Abstract
The liver is a complex organ governing several physiological processes
that define biological mechanisms affecting growth, feed efficiency and
performance traits in all livestock species, including pig. Proteomics
may contribute to a better understanding of the relationship between
liver functions and complex production traits in pigs and to
characterize this species as biomedical model. This study applied, for
the first time, a label‐free liquid chromatography-mass spectrometry
(LC‐MS) proteomic approach to compare the liver proteome profiles of
two important heavy pig breeds, Italian Duroc and Italian Large White.
Liver specimens were collected (after slaughtering) from performance
tested pigs of these two breeds, raised in standard conditions. The
label‐free LC‐MS method captured a total of 501 proteins of which 200
were subsequently considered in the between breeds comparison. A
statistical pipeline based on the sparse Partial Least Squares
Discriminant Analysis (sPLS-DA), coupled with stability and
significance tests, was applied for the identification of up or down
regulated proteins between breeds. This analysis revealed a total of 25
proteins clearly separating Italian Duroc and Italian Large White pigs.
Among the top proteins differentiating the two breeds, 3-ketoacyl-CoA
thiolase, mitochondrial (ACAA2) and histone H2B type 2-F (HIST2H2BF)
were up-regulated in Italian Duroc pigs and carboxylesterase 3 (CES3)
and ketohexokinase (KHK) were up-regulated in Italian Large White pigs.
Fatty acid synthase (FASN), involved in fatty acid metabolism and
encoded by a gene located in a QTL region for fatty acid composition,
was up-regulated in Italian Large White pigs. The in silico protein
interaction analysis showed that 16 of these proteins were connected in
one big module. Bioinformatic functional analysis indicated that
differentially expressed proteins were involved in several biological
processes related to the metabolism of lipids, amino-acids,
carbohydrates, cofactors and antibiotics/drugs, suggesting that these
functions might distinguish Italian Duroc and Italian Large White pigs.
This pilot comparative proteomic analysis of the porcine liver
highlighted several biological factors that could determine the
peculiar production potentials of these two heavy pig breeds, derived
by their different genetic backgrounds.
Introduction
The liver is an important metabolic organ that governs many
physiological processes that define biological mechanisms leading to
growth, feed efficiency and several other economically relevant traits
in all livestock species, including pig. The liver is involved in lipid
oxidation, is a primary source of proteins and the centre of amino acid
metabolism, is responsible for the secretion of many proteins into the
blood and interplays with this circulating tissue for the transfer of
amino acids and energy compounds and the disposal of waste metabolites,
i.e. derived by the protein degradation in the form of urea metabolism
[[32]1, [33]2].
The construction of liver proteome maps [[34]3, [35]4] and the
establishment of the Human Liver Proteome Project (HLPP) by the Human
Proteome Organisation (HUPO) [[36]5] are important resources for the
study of liver function not only in humans but also in all animal
species [[37]6, [38]7]. Indeed, a better understanding of the metabolic
processes being undertaken in the hepatocytes will eventually be a step
forward in managing animal growth and feed efficiency. In this context,
proteomics can help to unravel the variations in the metabolic pathways
of the hepatocytes in healthy and diseased animals and to identify
biomarkers that could be applied in breeding programmes. Proteomic
information in livestock species can be also useful to define animal
models to better characterize liver physiology and related diseases
[[39]8, [40]9].
Despite these key roles and envisaged potential applications, the
interest of the animal science sector on liver proteomics has been
slowly increasing over the last years. For example, Timperio et al.
[[41]10] compared the cattle liver proteome profiles using traditional
proteomic approaches [including two-dimensional polyacrylamide gel
electrophoresis (2DE) and mass spectrometry (MS)], assuming that
differences could be imputed to genetic diversity between dairy and
beef breeds [[42]10].
A first comprehensive proteomic analysis of porcine hepatic cells was
achieved by Caperna et al. [[43]11] who analysed three-lines hybrid
pigs (Landrace × Yorkshire × Poland China). This study produced 2DE
maps of cytosol and membrane fractions from hepatocytes that were
subsequently characterized by MS analysis. A similar approach was used
by Wang et al. [[44]12] and Liu et al. [[45]13] to investigate how
intrauterine growth restriction affected the liver proteome in new
borne and fetal pigs.
More recently, gel-free proteomic approaches have risen in popularity
[[46]14] and have been also applied in livestock. Tang et al. [[47]15]
investigated the heat stress response in broiler liver using a gel-free
method, i.e. sequential window acquisition of all theoretical spectra
(SWATH)-MS, reaching a good resolution that was able to quantify about
2,400 proteins. One tenth of these proteins was differentially
expressed between the controls and the heat stressed group. Other MS
based proteomics methods, such as label-free liquid chromatography
(LC)-MS, have become more frequently adopted for quantifying protein
expression and comparing different samples. This method can also give
the possibility to better investigate hydrophobic proteins with low or
high molecular weights that are quite challenging to be analysed using
traditional proteomic approaches [[48]14].
It is well known that pig breeds and lines differ in their potentials
for many production and economically relevant traits (like growth rate,
feed efficiency, carcass and meat quality traits, reproduction
performances and rusticity) that might affect their use or choice in
various production systems and breeding programmes. These traits can be
considered as external traits or end phenotypes and are the outcome of
complex biological processes and interactions. Therefore, a more
detailed description of these phenotypes can be obtained only adding
intermediate or molecular information, which can dissect the biological
mechanisms underlying their final expression [[49]16]. This
intermediate level that links the genetic layer (defined by the genetic
variability) and the external phenotypes (i.e. production traits)
includes internal phenotypes such as metabolite and protein
quantitative and qualitative profiles [[50]17]. Recently, comparative
metabolomic analyses of plasma and serum of two Italian heavy pig
breeds (Italian Duroc and Italian Large White) have identified
biomarkers that might explain the metabolic differences between these
breeds [[51]18]. Italian Duroc and Italian Large White are two heavy
pig breeds, selected under the national pig breeding program, that
differ for several economically important traits (e.g. growth rate,
feed efficiency, intermuscular fat deposition, meat quality, rusticity
and reproduction traits) [[52]19–[53]21]. These breeds are commonly
used in crossbreeding plans to produce final pigs, for the production
of high quality dry-cured hams [[54]19–[55]21].
