KR102163817B1 - Method and kit for predicting growth of piglet - Google Patents

Abstract

The present invention relates to a method for predicting growth of pigs, a composition and a kit for predicting growth, using genetic analysis present in pig fecal samples.

Description

Method and kit for predicting growth of piglet {Method and kit for predicting growth of piglet}

The present invention relates to a method for predicting growth of pigs, a composition and a kit for predicting growth, using genetic analysis present in pig fecal samples.

Due to many recent studies, pig breeding technology has improved significantly compared to the past, resulting in a significant increase in pig farming productivity. However, at the point of transition from lactation to weaning, stress occurs due to numerous changes such as separation from sows, rearrangement to mix with unfamiliar pigs, and conversion from milk to solid feed, and as immunity weakens, it becomes very vulnerable to diseases. It causes a decrease in productivity.

A large number of piglets experience growth retardation such as decreased intake or diarrhea due to dietary changes resulting from the conversion from milk to solid feed. Intestinal-related diseases such as E. coli diarrhea, which accounts for 43% of dietary diarrhea and gastrointestinal diseases, which are the most common in pigs, occur in a wide range from newborn piglets to weaned piglets. In the case of piglets that are not matured immediately after delivery, the mortality rate is very high, and even after recovery, most of them become atrophy pigs, causing enormous losses to pig farms.

Recently, a technology has been developed to analyze the effects of feed additives such as antibiotics, probiotics, and antibiotic substitutes added to pig feed through analysis of the microbial profile in pig intestines (Korea Patent Publication 10-2012-0007176). There is no disclosure of biomarkers for predicting productivity discovered through comparative analysis of intestinal microorganisms of piglets.

On the other hand, the sequencing technology of 16S ribosomal RNA genes (16S rRNA genes) using Next-generation Gene Sequencing, unlike the process of culturing and identifying microorganisms, has a large amount of research cost, labor, and time. By calculating bacterial gene information (DNA Sequence), it overcomes the limited research limitations of the existing culture and identification processes, and provides a means to analyze all bacterial flora. This technology can analyze all bacterial flora in each pig intestine using the ultra-variable region sequence of 16S rRNA genes using next-generation sequencing. Furthermore, bioinformatics techniques using nucleotide sequences can be used to evaluate differences in bacterial composition in the intestine.

Metagenomics is a technique of bioinformatics that analyzes and studies the overall genome of the bacterial flora. It goes beyond the limit of studying only the genome of cultivable bacteria and can analyze the gene functions of egg isolates. Using Next-generation Gene Sequencing, the nucleotide sequence of the bacterial flora in the intestinal tract is made, and the functional elements of the nucleotide sequence can be predicted by comparing this genome with various reference sequences.

Under this background, the present inventors have different intestinal microbial profiles of nursing piglets and weaning piglets, and when the intestinal microbial composition and gene expression patterns of nursing piglets are similar to those of weaning piglets, the probability of occurrence of dietary growth retardation after weaning is low, weaning. The present invention was completed by finding that there is a high probability of being an individual with high productivity such as an increase in weight.

Accordingly, an object of the present invention is to provide a method for predicting the growth of pigs by measuring the expression level of a specific gene from a pig fecal sample.

Another object of the present invention is to provide a composition for predicting growth of pigs and a kit including the same, which can measure the expression level of a specific gene from a pig fecal sample.

However, the problem to be solved by the present invention is not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

According to an embodiment of the present invention, the step of collecting a pig fecal sample; And measuring the expression level of the gene from the fecal sample. And, the gene including the LepA, DnaJ, sorA, dfx, ccp1, RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And At least one selected from the group consisting of asn , a method for predicting the growth of pigs is provided.

According to one side, the measurement of the expression level of the gene may be performed by a method selected from real-time PCR, RT-PCR, and enzyme-linked immunoprecipitation assay (ELISA).

According to one side, the pig may be a suckling piglet.

According to one side, when the expression level of the gene is measured to be significantly high, it can be determined that the probability of occurrence of dietary growth retardation after weaning is low.

According to one side, when the expression level of the gene is measured to be significantly high, it can be determined that early reason is possible.

According to another embodiment of the invention, LepA, DnaJ, sorA, dfx , ccp1, RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And A composition for predicting growth of pigs is provided, comprising a material for measuring the expression level of one or more genes selected from the group consisting of asn .

