A transcriptome analysis reveals that hepatic glycolysis and lipid synthesis are negatively associated with feed efficiency in dly pigs

ABSTRACT Feed efficiency (FE) is an important trait in the porcine industry. Therefore, understanding the molecular mechanisms of FE is vital for the improvement of this trait. In this

study, 6 extreme high-FE and 6 low-FE pigs were selected from 225 Duroc × (Landrace × Yorkshire) (DLY) pigs for transcriptomic analysis. RNA-seq analysis was performed to determine

differentially expressed genes (DEGs) in the liver tissues of the 12 individuals, and 507 DEGs were identified between high-FE pigs (HE- group) and low-FE pigs (LE- group). A gene ontology

(GO) enrichment and pathway enrichment analysis were performed and revealed that glycolytic metabolism and lipid synthesis-related pathways were significantly enriched within DEGs; all of

these DEGs were downregulated in the HE- group. Moreover, Weighted gene co-expression analysis (WGCNA) revealed that oxidative phosphorylation, thermogenesis, and energy metabolism-related

pathways were negatively related to HE- group, which might result in lower energy consumption in higher efficiency pigs. These results implied that the higher FE in the HE- group may be

attributed to a lower glycolytic, energy consumption and lipid synthesizing potential in the liver. Furthermore, our findings suggested that the inhibition of lipid synthesis and glucose

metabolic activity in the liver may be strategies for improving the FE of DLY pigs. SIMILAR CONTENT BEING VIEWED BY OTHERS IDENTIFICATION OF CANDIDATE GENES THAT SPECIFICALLY REGULATE

SUBCUTANEOUS AND INTRAMUSCULAR FAT DEPOSITION USING TRANSCRIPTOMIC AND PROTEOMIC PROFILES IN DINGYUAN PIGS Article Open access 18 February 2022 GLOBAL ANALYSIS OF THE ASSOCIATION BETWEEN PIG

MUSCLE FATTY ACID COMPOSITION AND GENE EXPRESSION USING RNA-SEQ Article Open access 11 January 2023 TRANSCRIPTIONAL RESPONSE TO AN ALTERNATIVE DIET ON LIVER, MUSCLE, AND RUMEN OF BEEF

CATTLE Article Open access 13 June 2024 INTRODUCTION Feed efficiency (FE) is an important economic trait in the porcine industry, as feed cost accounts for approximately 70% of the total

production cost1. Therefore, improving FE can greatly promote the economic benefits of pig production. The main indicators for measuring FE are residual feed intake (RFI) or feed conversion

ratio (FCR). RFI is defined as the difference between the animal’s actual feed intake and its predicted dry matter intake (DMI) based on production needs, to specifically capture the

efficiency of feed use independent from production needs2. A lower RFI value indicates a more efficient pig. FCR is the ratio of feed intake to the average daily gain (ADG) during a

specified period. Compared with FCR, RFI can more accurately reflect the differences of FE in pigs with different body weights and growth rate2. Therefore, RFI is preferred as the selection

indicator. Because of the strong genetic correlation with RFI (R equals 0.76–0.99)3, FCR can be used as a reference indicator to verify the selection based on RFI, which can measure FE more

intuitively. To date, based on genome-wide association analysis (GWAS) of pigs, some SNPs and candidate genes that might affect FE have been identified. SNPs located on SSC7, SSC13, SSC14,

and SSC17 and candidate genes, such as _MBD5_, _GTF3C5_, _HMGA2_, _PITX2_, and _MAP3K14_, were reported to be associated with FE in the previous studies4,5,6,7. However, GWAS studies are

limited to finding chromosomal regions or preselected genes that affect FE8 and finding candidate genes and pathways that affect FE is difficult. Instead, RNA-seq technology can

quantitatively measure gene expression in individuals to screen differentially expressed genes (DEGs)9,10. By analyzing the DEGs and related biological pathways, candidate genes and pathways

that affect FE can be identified. RNA-seq has been used to research FE in animals, including pigs, cattle, and poultry, and muscle, liver, and adipose tissues have been used as research