This study characterized and compared the liver proteomic profiles of
Italian Duroc and Italian Large White pigs using a quantitative
label-free LC-MS proteomic approach. The final aim was to identify
intermediate phenotypes that might capture biological differences
between these two porcine genetic types and then inform the biological
mechanisms underlying their different production performances.
Materials and methods
All animals used in this study were kept according to the Italian and
European legislations for pig production. All procedures described here
were in compliance with Italian and European Union regulations for
animal care and slaughter. Animals were slaughtered in a commercial
abattoir following standard procedures. Animals were not fasted for the
purpose of this study. Fasting was part of the standard
pre-slaughtering procedures. Animals were raised in an approved
performance tested structure. Pigs were not raised or treated in any
way for the purpose of this study and for this reason no other ethical
statement is needed.
Samples
Five Italian Duroc gilts and five Italian Large White gilts were
included in this study. Animals did not have common grandparents. Gilts
from both pig breeds were performance-tested at the Test Station of the
National Pig Breeder Association (ANAS). Performance evaluation started
when the pigs were 30 to 45 days of age and ended when the animals
reached about 155 ± 5 kg live weight. Details of performance testing
system in Italian heavy pigs of these two breeds are reported in
[[56]22–[57]24].
The management of all pigs included in this study with regard to
housing, feeding, handling and transportation was the same. All animals
were fed with the same standard commercial feed for fattening pigs
under the production rules of the Parma and San Daniele dry-cured ham
consortia. After a fasting period of 12 h, all animals, which were nine
months old (and weighting 155 ± 5 kg), were transported in the morning
in a commercial abattoir, where they were electrically stunned and then
slaughtered under controlled conditions. Then, after slaughtering,
liver specimens were collected from the extreme end of right lobe,
placed in a labelled tube, snap frozen in liquid nitrogen and then
stored at −80°C until they were analysed.
Protein extraction and digestion
Protein extraction and digestion were performed according to a slightly
modified Filter Aided Sample Preparation (FASP) protocol to improve
sensitivity, recovery and proteomic coverage for processed samples
[[58]25]. Briefly, proteins were extracted from approximately 100 mg of
liver tissue homogenised in 1 mL of extraction buffer [0.1% SDS
(Affymetrix/Thermo Fisher Scientific, USA), 100 mM Tris/HCl
(Affymetrix/Thermo Fisher Scientific, USA) pH 7.6, 10 mM DTT
(Affymetrix/Thermo Fisher Scientific, USA)]. Liver samples were
homogenised for 2 min using a homogenizer (G50 Tissue Grinder, Coyote
Bioscience, Inc., China) in an Eppendorf tube immersed into liquid
nitrogen. The lysates were vortexed at room temperature for 3 min and
then centrifuged at 16,000 × r.c.f. at 4°C for 5 min. Samples were
heated at 56°C for 30 min and then centrifuged at 16,000 × r.c.f. at
20°C for 20 min. The supernatants were transferred into new labelled
tubes, were mixed, aliquoted and stored at -20°C until usage. Qubit™
Fluorometric Quantitation was used to determine the protein
concentration according to the manufacturer’s instructions
(Affymetrix/Thermo Fisher Scientific, USA).
Protein samples were reduced and alkylated with dithiothreitol (DTT)
and iodoacetamide (IAA; Sigma-Aldrich/Merck, USA) and then digested
with trypsin according to the FASP method [[59]25]. Samples were then
purified from any contaminants using Pierce C18 Spin Columns
(Affymetrix/Thermo Fisher Scientific, USA), vacuum dried and stored at
-20°C. All 10 peptide samples (five from Italian Duroc and five from
Italian Large White pigs) were collected for MS analysis.
Mass spectrometry analysis
Dry peptides from each sample were resuspended in 25 μL of a mixture of
water:acetonitrile:formic acid 97:3:2 and sonicated for 10 min at room
temperature, then centrifuged at 12,100 x r.c.f. for 10 min. Mass
spectrometry analyses were performed on an ESI-Q-TOF Accurate-Mass
spectrometer (G6520A, Agilent Technologies, Santa Clara, CA, USA),
controlled by MassHunter software, Agilent (v. B.04.00)
([60]https://www.agilent.com/en/products/software-informatics/masshunte
r-suite/masshunter/masshunter-software) and interfaced with a CHIP-cube
to an Agilent 1200 nano-pump (Agilent Technologies, Santa Clara, CA,
USA).
Chromatographic separation was performed on a high-capacity loading
chip (Agilent Technologies) with a 75 μm internal diameter (I.D.), 150
mm, 300 Å C18 column, prior to a desalting step through a 500 nL trap
column. The injected samples (8 μL) were loaded onto the trap column
with a 4 μL/min 0.1% (v/v) formic acid (FA):acetonitrile (ACN) (98:2
v/v) phase flow. After 3 min, the precolumn was switched in-line with
the nanoflow pump (400 nL/min, phase A: water:ACN:FA (96.9:3:0.1
v/v/v), phase B: ACN:water:FA 94.5:5:0.1 v/v/v), equilibrated in 1%
(v/v) B. The peptides were eluted from the reverse phase (RP) column
through the following gradient: 1% for one min, 1->5% B in 7 min,
5->30% B over a period of 142 min, 30–60% B in 20 min, 60->90% B in 0.1
min, then held at 90% B for 8 min, and switched back to 1% B for column
reconditioning, for a total runtime of 190 min. Eight μL of each
samples were run twice; analytical controls (a mix of baker’s yeast
enolase and bovine serum albumin tryptic digests) were run daily to
monitor chromatographic performances. Ions were formed in a nano-ESI
source, operated in positive mode, 1860 V capillary voltage, with the
source gas heated at 350°C and at a 5 L/min flow. Fragmentor was set to
160 V, skimmer lens operated at 65 V. Centroided MS and MS^2 spectra
were recorded from 250 to 1700 m/z and 70 to 1700 m/z, respectively, at
scan rates of 8 and 3 Hz. The six most intense multi-charged ions were
selected for MS^2 nitrogen-promoted collision-induced dissociation. The
collision energy was calculated according to the following expression:
[MATH:
CE(V)=3.6∙m/z100−3 :MATH]
A precursor active exclusion of 0.22 min was set, and the detector was
operated at 2 GHz in extended dynamic range mode. Mass spectra were
automatically recalibrated with two reference mass ions.