According to one side, the substance for measuring the expression level of the gene may be a primer pair, a probe, or an antisense nucleotide.

According to another embodiment of the present invention, there is provided a kit for predicting growth of pigs, including the composition.

According to one side, the kit may be a real-time PCR kit, an RT-PCR kit, a DNA microarray or an ELISA kit.

According to one side, the pig may be a suckling piglet.

According to another embodiment of the invention, LepA in fecal samples of pigs, DnaJ, sorA, dfx, ccp1 , RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And A method of providing information for predicting growth of pigs is provided, comprising measuring the level of expression of one or more genes selected from the group consisting of asn .

The growth prediction method and growth prediction kit of the present invention can easily predict productivity by measuring the expression level of a specific gene from a pig fecal sample, and furthermore, feed additives such as probiotics are added to piglets predicted to be inferior in productivity. It can be usefully used to take appropriate measures, such as paying.

The effects of the present invention are not limited to the above effects, and should be understood to include all effects that can be deduced from the configuration of the invention described in the detailed description or claims of the present invention.

FIG. 1 shows the results of unweighted UniFrac PCoA plots (a) and weighted UniFrac distances of fecal samples of lactating and weaning piglets (b).
Figure 2 is a result of SEED analysis showing a group of genes related to the "stress response", "heat shock" and "oxidative stress" functions present in the feces of mammalian piglets and weaning piglets ([P <0.001], [P <0.01] and [P <0.05] is denoted by [***], [**] and [*] respectively).
3 is a result of SEED analysis showing a group of genes related to the "carbohydrate metabolism" and "amino acid metabolism" functions present in the feces of mammalian piglets and weaning piglets ([P <0.001], [P <0.01] and [P <0.05]] Are denoted by [***], [**] and [*] respectively).
Figure 4 shows the amount of prevotella in the fecal sample of piglets using the prevotella qPCR Kit as a Ct value.
5 is a heat map for the relative abundance of the level 1 SEED subsystem based on the entire metagenome sequencing data. Yellow clusters have a relative abundance of 7% or more, and green clusters have a relative abundance of less than 5%. System.
Figure 6 compares the relative abundance differences of the Level 1 SEED subsystem mapped to "Carbohydrates", "Amino acids and their derivatives", "Stress Response" and "Toxic, Disease and Defense".

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the rights of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes to the embodiments are included in the scope of the rights.

The terms used in the examples are used for illustrative purposes only and should not be interpreted as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of the reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the embodiments, the detailed description thereof will be omitted.

According to an embodiment of the present invention, the step of collecting a pig fecal sample; And measuring the expression level of the gene from the fecal sample. Including, the gene isLepA , DnaJ , sorA , dfx , ccp1 , RhaA , RhaB , RhaD , malA , mall, mtr, met, gla And asn At least one selected from the group consisting of, a method for predicting the growth of pigs is provided.

The genes were subjected to 16rRNA gene sequencing and whole metagenome sequence analysis on fecal samples collected from piglets after weaning, as shown in the following examples, and genes with statistically significant expression levels were selected compared with the profile of mammalian piglets. . Specifically, LepA, DnaJ, sorA, dfx , ccp1 stress resistance genes, RhaA , RhaB , RhaD , malA , and mall represent genes related to carbohydrate metabolism, and mtr, met, gla , and asn represent genes related to amino acid metabolism .

The measurement of the expression level of the gene may be performed by a method selected from real-time PCR, RT-PCR, and enzyme-linked immunoprecipitation assay (ELISA), but is not limited thereto.

The pig is preferably a suckling piglet, but is not limited thereto. In some cases, the method of the present invention may be applied to weaning piglets.

In the present invention, the term "mammal piglet" refers to a piglet that feeds on mother's milk, and "wet piglet" is a piglet up to 70 days old after being weaned after birth, and a piglet eating solid feed from the 21st to the 28th after birth Means.

On the other hand, the growth prediction method of the present invention can be used to select pigs with high productivity by predicting feed efficiency and stress resistance after weaning when applied to piglets.

In addition, when the expression level of the gene is measured to be significantly high, it can be determined that the probability of occurrence of dietary growth retardation after weaning is low or that early reason is possible.

In the present invention, the term "dietary growth retardation" refers to a phenomenon in which a piglet's weight gain is decreased due to various causes, such as physiological, psychological, and environmental stress of the piglet, and thus the weight is maintained or decreased.

