CN113174441B - Duck residual feed intake related lncRNA and application thereof - Google Patents

Abstract

The invention relates to the technical field of molecular biology, in particular to lncRNA related to residual feed intake of ducks and application thereof. The invention utilizes RNA-Seq technology and bioinformatics method to compare and analyze the gene expression profile of the liver tissue with high and low residual feed intake of duck, and identify and screen the key differential expression lncRNA related to residual feed intake and lipid metabolism. The results show that lncRNA and target genes GSK3 beta and ACVR1B, SORBS1 of the lncRNA are all related to the residual feed intake of ducks. The invention lays a theoretical foundation for molecular mechanism research of the duck feed utilization rate character, and has wide application prospect in molecular breeding of new duck varieties and new lines with high feed utilization rate.

Description

Duck residual feed intake related lncRNA and application thereof

Technical Field

The invention relates to the technical field of molecular biology, in particular to lncRNA related to residual feed intake of ducks and application thereof.

Technical Field

The residual feed intake refers to the difference between the actual feed intake of the livestock and the expected feed intake for maintenance and growth, which reflects the difference of feed utilization rate (Aggrey et al, 2010) caused by the difference of the genetic background of the livestock and the poultry, and can be used as an effective index for measuring the feed utilization rate, and in general, the low residual feed intake is superior to the high residual feed intake in feed utilization. Researches show that the residual feed intake is a medium genetic trait, the genetic power of which is generally 0.25-0.45 (Zhang, et al, 2017), and the research shows that a great amount of genetic variation exists among different individuals, and the residual feed intake can be genetically improved by effective individual selection or family selection on the premise of not affecting the growth, fattening and reproductive performance of livestock, so that the feed utilization rate is improved, and the feeding cost is reduced. Therefore, the breeding of the low-residual feed intake duck group is beneficial to reducing the cost.

Factors affecting residual feed intake mainly include factors such as body composition, digestibility of nutrients, metabolic capacity, immune response, energy metabolism, etc., wherein lipid metabolism is one of the important factors affecting residual feed intake of poultry (Yang et al, 2019;Zabielski et al, 2019). Liver is an important organ in animals and plays an important role in metabolism and energy balance of substances such as sugar, fat and protein. Studies have shown that lipid metabolism deposition is significantly related to feed utilization. Lipid metabolism is an important factor affecting the residual feed intake of meat ducks, and genes related to lipid metabolism are highly expressed in individuals with low residual feed intake. In view of this, liver lipid metabolism is an important factor affecting the residual feed intake of animals.

Long non-coding RNAs (lncRNA) are a class of RNAs greater than 200 nucleotides in length that can regulate gene expression from multiple planes, thereby affecting phenotypic traits (Weikard et al, 2017). Studies show that lncRNAs regulate gene expression in a plurality of ways such as transcriptional regulation, posttranscriptional regulation, epigenetic regulation and the like. LncRNAs are taken as a large class of RNA with regulatory action, and play a great role in the aspects of growth, development, metabolism and the like of organisms. The lncRNA can perform transcriptional activation and expression control on adjacent mRNA in a cis mode, and can also control the coded protein genes which are far away or on different chromosomes in a trans mode. Studies have shown that lncRNA plays an important role as an important regulatory factor in liver lipid metabolism.

Disclosure of Invention

In order to solve the problems in the prior art, the invention provides LncRNA related to the residual feed intake of ducks, wherein the LncRNA is named XR_001191699.1 and has a sequence of SEQ ID NO.1.

The invention further aims to provide application of the lncRNA, wherein the lncRNA is used for predicting or assisting in predicting the ducks with high feed utilization rate; or used for breeding ducks with different feed utilization rates.

The invention also provides another application of the lncRNA, which is used for preparing a reagent for detecting the utilization rate of duck feed.

Preferably, the reagent detects the expression level of the lncRNA by a nucleic acid amplification technique, and the primer sequence pairs for nucleic acid amplification are:

forward sequence SEQ ID No.2: CCAGCATTCTCGTCTT

Reverse sequence SEQ ID NO.3: CTTATAGTCGTGCCAGCGT.

Preferably, the agent detects the expression level of the target gene of the lncRNA by a nucleic acid amplification technique.

Preferably, the target gene is gsk3β and/or ACVR1B and/or sodbs 1.

Preferably, the primer sequence pairs for amplifying the GSK3 beta are:

forward sequence SEQ ID No.4: GGGCGAGATAAACCTGCACT; reverse sequence SEQ ID NO.5: GCTGTAGCGTTTGTAGGGGT;

the primer sequence pairs for amplifying the ACVR1B are:

forward sequence SEQ ID No.6: ATTGCACCGAACCAAAGGGT; reverse sequence SEQ ID NO.7: CCCAGTAGACCAAGCCCAAA;

the primer sequence pairs for amplifying the SORBS1 are:

forward sequence SEQ ID No.8: GAGCTGGAACTGAGGGATGG; reverse sequence SEQ ID NO.9: GAAGGTGCCGAATTGCCTTG.

Preferably, the sample detected by the reagent is liver tissue.

