breeding, characterized in that, The gene is selected from at least one of SRP19, PPIB, FOXL1, LPAR4, ARPC3, FABP4A, PHF5A, SLC6A15, TRIM69, APOO, TRX1, NDUFA1, NDUFA3, NDUFA6, NDUFA10, NDUFA13, COX6B1, COX6C, TIMM50, PRDX3, LTV1, AK2, HER6, TUBa2, SCFD1, CHST3a, NAGK, GPD1c, NAGS, ARG1, UCK2b, AK2, ATP5MF, IL10, PSME2, ​​TXN, CH25h, DENR, MMP13, TUBA, and MAMDC4, and the microorganism is selected from the genera Flavobacterium and / or Hydrogenobacterium.

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