In the present invention, the term "early reason" means to start weaning within 10 to 20 days of birth.

According to another embodiment of the invention, LepA, DnaJ, sorA, dfx , ccp1, RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And A composition for predicting growth of pigs is provided, comprising a material for measuring the expression level of one or more genes selected from the group consisting of asn .

"Substances that measure the level of expression of a gene" terms, in the present invention is a marker that the increased expression in the field of reason piglets LepA, DnaJ, sorA, dfx, ccp1, RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And It refers to a molecule that can be used for detection of a marker by specifically binding to mRNA encoding asn and confirming their expression level.

A substance for measuring the expression level of such a gene may be a primer pair, a probe, or an antisense nucleotide, but is not limited thereto.

The "primer" is a nucleic acid sequence having a short free 3'hydroxyl group and can form a complementary template and a base pair, and functions as a starting point for template strand copying. Means a short nucleic acid sequence. Meanwhile, the length of the sense and antisense primers can be modified based on those known in the art.

The "probe" refers to a nucleic acid fragment such as RNA or DNA corresponding to a short number of bases to hundreds of bases capable of specific binding to an mRNA, and is labeled so that the presence or absence of a specific mRNA can be confirmed. The probe may be manufactured in the form of an oligonucleotide probe, a single stranded DNA (DNA) probe, a double stranded DNA (DNA) probe, or an RNA probe. Here, selection of an appropriate probe and conditions for hybridization can be modified based on those known in the art.

According to another embodiment of the present invention, there is provided a kit for predicting growth of pigs, including the composition.

The kit of the present invention is capable of confirming the mRNA expression level of the genes, and the kit may be a real-time PCR kit, an RT-PCR kit, a DNA microarray or an ELISA kit.

The kit of the present invention may include a primer for measuring the gene expression level, a probe, or an antibody that selectively recognizes a marker, as well as one or more other components, solutions, or devices suitable for an assay method according to need.

According to another embodiment of the invention, LepA in fecal samples of pigs, DnaJ, sorA, dfx, ccp1 , RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And A method of providing information for predicting growth of pigs is provided, comprising measuring the level of expression of one or more genes selected from the group consisting of asn .

Hereinafter, the present invention will be described in more detail by examples. However, the following examples and experimental examples are for illustrative purposes only, and the present invention is not limited by the following examples and experimental examples.

Example One: Feces Sampling and DNA extraction

Fecal samples were collected from 10 piglets immediately before weaning (21 days old) and 1 week after weaning (28 days old), and the collected fecal samples were stored in a sterilized test tube and stored at -80°C. Then, DNA was extracted from 200 mg feces per sample using the QIAamp Fast DNA Stool Mini Kit (QIAGEN, Hilden, Germany). In the cell lysis process during DNA extraction, the sample was vortexed twice at 300 rpm for 2 minutes using a bead-beating method, and incubated in a water bath at 70° C. for 5 minutes between beating processes. DNA concentration was measured at OD260/280 using a Colibri Microvolume Spectrometer (Titertek Berthold, Pforzheim, Germany), and DNA with a concentration result of 1.80-2.15 was used.

Example 2: 16S rRNA gene sequencing

Primers 799F-mod6 (5'-CMGGATTAGATACCCKGGT-3' and 1114R (5'-GGGTTGCGCTCGTTGC-3') were used to amplify this part by targeting V5-V6, the super variable region of the 16S rRNA gene. Conditions are as shown in Tables 1 and 2 below.

PCR composition Μl 5X PrimeSTAR Buffer (Mg ^{2 +} ) (Takara Bio, Inc., Shiga, Japan) 10 dNTP 4 Primer (forward) One Primer (reverse) One PrimeSTAR HS DNA Polymerase (Takara company) 0.5 D.W 31.5 Template 2 Final volume (PCR tube) 50

In the composition of the PCR, 5ХPrimeSTAR Buffer (Mg ^{2 +} ) (Takara Bio, Inc., Shiga, Japan), 2.5 mmol/L of deoxynucleotide triphosphates (dNTPs), 2.5 IU/μL of PrimeSTAR HS DNA Polymerase, each 10 pmol primer, Then, 25 ng of DNA was used, and the total amount thereof was 50 μL.

