Abstract
Background
Ruminants play a great role in sustainable livestock since they
transform pastures, silage, and crop residues into high-quality human
food (i.e. milk and beef). Animals with better ability to convert food
into animal protein, measured as a trait called feed efficiency (FE),
also produce less manure and greenhouse gas per kilogram of produced
meat. Thus, the identification of high feed efficiency cattle is
important for sustainable nutritional management. Our aim was to
evaluate the potential of serum metabolites to identify FE of beef
cattle before they enter the feedlot.
Results
A total of 3598 and 4210 m/z features was detected in negative and
positive ionization modes via liquid chromatography-mass spectrometry.
A single feature was different between high and low FE groups. Network
analysis (WGCNA) yielded the detection of 19 and 20 network modules of
highly correlated features in negative and positive mode respectively,
and 1 module of each acquisition mode was associated with RFI
(r = 0.55, P < 0.05). Pathway enrichment analysis (Mummichog) yielded
the Retinol metabolism pathway associated with feed efficiency in beef
cattle in our conditions.
Conclusion
Altogether, these findings demonstrate the existence of a serum-based
metabolomic signature associated with feed efficiency in beef cattle
before they enter the feedlot. We are now working to validate the use
of metabolites for identification of feed efficient animals for
sustainable nutritional management.
Electronic supplementary material
The online version of this article (10.1186/s12864-018-5406-2) contains
supplementary material, which is available to authorized users.
Keywords: Residual feed intake, Nellore, Retinol, WGCNA
Background
The Food and Agriculture Organization of the United Nations estimates
the world population will reach 9 billion people by 2050 and as a
consequence, livestock production must double to meet the demand for
food [[40]1]. Sustainable livestock production is a field of intense
research where ruminants play a great role since they can transform
graze pastures, silage and high-fiber crop residues into high-quality
human food (i.e. milk and meat) [[41]2]. The goal is “sustainable
intensification” [[42]3], meaning increased productivity while reducing
the environmental impacts. In this context, feed efficiency (FE) has a
particular importance, since it is directly related to productivity,
greenhouse gas emission intensities, and resource use [[43]4, [44]5].
Due to its importance, more than two dozen feed efficiency measurements
have been proposed to select efficient animals and from those, residual
feed intake (RFI) is considered one of the most effective methods
[[45]6, [46]7]. As a complex trait, at least five major physiological
mechanisms contribute to RFI variation: feed intake behavior,
digestion, physical activity, thermoregulation and cell
anabolism/catabolism [[47]8]. Recently, our group proposed a new
biological process associated with FE in beef cattle: increased hepatic
inflammation in less efficient animals probably caused by altered lipid
metabolism and/or increased bacterial infection associated with higher
feed intake [[48]9].
Metabolomics is the systems-scale study of low-molecular-weight
biochemicals (< 1500 Da) involved in metabolism, including
carbohydrates, lipids, amino acids, biogenic amines, and organic acids
[[49]10, [50]11]. Due to the important role of metabolism across all
biological processes, metabolomics studies have been increasingly used
to understand physiological processes associated with economically
important traits in livestock such as meat quality in pigs [[51]12],
milk production in dairy cattle [[52]13–[53]15] and growth in beef
cattle [[54]16]. Also, metabolomics has been applied to RFI studies,
reporting blood metabolites in beef cattle during feedlot [[55]17,
[56]18].
Currently, there is an urgent need to develop new ways to predict FE in
livestock, since the use of the commercially available genomic markers
for genetic selection is not sensitive enough due to low to moderate
heritability (ranging from 0.08 to 0.49) of the FE trait
[[57]19–[58]22]. Therefore, we hypothesized there are specific serum
metabolome signatures that predict feed efficiency in beef cattle
before the feedlot which could be used for feed management of beef
cattle. To this end, we used serum samples from a previous feeding
trial with young Nellore bulls and performed a metabolomic approach on
high and low feed efficient animals. The resulting data were used to
investigate whether circulating metabolite levels could predict feed
efficiency.
Methods
Phenotypic data collection
All animal procedures were approved by the Institutional Animal Care
and Use Committee of the Faculty of Food Engineering and Animal Science
at the University of Sao Paulo (protocol 14.1.636.74.1). The serum of
98 Nellore young bulls (16 to 20 months old and 376 ± 29 kg BW) born
and raised in the University of Sao Paulo were collected 21 days prior
to a 70-d feedlot. Briefly, the feeding-trial period was preceded by
21 days of adaptation to diet and location and before that, the animals
were maintained in a single group on Brachiaria spp. pastures. On
adaptation period, animals received corn silage (ad libitum), gradually
replaced by trial diet (total mixed ration, including dry corn grain,
corn silage, soybean, citrus pulp pellets, urea, calcareous, mineral
salt and potassium chloride) offered at 8:00 h and 16:00 h. After the
experiment, all animals were slaughtered following the guidelines of
the Institutional Animal Care and Use Committee. More details regarding
animals, diet and experimental design can be found in Alexandre et al.
[[59]9] and Mota et al. [[60]23].
