A method for screening a key bacteria affecting the feed efficiency trait of poultry, probiotic bacteria and uses

Abstract

The application belongs to the field of biology, and discloses a screening method for key bacteria affecting the grain-saving trait of poultry, comprising the following steps: step 1: constructing two groups of poultry with different feed utilization rates; step 2: using the feces of the high feed utilization rate group as raw material to prepare a bacterial solution, and implanting the bacterial solution into the intestinal tract of the low feed utilization rate group through fecal transplantation technology to construct a fecal transplantation group; step 3: screening first difference information and second difference information; and finding bacteria that exist simultaneously from the first difference information and the second difference information. The method of the application can quickly and accurately screen key bacteria that have a significant effect on the grain-saving trait of poultry by constructing high / low feed utilization rate groups and a fecal transplantation group, and through detection of the cecal microbiota and IgA + microbiome, and the use thereof. A key bacterium and the use thereof are also disclosed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biology, and in particular to a screening method for key bacteria affecting the feed-saving trait of poultry, probiotics and uses. BACKGROUND

[0002] Feed costs account for about 70% of the total cost of poultry production, and low feed utilization is one of the bottleneck problems restricting the development of China's poultry industry. Intestinal microorganisms, as the second genome of the host, can affect the feed utilization efficiency of poultry by participating in the metabolism of nutrients, but the effect and mechanism are not clear.

[0003] In the prior art, the bacteria affecting the feed utilization of poultry are mostly screened from a single perspective, such as analyzing the dominant flora of the intestine of a certain chicken to further obtain the strain. However, this screening method has the problems of low efficiency and poor precision.

[0004] In the prior art, some people also use biomolecular technology for screening, such as the method for detecting the intestinal flora of Licha black pigs for assisted breeding by using 16S rRNA gene sequencing technology disclosed in CN115725709A, which uses 16S rRNA gene sequencing technology to detect the intestinal flora of Licha black pigs for assisted breeding, which includes: extracting total DNA from the feces of Licha black pigs and amplifying, building a library and sequencing, and then analyzing the relative abundance information of Prevotella, Spirochaeta, Ruminococcus and Lactobacillus related to feed utilization and the intestinal microbial ɑ-diversity index to assist the breeding of Licha black pigs. This method also screens in a single state of population, which has certain limitations.

[0005] Therefore, the technical problem to be solved by the present application is: how to screen the key bacteria affecting the feed-saving trait of poultry. SUMMARY

[0006] The purpose of the present application is to provide a screening method for key bacteria affecting the feed-saving trait of poultry. The method of the present application constructs high / low feed utilization groups and fecal transplantation groups, and uses cecal microbiota detection and IgA + Microbiome detection can quickly and accurately screen key bacteria that have a significant effect on the feed-saving trait of poultry.

[0007] Meanwhile, the present application also discloses a key bacteria and its uses.

[0008] To achieve the above-mentioned purpose, the present application discloses:

[0009] A screening method for key bacteria affecting the feed-saving trait of poultry, comprising the following steps:

[0010] Step 1: Construct two groups of poultry with different feed utilization rates, one of which is named the low feed utilization rate group and the other is named the high feed utilization rate group;

[0011] Step 2: Using feces from the high-feed utilization group as raw material to prepare bacterial liquid, the bacterial liquid was transplanted into the intestines of the low-feed utilization group through fecal transplantation technology to create a fecal transplant group;

[0012] Step 3: Obtain information on the cecal microbiota at the genus level in the low feed utilization group, high feed utilization group, and fecal transplant group, as well as IgA at the genus level in the cecal microbiota in the low feed utilization group, high feed utilization group, and fecal transplant group. + Information about the microbiome; the IgA + The microbiome is a microbiome that can bind to IgA;

[0013] The information of cecal microbiota, IgA in the low feed utilization group + The information of the microbiome is used as a control to screen and obtain the first difference information and the second difference information;

[0014] Among them, the first difference information is the information difference of cecal microbiome between the high feed utilization group and the low feed utilization group, IgA + The diverse collection of microbiome information;

[0015] The second difference information is the information difference of cecal microbiome between the fecal transplant group and the low feed utilization group, IgA + The diverse collection of microbiome information;

[0016] The bacteria that coexist in the first difference information and the second difference information are found to be the key bacteria that affect the utilization rate of poultry feed.

[0017] Immunoglobulin A (IgA) is the most secreted antibody in the intestine and plays an important role in microbial colonization and maintaining intestinal homeostasis. On the one hand, intestinal IgA can directly bind to commensal bacteria to promote their colonization in the intestine, and on the other hand, it can encapsulate pathogens to promote their clearance from the intestine. The present invention uses humans and mice as research objects and finds that IgA can regulate the composition of intestinal flora, affect microbial and host functions, maintain metabolic homeostasis, etc. by binding to microorganisms. Recent studies have found that in malnourished children, the binding of IgA to microorganisms changes, resulting in adaptive changes in the host's utilization of nutrients. Therefore, from the perspective of IgA binding to microorganisms, a new idea is provided for screening probiotics that improve the utilization rate of livestock and poultry feed.