PCR conditions Step Temperature Time Cycle Initiation 98℃ 3min One Denaturation 98℃ 10sec 35 Annealing 55℃ 15sec Extension 72℃ 30sec Termination 72℃ 3min One

PCR cycling is as follows: The initial denaturation process is performed at 98°C for 3 minutes, followed by 10 seconds at 98°C, 15 seconds at 55°C, and 30 seconds at 72°C, and finally 35 repetitions at 72°C for 3 minutes. . The PCR amplification product was purified using a PCR purification kit (Wizard®SV Gel and PCR Clean-Up System (Promega, Wisconsin, USA)), and the 16S rRNA gene amplicon thus produced was sequencing using the Illumina MiSeq platform. For 16S rRNA gene sequence analysis, low-quality sequences were removed using Mothur software, and chimeric sequences were deleted using UCHIME calculation method in Mothur software. QIIME (Quantitative Insights into Microbial Ecology) software (version 1.9.1) was used for the de novo operational taxonomic unit, and taxonomic assignment was performed by referring to the naive Bayesian RDP classifier and Greengenes database. . Microbial alpha diversity including Chao1, observed OTUs, phylogenetic diversity (PD) whole tree, Shannon index and Simpson index were calculated using QIIME. Statistical Analysis of Metagenomic Profiles (STAMP) software V2.1.3's two-sided Welch's t-test was used to confirm the difference in the relative abundance of the microbial taxa, the P-value was set to less than 0.05, and the beta-diversity was QIIME. The weighted and unweighted UniFrac distance metrics were measured. Unweighted UniFrac takes into account the presence or absence of OTUs, while weighted UniFrac takes into account the relative abundance of OTUs. Major coordinate analysis (PCoA) plots are prepared based on weighted and unweighted UniFrac distance metrics, and similarity analysis (ANOSIM) uses QIIME and weighted and unweighted UniFrac distance metrics to check whether the microbial composition between the two groups is significantly different. And decided.

As a result of performing 16S rRNA gene sequence analysis on the feces sample collected in Example 1 in the same manner as described above, the diversity of the microbial community was as shown through the Shannon, Simpson and Chao1 diversity indexes shown in Table 3, after weaning. It was confirmed that it decreased. In addition, the PD whole tree value, which shows the systematic difference between taxa, was significantly higher in nursing piglets than in weaning piglets (P<0.05).

Diversity index Piglets Weaning piglets P-value PD whole tree 17.58 ± 2.94 13.03 ± 2.45 0.003 Shannon 5.13 ± 0.80 4.58 ± 1.06 0.223 Simpson 0.89 ± 0.07 0.82 ± 0.13 0.189 Chao1 1192.99 ± 324.63 1090.64 ± 287.64 0.463 Observed OTUs 631.56 ± 159.71 543.72 ± 134.26 0.237

On the other hand, the analysis of unweighted UniFrac distance metrics showed that the fecal samples of lactating and weaning piglets had an R value of 0.7373, and the microbial profiles were significantly different (P=0001). Referring to (a) of Figure 1, the unweighted UniFrac PCoA plot can visually confirm that the microbial community of the suckling piglet and weaning piglet fecal samples are clearly separated, and the ANOSIM of the weighted UniFrac distance through Figure 1 (b) Is similar to the unweighted UniFrac distance, and the R value was 0.7158, indicating a significant difference between nursing and weaning piglets (P=001).

Example 3: prevotella Reason for using Piglet Productivity prediction

Using the microbial profile analyzed in Example 2, a qPCR Kit primer capable of selectively detecting prevotella (genus) was prepared using a CLC workbench in order to confirm the change in intestinal prevotella in piglets that have passed from mammal to weaning. .

target Sequence (5'→3') Annealing temperature (℃) Size (bp) Prevotella (genus) Forward TAATTCCGTGCCAGCAGCC (19mer) 55 180 Reverse GGGCGGAATTCGTGGTGTA (19mer)

As a result of confirming the coverage through RDP (Ribosomal database project) probe match, as shown in Table 5, it was confirmed that the coverage of the prevotella primer disclosed in the previous literature was similar or superior.