Preferably, the agent is used for predicting or assisting in predicting high feed utilization ducks; or used for breeding ducks with different feed utilization rates.

The invention has the beneficial effects that: the invention utilizes the RNA-Seq technology and the bioinformatics method to compare and analyze the gene expression profile of the liver tissue of the high and low residual feed intake of the duck, identify and screen the key differential expression lncRNA related to the residual feed intake and lipid metabolism, and provide a theoretical basis for molecular breeding for improving the feed utilization rate of the meat duck.

Drawings

FIG. 1 shows the results of Agilent 2100Bioanalyzer detection of RNA integrity, where 1-6 are HRFI and 7-12 are LRFI; the two bands, 28S and 18S, are clearly visible, demonstrating that the extracted RNA has better integrity.

FIG. 2 is a graph of 176 differentially expressed lncRNAs identified by differential expression analysis of high and low duck liver tissue genes, comprising 89 up-regulated genes (right panel) and 87 down-regulated genes (left panel);

FIG. 3 shows the results of fluorescent quantitative PCR assay for differentially expressed lncRNA and its target gene, wherein: * Represents p < 0.05; * Represents p < 0.0001.

Detailed Description

For easy understanding, the following description will make more specific use of the technical solution of the present invention in conjunction with the examples:

example 1: test materials

1. Test animals

The experimental duck group in the embodiment is provided by a breeding farm of Huang Shanjiang English duck industry limited company, is healthy and healthy, has complete pedigree record and has clear source of seeds. And (3) carrying out male and female identification, wearing fin numbers, inoculating a vaccine and weighing primary weight when the ducks are hatched. And selecting 1-day-old healthy and strong meat-type breeding male ducks with similar weight, and feeding all selected ducks in the same duck shed until the age of 21 days. And weighing the whole group at 21 days of age, randomly selecting 1000 meat ducks with similar weights, transferring the meat ducks into individual cages for feeding, and finishing the feeding until 42 days of age. The feeding management rules and the immunization program are carried out according to the unified standard during the test period, and all test ducks eat and drink water freely.

RFI assay and tissue acquisition

During the test period, 21-day-old Body Weights (BW) of the ducks were recorded ₂₁ ) Body weight at 42 days of age (BW) ₄₂ ) The Feed Intake (FI), the Body Weight Gain (BWG), the daily feed intake (ADFI), the daily weight gain (ADG), the Feed Conversion Rate (FCR) and the Residual Feed Intake (RFI) of all individuals are calculated according to the following calculation formulas:

RFI＝ADFI–(b ₀ +b ₁ ×MBW ^0.75 +b ₂ ×ADG)

wherein RFI is the residual feed intake; ADFI is daily feed intake; MBW (Membrane biological Membrane w) ^0.75 Is the average metabolic weight; ADG is daily gain; b ₀ For the intercept, b ₁ 、b ₂ Is a regression coefficient. A linear fitting function in SAS9.4 software was used to calculate RFI values for meat ducks.

Subsequently, sequencing RFI of all individuals, dividing the test meat duck population into a high RFI group (HRFI) and a low RFI group (LRFI) according to the RFI sequencing result, randomly selecting 6 meat ducks from the high RFI group and the low RFI group respectively according to FCR values, bleeding and slaughtering the test ducks, collecting livers, placing the collected livers in a 1.5mL centrifuge tube filled with RNALater liquid (America Thermo Fisher Sciences company), and storing the livers in a refrigerator at-80 ℃ for later use after the temperature is 4 ℃.

3. Main test instrument

Fluorescent quantitative PCR (ABI 7500), ABI Co., USA; nanodrop spectrophotometers, company Thermo Fisher Sciences, usa; ultra low temperature refrigerator (DW-86L 828W), china sea company; a tabletop high-speed refrigerated Centrifuge (Centrifuge 5424R), eppendorf corporation; a refiner (JXFSPRP-48), shanghai's net letters industry Co., ltd; analytical balance (AB 265-S), metrele-tolidol; full-automatic gel imaging analyzer, UVItec company, UVItec, UVLItec; electrophoresis apparatus (DYY-6C), beijing Liuyi instrument factory; PCR instrument, dongsheng scientific instrument Co., ltd; a bioanalyzer (Agilent Bioanalyzer 2100), agilent, usa; sequencer (Illumina HiSeq 400), illumina company, usa; pure water apparatus (Millipore tank 030), sanyo company; pipette gun, eppendorf company.

4. Main reagent

RNA-later, company Thermo Fisher Sciences, USA; RNA extraction kit, a kit for rapidly extracting the total RNA of the Baifeite TRIPURE; TSE202 (2×T5Fast qPCR Mix (SYBR Green I), optimaceae; goldenstar RT6 cDNA Synthesis Kit Ver 2, goldenstar reverse transcription kit, agarose, china Biosharp, chloroform, tin-free market prospect chemical reagent Co., ltd., 5×TBE, biological engineering (Shanghai) stock Co., ltd., 6× DNA loading buffer, beijing Tiangen Biotechnology Co., ltd., absolute ethyl alcohol, jiangsu-strongy functional chemical Co., ltd., superRed/Gelred nucleic acid dye (10000X aqueous solution), china Biosharp, NEB#7530 kit, an Nuolun (Beijing) biological technology Co., ltd.)