[0018] In the past ten years, we have successfully constructed a Huixiang chicken genetic resource population with significantly different feed utilization rates by selecting for residual feed intake (RFI) for 15 generations of continuous breeding selection, providing a unique biological resource for studying the effects of intestinal microorganisms on feed utilization. The present invention systematically compares the development patterns and composition differences of the cecal microbiome and IgA-bound microbiome of high feed efficiency (H-FE) and low feed efficiency (L-FE) chickens; determines the effects of intestinal microorganisms on feed utilization efficiency traits through fecal microbiota transplantation (FMT); integrates cecal microbiome and IgA-bound microbiome information to precisely identify Blautia as a key candidate bacterium affecting feed utilization efficiency traits; further verifies its effects on improving feed utilization efficiency in chickens and mice; and finally, combined with in vitro cell and microbial co-culture experiments, preliminarily analyzes the mechanism of the bacterium in promoting B cell activation to produce IgA.

[0019] The present invention provides a new approach for improving chicken feed utilization efficiency traits by identifying core microorganisms from the perspective of IgA-bound microorganisms.

[0020] In the above method, the information of the IgA+ microbiome is obtained by an IgA-SEQ detection method.

[0021] In the above method, the avian is one of a chicken, a duck, and a goose.

[0022] In the above method, the avian is a Huixiang chicken.

[0023] In the above method, in step 2, after perfusing the bacteria solution for 5-35 days, a successfully constructed fecal transplant group is obtained.

[0024] Meanwhile, the present invention also discloses a key bacterium affecting the feed efficiency traits of avians, which is screened by the above method.

[0025] Finally, the present invention also discloses the use of probiotics of the Blautia genus, particularly Blautia coccoides, for preparing probiotics for improving the feed utilization efficiency of avians.

[0026] The present application has at least the following beneficial effects:

[0027] 1. The present application is based on long-term breeding of high / low feed utilization genetic resource population, the development law and composition difference of intestinal microorganisms and IgA combined microorganisms of two chicken populations are systematically compared; through fecal microbiota transplantation (FMT) technology, it is clear that intestinal microorganisms are an effective strategy to improve chicken feed utilization; by integrating cecal microbiome and IgA combined microbiome information, the key candidate bacteria Blautia affecting chicken feed utilization efficiency is accurately mined, and the possible mechanism of regulating feed utilization efficiency by promoting B cell activation and secreting IgA is preliminarily analyzed. The present application provides an important reference for mining core microorganisms for regulating chicken feed utilization efficiency traits from the perspective of IgA combined microorganisms.

[0028] 2. By integrating cecal microbiome and IgA combined microbiome information, the present application accurately screens Blautia as a key candidate bacteria for improving feed utilization efficiency traits, and further reveals the potential mechanism of the bacteria to improve chicken feed utilization rate by inducing B cell activation and IgA production through animal experiments and in vitro cell and microbial co-culture experiments. In summary, the present application provides an important reference for accurately screening core strains for improving feed utilization efficiency traits of livestock and poultry. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A is a feed conversion rate chart of the low feed utilization group and the high feed utilization group, the detection time is from 49 to 70 days old (n≥70), and the statistical significance is analyzed by unpaired two-tailed Student's t test;

[0030] Figure 1 B is a chart of the average daily feed intake rate of the low feed utilization group and the high feed utilization group, the detection time is from 49 to 70 days old (n≥70), and the statistical significance is analyzed by unpaired two-tailed Student's t test;

[0031] Figure 1 C is a Shannon diversity chart of the low feed utilization group and the high feed utilization group at different ages, the Shannon diversity of the cecal microbiome (n≥9);

[0032] Figure 1 D is a bacterial abundance table of gram-negative bacteria, potential pathogenic bacteria gram-positive bacteria and stress-tolerant bacteria in the low feed utilization group, red indicates higher relative abundance in L-FE chickens, and blue indicates higher relative abundance in H-FE chickens;

[0033] Figure 1 E is a fecal microbiota transplantation (FMT) test flow chart (n≥15);

[0034] Figure 1 F is a feed conversion rate chart of the low feed utilization group, the high feed utilization group and the fecal transplantation group;

[0035] Figure 1 G is the body weight gain plot of the low feed utilization group, the high feed utilization group, and the fecal transplant group;

[0036] Figure 1 H is the caecal microbiome and IgA predicted by BugBase of the low feed utilization group, the high feed utilization group, and the fecal transplant group + The relative abundance plot of gram-negative bacteria and potential pathogenic bacteria in the microbiome;

[0037] Figure 1 I is the Spearman correlation analysis result plot between the relative abundance of IgA-binding bacteria Blautia and FCR;

[0038] Figure 2 A is the body weight of the 70-day-old chicken of the low feed utilization group, the high feed utilization group, and the Blautia gavage group;