Taxonomic Rank Hierarchy View (hits/total searched) Kingdom “Bacteria” 12187/3196041 Phylum “Bacteroidetes” 12175/396435 Class “Bacteroidia” 12175/229333 Order “Bacteroidales” 12175/229333 Family “Prevotellaceae” 12099/55057 Genus “Prevotella” 12049/45798

Using the primers, qPCR was performed with the composition shown in Table 6 below to measure the amount of prevotella present in the total DNA of bacteria extracted from fecal samples of 4-week-old weaning piglets (left graph of FIG. 7), and 10-week-old weaning piglets By repeating the same procedure in, the amount of prevotella was measured to compare the amount of prevotella in fecal samples of the experimental groups 1-3 pigs with excellent productivity and the control group. As a negative, pure distilled water without DNA was used.

qPCR reaction mixture Concentration ul SYBR Green I 1000-fold dilution One 10× PCR mixure 100 mM Tris-Hcl, pH 8.5
500mM Kcl,
15-30mM Mgcl ₂
±0.2 mg/mL BSA
±1.5% Triton X-100 2 taq DNA polymerase 0.5 U One
prevotella primer F
10pmol/ul One prevotella primer R
10pmol/ul One Distilled Water UltraPure DNase/RNase-Free 12 DNA template 50ng/ul 2 Total 20

As a result, as shown in Table 7 and Figure 4, there was no difference in the amount of prevotella in the feces of 4-week-old weaning piglets, but in 10-week-old weaning piglets after pig growth It was confirmed that more prevotella in the intestinal tract was detected in pigs of experimental groups 1-3 with high productivity.

4 weeks prevotella (genus) Ct value Control Experimental group 1 Experimental group 2 Experimental group 3 Negative 23.68 23.09 21.46 22.55 37.34 10 weeks prevotella (genus) Ct value Control Experimental group 1 Experimental group 2 Experimental group 3 Negative 25.72 23.99 23.14 23.84 34.71

Prevotella is a microorganism responsible for fermentation of non-starch polysaccharides of short-chain fatty acid plants and facilitating the improvement of feed digestibility. In piglets with more prevotella , the intestinal microflora change occurs most due to feed intake. Edo, it can be predicted that the probability of occurrence of dietary growth retardation is low.

Example 4: Whole metagenome sequence analysis

Whole metagenome shotgun sequencing was performed with 8 samples of the samples collected in Example 1 to investigate the functional aspects of microorganisms present in piglet feces. For whole metagenome sequencing, raw data in FASTQ format is transferred to CLC Genomics Workbench (version 10) (Qiagen Bioinformatics, Aarhus, Denmark) with CLC Microbial Genomics Module (version1.2), and the sequence quality evaluation process is performed. After that, high-quality sequences were collected using CLC's De novo type algorithm.

For functional analysis of microorganisms, contig was uploaded to Metagenomics Rapid Annotation using MG-RAST, a surf system, and then all sequences were analyzed, and DESEq was used to analyze count data. Artificial duplicates and genomic DNA of piglets were removed using MG-RAST. The bowtie setting of the MG-RAST pipeline was used to remove the sequence matching the host gonome, and the swine genome was used for metagenomics analysis derived from the host (Sus scrofa, NCBI v10.2). Here, the Swine genome is used as a reference for easily obtaining information from MG-RAST. The total metagenome shotgun sequencing analyzed through the above process produced a total of 50,440,732 sequences, and a total of 1,120,421 contigs were collected from 8 fecal samples after quality trimming.

Sample ID Number of sequences Contig length (bp) Number of contigs Identified protein characteristics Identified functional
category Before QC After QC Before QC After QC Before QC After QC N1 4,531,548 4,530,646 74,800,360 52,416,370 81,447 79,410 55,257 31,527 N2 6,870,302 6,868,798 130,241,602 103,118,418 164,528 160,633 103,541 61,547 N3 4,288,774 4,287,375 69,391,125 53,568,785 81,677 79,941 62,874 36,796 N4 4,654,022 4,653,262 82,589,526 66,491,857 108,853 106,095 71,088 40,136 W1 9,447,406 9,445,251 151,116,638 113,319,181 179,364 165,774 98,920 44,941 W2 7,113,170 7,111,597 151,002,242 112,874,763 161,617 155,573 105,330 55,088 W3 6,320,228 6,318,750 144,162,529 106,003,305 165,591 160,988 104,264 56,570 W4 7,215,282 7,213,515 173,768,565 131,398,512 224,894 212,007 132,418 67,400

N: Nursing piglets W: Weaning piglets, QC: Quality Control

Next, statistical processing at levels 1 to 4 of the output file was used through a double-sided Welch's t-test at each level to compare the microbial group and related genes in the intestine at the time of lactation and the weaning period, For the part, a hierarchical clustering-based analysis of the SEED subsystem was performed using a database (SEED Subsystems database) that can obtain information on functionally related proteins.