materials11,12,13. Recently, a growing number of transcriptome analysis has focused on the liver tissue of pigs to identify candidate genes and pathways associated with FE14,15,16. In

mammals, the liver plays a prominent and central role in regulating the metabolism and distribution of nutrients. On the one hand, macronutrients, such as carbohydrates, lipids, and

proteins, are metabolized in the liver17,18. On the other hand, the liver can synthesize and store nutrients, as well as release nutrients into the blood19. Several studies have revealed

that lipid metabolism, such as fatty acid synthesis, lipogenesis, and steroidogenesis, in the liver tissue was altered in FE-divergent pigs14,20,21. In addition, glucose metabolism and

energy metabolism in the liver have been reported to be essential for the regulation of FE in pigs, and lower glycolytic potential and energy loss were found in high-FE pigs15. Many

transcriptome studies focused on purebred pigs (including Yorkshire and Duroc) and crossbred pigs, such as Large White × (Landrace × Pietrain) and Maxgro × (Landrace × Large White) pigs,

have made some progress in identifying candidate genes and pathways linked with FE14,20,21,22. However, none of the transcriptome studies have been conducted in the liver of commercial Duroc

× (Landrace × Yorkshire) (DLY) pigs, while DLY pigs are by far the largest population in the porcine industry worldwide23. As a result, in this study, we used RNA-seq technology to profile

the liver transcriptome of 6 extremely high-FE DLY pigs (HE- group) and 6 extremely low-FE DLY pigs (LE- group) to identify candidate genes and pathways that significantly correlated with

the FE of DLY pigs. Furthermore, the identified genes and pathways that affect FE can provide theoretical support for pig selection, to improve the feed efficiency and economic benefits in

commercial pig production in the future. RESULTS PHENOTYPIC PARAMETERS IN PIGS FROM THE HE- AND LE- GROUPS Six extremely high-FE pigs had the phenotypic parameter of RFI = −0.18 ± 0.08 and

FCR = 2.19 ± 0.08, while six extremely low-FE pigs had the phenotypic parameter of RFI = 0.14 ± 0.09 and FCR = 2.68 ± 0.05. The phenotypic details of the HE- and LE- groups are shown in

Table S1. The FCR and RFI of the HE- group were significantly lower than those of the LE- group, which are displayed in the boxplot (Fig. 1A); thus, the HE- group was more efficient than the

LE- group. A high linear positive correlation between FCR and RFI (Fig. 1B) was identified in our study, which was consistent with previous studies3. MAPPING STATISTICS SUMMARY In this

study, the mapping statistics summaries for each sample are listed in Table S2. On average, the sequencing generated 40719764 and 40985844 effective reads in the HE- and LE- groups,

respectively. Among the effective reads, on average, 93.36% and 92.81% in the HE- and LE- groups, respectively, were uniquely mapped to the reference genome, and 3.62% and 3.69% were

multiple mapped to the reference genome. A TOTAL OF 507 DEGS BETWEEN THE HE- AND LE- GROUPS In the current study, a total of 507 DEGs satisfied the criteria of |log2(Foldchange)| > 1 and

_q_-value < 0.001. Among the 507 DEGs, 53 DEGs were upregulated and 454 DEGs were downregulated in the HE- group; the 5 most significantly upregulated named genes (including _NRN1,

CXCL13, DLK1, PLB1, and LYPD6B_) and 5 most significantly downregulated named genes (including _ADAMTS19, TRPV6, NME8, ANHX, and LRRC71_) are marked (Fig. 2). The details of all DEGs with

their log2(Foldchange) and _q_-value are listed in Table S3. GO ENRICHMENT ANALYSIS OF DEGS The biological process (BP), molecular function (MF), and cellular component (CC) GO terms of 507

DEGs were identified and the details of all identified GO terms are listed in Table S4. A total of 5 GO terms were significantly enriched (_q-_value <0.05), including 1 BP_GO term and 4

