Abstract
Social genetic effects (SGEs) refer to how the genotypes of other
individuals impact an individual’s phenotype within a population. These
effects significantly influence the feeding behavior and production
performance in pigs, though their mechanisms are not well understood.
This study examined two pig groups with extreme SGE values for residual
feed intake (RFI), analyzing their feeding behavior and the molecular
mechanisms involved using transcriptomics and proteomics analysis of
liver and ileum tissues. Pigs with higher SGE values exhibited distinct
feeding patterns, spending more time at the feeder but making fewer
visits. They consumed less overall feed but had a higher intake per
visit. Differentially expressed genes and proteins were identified in
the liver and ileum and were associated with processes such as
mitochondrial functions, oxidative phosphorylation, and cholesterol
metabolism. Integrated analysis supported these findings. Combined
transcriptome and proteome analysis identified potential key genes that
were associated with processes including mitochondrial processes,
oxidative phosphorylation, fat digestion and absorption, and
cholesterol metabolism. The results showed that pigs with differing SGE
values display different feeding behaviors and utilize distinct
molecular pathways affecting RFI. These findings offer valuable
insights into how SGEs influence feed efficiency and shed light on the
fundamental mechanisms underlying it.
1. Introduction
An individual’s phenotype is shaped by its genes, the environment, and
their interactions. While the genetic variation of an individual’s
phenotype is primarily determined by its genotype, it is also
influenced by gene expression interactions with other individuals
[[46]1]. The direct influence of an individual’s own genotype on its
phenotype is known as direct genetic effects (DGEs), whereas the
influence of other individuals’ genotypes (typically conspecifics) on
that phenotype is called social genetic effects (SGEs) [[47]2]. Social
genetic effects often manifest through interactions between individuals
within a group, such as social dominance, intraspecific competitive
ability, and mating systems in animals [[48]3]. In plants, SGEs are
observed in ecological interactions, where traits such as plant size
and fitness are influenced by neighboring plants [[49]4,[50]5]. With
the advancement of linear model analysis methods in recent years, the
estimation of SGEs has become easier and more accessible [[51]6,[52]7].
Applying best linear unbiased prediction (BLUP) models that incorporate
SGEs allows for the evaluation of the SGEs of a trait, reflecting the
influence of an individual’s genotype on other individuals. For most
traits, the SGEs’ variance accounts for 9–98% of the total genetic
variance [[53]8]. Baud et al. reported that SGEs explain up to 29% of
phenotypic variance, and their contribution exceeds that of DGEs for
several health and disease traits in mice [[54]9]. Studies have shown
that the use of traditional breeding models that do not consider the
SGEs among individuals has led to negative selection responses in terms
of body weight and mortality rate in Japanese quails [[55]10]. Also,
pigs reared in captivity may exhibit harmful behaviors such as ear
biting and tail biting [[56]11]. Similarly, overcrowding of domestic
chickens has been reported to lead to severe feather-pecking behavior,
resulting in death, indicating that the survival probability of an
individual depends on the genotype of its caged companions [[57]12].
More evidence suggests that the role of SGEs in the process of
evolution and domestic breeding cannot be ignored
[[58]13,[59]14,[60]15,[61]16].
In current pig production systems, pigs are housed in groups, and
social interaction-affected traits influence the phenotypes of group
mates, such as behavior [[62]17], growth rate [[63]18], body weight
[[64]19,[65]20], feed intake [[66]21], and average daily gain (ADG)
[[67]22]. Previous studies have demonstrated the genetic parameters of
SGEs [[68]23,[69]24] and the genetic correlation between DGEs and SGEs
in carcass and growth performance [[70]25,[71]26]. The genome-wide
association study (GWAS) approach has been employed to reveal key loci
for DGEs and SGEs on socially affected traits in pigs
[[72]27,[73]28,[74]29]. However, traditional breeding programs for
domestic pigs have relied on classical methods, such as selection based
on estimated breeding values. These methods primarily account for an
individual’s DGEs while neglecting the SGEs of other individuals
[[75]8]. Consequently, the use of classical selection methods for
traits influenced by social interactions has sometimes resulted in
unpredictable outcomes, including adverse effects like tail biting in
pigs, despite significant improvements in growth performance
[[76]30,[77]31]. Camerlink et al. assessed aggression in pigs that were
divergently selected for SGEs on growth. They found no significant
difference in aggression between the next-generation pigs selected for
low or high SGEs [[78]17]. Although SGEs are prevalent in various
traits of domesticated animals, the mechanisms and pathways associated
with these effects remain unclear.
Our previous study estimated the DGEs and SGEs for six socially
affected traits, revealing a high correlation between DGEs and SGEs for
RFI compared to the other traits [[79]32]. We found that SGEs
significantly affect the RFI in pigs. However, the intrinsic genetic
characteristics and regulatory mechanisms of SGEs influencing the RFI
from one individual to another remain unknown. This study aims to
identify key candidate genes, pathways, and fundamental mechanisms
related to SGEs on the RFI by examining the differences in the mRNA and
protein expression in the liver and ileal mucosa of Duroc pigs with
extremely low or high SGEs.
2. Methods
2.1. Ethical Approval
All the experimental procedures involving animals were approved by the
Institutional Review Board and the Institutional Animal Care and Use
Committee of the Sichuan Agricultural University under permit number
No. 2020202051. All the methods in this study were performed in
accordance with the institutional ethical standards in compliance with
the ARRIVE guidelines ([80]https://arriveguidelines.org, accessed on 4
October 2022) and all other relevant guidelines and regulations.
2.2. Animals
This study utilized 209 Duroc gilts from the breeding farm operated by
Sichuan New Hope Liuhe Pig Breeding Technology Co., Ltd. (Chengdu,
China). The exclusive use of gilts in this study minimized confounding
effects from sex-based physiological differences, hormonal influences,
and varied social behaviors, allowing for a clearer analysis of the
feeding behavior, residual feed intake, and associated molecular
mechanisms. The pigs were randomly assigned to 20 pens, with 10 to 12
pigs per pen. Each pen was equipped with feed intake recording
equipment (Osborne, KS, USA), which tracked individual feeding behavior
using the unique electronic identification tag on each pig’s ear. The
system recorded the start time of feeding, feeding duration (i.e., time
spent at the feeder per visit), feed consumption, and body weight at
each visit to the feeder. The pigs had an average initial age of 93
days, with an average weight of 33.6 kg. The recording period lasted 12
weeks, with the pigs reaching an average age of 177 days and an average
weight of 111 kg. All the pigs were fed a commercial corn–soybean diet
formulated according to body weight, in compliance with the Chinese
standard GB/T 5915-2020 [[81]33], without antibiotics or drugs. Clean
water was provided ad libitum, and a veterinarian regularly checked
their health condition throughout the experiment.
2.3. Data Collection and Social Status Evaluation
Firstly, we conducted quality control on the recorded feeding data for
each pig. We retained the data for daily feed intake ranging from 0.5
kg to 4.5 kg, daily feeding frequency of 2 to 20 times, and daily
feeding duration of 5 min to 2 h. Subsequently, we calculated the
initial body weight (W1), final body weight (W2), total time spent at
the feeder per day (TPD), total feed intake (TFI), number of visits to
the feeder per day (NVD), average feed intake per visit(AFI), average
daily feed intake (ADFI), and average daily gain (ADG) for each pig.
The backfat thickness between the 6th and 7th ribs was measured using
real-time B-mode ultrasound (MyLab™X7, ESAOTE, Genova, Italy).
The displacement success (DS) percentage was calculated using the
method outlined by Kranendonk et al. [[82]34]. The pigs were equipped
with individual electronic tags and monitored using the electronic
feeding system, which automatically recorded each visit to the feeder,
including the pig’s identification number, entry time, and exit time.