Example 2: experimental procedure and result analysis

1. Total RNA extraction from tissue

(1) Tissue: every 50-100 mg of tissue is added with 1. 1mL TRIpure Reagent, and the tissue sample is uniformly stirred by using glass or a strong homogenizer, and the sample stored in liquid nitrogen needs to be ground by using a mortar and then homogenized. The volume of the tissue sample cannot exceed 10% of the volume of the TRIpure Reagent.

(2) Incubation is carried out at 15-30℃for 5 minutes to allow complete decomposition of the nucleoprotein.

(3) 0.2mL of chloroform was added to each 1. 1mL TRIpure Reagent, the lid was closed, vigorously shaken for 15 seconds, and incubated at room temperature for 2 to 3 minutes.

(4) At 4 ℃,12000g is centrifuged for 10-15 minutes. After centrifugation the sample will be divided into three layers: the lower organic phase, the middle layer and the upper colorless aqueous phase, the RNA being present in the aqueous phase. The volume of the aqueous layer was about 60% of the volume of the added TRIpure Reagent.

(5) Carefully sucking 400-500 mu L of the upper water phase into a new RNase-free centrifuge tube, adding 70% ethanol with equal volume, immediately reversing and uniformly mixing, and a small amount of precipitation does not influence the extraction process.

(6) The RNA adsorption column is placed in a collection tube, the mixture is immediately added into one RNA adsorption column, the addition amount of each collection tube is less than 700 mu L, each collection tube can be added in two times at most, and then the collection tube is centrifuged for 60 seconds at the room temperature of 12000 Xg, and the waste liquid is discarded.

(7) Then, 700. Mu.L of deproteinized solution RW1 was added thereto, the mixture was left for 1 minute, and the mixture was centrifuged at 12000 Xg for 30 seconds at room temperature, whereby the waste liquid was discarded.

(8) Digestion of DNA on the membrane, which step may be omitted, is as follows,

1) 700. Mu.L of rinse solution RW was added, centrifuged at 12000 Xg for 1 minute at room temperature, and the permeate was discarded; 2) Centrifuging at 12000 Xg for 2min at room temperature to prevent residual ethanol from inhibiting DNase activity; 3) Adding 5-6 mu L of Recombinant DNase I into 45 mu L of DNase I Buffer, blowing and uniformly mixing to prepare DNase I working solution, adding the DNase I working solution into a centrifugal adsorption column, and standing for 15-20 minutes at room temperature; 4) mu.L of the rinse RW2 was added, centrifuged at 12000 Xg for 1 minute at room temperature, and the permeate was discarded.

(9) mu.L of the rinse solution RW was added, and the mixture was centrifuged at 12000 Xg for 30 seconds at room temperature to discard the waste liquid. 500. Mu.L of the rinse RW was added and repeated.

(10) The RNA adsorption column is put back into an empty collecting pipe, and is centrifuged for 2 minutes at the room temperature of 12000 Xg, so that the rinsing liquid is removed as much as possible, and the residual ethanol in the rinsing liquid is prevented from inhibiting downstream reaction.

(11) Centrifugation was performed at 12000 Xg for 2 minutes at room temperature to prevent residual ethanol from affecting RNA use.

(12) Transferring the centrifugal adsorption column into RNase-free collecting pipe, adding 50-100 mu LRNase-free H ₂ O, standing for 3-5 minutes at room temperature.

(13) And (3) centrifuging at the room temperature of 12000 Xg for 2 minutes, wherein the solution in the centrifuge tube is the RNA sample, and can be immediately used or stored at the temperature of-80 ℃ for standby.

RNA integrity identification and yield determination

5-10 mu L of RNA is dissolved in TE buffer (pH 7.5-8.2) and detected at OD ₂₆₀ Is not shown). RNA concentration (1 OD) can be obtained by light absorption ₂₆₀ RNA = 40 μg/mL), and thus yield (concentration x volume) and yield (RNA yield/tissue amount) of RNA were calculated.

1) Agilent 2100Bioanalyzer detects RNA integrity: 1.0. Mu.L of total RNA was diluted with DEPC water at a 1:5 ratio and denatured at 70℃for 2 minutes, and RNA mass integrity was checked using an Agilent 2100 Bioanalyzer. After detection, both 28S and 18S bands were clearly seen, with 28S band being approximately 2 times that of 18S band, and the integrity was better as shown in FIG. 1.

2) And (3) detecting the purity of RNA: detecting the OD value of RNA by using a Nanodrop2000 spectrophotometer, aiming at measuring the purity and OD of the RNA ₂₆₀ /OD ₂₈₀ The ratio should preferably be between 1.8 and 2.1, OD ₂₆₀ /OD ₂₃₀ The ratio should be around 2.2. 3) Detection of RNA integrity value (RIN, RNAintegrity number): RNA quality was determined by measuring RNA integrity values (RIN) using an Agilent 2100 Bioanalyzer. RIN value is from 0-10, can directly reflect the quality of RNA, and the larger RIN value indicates the better and more complete RNA quality.