MF_GO terms. All of the genes enriched in the 5 GO terms were downregulated in the HE- group (Table 1). None of the CC_GO terms was significantly enriched. The significantly enriched BP_GO

term was carbohydrate phosphorylation, which is involved in glycolysis. The most significantly enriched MF_GO term was carbohydrate kinase activity. Carbohydrate kinase includes hexokinase,

phosphofructokinase, and other kinases, which mainly catalyze glycolysis. Genes involved in the 2 terms were downregulated in the HE- group, indicating that glycolysis might decrease in the

liver of the HE- group. The remaining 3 MF_GO terms were related to guanyl-nucleotide exchange factor activity, which exchanges GDP for GTP24, and the DEGs enriched in these terms were

downregulated in the HE- group. PATHWAY ENRICHMENT ANALYSIS OF DEGS Our results showed that 25 significantly enriched pathways (_q-_value <0.05) were enriched in the Reactome or KEGG

database, 24 pathways were enriched in the Reactome database, and one pathway was enriched in the KEGG database (Table S5). The top 10 significantly enriched pathways and the genes contained

in each pathway are listed in Table 2. Among the 10 pathways, 6 pathways were related to carbohydrate metabolism, including metabolism of carbohydrates, glucose transport, glycolysis,

hexose transport, glucose metabolism, and starch and sucrose metabolism; the other 4 pathways were correlated with lipid synthesis, including lipid and lipoprotein metabolism, SREBP

activation gene expression, SREBP regulation of cholesterol biosynthesis and phase 1 - functionalization of compounds. Most of the genes involved in the 10 pathways were downregulated in the

HE- group. These results indicated that decreased lipid and glucose metabolism activity might occur in the liver of the HE- group. PROTEIN-PROTEIN INTERACTION (PPI) ANALYSIS According to

Protein-protein interaction (PPI) analysis, a network of some of the named genes was visualized to explore the interaction of them with each other (Fig. 3). The network diagram was centered

on the _ACACB_ gene, which had the largest degree, and 32 DEGs were directly or indirectly related to it. Genes highlighted in green (_GCK_, _HK3_, _ENO2_, _PFKFB1_, and _PFKFB4_) are

involved in glycolysis, and genes highlighted in red (_ACACB_, _SCD_, _FASN_, _HSD17B1_, and _CYP21A2_) are involved in lipid synthesis. All genes highlighted in green or red color were

downregulated in the HE- group, indicating that glycolysis and lipid synthesis might be reduced in the liver of the HE- group. WEIGHTED GENE CO-EXPRESSION ANALYSIS (WGCNA) AND ENRICHMENT

PATHWAYS OF MODULES CORRELATED TO FEED EFFICIENCY TRAITS WGCNA was conducted to identify gene co-expression modules that are correlated with the trait of interest (HE, LE, RFI, and FCR). A

total of 18 co-expressed gene modules were identified and named by different colors (Fig. 4A). The list of genes in these modules was presented in Table S6. Among them, two-thirds (12/18)

are negatively correlated and one-third (6/18) are positively correlated with HE- group. This may imply that high feed efficiency may be associated with a lower level of certain biological

processes. Two of these modules were significantly positively associated with RFI and FCR, namely the MEcyan module (r = 0.72, p = 0.02; r = 0.74, p = 0.01) and the MEpurple module (r = 

0.68, p = 0.03; r = 0.66, p = 0.04). The MEcyan module (r = −0.72, p = 0.02) and MEred module (r = −0.69, p = 0.03) were negatively correlated with HE- group, while positively correlated

with LE- group. The MEcyan module clustered 46 genes, and 56 genes were clustered in MEpurple module, while 163 in MEred (Fig. 4B). The functional enrichment analysis for the three modules

significantly correlated with FE-related traits was conducted in KEGG and Reactome database and were presented in Table S7. The MEcyan and MEpurple module identified 20 significantly

enriched pathways in KEGG database, while 4 in Reactome database (p.adjust <0.05) (Table 3). However, there was no significantly enriched pathway in MEred. Moreover, none of the

significantly enriched terms were identified in GO analysis in the MEcyan, MEpurple and MEred modules (Table S8). Oxidative phosphorylation and thermogenesis were the most significantly

enriched pathways identified in KEGG database, which contained 12 genes and related to energy consumption and negatively correlated with HE- group (Table 3). Three of four significantly

enriched pathways enriched in Reactome database were correlated with energy metabolism, including “The citric acid (TCA) cycle and respiratory electron transport”, “Respiratory electron

transport”, and “Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins” (Table 3). QUANTITATIVE REAL-TIME PCR VALIDATION OF SIX