Displacement events were inferred when one pig entered the feeding
station within 2 s of another pig’s departure. The percentage of
displacement success was determined as follows (the higher the
displacement success rate, the higher the social status): [the number
of times succeeding another gilt within 2 s ÷ (the number of times
succeeded by another gilt within 2 s + the number of times succeeding
another gilt within 2 s)] × 100.
RFI and SGE Calculation
The calculation of the RFI was based on the following formula [[83]35]:
[MATH: RFI=ADFI−1.41ADG−2.83BF−110.9AMW :MATH]
(1)
where ADFI = TFI/test days, ADG = (W2 − W1)/test days. The average
metabolic body weight (AMW) for each individual was calculated using
the following formula [[84]36]:
[MATH: AMW=W21.6−W11.61.6×W2−W1
:MATH]
(2)
Subsequently, we used the DMU software (version 6) [[85]37] to
calculate the direct genetic effects and social genetic effects of the
RFI. The following full model was built for the RFI trait [[86]7].
[MATH: YRFI= Xb + Zdad +Zsas +
Wl + Vg + e :MATH]
(3)
where
[MATH: YRFI :MATH]
is the phenotypic value vector of the RFI;
[MATH: b :MATH]
is the vector of the fixed effects, including the tested year and
month;
[MATH: ad :MATH]
is a vector of a random DGE;
[MATH: as :MATH]
is a vector of a random SGE;
[MATH: l :MATH]
is a vector of random litter effects, with
[MATH: l~N(0,Ilσl2
) :MATH]
;
[MATH: g :MATH]
is a vector of random group (pen) effects, with
[MATH: g~N(0,Igσg2
) :MATH]
;
[MATH: e :MATH]
is a random residual vector, with
[MATH: e~N(0,Ieσe2
) :MATH]
; and
[MATH: X :MATH]
,
[MATH: Zd :MATH]
,
[MATH: Zs :MATH]
,
[MATH: W :MATH]
and
[MATH: V :MATH]
are the incidence matrix of
[MATH: b :MATH]
,
[MATH: ad :MATH]
,
[MATH: as :MATH]
,
[MATH: l :MATH]
and
[MATH: g :MATH]
, respectively.
[MATH: Ie :MATH]
,
[MATH: Il :MATH]
and
[MATH: Ig :MATH]
are identity matrices. The additive genetic relationship matrix (A)
used in the model was constructed from pedigree information. The
variance–covariance matrix of
[MATH: ad :MATH]
and
[MATH: as :MATH]
is denoted as:
[MATH: σ<
mrow>Ad2<
/mn>σAdsσAdsσA<
/mi>s2
⊗A
:MATH]
(4)
where
[MATH: A :MATH]
is the additive genetic correlation matrix; and
[MATH:
σAd2 :MATH]
,
[MATH:
σAs2 :MATH]
and
[MATH:
σAds :MATH]
are the genetic variance and covariance between the direct effects and
social effects. At the ith row of
[MATH: Zs :MATH]
, the group members of individual i were set to 1 and the others were
set to 0. The group was defined based on the pen allocation, as pigs in
the same pen were assumed to interact socially.
2.4. Sample Collection
The HPD95% interval of an SGE was calculated based on the estimated
[MATH:
σAs2 :MATH]
(−
[MATH:
1.96σAs/n :MATH]
< M <
[MATH:
1.96σAs/n :MATH]
) and assumed a normal distribution of the posterior SGE estimates.
Animals with SGE values outside of this interval were classified as
extreme. From these, four pigs with the highest SGEs (HS) and four with
the lowest SGEs (LS) were selected from these extreme animals. These
pigs were not explicitly balanced for pen or other factors, which may
have introduced residual confounding. The pigs were slaughtered in
accordance with the Live Pig Slaughter Guidelines (GB/T 17236-2019)
[[87]38], approved by the General Administration of Quality
Supervision, Inspection, and Quarantine of the People’s Republic of
China and the Standardization Administration of the People’s Republic
of China. The pigs were starved overnight but allowed unlimited access
to water before being slaughtered the next day. They were stunned with
carbon dioxide prior to slaughter. The ileal mucosa and liver were
aseptically separated and transferred into sterile 15 mL cryovials
immediately before being frozen in liquid nitrogen for storage. To
prevent cross-contamination, separate utensils were used for each
sample.
2.5. RNA Sequencing and Quantification of Expression Levels
Approximately 0.1 g of liver and ileal mucosa samples were collected
for RNA extraction. RNA was extracted according to the TRIzol reagent
extraction instructions. The quality of total RNA was detected by
RNA-specific agarose electrophoresis and Agilent 2100. RNA samples with
RIN values greater than seven were considered qualified. Qualified RNA
was sequenced by strand-specific library construction in BGI Company
(Shenzhen, China), and the sequencing platform was DNBSEQ and
BGISEQ-500.
FastQC was used for quality control of the raw data and filtered using
SOAPnuke. Clean reads were then mapped to the reference genome Sscrofa
11.1 using Hisat2. Differential expression analysis was then performed
using DESeq2 to identify differentially expressed RNAs. Differentially
expressed genes (DEGs) were selected based on a false discovery rate
(FDR) < 0.05. This threshold ensures biologically meaningful expression
differences while controlling for multiple testing. Hierarchical
clustering analysis was performed to examine the overall expression
patterns of the DEGs.
2.6. iTRAQ-Based Quantitative Proteomic Analysis
Approximately 50 μg of liver and ileum mucosa tissue was processed for
protein extraction. The tissue was ground, lysed, centrifuged, and
precipitated to obtain protein solutions. The protein concentrations
were then determined using the Bradford assay kit. Afterward,
approximately 100 μg of the sample was taken for labeling according to
the instructions of the AB assay kit. The labeled samples were
subjected to liquid-phase separation using the Shimadzu LC-20AD liquid
phase system. After liquid phase separation, the peptides were ionized
by a nanoESI source and entered into a tandem mass spectrometer
Q-Exactive HF X (Thermo Fisher Scientific, San Jose, CA, USA) for
data-dependent acquisition (DDA) mode detection. The main parameters
were as follows: ion source voltage was set to 1.9 kV; primary mass
spectrometry scan range 350~1500 m/z; the resolution was set to 60,000;
secondary mass spectrometry starting m/z was fixed at 100; the
resolution was 15,000. The parent ions for the secondary fragmentation
were selected as the top 20 parent ions with charge 2+ to 6+ and peak
intensity over 10,000. The ion fragmentation mode used was HCD and
fragment ions were detected in Orbitrap. The dynamic exclusion time was
set to 30 s and the AGC was set to 3E6 for primary and 1E5 for
secondary.
The MS/MS data were converted into MGF format and compared to the NCBI
porcine genus (Sus) database using the protein identification software
MASCOT2.3.02 (Matrix Science, London, UK). The iTRAQ data were
quantified using IQuant v2.0.1 software, which was first filtered with
1% FDR at the spectrum/peptide level to obtain a list of significantly
identified spectra and peptides. The peptides were then used for
protein assembly and a series of proteomes were generated. An
additional filtering step was applied at the protein level using an FDR
threshold of 1% to control the false positive rate of protein
identification. The strategy employed was to pick proteins based on
their FDR values. Hierarchical clustering analysis was performed to
examine the differential protein patterns between samples and groups.
2.7. Weighted Gene Co-Expression Network Analysis
Weighted gene co-expression network analysis (WGCNA) was performed to
identify the modules of co-expressed genes associated with the RFI.