[0039] Figure 2 B is the feed intake plot of the 49-70 day-old chicken of the low feed utilization group, the high feed utilization group, and the Blautia gavage group;

[0040] Figure 2 C is the feed conversion rate plot of the 49-70 day-old chicken of the low feed utilization group, the high feed utilization group, and the Blautia gavage group;

[0041] Figure 2 D is the proportion of IgA + Bacteria detected by flow cytometry in the cecal contents of chickens (n=15);

[0042] Figure 2 E is the proportion of CD19 + B cells detected by flow cytometry in the mesenteric lymph nodes of mice (n=15);

[0043] Figure 2 F is the proportion of IgA + cells detected by flow cytometry in the mesenteric lymph nodes of mice (n=15); + cells and CD19 + IgA + cells detected by flow cytometry in the mesenteric lymph nodes of mice (n=15);

[0044] Figure 2 G is the proportion of CD19 - CD138 + plasma cells detected by flow cytometry in the B cell line after 3 hours of in vitro co-culture with Blautia coccoides (n=6);

[0045] Figure 2H is the flow cytometry detection of IgA in B cell lines after 3 hours of in vitro co-culture with Blautia coccoides + Ratio plot of cells (n=6);

[0046] Figure 2 I is the expression level plot of Blimp1, a B cell activation and IgA secretion related gene, after 3 hours of co-culture (n=6);

[0047] Figure 2 J is the expression level plot of Xbp1s, a B cell activation and IgA secretion related gene, after 3 hours of co-culture (n=6);

[0048] Figure 2 K is the expression level plot of Igha1, a B cell activation and IgA secretion related gene, after 3 hours of co-culture (n=6);

[0049] Figure 3 L is the concentration plot of IgA in supernatant after co-culture using enzyme-linked immunosorbent assay (ELISA); * indicates p<0.05, ** indicates p<0.01, *** indicates p<0.001, **** p indicates <0.0001;

[0050] Figure 3 A is the caecal microbial OTU level plot of different ages in the low feed utilization group;

[0051] Figure 3 B is the caecal microbial OTU level plot of different ages in the high feed utilization group;

[0052] Figure 3 C is the caecal microbial OTU level plot of common and unique in the high feed utilization group and the low feed utilization group at different ages;

[0053] Figure 3 D is the observed species diversity plot of the low feed utilization group, the high feed utilization group at different ages;

[0054] Figure 3 E is the Chao index diversity plot of the low feed utilization group, the high feed utilization group at different ages;

[0055] Figure 3 F is the caecal microbial door level composition column chart of the low feed utilization group, the high feed utilization group at different ages;

[0056] Figure 4 G is the caecal microbial genus level composition column chart of the low feed utilization group, the high feed utilization group at different ages;

[0057] Figure 4A is a relative abundance plot of low feed utilization group, high feed utilization group lipopolysaccharide biosynthesis-associated microorganisms at 1 day and 70 days of age;

[0058] Figure 4 B is a principal component analysis plot of low feed utilization group, high feed utilization group cecal microbial functions predicted by PICRUSt at 70 days of age;

[0059] Figure 5 C is a differential analysis plot of low feed utilization group, high feed utilization group cecal microbial functions predicted by PICRUSt at 70 days of age;

[0060] Figure 5 A is an observed species diversity plot of low feed utilization group, high feed utilization group IgA-binding microorganisms at different ages;

[0061] Figure 5 B is a principal component analysis plot of low feed utilization group, high feed utilization group IgA-binding microorganisms at different ages;

[0062] Figure 5 C is a bar plot of low feed utilization group, high feed utilization group IgA-binding intestinal microbial phylum composition at different ages;

[0063] Figure 6 D is a relative abundance plot of potential pathogenic bacteria of low feed utilization group, high feed utilization group cecal microbiome and IgA-binding microbiome at different ages analyzed by BugBase;

[0064] Figure 6 A is a cecal microbiome diversity analysis plot of low feed utilization group, high feed utilization group, and fecal transplant group;

[0065] Figure 6 B is an IgA-binding microbiome diversity analysis plot of low feed utilization group, high feed utilization group, and fecal transplant group;

[0066] Figure 6 C is a cecal microbial phylum composition plot of low feed utilization group, high feed utilization group, and fecal transplant group;

[0067] Figure 6 D is an IgA-binding microbial phylum composition plot of low feed utilization group, high feed utilization group, and fecal transplant group;

[0068] Figure 6 E is a cecal microbial genus composition plot of low feed utilization group, high feed utilization group, and fecal transplant group;

[0069] Figure 7F is the IgA-binding microorganism genus level composition chart of the low feed utilization group, the high feed utilization group, and the fecal transplant group;

[0070] Figure 7 A is the common key candidate bacteria chart screened from the cecal microbiome of the high feed utilization group and the fecal transplant group;

[0071] Figure 8 B is the common key candidate bacteria chart screened from the IgA-binding microbiome of the high feed utilization group and the fecal transplant group;