RANDOMLY SELECTED DEGS The reliability of the DEGs identified by RNA-seq was validated by qPCR. Six DEGs (_ACIN1_, _ACSS2_, _CCL21_, _HAMP_, _LSG1_, and _SAFB2_) were randomly selected for

qPCR. Moreover, all of 12 individuals from the HE- and LE- groups were selected for qPCR. A significant correlation (_P-value_ < 0.05) between the gene expression data calculated by

RNA-seq and the gene expression data calculated by qPCR was found in 5 selected DEGs (_ACIN1_, _CCL21_, _HAMP_, _LSG1_, and _SAFB2_) (Fig. 5). Although the _P-value_ of _ACSS2_ was more than

0.05, it had a trend line similar to the other 5 selected DEGs. These results revealed a significant correlation between the two measures and confirmed the reliability of the gene

expression data identified by RNA-seq. DISCUSSION In this study, the DEGs, at the mRNA level, in the liver of the HE- and LE- groups were identified. Furthermore, the relevant pathways and

candidate genes affecting the different FEs between the two groups were explored, illuminating the metabolic pathways and molecular mechanisms associated with the divergence in FE. We found

that decreased glycolytic, energy consumption and lipid synthesizing potential in the liver may be associated with improved feed efficiency in DLY pigs. The PPI analysis revealed some

candidate genes associated with glycolysis (including _GCK_, _ENO2_, _HK3_, _PFKFB1_, and _PFKFB4_). Combining the results of GO and pathway enrichment analysis, _HK3_, _PFKFB1_, _GCK_, and

_PFKFB4_ were enriched in the most significantly enriched GO terms and pathways, all of which were downregulated in the HE- group. The _GCK_ gene is involved in glycolysis and encodes

hexokinase 4 that catalyzes glucose transfer to glucose-6-phosphate25,26. The overexpression of _GCK_ increased glucose uptake and promoted glucose utilization in the liver27. Moreover,

hepatic _GCK_ mRNA expression was positively associated with the mRNA expression of lipogenic enzymes (_ACACB_ and _FASN_) and de novo lipogenesis in the liver26. Thus, the hepatic

glycolytic process probably stimulated hepatic lipid synthesis. The _ENO2_ gene increases glucose uptake in the liver and participates in hepatic glycolysis by converting 2-phosphoglycerate

into phosphoenolpyruvate28. The _HK3_ (hexokinase 3) gene, one of the four hexokinase family members, is a catalytic enzyme in glycolysis29. Similarly, the _PFKFB1_ and _PFKFB4_ genes encode

key enzymes involved in glycolysis30,31. In a previous study, Reyer found three adjacent SNPs in SSC13, two of which were located beside and in the _PFKFB4_ gene in pigs, and these SNPs

were significantly correlated with RFI32, suggesting that _PFKFB4_ might be a candidate gene affecting FE. Liver glycolysis is one of the most important metabolic pathways regulating FE and

is decreased in high-FE beef cattle33. Moreover, a study showed that genes involved in glycolysis were downregulated in high-FE pigs34. Carbohydrate phosphorylation is the first step in

glycolysis, in which glucose is phosphorylated to glucose-6-phosphate (G6P) by hexokinase35. In the fed state, G6P is metabolized to generate pyruvate through glycolysis. Pyruvate is the

main glycolytic product and links glycolysis to lipogenesis. Pyruvate can be completely oxidized in mitochondria to generate ATP through the citric acid cycle and oxidative

phosphorylation25. Glycolysis produces ATP to provide energy for growth, which is an extremely inefficient way of producing energy. In the complete oxidation of pyruvate, approximately 40%

of the energy produced is used to synthesize ATP, while the remaining energy (approximately 60%) is lost as heat energy36. Combining with the results of WGCNA, oxidative phosphorylation,

thermogenesis, and energy metabolism-related pathways (including TCA cycle, respiratory electron transport, ATP synthesis, and heat production) were significantly enriched in MEcyan and