The purity detection results of the RNA extracted in the embodiment are shown in the following table, and the complete value of the RNA is more than 8, so that the quality is good.

RNA library construction

After total RNA extraction, the purity of the sample was high and the quality was good, and rRNA meeting the cDNA library construction criteria was removed to retain mRNA and ncRNA (non-coding RNA includes lncRNA). The enriched mRNA and ncRNA are fragmented into short fragments by using a fragment buffer solution, and reverse transcription amplification is carried out by using a gold-brand reverse transcription kit Goldenstar RT6 cDNA Synthesis Kit Ver 2 of the Optimus in the family of the Optimus, and the sample is added according to the following components:

after mixing evenly, incubating for 2min at 42 ℃ and incubating for 5min at 60 ℃, rapidly placing on ice for cooling, and adding the following components after short centrifugation:

after mixing, incubating for 10min at 25 ℃, 30min at 50 ℃ and 5min at 85 ℃, and placing the reverse transcription product on ice or refrigerating for standby. Next, the cDNA fragment was purified using the Qiaquick PCR extraction kit, end repaired, poly (A) added, and ligated to the Illumina sequencing adapter. The second strand cDNA was then digested using UNG (uracil-N-glycosylase). The size of the digestion products was selected by agarose gel electrophoresis, PCR amplified, and then sequenced using Illumina HiSeqTM 4000 of Gene Denovo Biotechnology co (china, guangzhou). The off-machine data are raw sequencing data raw reads.

4. Raw data quality control

In order to ensure the data quality, the original data is subjected to quality control before information analysis, and data noise is reduced through data filtering. We further filtered the clean Reads off-line more tightly to get High quality clean Reads for subsequent information analysis. The filtering steps are as follows:

1) Removing reads containing adaptors;

2) Removing reads with the N content of more than 10%;

3) Removing low-quality reads (the number of bases with the mass value Q less than or equal to 20 accounts for more than 50% of the whole read).

And removing the reads containing the linker, ploy-N and low quality through quality control, and finally obtaining clean reads for subsequent analysis. The results are shown in Table 1,

TABLE 1 quality control results for raw data

Through quality control, the Q-score is more than or equal to 90 percent, the GC content is about 48 percent, and the sequencing result is reliable and can be used for subsequent analysis. Quality and comparison of sequencing data this study sequenced the high and low RFI meat duck liver tissue mRNA via Illumina HiSeqTM 4000 sequencing platform, with an average of about 10G data per sample. The Q20 of the sequencing data is more than 97.64%, the Q30 is more than 93.26%, and the sequencing quality is good.

5. Reference genome alignment and transcript splicing

The reads with the filtered ribosomes were aligned to the reference genome using the transcriptome data alignment software TopHat 2. TopHat uses Bowtie as an alignment "engine" to break reads that cannot be aligned into small segments and infer the reads segments and the locations of the segments. For reads that were not initially aligned, topHat will construct a reference set of splice sites without relying on annotation of known transcripts. Alignment of the RNA-seq sequences is useful for identifying expressed transcripts and for quantifying them, helping to find variable splicing and new transcripts.

In order to predict new transcripts, the transcripts need to be reconstituted and assembled.

Firstly, respectively comparing reads of each sample with a reference transcript component to obtain a comparison result of each sample; and assembling transcripts by using cufflinks to obtain an assembling result of each sample. Secondly, firstly grouping and combining a plurality of samples by utilizing cuffmerger according to different treatments, and then combining the results of different groups by utilizing cuffmerger as well, so as to finally obtain the expression levels of transcripts in different groups. Finally, differential analysis is performed on the predicted transcripts by using edge, then functional annotation is performed on the differential transcripts, annotation is performed on new transcripts, and the like.

General procedure overview: filtering off machine clear Data to remove low quality and joints to obtain HQ clear Data, comparing the rRNA-removed reads with a kit to obtain reference genome, reconstructing transcript, filtering FPKM and sequences with lower coverage, comparing the assembled transcript with known mRNA and lncRNA reference sequences to obtain transcripts of all novels, and predicting coding potential to distinguish coding and non-coding parts to obtain final predicted lncRNA.

Results: clear reads were aligned to the duck reference genome using bioinformatics software and the results are shown in table 2.

TABLE 2 clear reads alignment of reference genome results

After quality control of the raw data, 82035712-160487464 clean reads were obtained per sample on average. The number of reads of high quality is on average about 98.09% of the original reads, and the error rate of sequencing is controlled at 0.01%. After removal of reads aligned to ribosomal RNA, an average of 80.64% clean reads were aligned to the duck reference genome, with only one alignment to 79.56% of the genomic position. Each item of data shows that the quality of the sequencing data is better, and the subsequent analysis can be performed.

6. Potential lncRNA mining identification

LncRNA is a class of RNAs with a length greater than 200bp that do not encode proteins, and based on these two main features, potential LncRNA was identified, mainly screening intergenic LncRNA (llncRNA), introns lncRNA (intronic lncRNA), sense lncRNA (sense lncRNA) and antisense lncRNA (antisenselncRNA). The specific operation is as follows:

(1) Exon count and transcript length selection: the threshold value is that the exon number is more than or equal to 2, length is more than 200bp, and single exon transcripts with low credibility are filtered out.