Label-free quantitative profiling
Raw MS data were converted to MASCOT ([61]www.matrixscience.com)
generic file (mgf) using MassHunter Qualitative Analysis (v. B.05.00,
Agilent Technologies). Then data were used for peptide identification
with MASCOT (version 2.4, Matrix Science, London, UK) searched against
an implemented version of the Pig PeptideAtlas resource
([62]http://www.peptideatlas.org/) [[63]26], including contaminant
protein sequences as retrieved from the cRAP v.1.0 resource
([64]http://www.thegpm.org/crap/). The search parameters used were as
follow: 40 ppm precursor tolerance, 0.1 Da fragment mass error allowed,
two missed cleavage allowed for trypsin, carbamidomethyl as a fixed
modifier of cysteine residues, asparagine and glutamine deamidation and
methionine oxidation as variable modification. The peptide
identification results were imported into an in-house Trans-Proteomic
Pipeline server (TPP, v. 5.1.0) [[65]27], rescored and validated with
PeptideProphet [[66]28]. Protein inference was then achieved by
ProteinProphet [[67]29]. As last step, relative label-free
quantification was performed with Skyline v.4.1 [[68]30] using the
rescored search results [Protein probability ≥0.9, corresponding to a
global false discovery rate (FDR) ≤0.1]. PepXML files and raw mzXML
data were imported in Skyline and: (i) the protein database was
filtered by retaining only the proteins with a ProteinProphet score ≥
0.9 and (ii) MS^1 data were correlated to the corresponding peptide
identification, matches were filtered (idotP≥0.9).
Data were exported and analyzed with InfernoRDN v.1.1.6556.25534
([69]https://omics.pnl.gov/software/InfernoRDN; [[70]31]). Data were
Log2 transformed and normalized with the central tendency adjustment
method. Peptide measurements were then rolled up to corresponding
protein abundances through the Zrollup method. In the ZRollup procedure
a scaling method similar to z-scores is applied first to peptides that
originate from a single protein and then the scaled peptide measures
are averaged to obtain a relative protein abundance measure [[71]31].
Differences in protein abundance between Italian Duroc and Italian
Large White pigs were investigated as described below. Proteins were
considered only if identified with more than one peptide.
The mass spectrometry proteomics data have been deposited to the
ProteomeXchange Consortium via the PRIDE
([72]http://www.ebi.ac.uk/pride; [[73]32]) partner repository with the
dataset identifier PXD009771 and project DOI: [74]10.6019/PXD009771.
Analyses
Multivariate statistical analysis
Differentially abundant proteins were detected by applying the
multivariate approach of sparse Partial Least Squares Discriminant
Analysis (sPLS-DA) [[75]33] coupled with the validation procedure
detailed in Bovo et al. [[76]18, [77]34]. sPLS-DA is a multivariate
technique used in classification and discrimination problems especially
when variables are highly correlated [[78]35]. Briefly, breed was
modeled as response variable (Italian Duroc = 0, Italian Large White =
1) and proteins as predictors. The sPLS-DA penalization coefficient eta
(ranging from 0.1 to 0.9) and the number of hidden components K
(ranging from 1 to 5) were automatically selected by an internal 4-fold
cross-validation procedure (4CV). The selected proteins were then
assessed through a stability and significance test [[79]18]. The
stability test was based on a Leave One Out (LOO) procedure coupled
with a permutation test. For this purpose, 1,000 artificial datasets
were obtained by randomly permuting the breed trait values. While the
stability test is aimed at evaluating the frequency of selection of a
protein in the original dataset against the permuted ones, the
significance test evaluates the regression coefficient (β). These two
tests estimate the probability (P) that the selection of a given
protein inside the dataset is due to chance or to a particular
structure of the dataset. Proteins having a P < 0.10 (with the sign of
the regression coefficient that matched the fold change ratio) were
declared stable and significantly related to breed. Analyses were
performed in R v. 3.0.2 [[80]36] by using the “spls” package (function
“cv.splsda” and “splsda”). Scatter plot of the first two components was
drawn (each point represents an individual sample).
Functional and protein network analyses and relation to genomic information
Functional interpretation of differentially abundant proteins was
carried out in Cytoscape ([81]http://www.cytoscape.org/) [[82]37] using
the plug-in ClueGO ([83]http://www.ici.upmc.fr/cluego/) [[84]38]. Gene
enrichment analysis was carried out over the Gene Ontology
(GO)–Biological Process (BP) branch (release data: May 2018) by setting
the following parameters: (i) GO hierarchy level from 3 to 20; (ii)
minimum number of genes per GO term equal to 2; (iii) minimum of input
genes associated to the functional term greater than 2%; (iv) GO term
network connectivity (Kappa score) equal to 0.70 and right-sided
hypergeometric test. These adjustments were applied to maximize the
process of functional association. Indeed, the tuning of the
above-mentioned parameters allows to over-represent processes and
functions very specific (the lowest levels of the GO hierarchy) rather
than terms that are general and uninformative (the highest levels of
the GO hierarchy). Moreover, similar processes and functions are
clustered in functional groups representing closely related terms. The
analysis made use of Sus scrofa specific functional annotations. The
other parameters were kept with default values. GO:BP terms with a
Benjamini–Hochberg corrected p-value < 0.05 were considered
statistically over-represented. A pathway enrichment analysis was
separately carried out, with ClueGO, over the KEGG pathway database
(release data: May 2018).
The in silico Protein-Protein Interaction (PPI) analysis of
differentially abundant proteins identified in the breed comparison was
obtained using STRING v. 10.5 database ([85]https://string-db.org/)
[[86]39]. STRING is a web resource providing uniquely comprehensive
coverage of experimental and predicted interaction information. The
analysis was carried out considering the Sus scrofa specific
interactome. Only interactions having a STRING combined score > 0.4
were considered (i.e. medium confidence). Network indices such as the
number of nodes and edges, the average node degree (average no. of
connections), the expected number of edges and the PPI enrichment
p-value were computed. The expected number of edges gives how many
edges would be expected if the nodes were selected at random. A small
PPI enrichment p-value indicates that nodes are not random and that the
observed number of edges is significant ([87]https://string-db.org/).