Genes with fragments per kilobase million (FPKM) values less than 1
were excluded, and the remaining genes were used for the analysis. The
analyses were performed separately for the HS and LS groups using the
same parameters. The gene expression matrices were checked for outliers
using the goodSamplesGenes function to ensure data quality, and no
samples or genes were removed as outliers. Using the pickSoftThreshold
function, soft-threshold powers of 18 (liver) and 16 (ileum) were
selected to approximate the scale-free topology. A topological overlap
matrix (TOM) was computed to measure the network interconnectedness,
and hierarchical clustering based on the TOM dissimilarity was
performed. The gene modules were identified using a dynamic tree cut
method with the parameters set to minModuleSize = 30, deepSplit = 2,
and pamRespectsDendro = FALSE. The modules were then merged based on
the eigengene similarity using a threshold cut height of 0.25. The
relationships between the module eigengenes and the RFI were assessed
using the Pearson correlation, and the module–trait relationships were
visualized through heatmaps. Modules showing strong or significant
correlations with the RFI were selected for the subsequent functional
enrichment analysis to explore the associated biological processes and
pathways. All the WGCNA analyses were performed in R (version 4.4.1)
using the WGCNA package (version 4.3.3) [[88]39].
2.8. Analysis of DEG, DEP, and Module Enrichment Pathways
The enrichment analysis was performed using DAVID Bioinformatics
Resources 6.8 ([89]https://david.ncifcrf.gov/, accessed on 20 April
2025) to identify potential mechanisms and pathways. The analysis was
conducted within the context of the Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathway [[90]40] and Gene Ontology (GO) terms, including
the biological process (BP), cellular component (CC), and molecular
function (MF). Statistical significance was set at p-value < 0.05 and
gene count ≥ 2.0.
2.9. Validation of RT-qPCR
Real-time fluorescence quantitative PCR was employed to validate the
RNA sequencing results. The genes selected for validation were randomly
chosen from the RNA sequencing data. The quantitative primer design was
performed using Primer 5.0 and synthesized by Shanghai Biotech Co.,
Ltd. (Shanghai, China). The total RNA was isolated using the TRIzol
reagent (OMEGA, Norcross, GA, USA; Genstar, Beijing, China) and reverse
transcribed using the one-step gDNA Removal and cDNA Synthesis SuperMix
kit according to the manufacturer’s instructions. The qPCR of all the
genes was performed on a 7500 Real-Time PCR system (Applied Biosystems,
Warrington, UK) using the fluorescent quantitative reagent kit
(Biomiga, San Diego, CA, USA). Each sample was tested in triplicate.
The Ct values for each gene were calculated using the 2^−△△CT method.
To compare the qPCR results with the sequencing-based results, the
average 2^−△△CT value for each gene was converted to fold change.
2.10. Statistical Analysis
The production data and feeding information were analyzed using a
two-tailed Student’s t-test in the Statistical Package for Social
Science program (SPSS 22.0, Chicago, IL, USA). A p-value < 0.05 was
considered statistically significant. For all the differential
expression mRNAs and proteins, we report the nominal p-values, adjusted
for multiple testing using the modified Benjamini–Hochberg method to
control the false discovery rate (FDR) [[91]41]. Differentially
expressed mRNAs were defined as those with an FDR < 0.05 and |log[2]FC|
> 1, while differentially expressed proteins (DEPs) were defined as
those with an FDR < 0.05 and |log[2]FC| > 1.2.
3. Results
3.1. Variance Components of Residual Feed Intake from a Social Genetic Model
We calculated the direct genetic effects (DGEs) and social genetic
effects (SGEs) of the residual feed intake (RFI) for all 209 pigs. The
variance and covariance between the direct and social genetic effects
of the RFI were estimated and are presented in [92]Table 1. The HPD95%
interval for SGE was determined as −4.304 to 4.304. Based on this
interval, we selected the extreme animals that were higher than 4.304
(HS) or lower than −4.304 (LS). Detailed information about the HS and
LS is provided in [93]Supplementary File S1.
Table 1.
Variance components from a social genetic model of the RFI.
[MATH: σAd2 :MATH]
[MATH: σAs2 :MATH]
[MATH: σAds :MATH]
[MATH: rAds :MATH]
[MATH: σTBV2 :MATH]
[MATH: σl2 :MATH]
[MATH: σg2 :MATH]
[MATH: σe2 :MATH]
2594.808 ± 86.02 1007.688 ± 59.78 206.75063 ± 17.32 0.128 ± 0.11
105,451.9714 322.848 ± 19.34 40,354.019 ± 260.16 23,234.581 ± 249.36
[94]Open in a new tab
[MATH:
σAd2 :MATH]
,
[MATH:
σAs2 :MATH]
and
[MATH:
σAds :MATH]
are the additive genetic variance and covariance between the direct
effects and social effects.
[MATH:
rAds :MATH]
represents the genetic correlation between the direct and social
genetic effects.
[MATH:
σTB<
mi>V2 :MATH]
represents the total genetic variance (
[MATH:
σTB<
mi>V2=σAd
2+2n−1σA
ds+(
n−1)2σA
s2 :MATH]
).
[MATH: σl2 :MATH]
,
[MATH: σg2 :MATH]
, and
[MATH: σe2 :MATH]
represent the random litter, group (pen), and residual variances,
respectively. n = 10.9 mean pen size, RFI: g/unit.
3.2. Comparative Analysis of Feeding Behavior and Growth Traits in Pigs with
Extreme Social Genetic Effects
The feeding behavior and productive performance of the selected pigs
were analyzed and are presented in [95]Table 2. As expected, there were
significant differences in the residual feed intake (RFI), feed
conversion ratio (FCR), and average daily weight gain (ADG) between the
HS and LS groups (p < 0.001); the RFI, FCR, and ADG were significantly
higher in the LS group. We further analyzed the four feeding-related
behaviors with different SGEs, including the average daily feed intake
(ADFI), total time spent at feeder per day (TPD), average feed intake
per visit (AFI), and number of visits to the feeder per day (NVD).
Consistent with the findings on the ADG, the HS group exhibited a lower
ADFI compared to the LS group. The pigs in the HS group visited the
feeder less frequently, yet they consumed more feed per visit. Notably,
the TPD of the HS group was higher than that of the LS group,
indicating that these pigs spent more extended periods at the feeder
during each visit, enabling them to eat at a leisurely pace and fulfill
their satiety. This phenomenon may be attributed to individuals with a
high SGE possessing a higher social status, which enables them to feed
in a more relaxed environment and spend more time at the feeder.
Subsequently, we calculated the percentage of displacement success (DS)
for each individual as a measure of their social status within the
group ([96]Table 2). The results indicated that the HS group had a
higher social status than the LS group, suggesting that individuals in
the LS group experience comparatively lower social standing within the
group. Consequently, they may frequently encounter disturbances during
feeding, thereby being compelled to visit the feeder more often in
order to secure adequate food intake.
Table 2.
Growth performance parameters and feeding behavior of pigs with
different SGEs.
Parameter HS (n = 4) LS (n = 4) p-Value
RFI (g) −248.74 ± 8.70 335.57 ± 4.88 0.001
FCR 1.91 ± 0.11 2.54 ± 0.07 0.001
SGE 10.14 ± 18.85 ^a −15.8 ± 18.85
DGE −98.92 ± 99.84 ^b 161.82 ± 99.84
ADG (g) 971.47 ± 98.31 1000.95 ± 31.53 0.001
ADFI (g) 1856.47 ± 186.73 2541.41 ± 31.29 0.001
AFI (g) 328.36 ± 29.60 296.42 ± 16.86 0.032
NVD 5.66 ± 0.26 8.62 ± 0.40 0.001
TPD (min) 65.83 ± 2.82 50.51 ± 4.22 0.022
DS 76.20 ± 4.66 29.60 ± 5.82 0.001
[97]Open in a new tab
Results are presented as the mean ± SD, ^a and ^b denote the HPD95%
interval of an SGE and a DGE. RFI: residual feed intake. FCR: feed
conversion ratio. SGE: social genetic effect (estimated breeding
value). DGE: direct genetic effect (estimated breeding value). ADG:
average daily gain. ADFI: average daily feed intake. AFI: average feed
intake per visit. NVD: the number of visits to the feeder per day. TPD:
total time spent at feeder per day. DS: percentage of displacement
success. The p-value was determined using Student’s t-test.