[0072] Figure 8 A is the relative abundance chart of the common key candidate bacteria screened from the cecal microbiome;

[0073] Figure 9 B is the relative abundance chart of the common key candidate bacteria screened from the IgA-binding microbiome;

[0074] Figure 9 A is the bursa of Fabricius lymphoid follicle area chart of the low feed utilization group, the high feed utilization group, and the fecal transplant group;

[0075] Figure 9 B is the bursa of Fabricius Bu-1 + B cell proportion chart;

[0076] Figure 9 C is the test flow chart of the key candidate bacteria Blautia coccoides for effect verification on chickens;

[0077] Figure 9 D is the test flow chart of the key candidate bacteria Blautia coccoides for effect verification on C57BL / 6j mice;

[0078] Figure 9 E is the body weight chart of mice after gavage of the key candidate bacteria Blautia coccoides;

[0079] Figure 9 F is the feed conversion rate chart of mice after gavage of the key candidate bacteria Blautia coccoides;

[0080] Figure 10 G is the proportion chart of cecal IgA tubercle bacilli of mice after gavage of the key candidate bacteria Blautia coccoides;

[0081] Figure 10 A is the cell activity chart of primary B cells after in vitro co-culture of chicken primary B cells and Blautia coccoides for 3 hours;

[0082] Figure 10 B is the IgA concentration in the supernatant after 3 hours of in vitro co-culture of chicken primary B cells with Blautia coccoides + B cell ratio chart;

[0083] Figure 1 C is the IgA concentration chart in the supernatant after 3 hours of in vitro co-culture of chicken primary B cells with Blautia coccoides.

[0084] Embodiment Mode

[0085] The present application will be described in detail below with reference to the embodiments of the present application, and in the description of the present application, it should be noted that the specific conditions are not indicated in the embodiments, and the conventional conditions or the conditions recommended by the manufacturer are used. The reagents or instruments used are not indicated by the manufacturer, and are conventional products that can be purchased on the market. Unless otherwise specified, the parts used in the embodiments of the present application are all by weight.

[0086] Construction of the first part of the low feed utilization group and the high feed utilization group

[0087] Feed conversion rate (FCR) is one of the key indicators for measuring feed utilization efficiency traits. After 15 generations of breeding selection, the FCR and average daily feed intake (ADFI) of low feed utilization (L-FE) chickens were significantly higher than those of high feed utilization (H-FE) chickens (p<0.001, Figure 1 A, Figure 1 B), and the L-FE chicken group is the low feed utilization group, and the H-FE chicken group is the high feed utilization group.

[0088] Figure 1 A is a feed conversion rate chart of the low feed utilization group and the high feed utilization group, and the detection time is from 49 to 70 days old (n≥70), and the statistical significance is analyzed by unpaired two-tailed Student's t test;

[0089] Figure 3 B is a daily average feed rate chart of the low feed utilization group and the high feed utilization group, and the detection time is from 49 to 70 days old (n≥70), and the statistical significance is analyzed by unpaired two-tailed Student's t test;

[0090] Among them, the L-FE chickens in the low feed utilization group are 117; and the H-FE chickens in the high feed utilization group are 77.

[0091] 16S rRNA sequencing was performed on the cecal contents of L-FE chickens and H-FE chickens at different growth stages.

[0092] At five sampling time points, L-FE and H-FE chickens had 284 and 233 operational taxonomic units (OTUs) in common, respectively Figure 3 A、 Figure 3 B)。

[0093] The number of OTUs shared by both groups increased with age ( Figure 3 C), and a-diversity also increased with age from 1 to 49 days of age. From 49 days of age, the Chao index and the number of observed species remained relatively stable. Among them, the observed species and Chao index of L-FE chickens at 1 day of age were significantly higher than those of H-FE chickens ( Figure 1 D, E), and the Shannon diversity (a commonly used index to measure biodiversity, which integrates information on species richness and species evenness) of L-FE chickens at 9 days of age was also higher than that of H-FE chickens ( Figure 1 C). However, the Shannon diversity of H-FE chickens was significantly higher than that of L-FE chickens at 49 and 70 days of age ( Figure 1 C).

[0094] Figure 3 C is a graph of Shannon diversity of low feed utilization group and high feed utilization group at different ages, Shannon diversity of cecal microbiome (n≥9);

[0095] The microbial composition changed with age and showed significant differences between high / low groups ( Figure 3 F、 Figure 3 G). At 1 and 9 days of age, Firmicutes was the only dominant phylum, while the relative abundance of Bacteroidetes significantly increased from 49 to 140 days of age and subsequently became the first dominant phylum ( Figure 1 F). It is worth mentioning that the relative abundance of Proteobacteria (a genus of gram-negative bacteria, including many pathogenic bacteria) in L-FE chickens at 1 day of age was significantly higher than that in H-FE chickens (4.2% vs. 0.0059%, p=0.0001).