MEpurple modules, and all of these biological processes occurred in mitochondria and are related to energy consumption25,37. The MEcyan and MEpurple modules were negatively related to HE-

group, indicating that higher efficiency in HE- group might due to lower energy consumption in the liver. These results implied that decreased rates of hepatic glucose metabolism, oxidative

phosphorylation, thermogenesis, and energy metabolism might result in fewer energy losses in the HE- group. Hence, we hypothesized that the HE- group had more efficient energy utilization

and a higher FE because the decreased glycolysis process and reduced energy losses. Correspondingly, previous analyses of FE revealed that the genes associated with the glycolytic pathway

and mitochondrial activity were downregulated in the liver and muscle tissues of high-FE pigs. Less energy was lost due to the decreased glycolytic potential and mitochondrial activity in

the liver, which might result in higher energy efficiency in HE- group19,34,38,39,40. Furthermore, pyruvate can provide a carbon source for lipogenesis in the liver. The conversion of

glucose to lipids in the liver results in an approximately 23% energy loss41, which is much higher than the amount of energy lost by protein deposition in the muscle42. Previous studies

focusing on muscle tissue revealed that high-FE pigs accumulated more muscle mass compared with low-FE pigs42,43, which indicated that high-FE pigs needed to consume more glucose in the

muscle to generate ATP for protein deposition. In our study, the HE- group had lower glucose metabolic and lipid synthesizing potential in the liver, so we speculate that more glucose is

consumed in the muscle tissue to provide ATP for protein deposition in the HE- group. Protein synthesis has a higher energy efficiency than lipid synthesis; thus, the HE- group exhibited

higher feed efficiency. Our findings are consistent with previous studies and support the assumption that the HE- group had less heat production and greater energy utilization related to

decreased glycolytic processes in the liver, which may have positive effects on FE in commercial DLY pigs. The liver is the main organ that synthesizes fatty acids and other lipids. The

precursor substances for the synthesis of fatty acids are mainly derived from glucose metabolism, especially glycolysis25. In our study, lipid synthesis-related pathways were significantly

enriched in the pathway enrichment analysis (among the top 10 significantly enriched pathways, 4 pathways were related to lipid synthesis), and genes involved in lipid synthesis, including

_ACACB_, _CYP21A2_, _CHKA_, _SCD_, were downregulated in the HE- group. The _ACACB_ gene is a known candidate gene for lipid metabolism and is related to the _de novo_ synthesis of fatty

acids and other lipids44. Both _CYP21A2_ and _CHKA_ are involved in the synthesis of cholesterol, steroids and other lipids and are candidate genes that affect lipid synthesis in pigs45,46.

Lipid synthesis in the liver was negatively related to the FE trait in pigs, which has been reported in previous studies47,48. Several studies found that genes involved in lipid metabolisms,

such as fatty acid, steroid, and cholesterol biosynthesis, were downregulated in the liver of low-RFI (more efficient) pig16,49,50 and cattle51. The PPI analysis indicated that the genes

associated with lipid synthesis (including _ACACB_, _SCD_, _FASN, HSD17B1_, and _CYP21A2_) were candidate genes that affected the FE in commercial DLY pigs. The _SCD_ gene, which encodes

stearoyl CoA desaturase and promotes the synthesis of fatty acid in pigs52, is a candidate gene that correlates with FE14. Previous studies revealed that the _SCD_ gene was downregulated in

more efficient pigs52,53,54, and the high-FE pigs had a reduction of lipid synthesis and accumulation. The _FASN_ gene is a fatty acid synthase that plays an important role in the _de novo_

synthesis of fatty acids55 and was downregulated in high-FE pigs52,53,54. In the current study, the _SCD_ and _FASN_ genes were downregulated in the HE- group compared with the LE- group.