3.3. Differences in Transcriptome Profiles with Different Social Genetic
Effects
The SGE of the RFI is related to variations in growth performance and
feeding behavior, suggesting potential differences in gene expression.
Considering that the main biological processes influencing the RFI are
digestion and absorption, we selected the liver and ileum for
transcriptome sequencing. The processed clean data were used for the
subsequent analysis ([98]Supplementary File S2).
3.4. Differential Gene Expression in the Liver Is Mainly Related to
Mitochondria Functions and Oxidative Phosphorylation
Transcriptome expression analysis of the liver revealed 360
differentially expressed genes (DEGs) ([99]Figure 1A). The HS group had
262 up-regulated and 98 down-regulated significant DEGs compared to the
LS group (criterion: |log[2]FC| > 1, FDR < 0.05). Hierarchical
clustering analysis of the DEGs indicated that our grouping results are
relatively ideal ([100]Figure 1B). TCN1 and CA3 were the most up-
(log[2]FC = 5.48) and down-regulated (log[2]FC = −3.88) genes,
respectively ([101]Table 3).
Figure 1.
[102]Figure 1
[103]Open in a new tab
Significant differentially expressed mRNA in the liver tissue between
the HS and LS groups. (A) Volcano plot of the differentially expressed
mRNAs; upward-pointing red triangles represent upregulated mRNAs,
downward-pointing green triangles represent downregulated mRNAs, and
black dots represent mRNAs that are not significantly differentially
expressed, (B) heatmap of the differentially expressed mRNA under
clustering conditions, (C) top 5 GO terms: biological process (BP),
cellular component (CC), molecular function (MF), and (D) analysis of
the top 15 KEGG-enriched pathways of the DEGs.
Table 3.
Gene information of the top 10 up-regulated and down-regulated genes in
the liver in the HS group.
Gene Name log[2]FC p-Value FDR In the HS Group
TCN1 5.48 3.14 × 10^−6 3.31 × 10^−4 Up
HBM 3.77 4.57 × 10^−4 1.20 × 10^−2 Up
HBB 3.31 2.23 × 10^−4 7.29 × 10^−3 Up
C2H11orf86 2.73 2.84 × 10^−7 5.26 × 10^−5 Up
LOC100737768 2.62 9.25 × 10^−4 1.98 × 10^−2 Up
ARF4 2.54 7.27 × 10^−14 3.59 × 10^−10 Up
PNPLA3 2.53 3.36 × 10^−5 1.84 × 10^−3 Up
LOC100517779 2.41 1.75 × 10^−7 3.45 × 10^−5 Up
APOA4 2.41 4.93 × 10^−7 8.21 × 10^−5 Up
SPATA22 2.39 1.78 × 10^−7 3.47 × 10^−5 Up
LOC102164346 −2.43 4.47 × 10^−5 2.25 × 10^−3 Down
CYP1A1 −2.44 1.99 × 10^−5 1.22 × 10^−3 Down
COLCA1 −2.46 2.95 × 10^−4 8.86 × 10^−3 Down
LOC110259967 −2.47 5.12 × 10^−5 2.47 × 10^−3 Down
GALP −2.69 8.77 × 10^−4 1.90 × 10^−2 Down
LOC100154757 −3.01 1.77 × 10^−4 6.13 × 10^−3 Down
KCNH7 −3.14 1.14 × 10^−4 4.49 × 10^−3 Down
LOC110261964 −3.24 5.52 × 10^−4 1.37 × 10^−2 Down
ASIC1 −3.35 1.74 × 10^−4 6.08 × 10^−3 Down
CA3 −3.88 6.27 × 10^−5 2.87 × 10^−3 Down
[104]Open in a new tab
To further investigate the functions of the DEGs, functional annotation
was performed. The up-regulated DEGs were significantly involved in GO
terms associated with proton motive force-driven ATP synthesis, proton
motive force-driven mitochondrial ATP synthesis, and other
mitochondrial functions, including electron transport, respiratory
chain complex I assembly, and mitochondrial inner membrane ([105]Figure
1C). The down-regulated DEGs were significantly associated with
transmembrane transport, peripheral nervous system neuron development,
and regulation of membrane potential ([106]Supplementary File S4). We
also performed a KEGG enrichment analysis of the DEGs to identify the
central pathways. The up-regulated DEGs were significantly enriched in
biological pathways associated with metabolic pathways, oxidative
phosphorylation, thermogenesis, and pathways associated with other
diseases ([107]Figure 1D). The down-regulated DEGs were enriched in
nitrogen metabolism, cell adhesion molecules, and metabolic pathways.
3.5. Differential Gene Expression in the Ileum Is Mainly Related to
Cholesterol Metabolism, Fat Digestion and Absorption, and Amino Acid
Biosynthesis
We also identified 546 significant DEGs (375 up-regulated genes and 171
down-regulated genes) in the ileum (criterion: |log[2]FC| > 1, FDR <
0.05) ([108]Figure 2A). RTL4 was the most up-regulated gene (log[2]FC =
6.09), while GRM8 was the most down-regulated gene (log[2]FC = −3.82)
in the HS group ([109]Table 4). The hierarchical clustering heatmap
analysis of the DEGs is presented in [110]Figure 2B. Compared to the
liver, the ileum exhibited a greater number of DEGs with larger fold
changes. Furthermore, the gene expression patterns within each group
were not entirely consistent.
Figure 2.
[111]Figure 2
[112]Open in a new tab
Significant differentially expressed mRNA in the ileum between the HS
and LS groups. (A) Volcano plot of the differentially expressed mRNAs;
upward-pointing red triangles represent upregulated mRNAs,
downward-pointing green triangles represent downregulated mRNAs, and
black dots represent mRNAs that are not significantly differentially
expressed, (B) heatmap of the differentially expressed mRNA under
clustering conditions, (C) top 5 GO terms: biological process (BP),
cellular component (CC), molecular function, and (D) analysis of the
KEGG pathway enrichment of the DEGs.
Table 4.
Gene information of the top 10 up-regulated and down-regulated genes in
the ileum in the HS group.
Gene Name log[2]FC p-Value FDR In the HS Group
RTL4 6.09 5.23 × 10^−7 5.95 × 10^−4 Up
ADTRP 5.43 3.75 × 10^−9 1.85 × 10^−5 Up
SDSL 5.36 5.81 × 10^−10 8.59 × 10^−6 Up
NTS 5.29 1.66 × 10^−8 6.12 × 10^−5 Up
KCTD8 5.24 6.22 × 10^−6 2.56 × 10^−3 Up
FEV 5.11 6.16 × 10^−4 2.91 × 10^−2 Up
AQP7 5.04 5.14 × 10^−6 2.29 × 10^−3 Up
MRO 5.03 7.80 × 10^−4 3.29 × 10^−2 Up
FGFBP1 5.01 1.20 × 10^−3 4.20 × 10^−2 Up
CXH4orf3 4.88 2.13 × 10^−9 1.58 × 10^−5 Up
C13H3orf62 −3.82 2.13 × 10^−4 1.64 × 10^−2 Down
IFNA1 −3.90 1.8 × 10^−3 4.94 × 10^−2 Down
LOC102165987 −4.08 7.27 × 10^−4 3.21 × 10^−2 Down
FAT2 −4.17 1.14 × 10^−3 4.10 ×10^−2 Down
AIRE −4.24 1.15 × 10^−3 4.48 × 10^−2 Down
CDH8 −4.38 1.02 × 10^−3 3.84 × 10^−2 Down
PTPRQ −4.82 1.54 × 10^−6 1.14 × 10^−3 Down
ITGB1BP2 −4.93 1.07 × 10^−3 3.96 × 10^−2 Down
LOC100624648 −5.89 9.34 × 10^−8 1.59 × 10^−4 Down
GRM8 −6.10 2.66 × 10^−5 5.54 × 10^−3 Down
[113]Open in a new tab
The results of the GO enrichment analysis of the up-regulated DEGs
showed that the most significant GO terms were related to cholesterol
homeostasis, cholesterol efflux, biosynthetic process, intestinal
D-glucose absorption, and lipoprotein metabolic process ([114]Figure
2C). The down-regulated DEGs were associated with terms such as DNA
integration, sodium ion transmembrane transport, positive regulation of
transcription by RNA polymerase II, and amino acid transport. The KEGG
pathway enrichment analysis revealed significantly enriched biological
pathways for the up-regulated DEGs. These enriched pathways
predominantly involve metabolic pathways, carbon metabolism, fat
digestion and absorption, biosynthesis of amino acids, and PPAR
signaling pathway ([115]Figure 2D). The down-regulated DEGs were
associated with KEGG pathways involving other types of O-glycan
biosynthesis, Fc gamma R-mediated phagocytosis, Th17 cell
differentiation, and glutamatergic synapse ([116]Supplementary File
S4).