[0096] The results of phenotypic prediction of microorganisms by BugBase showed that L-FE chickens had higher relative abundance of gram-negative bacteria and potential pathogenic bacteria. On the contrary, H-FE chickens showed higher abundance of gram-positive bacteria and stress-tolerant bacteria Figure 4D) PICRUSt-based microbial functional prediction results showed that the relative abundance of LPS biosynthesis-related bacteria was significantly higher in the caecal microbiota of L-FE chickens than in H-FE chickens. LPS is the main component of the outer membrane of most Gram-negative bacteria and can induce host inflammatory responses Figure 4 A to Figure 1 C) The higher relative abundance of Gram-negative bacteria and LPS biosynthesis-related bacteria in L-FE chickens suggests that their intestinal microbiota may be disturbed.

[0097] Figure 3 D is the bacterial abundance table of Gram-negative bacteria, potential pathogenic bacteria Gram-positive bacteria and stress-tolerant bacteria in the low feed utilization group. Red indicates a higher relative abundance in L-FE chickens, while blue indicates a higher relative abundance in H-FE chickens;

[0098] Figure 3 A is the caecal microbial OTU level graph at different ages in the low feed utilization group;

[0099] Figure 3 B is the caecal microbial OTU level graph at different ages in the high feed utilization group;

[0100] Figure 3 C is the caecal microbial OTU level graph of the high feed utilization group and the low feed utilization group at different ages;

[0101] Figure 3 D is the observed species diversity graph of the low feed utilization group and the high feed utilization group at different ages;

[0102] Figure 3 E is the Chao index diversity graph of the low feed utilization group and the high feed utilization group at different ages;

[0103] Figure 3 F is the caecal microbial door level composition column chart of the low feed utilization group and the high feed utilization group at different ages;

[0104] Figure 4 G is the caecal microbial genus level composition column chart of the low feed utilization group and the high feed utilization group at different ages;

[0105] Figure 4 A is the relative abundance graph of LPS biosynthesis-related microorganisms in the low feed utilization group and the high feed utilization group at 1 day and 70 days of age;

[0106] Figure 4 B is the principal component analysis chart of the PICRUSt-predicted caecal microbial functions of the low feed utilization group and the high feed utilization group at 70 days of age;

[0107] Figure 5 C is the analysis diagram of the difference in cecal microbial functions between the low feed utilization group and the high feed utilization group predicted by PICRUSt at 70 days of age.

[0108] Part II IgA + Microbiome determination

[0109] IgA binding (IgA) in high / low feed utilization chickens was determined using IgA-SEQ. + ) microbiome.

[0110] The IgA-SEQ assay method is to use fluorescence activated cell sorting (FACS) technology to sort IgA-binding bacteria in the cecal samples of chickens with high / low feed utilization rates and collect IgA + The microorganisms were analyzed by 16S rRNA sequencing.

[0111] The results showed that IgA + Microbiome α diversity increased with age. At 140 days of age, IgA + The observed species of microorganisms were significantly higher than those in L-FE chickens ( Figure 5 A) PCA results showed that IgA in chickens with high and low feed utilization rates + The microbial composition was significantly different ( Figure 5 B).

[0112] Very importantly, IgA + The abundance of Firmicutes is not entirely determined by their relative abundance in the cecal microbiota. For example, at 49 days of age, Firmicutes are the most abundant IgA + The first dominant phylum of microorganisms, and the dominant bacteria of cecal microorganisms are Bacteroidetes ( Figure 5 C). In addition, at 49 and 70 days of age, the relative abundance of potential pathogens in the cecal microbiota of L-FE chickens was higher, and IgA + However, at 140 days of age, although there were no significant differences in potential pathogens in the cecal microbiota between high and low feed utilization chickens, IgA + The relative abundance of potential pathogens was significantly higher in L-FE chickens ( Figure 5 D).

[0113] Figure 5 A is the observed species diversity diagram of IgA-bound microorganisms in the low feed utilization rate group and the high feed utilization rate group at different ages;

[0114] Figure 5B is a principal component analysis plot of IgA-binding microbes in low feed efficiency group and high feed efficiency group at different ages;

[0115] Figure 5 C is a column chart of IgA-binding intestinal microbial phylum composition in low feed efficiency group and high feed efficiency group at different ages;

[0116] Figure 1 D is a BugBase analysis of the relative abundance of potential pathogenic bacteria in the cecal microbiome and IgA-binding microbiome of low feed efficiency group and high feed efficiency group at different ages.

[0117] Third part: Screening of key bacteria affecting the feed efficiency of poultry

[0118] 3.1 Construction of fecal transplant group

[0119] In order to clarify the regulation of intestinal microorganisms on feed utilization efficiency traits, the feces of H-FE chickens were collected to prepare bacterial suspension, and FMT test was performed on L-FE chickens Figure 1 E).