The Pig Quantitative Trait Locus Database (Pig QTLdb release 35)
[[88]40] was used to explore possible links between up or
down-regulated proteins and genomic QTLs for economically relevant
traits and parameters that could be affected by genes encoding these
proteins. The whole set of 27,465 QTLs/associations was downloaded and
their genome coordinates were compared with genome coordinates of the
genes encoding differentially abundant proteins observed in this study.
Borders of the considered genomic regions were defined by the gene
coordinates ± 50 kbp. QTL relationships were filtered by manually
curating functional relationships of the genes encoding these proteins.
Results
Label-free proteomic data of porcine liver samples
Label-free proteomic analysis was performed from proteins extracted
from the liver of pigs of two heavy pig breeds, Italian Duroc and
Italian Large White. The analysis of the Italian Duroc liver specimens
identified a total of 1,696 peptides belonging to 467 proteins, with an
average of 3.6 peptides per protein. The analysis in the Italian Large
White liver samples identified 1,253 peptides related to 350 proteins,
with an average of 3.6 peptides per protein. Combining these two
datasets, this study identified a total of 1,873 peptides related to
501 proteins (average of 3.7 peptides per protein), that were then
re-defined using stringent parameters in the InfernoRDN analysis,
obtaining 329 unique proteins ([89]S1 Table). These proteins were
included in a total of 13 GO:Biological Processes ([90]Fig 1), four of
which (organonitrogen compound metabolic process, lipid metabolic
process, carbohydrate metabolic process and cellular amino acid
metabolic process) accounted each for more than 10% of the total listed
proteins.
Fig 1. Percentage of liver proteins (over all 329 identified proteins; [91]S1
Table) grouped according to different biological processes.
[92]Fig 1
[93]Open in a new tab
Only proteins having at least two supporting peptides were kept and
subsequently used in the study. Bioinformatic analyses were carried out
on a final dataset counting 200 proteins (including a total of 1,041
peptides) identified with high confidence and subsequently quantified.
The full list of the 200 proteins analysed in this study, as well as
the information on the identified peptides, is shown in the [94]S2
Table.
Comparative analysis of breed derived proteomic liver profiles
Differences at the proteome level between Italian Duroc and Italian
Large White pigs were investigated by using the multivariate approach
of sPLS-DA. This technique was coupled with a statistical procedure
aimed at evaluating the stability and significance of the proteins
selected as differentially expressed. The scatter plot of the first two
sPLS-DA components shows that animals of the same breed clustered
together, indicating that the identified proteins can discriminate
animals of these two pig breeds ([95]Fig 2). According to the stability
test, 33 proteins (16.5% on the total) had a P ≤ 0.10 (defined as a
threshold of significance, based on the validation procedure;
[[96]18]). The sPLS-DA regression coefficient of 57 proteins (28.5%)
was equal to 0, indicating that these proteins do not have any weight
in the classification derived by the breed. At the significance test,
56 out of 57 proteins had a P ≤ 0.10. A total of 25 out of 200
identified proteins (12.5%) were both stable (in terms of statistically
assigned condition) and significantly differentially expressed (P ≤
0.10) ([97]Table 1). Among these proteins, 14 (56%) showed an increased
abundance in Italian Duroc pigs, while the remaining 11 (44%) had a
higher quantification level in Italian Large White pigs. Details about
proteins with significant different quantification between the two
breeds are presented in [98]Table 1. Four of these proteins (two with
the highest expression in Italian Duroc pigs: 3-ketoacyl-CoA thiolase,
mitochondrial (ACAA2) and histone H2B type 2-F (HIST2H2BF); and two
with the highest expression in Italian Large White pigs: ketohexokinase
(KHK) and carboxylesterase 3 (CES3) showing one of the two calculated P
values ≤0.01 and the other with ≤0.05, were considered the top proteins
identified in this study differentiating the two analysed breeds.
Fig 2. Scatter plot of the first two sPLS-DA components obtained from the
liver proteome of five Italian Duroc pigs (blue circles) and five Italian
Large White pigs (orange diamonds).
Fig 2
[99]Open in a new tab
Table 1. Differentially abundant proteins identified from label-free mass
spectrometry analysis of liver samples of Italian Duroc (ID) and Italian
Large White (ILW) pigs.
Proteins are ordered according to their fold change score.