Moreover, by analyzing the top 5 significantly upregulated DEGs, we found that _DLK1_ was related to lipid metabolism, and _DLK1_ was upregulated by a log2 (Foldchange) of 3.81 in the HE-

group. Previous studies indicated that the overexpression of _DLK1_ would suppress lipid synthesis, and this gene was upregulated in more efficient pigs and cattle51,56. Consistent with

previous studies, our results indicated that the increased FE in the HE- group might be associated with the reduction of lipid synthesis and accumulation in the liver. CONCLUSION In this

study, we investigated the liver transcriptome of 6 extremely high feed efficiency pigs (HE- group) and 6 extremely low feed efficiency pigs (LE- group). A total of 507 DEGs were found

between the HE- and LE- groups. GO and pathway enrichment analyses revealed that the DEGs were mainly enriched in glycolysis and lipid synthesis. The vast majority of DEGs involved in

glycolysis and lipid synthesis were downregulated in the HE- group, such as _SCD_, _ACACB_, _FASN_, _GCK_, and _ENO2_. The expression patterns of these genes suggest that the related

pathways might influence feed efficiency in pigs. Moreover, the results of WGCNA indicated that oxidative phosphorylation, thermogenesis, and energy metabolism-related pathways were

decreased in HE- group, which resulted in higher energy efficiency in it. Briefly, the results indicated that the HE- group may have decreased glycolytic, energy consumption and lipid

synthesizing potential in the liver, thereby increasing energy efficiency. Our findings provide an understanding of the molecular mechanisms in the liver in regulating the feed efficiency of

DLY pigs. The key pathway and candidate genes identified in this study are potentially useful for improving porcine feed efficiency. METHODS ETHICS STATEMENT The experimental procedures

used in this study met the guidelines of the Animal Care and Use Committee of the South China Agricultural University (SCAU) (Guangzhou, People’s Republic of China), and every effort was

taken to minimize animal suffering. All animal experiments in this study were approved by the Animal Care and Use Committee (ACUC) of the SCAU (approval number SCAU#0030). ANIMALS AND

TISSUES In this study, a total of 225 commercial Duroc × (Landrace × Yorkshire) sows, provided by Guangdong Wen’s Foodstuffs Group Co., Ltd., (Yun fu, China), were selected as the

experimental animals. During the experiment, the pigs were housed in an environment-controlled shed, and feed and water were offered ad libitum. The phenotypic data of 225 sows were recorded

by the Osborne FIRE pig performance test system (Osborne, KS, USA). The recording period was approximately 12 weeks, during which time the weight of the animals was measured from

approximately 30 kg of body weight (BW) to 100 kg BW. Each individual had a unique electronic identification tag on its ear that could be captured by an automatic feeder. Each individual’s

feeding time, feeding duration, feed consumption, and body weight were recorded at each visit to the feeder. The standard A-scan and contact B-scan ultrasonography were used to measure back

fat (BF) of pigs in approximately 100 kg BW. The FCR and RFI were calculated for each individual during the trial. The RFI calculation method was similar to that of Cai _et al_.57. The RFI

was estimated by the linear regression of DFI on metabolic BW at mid-test (MWT), average daily gain from 30 to 100 kg (ADG), and BF. MWT was equal to [(BW at on-test + BW at off-test)/2]

0.75. Then, the RFI values of all individuals were ranked, and 6 pigs with extremely high-FE and 6 pigs with extremely low-FE were selected and designated as the HE- group and LE- group,

respectively. Finally, a correlation analysis was conducted to calculate the correlation between RFI and FCR to verify the selection of 12 individuals. At the end of the experiment, all 12

individuals from the HE- and LE- groups were slaughtered, and liver tissues (the middle portion of the left lateral lobe) were collected immediately. These samples were rapidly frozen in

liquid nitrogen and stored at −80 °C. RNA EXTRACTION AND SEQUENCING Total RNA was extracted from all 12 liver tissue samples using Total Kit II (OMEGA, USA) and the procedures and standards

were performed according to the manual. The quantification and quality of RNA were assessed by a NanoDrop2000 microspectrophotometer (Thermo Scientific, Wilmington, DE, USA). The

concentration of the mRNA ranged from 624 to 1218 ng/μl, and the absorbance (A260/280) of all samples was between 1.8 and 2.1. Besides, an Agilent 2100 Bioanalyzer device (Agilent