[0120] The method for constructing the fecal transplant group is as follows: Fresh feces were collected in the morning from the high feed efficiency group for fecal microbiota transplantation (FMT). The white part of the excrement was excluded and stored in liquid nitrogen. These samples were mixed uniformly with 0.9% sterile normal saline at a volume ratio of 1:2, and then filtered through sterile gauze. Then, sterile glycerol was added to the suspension at a ratio of 1:9 (9 ml of suspension added with 1 ml of sterile glycerol), and the obtained microbial suspension was stored at -80°C for subsequent use. Before use, the stored suspension was thawed, and methylene blue staining was used to count the viable bacteria. Then the suspension was diluted with sterile normal saline to a concentration of 1×10 3 ~ 1×10 8 colony forming units (CFU) per milliliter. Fecal microbiota transplantation was performed on 1-day-old low feed efficiency chicks (low feed efficiency resource population obtained after 15 generations of breeding selection in the first part). The chickens in the fecal microbiota transplantation group were given 0.1-1 ml of fecal microbial suspension (1×10 3 ~ 1×10 8 CFU / mL) continuously or intermittently for 5-35 days, and the body weight gain and feed intake were recorded from the 49th day to the 70th day to calculate the feed conversion ratio (FCR).

[0121] The results showed that FMT significantly improved the feed utilization efficiency (reduced FCR value) and growth performance (body weight gain) Figure 1 F、 Figure 1G), which demonstrated that microbiota intervention was an effective strategy to improve the feed utilization efficiency traits of poultry.

[0122] Figure 1 E is the flow chart of the fecal microbiota transplantation (FMT) experiment (n≥15);

[0123] Figure 1 F is the feed conversion rate chart of the low feed utilization group, the high feed utilization group, and the fecal transplantation group;

[0124] Figure 6 G is the body weight gain chart of the low feed utilization group, the high feed utilization group, and the fecal transplantation group;

[0125] 3.2 Study on the changes of cecal microbiota composition in the fecal transplantation group

[0126] This part further explores whether FMT will change the intestinal microorganisms and IgA + microbial composition.

[0127] The results show that FMT significantly affects the cecal microbiota composition and diversity of L-FE chickens, making it close to the H-FE chicken composition structure( Figure 6 A, Figure 6 B).

[0128] Among them, the relative abundance of Gram-positive bacteria Actinobacteria in H-FE chickens and FMT chickens is significantly higher than that in L-FE chickens (p<0.05), while the relative abundance of Gram-negative bacteria Proteobacteria is significantly reduced after FMT( Figure 6 C). IgA + Microbiome results show that the relative abundance of Bacteroidetes, Synergistetes, and Tenericutes in the H-FE and FMT groups is significantly higher than that in the L-FE group( Figure 1 D). BugBase analysis shows that the relative abundance of Gram-negative bacteria and potential pathogenic bacteria in the cecal microbiota of L-FE chickens is significantly higher than that in H-FE and FMT chickens. However, no significant difference was observed between the three groups in IgA + microorganisms( Figure 1 H). This result suggests that the L-FE chicken's IgA may have lower binding capacity for pathogenic bacteria.

[0129] Figure 6 H is the chart of the relative abundance of Gram-negative bacteria and potential pathogenic bacteria in the cecal microbiota and IgA + microbiome of the low feed utilization group, the high feed utilization group, and the fecal transplantation group predicted by BugBase.

[0130] Figure 6A is the cecal microbiome diversity analysis diagram of the low feed utilization group, high feed utilization group, and fecal transplantation group;

[0131] Figure 6 B is the IgA-bound microbiome diversity analysis diagram of the low feed utilization group, high feed utilization group, and fecal transplantation group;

[0132] Figure 6 C is the phylum composition diagram of cecal microorganisms in the low feed utilization group, high feed utilization group, and fecal transplantation group;

[0133] Figure 6 D is the IgA-bound microbial phylum composition diagram of the low feed utilization group, high feed utilization group, and fecal transplantation group;

[0134] 3.3 Screening of key bacteria affecting poultry grain-saving traits

[0135] In order to accurately explore the key candidate bacteria that affect feed utilization, this section analyzes the cecal microbiome and IgA after FMT. + The microbiome was comprehensively analyzed.

[0136] Taking L-FE as the control, the dominant bacteria shared by FMT and H-FE were considered to be core functional bacteria. At the genus level, the abundance of Peptococcus, Dorea, and Blautia in the cecal microbiota of FMT and H-FE chickens was significantly higher than that of L-FE chickens, while the relative abundance of Coprococcus and Sutterella was lower ( Figure 6 E). Similarly, IgA in FMT and H-FE chickens + Among the bacteria, the abundance of Prevotella, YRC22, Enterococcus, and Blautia was high, while the relative abundance of Megamonas and Coprococcus was low ( Figure 6 F).

[0137] Figure 6 E is the genus-level composition of cecal microorganisms in the low feed utilization group, high feed utilization group, and fecal transplant group;

[0138] Figure 7 F is the IgA-bound microbial genus composition diagram of the low feed utilization rate group, high feed utilization rate group, and fecal transplantation group.