(2) Coding potential screening: for the transcripts screened above, four kinds of software, PLEK (Li et al, 2014), CNCI (Sun et al, 2013), CPC (Kong et al, 2007), pfam (Finn et al, 2014), were used to predict their protein coding potential, and the intersection was taken to obtain the final results of lncRNA. PLEK is based on an optimized k-mer strategy, threshold score <0, CNCI is based on sequence adjacent nucleotide triplet spectra, threshold score <0, CPC is based on transcript open reading frame sequence characteristics and aligned to the UniProt reference database BLASTX, threshold score <0, pfam is a protein family database, transcripts encoding frames are aligned to the database homologously, and unaligned transcripts are lncRNA.

(3) Identification of lncRNA is known, ALDB (ALDB: a dometic-Animal Long Noncoding RNA Database) (Li et al 2015) is a database of livestock lncRNA, candidate lncRNA is aligned with lncRNA in the database by BLASTN tool, and known lncRNA is strictly identified on condition that identity=100%, mismatch=0, e-value <1e-10, gap_opening=0.

This step was performed mainly for sorting of lncRNA, length distribution and number of exons, and at the same time, for comparison analysis with the identified known mRNA.

7. Analysis of differential Gene expression between different samples

Constructing a known mRNA, predicting a new transcript and an lncRNA data set, and calculating the expression quantity by using a FPKM (Fragments Per Kilobase of transcript per Million mapped reads) method, wherein the calculation formula is as follows:

assuming that FPKM (A) is the expression level of transcript A, C is the number of sequenced fragments aligned to transcript A, N is the total number of sequenced fragments aligned to the reference transcript, and L is the number of bases of transcript A. The FPKM method can eliminate the influence of the difference of the length and the sequencing quantity of the transcripts on the expression of the calculated transcripts, and the calculated expression quantity of the transcripts can be directly used for comparing the expression difference of the transcripts among different samples. The transcripts were statistically analyzed, and the number of all transcripts of the high and low RFI ducks was 36481 and 37269, respectively, with 11338 and 11423 new transcripts detected, respectively. As shown in FIG. 2, 176 differentially expressed lncRNAs (89 up-regulated, 87 down-regulated) were identified by differential expression analysis of high and low duck liver tissue genes.

The edder software package was used to screen for differentially expressed genes, with the conditions of significant differential expression being FDR <0.05 and |log2fc| >1. The results showed a total of 1297 significant differences in genes between the two groups, with 686 significant up-regulated and 611 significant down-regulated in high RFI relative to the low RFI group. The lncRNA in the transcripts were identified by cufflinks, CPAT and CPC procedures, giving a total of 2101 new lncRNA. Of the new lncRNA identified, 4443 lncRNA expressed in both groups, 579 lncRNA specific to the high group (HRFI) and 889 lncRNA specific to the low group (LRFI). The differential expression analysis was also screened using the edder analysis software and showed a total of 176 non-coding genes significantly different between the high and low RFI groups, with 89 lncRNA upregulated in the low group (LRFI) relative to the high group (HRFI) and 87 lncRNA downregulated.

Wherein, the differential expression long non-coding RNA represented by XR_001191699.1 and differential expression genes represented by ACVR1B, SORBS1 and GSK3 beta are included in the study subjects.

8. Differential expression Gene GO and KEGG Pathway enrichment analysis

Gene Ontology (GO, http:// www.geneontology.org /) is an international classification of Gene function, consisting of molecular functions (molecular function), biological processes (biological process) and cellular components (cellular component). Pathway enrichment analysis can determine the main metabolic pathway and signal pathway of the differentially expressed genes, and a database KEGG (Kyoto Encyclopedia of Genes and Genomes, http:// www.genome.jp/KEGG) is used as a related main public database, so that the pathway enrichment analysis is a main tool for metabolic analysis and regulation network research. To further investigate the major biological functions of differentially expressed genes, we mapped the differentially expressed transcripts to each term of the GO database (http:// www.geneontology.org /), and calculated the number of transcripts per term, resulting in a list of transcripts with a certain GO function and statistics of transcript numbers. Then, applying a super-geometric test to find out GO entries significantly enriched in differentially expressed transcripts compared to the whole transcriptome background, assuming that the test uses a p-value calculation formula.

After the calculated p-value is corrected by FDR, taking Q-value less than or equal to 0.05 as a threshold value, and the GOterm meeting the condition is defined as the GOterm remarkably enriched in the differential expression transcripts. The primary biological function exerted by the differentially expressed transcripts can be determined by GO functional significance enrichment analysis.

To understand the biological functions involved in differentially expressed genes, functional enrichment analysis of GO entry and KEGG pathways was performed on differentially expressed genes using a method of hypergeometric testing. The results of enriching GO entries show that differentially expressed genes are mainly distributed in 12094 GO entries in three major classes of biological processes, molecular functions and cell composition, of which 355 are significantly enriched (Q-value. Gtoreq.0.05). Mainly participate in the processes of energy metabolism, oxidative phosphorylation and the like. The differential expression genes were subjected to KEGG pathway function enrichment assays, which included signaling pathways such as insulin, rapamycin (mTOR), lipid metabolism carbon metabolism, citric acid cycle, TCA cycle, glutathione metabolism, FOXO, MAPK, fatty acid degradation, PPAR, adipocyte factor, and fatty acid metabolism.