Technologies, Santa Clara, CA, USA) was used to assess the integrity of the RNA. The RNA integrity value (RIN) of our samples ranged between 6.2–8.8. The cDNA library was constructed using a

TruSeq RNA Library Prep Kit v2 (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions, where mRNA was purified and enriched from 1 μg of each of the total RNA samples

and then fragmented. After quality control, the libraries were sequenced on an Illumina HiSeq 4000 platform. READ ALIGNMENT AND DIFFERENTIAL EXPRESSION ANALYSIS The raw reads of each sample

were discriminated based on the indexing adaptors. The FastQC58 software was used to evaluate the quality of the reads. Then, the adapter sequences and low-quality reads (the reads that

adapter contamination is greater than 5 bp, Q20 ratio does not reach 85% or containing N ratios greater than 5%) were filtered out, and the high-quality reads were available for downstream

analysis. The STAR: ultrafast universal RNA-seq aligner STAR_2.3.059 was used to map the sequencing reads with the reference genome (_Sus scrofa_ 11.1), and all the options were set as STAR

default values. The HTSeq60 software was used to generate the read count tables for further differential expression analysis. Differential expression analysis was performed between the HE-

and LE- groups by using the Gfold (V1.1.2)61 software and the methods described by Audic and Claverie62. The Gfold (V1.1.2) software was used to count the expression level of the reads and

convert them into FPKM (Fragment Per Kilobase of exon model Per Million mapped reads). The expression fold change between the two groups was calculated by the methods described by Audic and

Claverie, and the Benjamini-Hochberg (BH) method was performed to calculate _q_-value for multiple testing. All genes were filtered by the criteria of |log2(Foldchange)| > 1 and _q_-value

< 0.001. Genes with log2(Foldchange) > 1 and _q_-value < 0.001 were defined as upregulated DEGs, while the gene with log2(Foldchange) < −1 and _q_-value < 0.001 were defined

as downregulated DEGs. GO AND PATHWAY ENRICHMENT ANALYSIS OF DEGS To explore the major metabolic pathways and cell signaling pathways related to FE, Gene Ontology (GO) and pathway enrichment

analysis were carried out by KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/anno_iden.php)63, and both the Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used for pathway

enrichment analysis. The BH method was applied to adjust the _P-value_ for multiple testing. The GO terms or pathways meeting the screening criteria (with _q_-value <0.05) were the

significantly enriched terms or pathways. PROTEIN-PROTEIN INTERACTION NETWORK CONSTRUCTION A protein-protein interaction (PPI) analysis of DEGs was implemented in the Search Tool for the

Retrieval of Interacting Genes (STRING) database. The intensity of interaction among the input genes was evaluated and the hub gene was determined according to the degree of relationship to

other genes. Then, the interaction network diagram of these genes was plotted by the Cytoscape (3.6) software64. WEIGHTED GENE CO-EXPRESSION ANALYSIS (WGCNA) Gene co-expression network

analysis was performed using the R package WGCNA65. Detailed steps are as follows. (1) Data input and cleaning: The gene expression matrix (genes that expression with variance variation

accounting for top 30%) and the phenotypic matrix (including HE, LE, RFI, and FCR) for 12 individuals were used for subsequent analysis. In the phenotypic matrix, the RFI and FCR values in

the matrix came from the RFI and FCR original values of each of 12 individuals. Six individuals belonging to high-FE group were marked as 1 and the remaining 6 as 0 for the “HE” values of

the phenotypic matrix. Similarly, six individuals belonging to low-FE group were marked as 1 and the remaining 6 as 0 for the “LE” values of the phenotypic matrix. Gene and individual

quality control were performed with the default settings of the R package WGCNA. (2) Best soft-Threshold Confirmation: Using the pickSoftThreshold function of the R package WGCNA to analyze

the expression matrix obtained in the first step, the most appropriate soft threshold value was 7. (3) Network construction and module detection: Using the blockwiseModules function to

analyze the expression matrix obtained in the first step for module detection with power = 7 and mergeCutHeight = 0.2, the gene set was divided into 18 modules. (4) Relating modules to

phenotypic traits and identifying important genes: Correlation analysis and significance test were performed on the phenotypic matrix obtained in the first step and the modules obtained in

the third step. Modules with statistically significant (_p_-value <0.05) correlations were selected for downstream analysis. (5) Functional annotation of significant module genes: gene

pathway analysis and gene function annotation were analyzed using R package clusterProfiler66 with default parameters. Biological terms were considered significant if the adjusted _p_-value

was less than 0.05. REAL-TIME QUANTITATIVE PCR To validate the results of the differential expression analysis from the RNA-seq data, the relative expression levels of 6 randomly selected