[0139] Subsequently, this part conducted a linear discriminant analysis (LDA) to mine biomarkers related to feed utilization (threshold ≥ 2). The results showed that Peptococcus, Dorea, and Blautia were biomarkers of the cecal microbiome, and Blautia was also an IgA +Microbiome-sorted biomarker Figure 7 A and Figure 8 B). In summary, Blautia is a common biomarker in cecum and IgA + Microbiome-sorted biomarker Figure 8 A and Figure 1 B). In addition, IgA + There is a significant negative correlation between relative abundance of Blautia and FCR Figure 7 I). Therefore, by integrating cecum and IgA + Microbiome information, we precisely sorted Blautia as a key candidate bacterium affecting feed utilization.

[0140] Figure 7 A is a plot of linear discriminant analysis to sort common key candidate bacteria in cecum microbiota of high feed utilization group and fecal transplantation group;

[0141] Figure 8 B is a plot of linear discriminant analysis to sort common key candidate bacteria in IgA-binding microbiota of high feed utilization group and fecal transplantation group;

[0142] Figure 8 A is a plot of relative abundance of common key candidate bacteria sorted from cecum microbiota;

[0143] Figure 1 B is a plot of relative abundance of common key candidate bacteria sorted from IgA-binding microbiota.

[0144] Figure 9 I is a plot of Spearman correlation analysis between relative abundance of IgA-binding bacterium Blautia and FCR; * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001; L-FE indicates low feed utilization group; H-FE indicates high feed utilization group

[0145] Since B cells producing IgA are mainly derived from bursa of Fabricius of avian, this part also detected the size of bursa of Fabricius lymphoid follicles and the proportion of Bu-1 + B cells, and the results showed that FMT significantly increased the levels of these two key indicators, making them closer to H-FE group Figure 9 A, Figure 9 B).

[0146] Figure 9 A is a plot of bursa of Fabricius lymphoid follicle area of low feed utilization group, high feed utilization group, and fecal transplantation group;

[0147] Figure 9 B is a plot of bursa of Fabricius Bu-1 +B cell proportion map.

[0148] Verification of the effect of the screened strains on feed utilization rate in the fourth part

[0149] Blautia is a potential probiotic with probiotic function, which can improve metabolic disorders by metabolizing tryptophan into indole-3-acetic acid, and up-regulate regulatory T cells in the intestine to relieve inflammation. However, whether it can affect animal feed utilization rate and its mechanism are still unclear. Therefore, in this part, Blautiacoccoides, a probiotic with important role in regulating host energy metabolism and mucosal immunity, was orally gavaged to newly hatched chickens and weaned C57BL / 6j mice Figure 9 C, Figure 9 D) to form Blautia gavage groups, with a gavage dose of 1×10 3 ~ 1×10 9 CFU / mL, using continuous or every other day gavage for 3 to 5 weeks.

[0150] Figure 9 C is the test flow chart of the key candidate bacteria Blautia coccoides for effect verification on chickens;

[0151] Figure 2 D is the test flow chart of the key candidate bacteria Blautia coccoides for effect verification on C57BL / 6j mice.

[0152] The results showed that B. coccoides had no significant effect on the body weight of chickens and mice Figure 9 A, Figure 2 E), but reduced the feed intake of L-FE chickens Figure 2 B). In addition, B. coccoides had the potential to improve the feed utilization rate of L-FE chickens and mice Figure 9 C, Figure 2 F). The FCR value of L-FE chickens decreased from 3.86 to 3.75. After B. coccoides treatment, the number of IgA + bacteria in the cecal contents of chickens and mice increased significantly Figure 9 D, Figure 2 G). In addition, the proportion of CD19 + cells, IgA + cells and CD19 + IgA + cells in the mesenteric lymph nodes of mice increased significantly after B. coccoides treatment Figure 2 E, F). The above results showed that B. coccoides had the potential to improve feed utilization rate and enhance B cell activation.

[0153] Figure 2 A is the body weight of chickens at 70 days of age in the low feed utilization group, the high feed utilization group, and the Blautia gavage group;

[0154] Figure 2 B is the feed intake graph of chickens at 49 to 70 days of age in the low feed utilization group, the high feed utilization group, and the Blautia gavage group;

[0155] Figure 2 C is the feed conversion rate graph of chickens at 49 to 70 days of age in the low feed utilization group, the high feed utilization group, and the Blautia gavage group;

[0156] Figure 2 D is the proportion of IgA + cells in the cecal contents of chickens detected by flow cytometry (n=15);

[0157] Figure 2 E is the proportion of CD19 + cells in the mesenteric lymph nodes of mice detected by flow cytometry (n=15);

[0158] Figure 9 F is the proportion of IgA + cells in the mesenteric lymph nodes of mice detected by flow cytometry (n=15); + cells and CD19 + IgA + cells in the mesenteric lymph nodes of mice detected by flow cytometry (n=15);

[0159] Figure 9 E is the body weight graph of mice after gavage with the key candidate bacteria Blautia coccoides;

[0160] Figure 9 F is the feed conversion rate graph of mice after gavage with the key candidate bacteria Blautia coccoides;