UniProtKB[100]^1 Gene Name Protein Peptides PA[IDU][101]^2
PA[ILW][102]^3 FC[103]^4 Direction[104]^5 P[st][105]^6 P[si][106]^7
[107]P51781 GSTA1 Glutathione S-transferase A1 5 -0.41 0.54 0.52 ILW
0.077 0.026
I3LVE1 KHK Ketohexokinase 3 -0.24 0.64 0.54 ILW 0.005 0.027
I3LEI5 CES3 Carboxylesterase 3 2 -0.64 0.23 0.55 ILW 0.006 0.014
B1A8Z3 PYGL Glycogen phosphorylase, liver form 5 -0.45 0.38 0.56 ILW
0.022 0.036
[108]Q9TV69 DHDH (SUS2DD) Trans-1,2-dihydrobenzene-1,2-diol
dehydrogenase 3 -0.39 0.30 0.62 ILW 0.091 0.016
A0A286ZKH3 SPR Sepiapterin reductase 5 -0.41 0.19 0.66 ILW 0.042 0.044
A0A286ZIW5 ILVBL Acetolactate synthase-like protein 2 -0.21 0.34 0.68
ILW 0.031 0.027
F1RII7 HBB Hemoglobin subunit beta 8 -0.20 0.33 0.69 ILW 0.080 0.042
F1RQP0 PRDX5 Peroxiredoxin-5, mitochondrial 8 -0.20 0.31 0.70 ILW 0.042
0.024
F1SLX5 AASS Alpha-aminoadipic semialdehyde synthase, mitochondrial 2
-0.42 -0.02 0.76 ILW 0.073 0.029
A5YV76 FASN Fatty acid synthase 7 -0.12 0.17 0.82 ILW 0.086 0.035
F1SSS0 CPS1 Carbamoyl-phosphate synthase [ammonia], mitochondrial 39
0.12 -0.07 1.14 IDU 0.084 0.066
F1RKG8 PEBP1 Phosphatidylethanolamine-binding protein 1 7 0.21 -0.24
1.37 IDU 0.073 0.038
D0G0B3 ACAA2 3-ketoacyl-CoA thiolase, mitochondrial 10 0.36 -0.20 1.48
IDU 0.010 0.016
F1S2X3 ECHDC1 Ethylmalonyl-CoA decarboxylase 2 0.09 -0.49 1.50 IDU
0.086 0.041
F1S0C1 ADH5 Alcohol dehydrogenase class-3 4 0.38 -0.24 1.53 IDU 0.068
0.052
F1RMH5 UROC1 Urocanate hydratase 3 0.25 -0.41 1.58 IDU 0.029 0.035
I3LP02 ACAT1 Acetyl-CoA acetyltransferase, mitochondrial 2 0.22 -0.57
1.73 IDU 0.099 0.023
F1RL81 HSD17B14 17-beta-hydroxysteroid dehydrogenase 14 4 0.29 -0.51
1.74 IDU 0.047 0.026
A0A287AR55 FH Fumarate hydratase, mitochondrial 3 0.40 -0.43 1.78 IDU
0.059 0.044
F1RWY0 RGN Regucalcin 9 0.40 -0.45 1.80 IDU 0.051 0.055
F2Z558 YWHAZ 14-3-3 protein zeta/delta 2 0.33 -0.64 1.96 IDU 0.082
0.048
UPI0002105648 HIST2H2BF Histone H2B type 2-F 2 0.58 -0.40 1.97 IDU
0.009 0.008
F1RIF3 FAH Fumarylacetoacetase 3 0.54 -0.48 2.02 IDU 0.005 0.052
I3LDY2 LOC100625049 Uncharacterized protein 3 0.54 -0.85 2.63 IDU 0.012
0.020
[109]Open in a new tab
^1UniProtKB accession number as derived from the Pig PeptideAtlas
resource
^2Average abundance level of protein in Italian Duroc pigs
^3Average abundance level of protein in Italian Large White pigs
^4Fold change
^5“ID” indicates protein abundance higher in Italian Duroc pigs than
Italian Large White pigs while ‘ILW’ indicates protein abundance higher
in Italian Large White pigs than Italian Duroc pigs
^6p-value at the stability test
^7p-value at the significance test.
Functional inference of proteomic differences between breeds
Biological processes and pathways involving the 25 differentially
abundant proteins were investigated through functional association
analysis. Enrichment analyses were carried out in Cytoscape (ClueGO
package). Analyses were run over the GO:BP and KEGG pathway databases,
separately. Two proteins (HIST2H2BF and LOC100625049) were not in the
ClueGO annotation sets. A total of 24 GO:BP terms (involving 14
differentially abundant proteins) were retrieved ([110]Table 2,
[111]Fig 3A). These biological processes can be summarized in the
following functional groups (G, in [112]Table 2): (i) metabolism of
carbohydrates and energy (G1—G3), (ii) metabolism of antibiotics/drugs
(G4—G5), (iii) metabolism of cofactors (G6), (iv) metabolism of
amino-acids (G7—G8; G10), (v) metabolism of lipids (G9—G10). It is
interesting to note that processes related to the glutamine family
amino acids and lipids involve proteins with a higher abundance in
Italian Duroc than in Italian Large White pigs. Processes related to
organic acid catabolism cellular/alpha amino acid metabolism showed the
same direction, except for the aminoadipate-semialdehyde synthase
(AASS) protein, that was more expressed in the Italian Large White
pigs. This inversion was also evident for proteins involved in the
carbohydrate catabolic process that showed a higher abundance in
Italian Large White than in Italian Duroc pigs.
Table 2. Over-represented biological processes (GO:BP) associated to up or
down regulated proteins in the breed comparison.
Term Description Functional group[113]^1 p-value[114]^2 % of associated
proteins[115]^3 N. of proteins Up or down regulated proteins [116]^4
GO:0005996 monosaccharide metabolic process G1 2.26E-03 2.05 3 KHK↓,
RGN↑, SUS2DD↓
GO:0006091 generation of precursor metabolites and energy G2 1.23E-04
2.03 5 AASS↓, ACAT1↑, ADH5↑, FH↑, PYGL↓
GO:0016052 carbohydrate catabolic process G3 7.63E-03 2.47 2 PYGL↓,
SUS2DD↓
GO:0016999 antibiotic metabolic process G4 6.36E-03 2.78 2 ADH5↑, FH↑
GO:0042737 drug catabolic process G5 5.31E-03 3.13 2 AASS↓, ACAT1↑
GO:0009108 coenzyme biosynthetic process G6 1.40E-03 2.48 3 ACAT1↑,
RGN↓, SPR↓
GO:0008652 cellular amino acid biosynthetic process G7 2.95E-03 4.44 2
AASS↓, CPS1↑
GO:1901607 alpha-amino acid biosynthetic process G7 2.86E-03 4.65 2
AASS↓, CPS1↑
GO:0009064 glutamine family amino acid metabolic process G7 3.05E-03
4.26 2 CPS1↑, FAH↑
GO:0006525 arginine metabolic process G8 2.81E-04 16.67 2 CPS1↑, FAH↑
GO:0016042 lipid catabolic process G9 4.45E-05 2.59 5 ACAA2↑, ACAT1↑,
CPS1↑, ECHDC1↑, HSD17B141↑
GO:0044242 cellular lipid catabolic process G9 1.16E-04 3.25 4 ACAA2↑,
ACAT1↑, CPS1↑, ECHDC1↑
GO:0034440 lipid oxidation G9 2.82E-04 4.62 3 ACAA2↑, ACAT1↑, ECHDC1↑