To validate the reliability of the RNA-seq results, we randomly
selected six genes from the commonly enriched differentially expressed
genes. The results demonstrated a strong consistency between the
RT-qPCR and RNA-seq data, indicating the reliability and high
reproducibility of the RNA-seq results ([117]Supplementary File S2).
3.6. Co-Expression Modules in the Liver Are Associated with Immune
Regulation, Cholesterol Metabolism, and Mitochondrial Function
Weighted gene co-expression network analysis (WGCNA) was performed to
identify the modules associated with the RFI of the SGE groups. After
quality control, that is, removing genes with low expression levels,
8752 (HS group) and 9751 (LS group) genes from the liver were retained
for network construction. Eight co-expression modules were identified
in the HS group, with the number of genes per module ranging from 43 to
3955. In the LS group, six modules were detected, containing between 71
and 5406 genes ([118]Supplementary File S5).
For the HS group, the brown4 and sienna3 modules were identified as the
top modules associated with the RFI ([119]Figure 3A). The brown4
module, consisting of 43 genes, was negatively correlated with the RFI
(r = −0.98, p < 0.05), while the sienna3 module, comprising 64 genes,
was positively correlated with the RFI (r = 0.99, p < 0.05). In the LS
group, the black module ([120]Figure 3B), containing 2057 genes, showed
a negative correlation (r = −0.89, p = 0.1).
Figure 3.
[121]Figure 3
[122]Open in a new tab
Moduletrait correlations and functional enrichment analysis of key
co-expression modules identified by WGCNA in the liver tissue. (A)
Module–trait correlation for the HS group, (B) module–trait correlation
for the LS group, (C) top 5 GO terms: biological process (BP), cellular
component (CC), molecular function of brown4 module, (D) KEGG pathways
enrichment of the brown4 module, (E) top 5 GO terms: biological process
(BP), cellular component (CC), molecular function of sienna3 module,
(F) KEGG pathways enrichment of the sienna3 module, (G) top 5 GO terms:
biological process (BP), cellular component (CC), molecular function of
black module, and (H) KEGG pathways enrichment of the black module.
Functional enrichment analysis revealed biological processes associated
with the top modules. In the HS group, genes in the brown4 module were
significantly enriched in GO terms related to the regulation of
immunoglobulin production and plasma membrane processes ([123]Figure
3C). Genes within the sienna3 module were primarily involved in
cholesterol biosynthetic processes and protein folding ([124]Figure
3E). In the LS group, black module genes were associated with
translation, protein transport, and mitochondrial function ([125]Figure
3G).
Pathway analysis further demonstrated the enrichment of specific KEGG
pathways. In the HS group, the brown4 module genes were significantly
enriched in the sphingolipid signaling pathway ([126]Figure 3D), while
genes in the sienna3 module were enriched in the spliceosome and
steroid biosynthesis pathways ([127]Figure 3F). In the LS group, black
module genes were enriched in oxidative phosphorylation and various
disease-related pathways ([128]Figure 3H).
3.7. Co-Expression Modules in the Ileum Are Associated with Fatty Acid
Metabolism and Protein Degradation Pathways
From the ileum transcriptome data, 10,658 genes (HS group) and 9634
genes (LS group) were retained for the WGCNA analysis. Eight
co-expression modules were identified in the HS group, with module
sizes ranging from 372 to 3114 genes. In the LS group, seven modules
were identified, with module sizes ranging between 69 and 4618 genes
([129]Supplementary File S5).
In the HS group, the antiquewhite4 module was identified as the top
module ([130]Figure 4A), showing a strong negative correlation with the
RFI (r = −0.96, p < 0.05). In the LS group, the lightyellow module was
identified as the top module ([131]Figure 4B) and was positively
correlated with the RFI (r = 0.99, p < 0.05).
Figure 4.
[132]Figure 4
[133]Open in a new tab
Module–trait correlations and functional enrichment analysis of key
co-expression modules identified by WGCNA in the ileum tissue. (A)
Module–trait correlation for the HS group, (B) module–trait correlation
for the LS group, (C) top 5 GO terms: biological process (BP), cellular
component (CC), molecular function of antiquewhite4 module, (D) KEGG
pathways enrichment of the antiquewhite4 module, (E) top 5 GO terms:
biological process (BP), cellular component (CC), molecular function of
the lightyellow module, and (F) KEGG pathways enrichment of the
lightyellow module.
Functional enrichment analysis showed that the genes in the
antiquewhite4 module (HS group) were significantly involved in fatty
acid metabolic processes and other mitochondrial-related functions
([134]Figure 4C). In the LS group, the genes in the lightyellow module
were enriched in processes related to the regulation of ribosome
biogenesis and reactive oxygen species metabolism ([135]Figure 4E).
KEGG pathway analysis further indicated that the antiquewhite4 module
in the HS group was significantly enriched in pathways including
metabolic pathways, fatty acid degradation, and fatty acid metabolism
([136]Figure 4D). In the LS group, the lightyellow module genes were
enriched in the ubiquitin-mediated proteolysis pathway ([137]Figure
4F).
3.8. Proteomic Analysis Revealed Differentially Expressed Proteins
Based on the transcriptome sequencing, we identified differentially
expressed genes in the liver and ileum, which may serve as the
molecular basis for the SGEs on the RFI. To further screen for core
genes, we performed proteomic analysis of the liver and ileum using
iTRAQ technology. We aimed to identify functional proteins that are
associated with the SGEs on the RFI and performed a combined analysis
with the transcriptome data.
We identified 6424 proteins in the liver and 7866 proteins in the
ileum, with 4716 co-expressed proteins. In the liver, 606
differentially expressed proteins (DEPs) were found, comprising 446
up-regulated and 160 down-regulated proteins in the LS group
([138]Figure 5A). In the ileum, there were 396 DEPs, of which 302 were
up-regulated and 94 were down-regulated (|log[2]FC| > 1.2, FDR < 0.05)
([139]Figure 5B). The hierarchical clustering analysis indicated that
the two groups showed distinct patterns of protein changes, with most
samples within each group consistently showing up-regulation or
down-regulation, suggesting good reproducibility ([140]Figure 5C,D).
Figure 5.
[141]Figure 5
[142]Open in a new tab
Quantitative proteome analysis of differentially expressed proteins in
different tissues using iTRAQ. (A) Volcano plot of the log[2]-fold
changes in protein abundance in the liver and their statistical
significance, (B) volcano plot of the log[2]-fold changes in protein
abundance in the ileum and its statistical significance, (C)
differential protein map in the liver, and (D) differential protein map
in the ileum.