[0161] Figure 2 G is the proportion of IgA

[0162] To further explore its mechanism, this part carried out the co-culture experiment of B cells (Ramos line) and B. coccoides in vitro, and the results showed that B. coccoides could induce B cell activation, increase the proportion of CD19 - CD138 + plasma cells and IgA + cells, and the proportion of IgA Figure 2G, H), while the expression of B cell activation-related genes (Blimp1 and Xbp1s) and IgA production-related genes (Igha1) were significantly upregulated Figure 2 I-K), the secretion level of IgA was also significantly increased Figure 10 L). We further successfully isolated primary B cells from the bursa of Fabricius of 49-day-old chickens and performed co-culture experiments of primary chicken B cells with B. coccoides. The results showed that IgA + The proportion of B cells increased, and the secretion level of IgA was significantly improved Figure 10 A to Figure 2 C), which was consistent with the above results.

[0163] Figure 2 G is the proportion of CD19 - CD138 + plasma cells in the B cell line after 3 hours of in vitro co-culture with Blautia coccoides (n = 6);

[0164] Figure 2 H is the proportion of IgA + cells in the B cell line after 3 hours of in vitro co-culture with Blautia coccoides (n = 6);

[0165] Figure 2 I is the expression level of Blimp1, a B cell activation and IgA secretion-related gene, after 3 hours of co-culture (n = 6);

[0166] Figure 2 J is the expression level of Xbp1s, a B cell activation and IgA secretion-related gene, after 3 hours of co-culture (n = 6);

[0167] Figure 2 K is the expression level of Igha1, a B cell activation and IgA secretion-related gene, after 3 hours of co-culture (n = 6);

[0168] Figure 10 L is the concentration of IgA in the supernatant after co-culture using enzyme-linked immunosorbent assay (ELISA); * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, **** p indicates < 0.0001;

[0169] Figure 10 A is the cell activity of primary B cells after 3 hours of in vitro co-culture with Blautia coccoides;

[0170] Figure 10B is the IgA concentration in the supernatant after the chicken primary B cells were co-cultured with Blautia coccoides in vitro for 3 hours + B cell ratio chart;

[0171] ​ C is the IgA concentration chart in the supernatant after the chicken primary B cells were co-cultured with Blautia coccoides in vitro for 3 hours.

[0172] Note: The 16S rRNA gene sequencing raw data of the cecal microbiome and the IgA-binding microbiome of the present application have been uploaded to the Sequence Read Archive (SRA) database with accession number PRJNA994595 (https: / / www.ncbi.nlm.nih.gov / sra / ?term=PRJNA994595). The main data and code have been uploaded to the Github website at: https: / / github.com / cylinqueen / chicken-micro.

[0173] It is apparent to a person skilled in the art that the present application is not limited to the details of the above exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the present application being defined by the appended claims rather than the above description, and it is intended to fall within the meaning and range of equivalent elements of the claims. All variations within the meaning and range of the essential elements of the claims are encompassed within the present application. Any reference signs in the claims should not be considered as limiting the claims involved.

Claims

1. A method for screening key bacteria that affect poultry grain-saving traits, characterized in that: The steps include: Step 1: Construct two groups of poultry with different feed utilization rates, one of which is named the low feed utilization rate group and the other is named the high feed utilization rate group; Step 2: Using feces from the high-feed utilization group as raw material to prepare bacterial liquid, the bacterial liquid was transplanted into the intestines of the low-feed utilization group through fecal transplantation technology to create a fecal transplant group; Step 3: Obtain information on the cecal microbiota at the genus level in the low feed utilization group, high feed utilization group, and fecal transplant group, as well as IgA at the genus level in the cecal microbiota in the low feed utilization group, high feed utilization group, and fecal transplant group. + Information about the microbiome; the IgA + The microbiome is a microbiome that can bind to IgA; The information of cecal microbiota, IgA in the low feed utilization group + The information of the microbiome is used as a control to screen and obtain the first difference information and the second difference information; Among them, the first difference information is the information difference of cecal microbiome between the high feed utilization group and the low feed utilization group, IgA + The diverse collection of microbiome information; The second difference information is the information difference of cecal microbiome between the fecal transplant group and the low feed utilization group, IgA + The diverse collection of microbiome information; Finding bacteria that coexist in the first difference information and the second difference information is the key bacteria that affects the utilization rate of poultry feed; The poultry is chicken.

2. The method according to claim 1, characterized in that The IgA + The method for obtaining microbiome information is to determine it through the IgA-SEQ detection method.

3. The method according to claim 1, characterized in that The poultry is bearded chicken.

4. The method according to any one of claims 1 to 3, characterized in that In step 2, after 5 to 35 days of infusion of the bacterial solution, a fecal transplant group was successfully constructed.

5. Use of probiotics of the genus Blautia in improving the utilization rate of poultry feed, wherein the probiotics of the genus Blautia are Blautia coccoides bacteria.

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