GO:0019395 fatty acid oxidation G9 2.81E-04 4.76 3 ACAA2↑, ACAT1↑,
ECHDC1↑
GO:0009062 fatty acid catabolic process G9 2.54E-04 5.08 3 ACAA2↑,
ACAT1↑, ECHDC1↑
GO:0072329 monocarboxylic acid catabolic process G9 2.89E-04 4.35 3
ACAA2↑, ACAT1↑, ECHDC1↑
GO:0006635 fatty acid beta-oxidation G9 1.33E-04 6.52 3 ACAA2↑, ACAT1↑,
ECHDC1↑
GO:0044282 small molecule catabolic process G10 2.21E-07 3.85 7 AASS↓,
ACAA2↑, ACAT1↑, ECHDC1↑, FAH↑, SUS2DD↓, UROC1↑
GO:0016054 organic acid catabolic process G10 5.77E-07 4.51 6 AASS↓,
ACAA2↑, ACAT1↑, ECHDC1↑, FAH↑, UROC1↑
GO:0006520 cellular amino acid metabolic process G10 4.45E-05 2.59 5
AASS↓, ACAT1↑, CPS1↑, FAH↑, UROC1↑
GO:1901605 alpha-amino acid metabolic process G10 1.00E-05 3.82 5
AASS↓, ACAT1↑, CPS1↑, FAH↑, UROC1↑
GO:0009063 cellular amino acid catabolic process G10 8.06E-06 7.14 4
AASS↓, ACAT1↑, FAH↑, UROC1↑
GO:0046395 carboxylic acid catabolic process G10 5.77E-07 4.51 6 AASS↓,
ACAA2↑, ACAT1↑, ECHDC1↑, FAH↑, UROC1↑
GO:1901606 alpha-amino acid catabolic process G10 7.81E-06 8.16 4
AASS↓, ACAT1↑, FAH↑, UROC1↑
[117]Open in a new tab
^1Groups of closely related terms
^2Benjamini-Hochberg corrected p-values
^3Percentage of input proteins found associated with respect to the
number of proteins directly annotated with the functional term
^4The symbol ↑ indicates protein abundance higher in Italian Duroc pigs
than Italian Large White pigs while the symbol ↓ indicates protein
abundance higher in Italian Large White pigs than Italian Duroc pigs
Fig 3. Functional analysis of the 25 differentially abundant proteins between
Italian Duroc and Italian Large White pigs.
[118]Fig 3
[119]Open in a new tab
The two panels show the result of gene enrichment analyses over the A)
Gene Ontology–Biological Process branch, and B) over the KEGG pathway
database. Bars represent the percentage of input proteins found
associated with respect to the number of proteins directly annotated
with the functional term. The number of input proteins related to the
term and the term significance are reported next to each bar. Detailed
statistics are reported in [120]Table 2 and [121]Table 3, respectively.
In each panel, bars sharing a specific color are clustered in the same
functional group (see [122]Table 2).
Over-representation analysis over the KEGG pathway database highlighted
a total of nine pathways ([123]Table 3, [124]Fig 3B) related to the
metabolism of lipids, amino-acids, carbohydrates and chemicals (as
previously showed from the analysis over the GO:BP database). Nine
proteins were involved in these pathways. Again, in this case, all the
differentially abundant proteins involved in the fatty acid catabolism
had higher concentration in Italian Duroc pigs than in Italian Large
White pigs. Moreover, in addition to the glutamine family amino acids,
the pathways involving valine, leucine, isoleucine and tyrosine showed
the same behaviour.
Table 3. Over-represented KEGG pathways associated to up or down regulated
proteins in the breed comparison.
Term Description p-value[125]^1 % of associated proteins[126]^2 N. of
proteins Up or down regulated proteins[127]^3
KEGG:00310 Lysine degradation 1.06E-02 3.45 2 AASS↓, ACAT1↑
KEGG:00350 Tyrosine metabolism 7.88E-03 5.71 2 ADH5↑, FAH↑
KEGG:00620 Pyruvate metabolism 7.04E-03 5.41 2 ACAT1↑, FH↑
KEGG:00640 Propanoate metabolism 8.27E-03 6.45 2 ACAT1↑, ECHDC1↑
KEGG:00071 Fatty acid degradation 1.28E-03 7.32 3 ACAA2↑, ACAT1↑, ADH5↑
KEGG:00280 Valine, leucine and isoleucine degradation 9.07E-03 4.00 2
ACAA2↑, ACAT1↑
KEGG:00980 Metabolism of xenobiotics by cytochrome P450 1.09E-03 6.12 3
ADH5↑, GSTA1↓, SUS2DD↓
KEGG:00982 Drug metabolism 9.38E-03 4.26 2 ADH5↑, GSTA1↓
KEGG:05204 Chemical carcinogenesis 1.04E-02 3.28 2 ADH5↑, GSTA1↓
[128]Open in a new tab
^1Benjamini-Hochberg corrected p-values
^2Percentage of input proteins found associated with respect to the
number of proteins directly annotated with the functional term
^3The symbol ↑ indicates protein abundance higher in Italian Duroc pigs
than Italian Large White pigs while the symbol ↓ indicates protein
abundance higher in Italian Large White pigs than Italian Duroc pigs.
STRING was used to highlight the functional connections established
among the differentially abundant proteins. The analysis revealed a
connected protein network ([129]Fig 4) divided in: (i) one big module
composed by 16 nodes (64%) and 19 links, (ii) a small component of two
proteins (8%) and (iii) seven singletons (28%). The resulting network
showed a PPI enrichment p-value of 3.67×10^−10 (three expected edges
vs. 20 detected edges) indicating that proteins are at least partially
biologically connected. In this network most of the proteins interacted
with only one or two other partners (average node degree equal to 1.6).
However, two proteins (ADH5 and HSD17B14; with higher expression in
Italian Duroc than in Italian Large White pigs) presented the highest
degree of connection (six edges), which may assign to them a role as
“hub” proteins playing a putative function of controllers inside
biochemical pathways that could potentially lead to cascade of protein
expression differences. In addition, the big module clustered all
proteins that were included in the GO and KEGG enriched processes
clearly differentiating (in terms of direction of the relative level of
expression) Italian Duroc and Italian Large White liver proteomic
profiles.