9. Target gene prediction for differentially expressed lncRNA

lncRNA is used as a non-coding RNA, and the functions of the lncRNA are mainly expressed in the regulation of target genes, and mainly comprise trans-action regulation (trans-regulation) of genes encoding proteins at a relatively long distance, and meanwhile, transcription activation and expression regulation are carried out on adjacent mRNA through the lncRNA: cis-acting regulation (cis-regulation). Thus, the target gene of lncRNA was studied by analysis of trans and cis.

The basic principle of LncRNA cis-acting target gene prediction considers that the function of LncRNA is related to a protein coding gene whose coordinates are adjacent, and the LncRNA positioned upstream possibly has intersection with a promoter or other cis-acting elements of a co-expressed gene, so that the expression of the gene is regulated at the level of transcription or post-transcription; lncRNA located downstream of the 3' utr or gene may be involved in other regulatory actions. We therefore annotate lncRNA that were annotated as being located in "unknown regions" in previous assays, which are likely to intersect with the region in which the cis-acting element is located if they are located upstream or downstream of a gene, thereby participating in the process of transcriptional regulation. And (3) analyzing the lncRNAtrans-action target gene, predicting the trans-action target gene of the differentially expressed lncRNA through the interaction relationship between the lncRNA and the mRNA sequence, and predicting the target gene by an expression quantity correlation analysis or a co-expression analysis method of the lncRNA and the protein coding gene among samples. When the number of samples > =6, analyzing the correlation between lncRNA and the protein coding gene by using a Pearson correlation coefficient method, and then only retaining the maximum positive correlation result and the maximum negative correlation result; when the number of samples > =24, the WGCNA method was used to cluster genes with similar expression patterns to obtain different co-expression modules, and the function of lncRNA was studied according to the gene function analysis within the modules.

And predicting the cis-acting and trans-acting target genes of the differentially expressed lncRNA, and combining the mRNA data of the differential expression to improve the accuracy of the prediction. The UCSC Genome Bioinformatics software was used to find differentially expressed mRNA within 300kb upstream and downstream of the differentially expressed lncRNA as the cis-acting target gene for the differentially expressed lncRNA. The BLAST software (E < E-5) and RNAplex software (-E-20) were used to predict the trans-acting target gene for differentially expressed lncRNA. Based on the mode of action of lncRNA, target genes that differentially express lncRNA are predicted by cis-action and antisense action. Based on the location of lncRNA on the chromosome, 2917 coding genes were found in the 10k upstream and downstream of 732 newly predicted lncRNA. Meanwhile, 2187 encoding genes were found in 1350 known lncRNA. RNAplex software was used to predict the antisense target gene of the lncRNA, and a total of 84 lncRNAs were identified as having a base complementary pairing relationship with the encoding gene. Combining the results of differential expression analysis and target gene prediction between the two groups, several pairs of differentially expressed lncRNA and genes may have potential regulatory relationships.

Xr_001191699.1 and target genes ACVR1B, SORBS1, gsk3β associated with fat metabolism were found by analysis.

10. Fluorescent quantitative PCR verification of differentially expressed lncRNA

6 tissue samples are respectively collected from high and low RFI duck liver tissues to carry out fluorescence quantitative verification of candidate genes GSK3 beta and ACVR1B, SORBS1, and the specific steps are as follows.

6 tissue samples were collected from high and low RFI duck liver tissue, each gene was set with 3 biological replicates, and primers were designed as follows:

the specificity of the designed primer sequence is detected by BLAST in NCBI, the mallard-GAPDH gene is used as an internal reference, and the expression level of the gene is verified by a qRT-PCR method.

qRCR system

qPCR cycle conditions

The PCR system was mixed well, centrifuged after reaction, and split into 96-well plates for qRT-PCR reaction and analysis on ABI7500 (ThermoFisher Scientific, USA).

2 ^–ΔΔCt Calculating the relative expression quantity of genes among all groups of samples by using a method, carrying out statistical analysis on the relative expression quantity by using t-test, and carrying out P<0.05 indicates significant differences.

As shown in fig. 3, xr_001191699.1, ACVR1B significantly up-regulated expression in low RFI duck liver tissue, and sodbs 1, gsk3β up-regulated expression in low RFI duck liver tissue. The results are consistent with the sequencing results, and indicate that the sequencing results of the liver tissue transcriptome of the meat ducks with the high-low RFI group are reliable.