To further investigate the differential proteins involved in the SGEs
on the RFI in pigs and to unravel the underlying molecular mechanisms,
we performed GO annotation and KEGG enrichment pathway analysis on the
DEPs in each comparison group. In the liver, the up-regulated DEPs were
significantly enriched in biological processes such as actin
cytoskeleton organization, cholesterol biosynthetic process, lipid
metabolic process, and sterol biosynthetic process, as well as cellular
components such as mitochondrion, mitochondrial inner membrane, and
others ([143]Figure 6A). The down-regulated DEPs in the liver were
associated with GO terms such as positive regulation of mRNA splicing,
via spliceosome, regulation of translation, and extracellular matrix
disassembly. In the ileum, the up-regulated DEPs were significantly
enriched in biological processes such as cholesterol homeostasis,
triglyceride homeostasis, and fatty acid transport, while the cellular
structures included chylomicron, very low-density lipoprotein particle,
and cytosol ([144]Figure 6B). The down-regulated DEPs in the ileum were
involved in processes such as positive regulation of canonical
NF-kappaB signal transduction, high-density lipoprotein particle
assembly, phospholipid efflux, and others ([145]Supplementary File S4).
Figure 6.
[146]Figure 6
[147]Open in a new tab
Functional enrichment analysis of differentially expressed proteomes in
the liver and ileum. (A) GO enrichment analysis of the DEPs in the
liver: biological process (BP), cellular component (CC), molecular
function (MF), (B) GO enrichment analysis of the DEPs in the ileum:
biological process (BP), cellular component (CC), molecular function
(MF), (C) KEGG pathway enrichment analysis of the DEPs in the liver,
and (D) KEGG pathway enrichment analysis of the DEPs in the ileum.
We also performed a KEGG pathway enrichment analysis of the DEPs from
both tissues. The pathways involved in the up-regulated DEPs in the
liver include metabolic pathways, lipid and atherosclerosis,
biosynthesis of cofactors, peroxisome, and steroid biosynthesis
([148]Figure 6C). The down-regulated DEPs were associated with
metabolic pathways, spliceosome, RNA degradation, and biosynthesis of
amino acids. In the ileum, the up-regulated DEPs enrichment primarily
occurred in metabolic pathways, fat digestion and absorption,
cholesterol and various amino acid metabolism, the PPAR signaling
pathway, and pathways related to viral infection and cancer
([149]Figure 6D). The down-regulated DEPs in the ileum were associated
with pathways related to ABC transporters.
3.9. Protein–Protein Interaction Network Analysis and Hub Protein Selection
The DEPs in the liver and ileum were separately introduced into the
STRING database to obtain the functional protein association networks.
The protein–protein interaction (PPI) network in the liver tissue was
constructed with 533 nodes and 206 edges, with a high confidence score
of 0.400 and an enriched p-value of 3.29 × 10^−7 ([150]Figure 7A). Nine
hub genes were selected using the cytoHubba plugin: SQLE, SC5D, HMGCS1,
DHCR7, CYP51, MSMO1, HSD17B7, FDFT1, and TM7SF2 ([151]Figure 7B). Our
functional analysis of the core gene found that the core gene in the
liver was involved in the cholesterol biosynthetic process and steroid
biosynthesis. The PPI network in the ileum consisted of 334 nodes and
312 edges, with a confidence score of 0.400 and an enrichment p-value
of 0.00236 ([152]Figure 7C). Eight hub genes were selected using the
cytoHubba plugin: APOA1, APOA4, APOC3, FABP1, FABP2, FABP6, HRG, and HP
([153]Figure 7D). The KEGG enrichment pathways of these hub genes were
mainly fat digestion and absorption, PPAR signaling pathway, and
cholesterol metabolism ([154]Supplementary File S4).
Figure 7.
[155]Figure 7
[156]Open in a new tab
The differential proteins screened from the two tissues were subjected
to protein–protein interaction network analysis. (A) PPI network of the
DEPs in the liver, (B) the core gene of the PPI network in the liver,
(C) PPI network of the DEPs in the ileum, and (D) the core gene of the
PPI network in the ileum.
3.10. Association Analysis of the Differentially Expressed Genes and Proteins
To further identify critical genes related to the SGEs, the combined
transcriptome and proteome data were analyzed. First, the co-expressed
proteins in the two omics were screened, and a nine-quadrant map was
drawn according to the fold difference between the mRNA and the
protein. Quadrants 1, 2, and 4 indicate that the protein abundance is
lower than the RNA abundance; in quadrants 3 and 7, the RNA corresponds
to related proteins; quadrant 5 shows the protein and RNA ubiquitous
expression, with no difference; and quadrants 6, 8, and 9 show the
protein abundance is higher than the RNA abundance ([157]Figure 8A,B).
Among them, 29 and 19 DEG-DEPs were co-upregulated in the liver and
ileum, respectively.
Figure 8.
[158]Figure 8
[159]Open in a new tab
The differential proteins screened from the two tissues were subjected
to PPI interaction network analysis. (A) Nine-quadrant chart of the
mRNA expression ratios and protein expression ratios in the liver;
different colors denote different quadrants, representing various
patterns of expression between mRNA and protein levels, (B)
nine-quadrant chart of the mRNA expression ratios and protein
expression ratios in the ileum; different colors denote different
quadrants, representing various patterns of expression between mRNA and
protein levels, (C) GO analysis of the DEG-DEPs in the liver tissue:
biological process (BP), cellular component (CC), molecular function
(MF), (D) KEGG pathways co-enriched by the DEG-DEPs in the liver
tissue, (E) GO analysis of the DEG-DEPs in the ileum: biological
process (BP), cellular component (CC), molecular function (MF), and (F)
KEGG pathways co-enriched by the DEG-DEP in the ileum.
We performed PPI network analysis of these 29 and 19 DEG-DEPs using the
STRING network. Details of the DEG-DEPs are shown in [160]Table 5.
Analysis of their functions revealed that most of the differential
genes in the liver were involved in various metabolic processes,
mitochondrial functions, and oxidative phosphorylation ([161]Figure
8C,D). The differential genes in the ileum were mainly involved in
various catabolic processes, digestion and absorption of fat,
cholesterol metabolism, glycine, serine, and threonine metabolism, and
arginine and proline metabolism ([162]Figure 8E,F).
Table 5.
DEGs and DEPs with the same expression trend in the HS group.