Fig 4. Protein-protein interaction map of the 25 differentially abundant
proteins.
[130]Fig 4
[131]Open in a new tab
Interactions are based on STRING v.10.5 and are shown in different
colors: cyan is from curated databases, magenta is experimentally
determined, dark green is gene neighborhood, blue is gene
co-occurrence, light green is textmining, black is co-expression and
purple is protein homology.
[132]S3 Table reports information on the potential links between
differentially expressed proteins identified in the comparison between
breeds and QTL mapped in the corresponding gene regions, as retrieved
from the pigQTL database. A total of 12 out of 25 up or down regulated
proteins identified in the breed comparative analysis resulted located
in ± 50 kbp surrounding 23 QTL regions (for a total of 40 protein/QTL
combinations), that might be directly or indirectly (in a broad sense)
affected by the protein functions. Six of these proteins were
up-regulated in Italian Duroc pigs (ECHDC1, HSD17B14, FAH, UROC1,
ACAT1, CPS1) and six in Italian Large White pigs (PRDX5, SPR, GSTA1,
FASN, KHK, CES3).
Discussion
The pig is one of the most important animal species used as protein
source for human consumption that is also considered a suitable model
for several biomedical aspects related to liver functions [[133]41,
[134]42]. In spite of that, thus far only few studies have investigated
the pig liver proteome [[135]9, [136]11–[137]13, [138]43–[139]52]. Most
of these liver proteomic investigations compared proteomic profiles of
pigs under different experimental conditions or treatments or just
wanted to extend the porcine liver proteome map. Our work not only
contributed to obtain a more detailed picture of the protein expressed
in pig liver but also provided a first comparative analysis of the
liver proteome of two important heavy pig breeds, Italian Duroc and
Italian Large White. These breeds are used in crossbreeding programmes
to obtain commercial slaughtered pigs, maximizing the effect of
heterosis. Italian Large White pigs are mainly selected for meat
quality and carcass traits and to maximize maternal reproduction
efficiency. Italian Duroc pigs are mainly used as terminal sires and
are selected to maximize growth, feed efficiency and improve meat
quality parameters [[140]21]. As the liver can be considered a key
organ for translating growth rate, feed efficiency and performance
potentials of the animals by exploiting tissue specific metabolic
functions, the evaluation of proteome differences between Italian Duroc
and Italian Large White pigs could highlight biological aspects (that,
in turn, are derived by genetic factors) that distinguish and
characterize these breeds and that might determine their peculiar
production potentials.
It is clear that any proteomic study cannot disclose in a single
analysis or method all proteins that construct a tissue or an organ for
intrinsic limits of the available technologies and difficulties to
discriminate and separate all proteins present in complex matrices. The
predicted human proteome has been estimated to be constituted by at
least 20,000 proteins (without considering splicing variants), about
59% of which might be expressed in the liver [[141]53]; a similar
fraction could be expected in the porcine liver. Therefore, the liver
could be considered one of the richest organs in terms of number of
proteins. However, studies in pig liver reported, in most cases, tens
or just a few hundreds of proteins. For example, Caperna et al.
[[142]11] using 2DE produced two liver porcine proteomic maps, one from
the cytosol fraction and another one from the membrane fraction. A
total of 728 proteins spots were picked and analysed by MS resulting in
a total of 282 unique identified proteins. Golovan et al. [[143]44]
using isobaric tag for relative and absolute quantification (iTRAQ)
proteomics identified a higher number of proteins (i.e. n. = 880) in
the liver proteomic profiles obtained from two pig lines (transgenic
Enviropig and conventional Yorkshire).
In our study, the label‐free LC‐MS analysis revealed a total of 501
different proteins that, after stringent filtering were identified as
329 unique proteins, of which 200 were then considered in the
comparison between the two breeds ([144]S1 and [145]S2 Tables). Many of
the identified proteins (e.g. peroxiredoxin, acetyl-CoA,
carboxylesterase, fumarylacetoacetase) were also identified in other
studies carried out in pigs or other livestock species [[146]11,
[147]44, [148]54]. Most of the identified proteins were involved in
several metabolic processes that characterize the liver functions
([149]Fig 1). Differences among studies might be due to different
methods of protein extraction employed and by the peptide separation
and identification technologies. Different approaches and methods have
different efficiencies and throughput and resolution potentials which
determine, in turn, the number of detected proteins. For example, the
2DE study of Caperna et al. [[150]11] identified a total of 282 unique
porcine liver proteins, the majority of which were from the cytosol
fractions. It is well know that this method is technically challenging
and is not optimized for the analysis of very hydrophobic and/or
membrane proteins [[151]55]. Using iTRAQ, Golovan et al. [[152]44]
identified a higher range of proteins in the pig proteome, but only few
of them (i.e. two and four) were differentially expressed between
breeds and sexes, respectively. In this label-based method, stable
isotopes are employed to create specific mass tags that can be
recognized by the mass spectrometer and can provide, at the same time,
the basis for the quantification. One of the main advantage of this
method is that it allows, in a single MS run, the simultaneous analysis
of many samples, reducing analytical variability [[153]56]. On the
other hand, the label-free method that we applied provides a higher
dynamic range of quantification than that obtained by stable isotope
labelling methods [[154]57].
To our knowledge, this work is the first study which applies a
label-free method to unravel the pig liver proteome. In the label-free
study of Miller et al. [[155]54], that compared the liver proteome of
two sheep breeds, a larger number of proteins was identified (2,445 vs
501 of our work), probably due to differences in sensitivity of the
used instruments. Miller et al. [[156]54] and our work had a similar
objective, i.e. identify proteomic differences between breeds. In both
studies that were based on a similar experimental design (both included
comparative analyses between breeds), the same percentage of proteins
(about 8%) were declared as differentially expressed between the two
analysed genetic types (within species). Even if it could be quite
speculative, this general overlapping gross picture might define
similar levels of underlying genetic diversity between the compared
groups of the two species.
sPLS-DA plot showed distinct clustering, which clearly segregated the
samples of the two analysed pig breeds based on their liver proteomic
profiles ([157]Fig 2). This pattern is in agreement with our previous
results obtained by analyzing targeted metabolomic profiles of plasma
and serum of Italian Duroc and Italian Large White pigs [[158]18].