The above embodiments are only for illustrating the technical scheme of the present invention, and are not limiting to the present invention; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Sequence listing

<110> Anhui university of agriculture

<120> an IncRNA related to residual feed intake of duck and application thereof

<141> 2021-04-23

<160> 9

<170> SIPOSequenceListing 1.0

<210> 1

<211> 4891

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tccaaacggt cctgttcggc accacagcgc tcaccccggg gcgcttgatc ttctggaaac 480

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taaggtctga ctcactgtgc agctgggaaa ctggaagctg agtgctgcgc cctcacaggg 600

gctgaacagc ctgaagtgac agtaaccacg ctgcactcgt catgttggat ctgctctgat 660

tcaccgcttt atcaaatgtc cctgagatac aaactcaata aaccaccaac caccagaagg 720

accagaagtt tttgcctctt ttggtaaaac aactttcctc cacctgaaat gcaccaccag 780

gcattccagt cgtcttgtag agttgcgctt gaaattggat ttatttattg gcctcgagca 840

tttgatgcca acacaaatca aattttctaa ttttctgatg aagttctttc taatagctac 900

gtaaacgctg gcaccgacta taagagagat gcttgagcat tattacatga caaagtgcgt 960

ggatggtctg aaaaatagca tgggtagggg gaactctgtg atagcagtaa atacgaatca 1020

caccgatgcg aaatttatcc taacggtatc tatttgagaa acgtccttta tgatgagtct 1080

gcaagttgat aatgccaatg gagaaccaac tgcatttctg tacagagaac aaggctaatt 1140

cgggagaaca gcctcacgct tacgctcctt tgtgaggctt agccttttgc tgttgacccc 1200

cagttccctc accagtaccc aaacaacccg tatcaacccc aaaactccca gataccggca 1260

cccctgctgt tctaaatgcc agtcctatcc atccacttgg ggaatgcacg ctacagacct 1320

cccagctgct ctgcttccca tcgggcaatc ctgcctgtat ttccaagtaa tcattaacca 1380

atgaactctc caaaacctgt ccgtttcaat cacagcgtct tctccgctac tagaaaacca 1440

cacaagtata cttaaaggag ggctattatt cgtttgaagg gacagaaagc tattaaccac 1500

ctccaatttc tgttagtctt ttcacagatt atttccccag ctagggctac catcagaaaa 1560

cccattagca tataagaagg gcatctgtta agctttcatc ctcaatgtca aaaatgcacc 1620

aaattaaacc catttgagga aatcatgagt gagctggttg cttttttaga ctgaaaaatt 1680

ctcttttgac aacattgcaa agttcgcggt tatttttggc ctcttggctg aagaaatgac 1740

ctccctttag actccgcaaa tataaataga attgacactt taatgatcag ttttcacagg 1800

caagaataat ccagtatccc aggcattaat attgttccaa atctggatgc agaggatggt 1860

tttacagctt ttgtgactca aaattaaagc gtccccgtca catctaagat cttggggagg 1920

gtgcctgaaa ataagaagca tctgagggga attactgaag tgttatctct ctgaaatgcc 1980

ataaggtggc agtgaattag aaaatcggtg ctatttctgg gtagaacaaa gcttttaaaa 2040

gctcggtagc ggtggtgggg aggctttatt tttttttgtt tgtttgtctg ggggctcgca 2100

tttgtctgct ttcatttcag gtatcaccct gaacatcaca tccaatcgtc ttaatacact 2160

gagcagggct gctccaatgt gcacaagcac tattactggg tcattccaac atcagccacg 2220

gtgaagaggg caggagggtg gtgtattgtg cctcggtaag gctgataagg atgatcgtgc 2280

aaggctgctg tgaactttgg tacgccagtg catcacactc agattaatgg atctaccagt 2340

ttggcatcta attccttaat attgccaagg gaattagcca agaaacaacg cagcgagtgg 2400

aaaatcagag attgtaacag taacaaattt accgactgct caggcgttaa tgattagaca 2460

ataaatcctg aaaagtggtt ttgatttttg tcccagaaca aaatccacac caatctatgt 2520

ttagaacaca gcacttgcgt ttgtttttct gagcgggaca gaggttttta atcctgagaa 2580

cccagcattt tgcaacgatt tcctgtcaca gaacatgtca ccaccagcaa ttgcttcaga 2640

gaacagctga tgttttgccc atctgggtgt gcgtgggcgc acctaggcct tgcaggatgc 2700

caggtaggtt cgccaggacc aacagagcca ggtggtccga gcactttaat gcaacgggag 2760

ctacaaaaaa cttctgcttc ccagttttgc agtgcatcat aagaagccaa gacaccccgt 2820

gcaggttcac ggggacattg cgacacacag aaatcatctg tgactcactc agtgcaccct 2880

gagagagaca tcgggtttca caccgtgcca ggagaacgtg tggctaagtt tgatgtcttc 2940

ccttgctcca gcatttgtca gaccacacgt cattcataaa taaccctgga aaaacagcgc 3000

ccagagccag agaagccgtg tcagtctggt ggtcctttga caagccttcc agatatttgt 3060

gctcccgaaa gcaaagatga tgcctgttcc tgcaggactc tgaaaaggtt taaatccaga 3120

aaagtggtgg ggtcttctat gtgcatcatc tacctccttc acatcagatt cactcgagcc 3180

acccatgaca gacatctatc tgctttctgt cgtgcttccc agtgctccca gcaggcctgt 3240