Name mRNA-log[2]FC mRNA-p-Value Pro-log[2]FC Pro-p-Value Tissue Type
APOA1 3.188 0.014 0.278 0.048 ileum
APOA4 3.829 0.018 0.397 0.002 ileum
APOC3 4.853 0.055 0.337 0.001 ileum
ASS1 4.382 0.000 0.473 0.001 ileum
CDHR2 2.976 0.001 0.323 0.013 ileum
DAO 4.201 0.005 0.367 0.001 ileum
FABP1 3.445 0.004 0.598 0.036 ileum
FABP2 4.188 0.000 0.839 0.004 ileum
GSTA1 5.027 0.016 0.396 0.000 ileum
LOC100512780 3.109 0.008 0.363 0.038 ileum
LOC100738425 2.239 0.013 0.284 0.010 ileum
LOC100739663 4.240 0.024 0.321 0.026 ileum
LOC106509660 3.134 0.049 0.295 0.010 ileum
OAT 2.964 0.057 0.898 0.049 ileum
RBP2 4.029 0.041 0.513 0.002 ileum
REEP6 3.219 0.005 0.266 0.000 ileum
SDSL 5.510 0.024 0.531 0.000 ileum
SLC5A1 3.208 0.001 0.410 0.017 ileum
STARD4 2.373 0.014 0.287 0.019 ileum
ABHD5 1.133 0.002 0.798 0.000 liver
ARF4 2.604 0.000 0.498 0.011 liver
ARL1 1.267 0.000 0.734 0.023 liver
ATOX1 1.024 0.006 0.266 0.000 liver
ATP5F1E 1.147 0.005 0.352 0.000 liver
ATP5MC1 2.048 0.000 0.768 0.002 liver
ATP5PO 1.138 0.000 0.523 0.000 liver
CFL1 1.232 0.000 1.024 0.000 liver
COX5B 1.414 0.000 0.867 0.023 liver
COX6C 1.456 0.000 0.420 0.002 liver
COX7A2 1.632 0.000 0.413 0.023 liver
COX7C 1.245 0.000 0.419 0.001 liver
CYCS 1.577 0.000 0.304 0.023 liver
FKBP1A 1.146 0.000 0.641 0.001 liver
H2AFZ 1.127 0.000 0.337 0.048 liver
HBB 3.335 0.010 0.284 0.000 liver
HMOX1 1.150 0.001 1.082 0.000 liver
LDHB 1.354 0.003 0.358 0.001 liver
MIF 1.353 0.000 0.489 0.001 liver
NDUFA4 1.353 0.000 0.423 0.000 liver
NNMT 1.423 0.005 0.332 0.016 liver
NQO1 1.279 0.030 0.277 0.005 liver
PGK1 1.324 0.000 0.389 0.010 liver
PSMB6 1.314 0.000 0.283 0.001 liver
RTCB 1.345 0.000 0.343 0.000 liver
S100A11 1.063 0.013 1.012 0.000 liver
SLIRP 1.333 0.047 0.420 0.007 liver
TPI1 1.044 0.000 0.657 0.000 liver
UBE2D3 1.857 0.012 0.460 0.002 liver
[163]Open in a new tab
Furthermore, the correlation coefficients between the mRNA and the
protein in these two tissues were 0.121 and 0.1087, respectively. The
majority of the differentially expressed proteins showed a low
correlation with their corresponding transcript levels, indicating that
post-transcriptional modifications might play a primary role in
regulating the SGEs on the RFI in pigs.
4. Discussion
Social genetic effects (SGEs) are the heritable influence of one
individual on the phenotype of its social partners. They are critical
in livestock and aquaculture, where selecting for SGEs has been
proposed to reduce harmful behaviors [[164]42]. SGEs significantly
affect feeding behavior and production traits. For example, low-SGE
individuals often display competitive and aggressive behaviors, while
high-SGE individuals exhibit cooperative traits [[165]17]. Studies
revealed a strong negative correlation between the SGEs and the direct
genetic effects (DGEs) on the feeding station occupation time
[[166]21]. However, this study observed a low positive correlation
(0.128 ± 0.11) between the SGEs and the DGEs on the residual feed
intake (RFI), suggesting that the relationship between these effects
may be trait-dependent. The positive correlation may indicate that pigs
with favorable DGEs for the RFI may also exhibit beneficial SGEs for
the same trait. The results from this study indicated that pigs with a
lower RFI exhibit higher SGEs. However, classical animal models do not
take into account the effects of group members on individuals and may
lead to biased estimates of the DGEs on the RFI. Therefore, in the
selection for the RFI, it is advisable to utilize animal models that
incorporate SGEs in order to fully consider their impact on the
phenotype.
In this study, we found that individuals with higher SGEs spent a
longer time but had fewer visits to the feeder. They consumed a lesser
overall amount of food while exhibiting a higher intake per visit.
Similar feeding patterns and social behaviors were observed in other
studies [[167]43]. A previous study ranked 12 pigs within the same
group and found that pigs with higher rankings visited the electronic
feeding station less frequently but stayed for a longer time and
consumed more feed. A study by Camerlink et al. observed that piglets
from a line selected for high SGEs were slower to explore the feed upon
weaning in a new environment, which may indicate that these pigs
prioritized social assessment or hierarchy establishment over immediate
feeding [[168]20]. Among captive macaques, females with lower social
status exhibit signs of chronic stress and consume more food than those
with higher social status [[169]44]. Therefore, we speculate that
individuals with high SGEs have higher social status, allowing them to
feed in a more relaxed environment. This enables them to occupy the
feeder for longer periods while consuming more food without being
disturbed. In contrast, individuals with low SGEs may experience
frequent interruptions during feeding due to their lower social status,
leading to a reduced intake per visit and increased feeder visits.
However, other studies produced differing results. For instance,
Herrera-Cáceres et al. [[170]21] found that dominant animals exhibit a
higher feeding frequency, while Nielsen et al. reported no relationship
between feeding behavior and social class. They suggested that the
correlations between performance and social behavior may have been
masked by environmental factors such as the space allowance and straw
provision. Alternatively, they proposed that aggression and growth
might be independent traits [[171]45,[172]46]. These conflicting
findings suggest that the relationship between feeding behavior and
social status may be complex and may be influenced by various factors.
Overall, evaluating the social status of pigs in group feeding remains
challenging, requiring further exploration to elucidate the
relationship between SGEs, social status, and feeding behavior.
This study analyzed the liver and ileum transcriptomes and proteomes of
pigs with extremely high or low SGEs. Although the sample size was
limited, which impaired the inclusion of potential confounders such as
pen as covariates in the differential analysis, this study therefore
provides preliminary insights into the biological mechanisms underlying
SGEs and their potential links to feed efficiency. The differential
gene and protein expressions observed in the liver potentially reflect
physiological responses to feeding behaviors associated with the SGEs
on the residual feed intake. In addition, the ileum’s role in nutrient
absorption and neurotransmitter secretion, which potentially influence
gut–brain signaling, may play a more direct role in the causation of
SGE-mediated effects on the RFI. A comparison between the LS and HS
groups revealed a significant number of differentially expressed genes
in both the liver and ileum. In the liver, TCN1 and CA3 were the most
up-regulated and down-regulated genes, respectively. TCN1 encodes a
vitamin-B12-binding protein essential for nutrient utilization. TCN1
has been reported to be located near a quantitative trait locus for the
RFI in cattle. Also, vitamin B12 supplementation has been linked to
improved weight gain in calves [[173]47] and improved average daily
gain and feed conversion in pigs [[174]48], suggesting TCN1-mediated
B12 uptake could influence feed efficiency. CA3 is involved in muscle
metabolism and energy homeostasis. In pigs, the CA3 expression in
muscle has been reported to correlate positively with the intramuscular
fat levels, indicating a role in muscle fuel utilization and lipid
metabolism [[175]49]. Differences in CA3 expression or activity could
reflect a shift in how muscle uses nutrients, thereby influencing an
animal’s energy expenditure and feed efficiency. Furthermore, RTL4 was
the most up-regulated gene, while GRM8 was the most down-regulated gene
in the HS group in the ileum. In mice, RTL4 encodes a secreted protein
that responds to noradrenaline signaling. Knockout mice lacking RTL4
show pronounced behavioral changes, such as increased impulsivity,
impaired short-term memory, and poor adaptation to new environments,
and it has been implicated as a causative gene in certain
neurodevelopmental disorders in humans [[176]50]. The behavioral
phenotypes observed in RTL4 knockout mice may suggest a potential role
for this gene in modulating social and feeding behaviors, which could
be relevant to the SGEs in pigs. GRM8 is involved in appetite
regulation and social feeding interactions. For instance, a
polymorphism in GRM8 (rs2237781) was associated with higher dietary
restraint in humans, suggesting that glutamate signaling could
influence how individuals regulate their eating behavior [[177]51].
This potential role remains hypothetical and requires further
investigation in pigs.
The functional enrichment results of the DEGs in both tissues were
quite consistent, highlighting the functional differences. In the
liver, the DEGs were primarily related to mitochondrial functions and
oxidative phosphorylation. The ileum plays a critical role in nutrient
absorption and gut microbiota regulation, both of which influence feed
efficiency. The DEGs in the ileum were mainly associated with
cholesterol metabolism and metabolic pathways related to fat digestion
and absorption, as well as amino acid biosynthesis, suggesting that the
SGEs may impact how efficiently nutrients are extracted from feed.