DEGs were detected by real-time quantitative polymerase chain reaction (qPCR) technology. A total of 6 HE- and 6 LE- pigs were selected for qPCR. RNA samples were prepared by the methods

mentioned above. Reverse transcription was performed by the PrimeScriptTM RT reagent kit (Takara, Japan). Then, all qPCR reactions were performed in a QuantStudioTM 7 flex device (Invitrogen

Life Technologies, Carlsbad, CA, USA) following the manufacturer’s instructions and three biological replicates were used in the experiment. The parameters used in the qPCR reaction were:

denatured at 95 °C for 5 min; performed 40 PCR cycles (95 °C, 10 s; 60 °C, 15 s; 72 °C, 20 s); dissolution curve (95 °C, 15 s, 55 °C, 15 s, 95 °C, 15 s). Thereafter, the comparative Ct

method67 was performed to quantify the gene expression of 6 selected genes in 12 individuals. The primer sequences of these genes were designed by the Oligo 7.0 software, and the details of

the primers are displayed in Table S9. DATA AVAILABILITY The raw reads have been submitted to the NCBI Sequence Read Archive database (SRA) under BioProject accession number of PRJNA578377

and SRA accession number SRR10315359 - SRR10315370. REFERENCES * Teagasc. Pig Herd Performance Report 2018. Foster City, CA: Teagasc Pig Development Department.

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Protocols_ 3; https://doi.org/10.1038/nprot.2008.73 (2008). Download references ACKNOWLEDGEMENTS This study was supported by the National Natural Science Foundation of China (Grant No.

31972540), the Project of Swine Innovation Team in Guangdong Modern Agricultural Research System (Grant No. 2019KJ26), the Pearl River Nova Program of Guangzhou (Grant No. 201906010011) and

the Natural Science Foundation of Guangdong Province (Grant No. 2017A030313213). AUTHOR INFORMATION Author notes * These authors contributed equally: Cineng Xu and Xingwang Wang. AUTHORS AND

AFFILIATIONS * College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China Cineng Xu, Xingwang

Wang, Zhanwei Zhuang, Jie Wu, Shenping Zhou, Jianping Quan, Rongrong Ding, Yong Ye, Longlong Peng, Zhenfang Wu, Enqin Zheng & Jie Yang Authors * Cineng Xu View author publications You

can also search for this author inPubMed Google Scholar * Xingwang Wang View author publications You can also search for this author inPubMed Google Scholar * Zhanwei Zhuang View author

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author inPubMed Google Scholar CONTRIBUTIONS Z.W., E.Z. and J.Y. conceived and designed the experiments. C.X., X.W., Z.Z., J.W., S.Z., J.Q., R.D., Y.Y., L.P., Z.W. and E.Z. collected the

samples and recorded the phenotypes. C.X. and J.W. performed the P.C.R. C.X. and X.W. analyzed the data. C.X., X.W. and J.Y. wrote and revised the manuscript. Z.W., E.Z. and J.Y. contributed

the materials. All authors reviewed and approved the manuscript. CORRESPONDING AUTHORS Correspondence to Enqin Zheng or Jie Yang. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Xu, C., Wang, X., Zhuang, Z. _et al._ A Transcriptome Analysis Reveals that Hepatic Glycolysis and Lipid Synthesis Are Negatively Associated

with Feed Efficiency in DLY Pigs. _Sci Rep_ 10, 9874 (2020). https://doi.org/10.1038/s41598-020-66988-6 Download citation * Received: 10 December 2019 * Accepted: 01 June 2020 * Published:

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