ctgtttctgc aaaggggata ccaaggagca gatgggtcac tctctcagca agaaattaaa 3300

attaaaccca gtggcgttgt gttaacagtg gtccccagag cttaccgggg ctctctacaa 3360

cacaatatta gaggaagggc ccatgtgtgt cccgtgctca ctatttccac acctccccag 3420

tcgactgcca acctgtctgt ccaaataaaa ggagttaaag ccagcctgat cccagaggcc 3480

atcagtccct tcaagcaaat cttgtaggtc tggaaagcct tggcttgaga gactgggagc 3540

caggcccgac agccaacaaa cgagattttt ctccacttcc cccttcaaac agcgggggga 3600

tgttgcagca gcgtagtgag acttgctgtg cctccacaca aacacaggaa ggggattcac 3660

tcatccctac tgaaagagtt ttcagaaatt gagcaccgct tacagtgcaa ggtgccaaca 3720

gaaggagaaa gggccttcgg gaatagacgt ctgcatggtg aaaagaagca aagaaagcca 3780

tcccgtgcct ggcacagcct gcctgggctg ctggcctcac tgcaagggct ccctgcaggc 3840

tgagatcacc acccatggct gcaatgaaat gctcatccag cccttgccgg agcatctgct 3900

cttactctag ctgaaaataa ccacatgggc atggctctcc ctgggggagg aaggagaaga 3960

tggctcttgg tgggttacag ccacgcttga tatccacagg gcagactgtg ggaagcgtgc 4020

tttgcagtat gctttgtgca tagcaacatg caacatccag aaaaatgttt gagaagggct 4080

ataagaactt ggtcatacac cagccacagc agcgagcccg ctgtgttgtt acctggggtc 4140

ttcagttcgc agcctggaat taacggtggt gattctcctc aattttgtcc tattttaaga 4200

ggtgacggaa tggaaagtaa gcggaacccg tgagacgatg cgtagataat tacgaggtgg 4260

ccagaagtgt aacagtattt tcattctgct cattgagttt ttgccaagca atctaattaa 4320

tttaatgatc cctgtccctg gctgctcgct aatcaccatc acacactact tccttcatat 4380

ctggcttgca aacggttact aattgttgct gcgtcttgcg ttgtacatct ggtgatgaca 4440

gctgccgtgg ccttgagatg gagagacagc tgtttttaaa aggagacttc cctcccagat 4500

ctctaaaaca tctggaaaag tgtggcgttg aaacccagtt gctgaatatg aaaacttatc 4560

agataggtgc acgggctaaa gggaaggggt ctgcaaacac aaagcttaaa agtgtttcac 4620

tgcaactgga gtggaagaga ctcactgctg tgtctttaaa gcacaggtcc tggatgtttg 4680

ccataaaaaa tgcctatttc ccctaattcc ccgttgaccg cttggcatgg caaaatattt 4740

tacactctgc acagcagagc acacctccta atttgatcaa actaacaact cacattactt 4800

ttgcttcagc gtgtcggacg taattaaaaa acctttttac gagaaaaggg ttcgggatca 4860

ggcagcaaga tcttgctggc tgcaaacaag a 4891

<210> 2

<211> 16

<212> DNA

<213> Anas Platyrhynchos

<400> 2

ccagcattct cgtctt 16

<210> 3

<211> 19

<212> DNA

<213> Anas Platyrhynchos

<400> 3

cttatagtcg tgccagcgt 19

<210> 4

<211> 20

<212> DNA

<213> Anas Platyrhynchos

<400> 4

gggcgagata aacctgcact 20

<210> 5

<211> 20

<212> DNA

<213> Anas Platyrhynchos

<400> 5

gctgtagcgt ttgtaggggt 20

<210> 6

<211> 20

<212> DNA

<213> Anas Platyrhynchos

<400> 6

attgcaccga accaaagggt 20

<210> 7

<211> 20

<212> DNA

<213> Anas Platyrhynchos

<400> 7

cccagtagac caagcccaaa 20

<210> 8

<211> 20

<212> DNA

<213> Anas Platyrhynchos

<400> 8

gagctggaac tgagggatgg 20

<210> 9

<211> 20

<212> DNA

<213> Anas Platyrhynchos

<400> 9

gaaggtgccg aattgccttg 20

Claims (5)

1. The application of the lncRNA related to the residual feed intake of the ducks is characterized in that the lncRNA is named XR_001191699.1, the sequence of the lncRNA is SEQ ID NO.1, and the application is that the lncRNA is used for predicting or assisting in predicting meat ducks with high feed utilization rate; or the application is that the lncRNA is used for breeding meat ducks with different feed utilization rates.

2. Use of a primer for amplifying the lncRNA of claim 1 in the preparation of a reagent for detecting meat duck feed utilization.

3. The use according to claim 2, wherein in the reagent the primers detect the expression level of the lncRNA by nucleic acid amplification technique and the pair of primer sequences used for nucleic acid amplification are SEQ ID No.2 and SEQ ID No.3.

4. The use of claim 3, wherein the sample detected by the reagent is liver tissue.

5. The use of claim 4, wherein the agent is used to predict or assist in predicting high feed utilization meat ducks; or used for breeding meat ducks with different feed utilization rates.

Images