Weighted gene co-expression network analysis was performed on the liver
and ileum transcriptomes to identify biologically meaningful modules
associated with the RFI. In the liver of the HS group, the brown4
module was enriched in processes related to immunoglobulin production
and plasma membrane regulation, while the sienna3 module was associated
with cholesterol biosynthesis and protein folding. These findings
suggest that immune regulation and cholesterol metabolism processes may
be important contributors to the improved feed efficiency in the HS
group. In the LS group, the black module was enriched in processes
related to translation, protein transport, and mitochondrial function.
In the ileum, the antiquewhite4 module in the HS group was associated
with fatty acid metabolic mechanisms, while the lightyellow module in
the LS group was enriched in ribosome biogenesis and reactive oxygen
species metabolism. These results suggest that the nutrient metabolism
mechanisms in the HS group and the stress response mechanisms in the LS
group may contribute to the differences in feed intake efficiency under
social interactions.
Proteomic analysis was performed to further explore the biological
changes associated with the SGE groups. However, the number of
differentially expressed proteins between the two groups was
significantly lower across both tissues compared to the number of
differentially expressed genes, leading to a lower correlation between
the proteome and the transcriptome. This might be due to complex
post-transcriptional regulation. Additionally, the enrichment analysis
of the differentially expressed proteins predominantly highlighted
biosynthetic processes and mitochondrial-related functions in the
liver, as well as cholesterol metabolism, including cholesterol
homeostasis, and related processes such as fatty acid transport, and
fat digestion and absorption, in the ileum. These findings align with
the functional enrichment results of the differentially expressed
genes. Consequently, an integrated analysis of the transcriptome and
proteome is essential to further identify key genes.
Mitochondria are double-membrane organelles primarily responsible for
energy generation. In recent years, increasing evidence has shown a
clear link between mitochondrial function and social behavior
[[178]52,[179]53]. For example, in a rat model of autism, treatment
with a ketogenic diet enhances mitochondrial function and ameliorates
impaired mitochondrial respiration and deficits in social behavior
[[180]54]. The mitochondrial respiratory chain is located on the inner
mitochondrial membrane, which consists of five complexes, namely
complex I: NADH-Q oxidoreductase; complex II: succinate-Q
oxidoreductase; complex III: UQ-cytochrome C oxidoreductase; complex
IV: cytochrome C oxidase; and complex V: ATP synthase. It eventually
forms ATP through a series of redox processes to provide energy for
body tissues. In this assay, related genes on complex I (NDUFA4),
complex IV (COX5B, COX6C, COX7C, COX7A2), and complex V (ATP5PO,
ATP5F1E, ATP5MC1) were significantly up-regulated in the HS group in
both omics. Pigs with abundant energy in their bodies may have a more
robust physique, which can be advantageous for securing a strong
position in the group competition. These results indicate that the
impact of the SGEs on the RFI in the digestive system occurs primarily
through substances and energy metabolic pathways.
Furthermore, some reported genes related to social behavior influence
the SGEs on the RFI, although the underlying mechanism remains unclear.
Apolipoprotein A1 (APOA1), the most abundant component of the APOA
family and the primary apolipoprotein in lipoprotein, was previously
reported to be significantly up-regulated in children and adolescents
from high social status and wealthy families, suggesting a possible
link between apolipoprotein expression and social ranking [[181]55]. In
line with this, we observed that APOA1, along with APOC3 and APOA4, was
significantly down-regulated in individuals with low SGEs. Fatty
acid-binding proteins (FABPs) regulate fatty acid absorption and
intracellular transport. Studies reveal that there is a close
relationship between the FABP family and animal behavior. For instance,
FABP3 knockout mice exhibit reduced social memory and novelty-seeking
behaviors, while FABP7 knockout mice exhibit hyperactivity and
anxiety-related phenotypes [[182]56]. This experiment found that both
FABP1 and FABP2 were significantly up-regulated in the ileum of
high-SGE pigs. These findings may point to a potential role for FABPs
in behavior regulation, though their functional significance in pigs
remains to be explored.
The core genes found in the liver are associated with cholesterol
biosynthesis, while those in the ileum are related to cholesterol
metabolism. Cholesterol is essential for neuronal development and brain
function [[183]57]. Low total cholesterol is widely recognized to be
connected with negative psychiatric symptoms such as hostility and
impulsivity in severe mental disorders [[184]58], suicide [[185]59],
and depression [[186]60]. Disturbed cholesterol homeostasis impairs
neuronal survival and susceptibility to excitotoxicity [[187]61].
Cholesterol-rich cells are more resistant to oxidative stress and
beta-amyloid toxicity [[188]62]. Therefore, the differences in
cholesterol metabolism and maintenance of cholesterol homeostasis could
represent one of several possible pathways through which the SGEs may
influence the RFI.
5. Conclusions
The results from this study indicated that pigs with differing SGE
values exhibit distinct feeding patterns. Transcriptomic and proteomic
analyses of the liver and ileum identified pathways associated with
low- and high-RFI-SGE pigs. Integrated analysis identified potential
key genes enriched in mitochondrial processes and oxidative
phosphorylation in the liver and in fat digestion and absorption and
cholesterol metabolism in the ileum. These findings provide valuable
insights into the biological pathways associated with the SGEs and RFI,
offering a foundation for future research aimed at improving feed
efficiency in the swine industry. Further studies with larger sample
size are needed to elucidate the mechanisms underlying this
association.
Supplementary Materials
The following supporting information can be downloaded at
[189]https://www.mdpi.com/article/10.3390/ani15091345/s1, Supplementary
File S1. Animal information and basic data of the HS and LS groups;
Supplementary File S2. Transcriptome sequencing data summary and
validation; Supplementary File S3. All the differential expression of
the mRNA and protein in the ileum and liver; Supplementary File S4.
Enrichment analysis of genes and proteins; Supplementary File S5.
Cluster dendrogram of modules and module gene counts.
[190]animals-15-01345-s001.zip^ (636.8KB, zip)
Author Contributions
Conceptualization, P.K.M.T., Z.Z. and G.T.; formal analysis, D.C.,
Y.Y., S.C., J.W., Z.C. and J.X.; funding acquisition, G.T.;
investigation, X.J., Q.S. and J.W.; methodology, P.K.M.T., Z.Z. and
K.W.; software, D.C., S.C. and Z.C.; supervision, G.T.; validation,
Q.S. and Y.Y.; writing—original draft, P.K.M.T. and Z.Z.;
writing—review and editing, P.K.M.T., Z.Z., K.W., X.J., J.X. and G.T.
All authors have read and agreed to the published version of the
manuscript.
Institutional Review Board Statement
This study was approved by the Institutional Review Board and the
Institutional Animal Care and Use Committee of the Sichuan Agricultural
University (No. 2020202051). All the methods in this study were
performed in accordance with the institutional ethical standards in
compliance with the ARRIVE guidelines
([191]https://arriveguidelines.org, accessed on 4 October 2022) and all
other relevant guidelines and regulations.
Informed Consent Statement
Not applicable.
Data Availability Statement
All data generated or analyzed during this study are included in this
published article and its [192]Supplementary Information Files. The raw
transcriptome and proteome data reported in this paper have been
uploaded to the NCBI (PRJNA1032745) and iProX database (IPX0007506000),
respectively.
Conflicts of Interest
Author Kai Wang was employed by the company New Hope Liuhe Co., Ltd.
The remaining authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Funding Statement
This study was supported by grants from the Sichuan Science and
Technology Program (2020YFN0024, 2021ZDZX0008, 2021YFYZ0030), the
Sichuan Innovation Team of Pig (sccxtd-2022-08), and the Earmarked Fund
for the China Agriculture Research System (No. CARS-35-01A).
Footnotes
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References