Liver interplays with blood exchanging the products of their respective
anabolism and catabolism and many other components, depicting a
metabolic picture that could be observed at the organ or tissue level
as well in the circulating tissue and in its fractions (i.e. plasma and
serum).
Among the 25 proteins that were up- or down-regulated between the two
pig breeds, the top differentially expressed proteins (ACAA2,
HIST2H2BF, CES3 and KHK) according to their functions, may contribute
to explain in part the phenotypic differences between Italian Duroc and
Italian Large White animals, complementing the metabolomic profile
differences already reported in a previous study [[159]18].
Carboxylesterase 3 (CES3) was up-regulated in Italian Large White. This
is a member of enzymes of the endoplasmic reticulum that hydrolyses a
variety of esters, carbamates, amides and similar structures of drugs
and xenobiotics [[160]58]. This enzyme is involved in hepatic very
low-density lipoprotein (VLDL) assembly and in basal lipolysis of
adipose tissues. Ablation of carboxylesterase 3 expression in mice
results in reduced circulating plasma triacylglycerol, apolipoprotein
B, and fatty acid levels and increased food intake and energy
expenditure [[161]59, [162]60]. Moreover, the porcine CES3 gene is
located on porcine chromosome (SSC) 6 in a QTL region for residual feed
intake (that might be related to feed intake and metabolism efficiency;
[163]S3 Table) [[164]40]. Differences of hepatic CES3 levels between
Italian Large White and Italian Duroc might be in line with a higher
feed efficiency of the latter breed. ACAA2, among the top up-regulated
proteins in Italian Duroc pigs, is a mitochondrial enzyme catalysing
the last step in fatty acid oxidation which releases acetyl CoA for the
Krebs cycle. For its role, it is considered one of the key enzymes in
lipid metabolism [[165]61], further evidencing differences related to
fat related pathways. Again, ACAT1, that is involved in cellular
cholesterol homeostasis, macrophage cholesterol metabolism, isoleucine
metabolism and ketogenesis (i.e. Hai et al. [[166]62]), was
up-regulated in Italian Duroc pigs. The ACAT1 gene is located on SSC9
in a region in which several QTLs for average daily gain and
intramuscular fat content have been identified ([167]S3 Table). In
addition, this protein has been already identified to be differentially
expressed in a proteomic analysis of skeletal muscles between pigs with
extreme values of intramuscular fat content [[168]63], strengthening a
role of lipid metabolism differences between pig breeds that could
result in different phenotypic traits [[169]23, [170]64]. Moreover, a
few other hepatic differences between Italian Duroc and Italian Large
White pigs highlighted also metabolic differences in fatty acid
metabolism. The ECHDC1 gene, which encodes for a protein involved in
the mitochondrial fatty acid oxidation, is located in a region of the
SSC1 overlapping a QTL region involved in the saturated fatty acid
content ([171]S3 Table). FASN (up-regulated in Italian Large White
pigs) is an enzyme that catalyses the biosynthesis of palmitic acid.
The porcine FASN gene is located on SSC12 in a region in which two QTLs
affecting the myristic and palmitic acids have been mapped and for
which FASN was pointed out as the most plausible candidate gene
[[172]65]. Histone H2B (HIST1H2BA), is a member of the histone family,
which are basic nuclear proteins that are responsible for the
nucleosome structure of the chromosomal fiber in eukaryotes [[173]66].
This family of proteins and peptides derived from them were found to be
part of the antimicrobial defense of many species [[174]67–[175]69].
For example, Li et al [[176]68] highlighted the antimicrobial activity
of HIST1H2BA and other histone proteins in the chicken liver extract.
In our study, HIST2H2BF was up-regulated in the Italian Duroc breed,
which may allude to a stronger resistance to disease of this breed,
that is, in general considered the most rustic breed among all
commercial pig breeds [[177]70].
These specific differences highlighted by the function of the proteins
mentioned above (and all other proteins that contributed to produce
“breed specific” proteomic profiles; [178]Table 1) can be summarized in
few biological processes that could be identified summing up their
roles, matching again some of the metabolomic differences (described
with biogenic amines and sphingomyelins) that we already reported
between these two breeds [[179]18]. Protein differentially expressed
were widely distributed among the known biological processes associated
with liver metabolism such as metabolism of lipids, metabolism of
amino-acids, metabolism of carbohydrates, metabolism of cofactors and
metabolism of antibiotics/drugs. Liver proteomic profiles we obtained
for Italian Duroc and Italian Large White pigs seems to match
production characteristics of these breeds that have been developed
over decades of divergent directional selection originally determined
by the genetic pools constituting these two heavy pig breeds.
Conclusions
In this study we reported results from the first proteomic
investigation of the liver of heavy pigs using a label-free proteomic
approach. Despite this work should be considered a pilot study and
proteomic differences might be confirmed with other approaches, the 25
identified proteins up- or down-regulated in the compared breeds were
able to discriminate the liver proteomic profile of pigs belonging to
the Italian Duroc and Italian Large White breeds. These results
indirectly demonstrated that breed differences (underlying general
genetic differences) can be highlighted at the liver proteome level.
These different proteomic profiles could be useful to describe
breed-specific metabolic characteristics. We also provided evidences
that quantitative proteomic approaches are useful to describe internal
phenotypes that could be important to link and dissect external
(production) and complex traits. The obtained proteomic differences
highlighted several biomarkers that can be potentially useful to define
new molecular phenotypes for novel applications in pig breeding
programmes.
Supporting information
S1 Table. List of the 329 proteins used to group the liver proteins
according to their different biological processes.
(XLSX)
[180]Click here for additional data file.^ (56.9KB, xlsx)
S2 Table. Full list of the 200 proteins analysed in this study.
(XLSX)
[181]Click here for additional data file.^ (19KB, xlsx)
S3 Table. Links between differentially expressed proteins identified in
the comparison between breeds and QTLs mapped in the corresponding gene
regions (± 50 kbp).
(DOCX)
[182]Click here for additional data file.^ (27.2KB, docx)
Acknowledgments