In the nursing and weaned piglet metagenome, 28 SEED subsystems were identified and a hierarchical cluster-based analysis was performed. The level 1 SEED subsystem was found to be significantly abundant in weaning piglets (see FIGS. 5 and 6). Even in the level 3 SEED subsystem, it was confirmed that the gene group mapped to carbohydrate and amino acid metabolism was significantly higher in weaned piglets (see Fig. 3).

In the level 2 SEED subsystem, the gene groups related to "heat shock" and "oxidative stress" among "stress response" were identified, and as shown in Fig. 2a, it was confirmed that it was quite abundant in weaned piglets (P<0.05 ). In addition, the level 4 SEED subsystem during "heat shock and oxidative stress" also included translation elongation factor LepA, chaperone protein DnaJ, signal peptidase-like protein, heat shock protein GrpE, peroxide reductase and cytochrome c551 peroxidase in weaned piglets. It was confirmed that a number of proteins and enzymes involved in heat shock and oxidative stress were remarkably abundant (Fig. 2b, c).

Based on these results, the Enzyme Commission number, which is judged to be involved in the enzyme-catalyzed reactions necessary for the growth of pigs, was derived. Then, the bacteria were selected genes corresponding to the function of the enzyme number, the heat shock gene (LepA and DnaJ), oxidative stress gene (sorA, dfx, and ccp1 ), carbohydrate metabolism ( RhaA , RhaB , RhaD , malA , and mall) , amino acid metabolism ( mtr, met, gla And asn ) related genes were finally selected.

Therefore, when the expression level of the genes is measured from a pig fecal sample, the probability of occurrence of dietary growth retardation after weaning is low, and thus it can be predicted that the pig is excellent in growth.

As described above, although the embodiments have been described by the limited drawings, a person of ordinary skill in the art can apply various technical modifications and variations based on the above. For example, even if the described techniques are performed in a different order from the described method, and/or the described components are combined or combined in a form different from the described method, or are replaced or substituted by other components or equivalents. Appropriate results can be achieved.

Therefore, other implementations, other embodiments and claims and equivalents fall within the scope of the following claims.

Claims (11)

Collecting a sample of pig feces; And
Measuring the expression level of the gene from the fecal sample; Including,
The gene LepA, DnaJ, sorA, dfx, ccp1, RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And At least one selected from the group consisting of asn , pig growth prediction method.
The method of claim 1,
The measurement of the expression level of the gene is performed by a method selected from real-time PCR, RT-PCR, and enzyme-linked immunoprecipitation assay (ELISA).
The method of claim 1,
The pig is a mammalian piglet, a method for predicting growth of pigs.
The method of claim 1,
When the expression level of the gene is measured to be significantly high, it is determined that the probability of occurrence of dietary growth retardation after weaning is low.
The method of claim 1,
When the expression level of the gene is measured to be significantly high, it is determined that early reason is possible, the growth prediction method of pigs.
LepA, DnaJ, sorA, dfx, ccp1, RhaA, RhaB, RhaD, malA, mall, mtr, met, gla and A composition for predicting growth of pigs, comprising a substance for measuring the expression level of one or more genes selected from the group consisting of asn .
The method of claim 6,
The material for measuring the expression level of the gene is a primer pair, a probe or an antisense nucleotide, a composition for predicting growth of pigs.
A kit for predicting growth of pigs, comprising the composition of claim 6 or 7.
The method of claim 8,
The kit is a real-time PCR kit, RT-PCR kit, DNA microarray or ELISA kit, a kit for predicting growth of pigs.
The method of claim 8,
The pig is a mammalian piglet, a kit for predicting the growth of pigs.
LepA in the fecal samples of the pigs, DnaJ, sorA, dfx, ccp1 , RhaA, RhaB, RhaD, malA, mall, mtr, met, gla And A method for providing information for predicting growth of pigs, comprising measuring the level of expression of one or more genes selected from the group consisting of asn .

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