RFI was calculated as the difference between the expected and observed
feed intake, considering the average metabolic weight (MBW) and ADG to
predict DMI [[61]6]. The 98 animals were ranked by RFI, and two groups
of 8 animals each were selected for further analysis (total of 16
animals): high feed efficiency (HFE, low RFI) and low feed efficiency
(LFE, high RFI). Sire and age effect on RFI were estimated by
completely randomized design on linear model:
[MATH: Yijk=μ+βi+βk+eij
:MATH]
where Yij is the observation of jth individual, son of ith sire, with k
age; μ is the general mean of the RFI; βi is the sire effect; βj is the
age effect and eij is the random residual error, ~NID (0, σ^2[e]); and
σ^2[e] is the residual variance. The phenotypic measures included:
initial body weight (BWi), final body weight (BWF), dry matter intake
(DMI), average daily gain (ADG), feed conversion ratio (FCR), residual
feed intake (RFI), residual body weight gain (RWG), residual intake and
body weight gain (RIG), initial ribeye area (REAi), final ribeye area
(REAf) and gain of ribeye area (REAg). Normality of data was tested by
the Shapiro-Wilk test. Student’s t-test was applied to compare the
groups for normally distributed variables and Mann-Whitney-Wilcoxon
test for nonparametric variables using R STATS package. Results were
considered significant when p-value (P) ≤ 0.05. The RFI values were
adjusted using regression model, in which the age was fitted as a
covariate for network analysis (Additional file [62]1).
Sample collection
Serum samples were collected 21 days before the start of the feeding
trial (before the adaptation period) by jugular venipuncture using
vacutainer tubes. After 30 min at room temperature for clot formation,
all samples were centrifuged at 3500×g for 15 min at 4 °C and stored at
− 80 °C until further analysis, following the recommendations of Tuck
et al. [[63]24].
LC-MS analysis
Protein precipitation of serum samples was performed at 4 °C by adding
methanol (1:4 serum: methanol) and vortexing for 120 s at 5000 rpm
[[64]10]. The samples were then centrifuged at 16000 g for 4 min at
room temperature, and the supernatants were dried in a vacuum
centrifugal evaporator for 3 h at 30 °C and stored at − 20 °C prior to
analysis. The samples were reconstituted in 200 μL H[2]O and
centrifuged at 12000 rpm for 15 min. The supernatants were transferred
to analytical vials for analysis using a Xevo G2 XS
quadrupole-time-of-flight mass spectrometer (Q-TOF-MS) in positive and
negative modes (Waters Corporation, Milford, MA, USA). Chromatographic
separation was performed by an Acquity I-Class UPLC system (Waters
Corporation, Milford, MA, USA) using a Waters Acquity BEH C18 column
(2.1 mm × 100 mm, 1.7 μm) (Waters Corporation, Milford, MA, USA) at
50 °C. The injected sample volume was 5 μL. The mobile phase consisted
of 0.1% formic acid-water (eluent A) and 0.1% formic acid-methanol
(eluent B). The gradient elution in positive mode was performed at a
flow rate of 0.4 ml/min, as follows: between 0 and 1 min 0% eluent B;
1–16 min increasing up to 100% eluent B;16–20 min at 100% eluent B and
20–22 min decreasing back to 0% eluent B. The elution flow rate was
0.36 ml/min in negative mode, with an elution gradient as follows:
0–2 min 0% eluent B; 2–17 min increasing up to 100% eluent B; 17–22 min
at 100% eluent B and 22–24 min decreasing back to 0% eluent B.
The UPLC was connected to the electrospray ionization (ESI) interface,
operating in negative and positive modes, with a capillary voltage of
− 2.5/+ 3 KV, source temperature of 150 °C, cone gas flow of 50 L/h,
cone voltage of 40 V, desolvation temperature of 550 °C and desolvation
gas flow of 800 L/h. The spectra were collected at high resolution
(mass resolving power 30,000 M/ΔM at fwhm) from 100 m/z (mass/charge
ratio) to 1200 m/z, collected over 250 ms per spectrum in centroid
mode. To avoid problems due to instrument drift, the sequence of
samples was randomized and pooled quality-control samples (QC) were
injected periodically for use in downstream data processing and
correction [[65]10]. QC samples were prepared by pooling equal volumes
of all samples; these samples were run after every four sample
injections to provide a measurement of the stability and performance of
the system.
Data treatment and pre-processing
LC-MS raw data were created and processed and using Waters MassLynx™
(Waters Corporation, Milford, MA, USA) Software v4.1 and Progenesis QI
(Nonlinear Dynamics, Newcastle, UK). Following the manufacturer’s
instructions, a reference run was automatically selected, and the
precursor ion traces were processed for alignment, peak picking and
normalization with default parameters. Locally estimated scatterplot
smoothing (LOESS) signal correction based on QC samples was performed
using MATLAB 2016 software with a script built for this purpose
[[66]25].
Afterward, a quality assurance (QA) step was used for analytical
validation: variables with unacceptable reproducibility in QC samples
(RSD > 20% in QCs or detected in less than 50% of QCs) and samples
(detected in less than 90% of QC) were removed from the dataset
[[67]10]. The confidence scores of annotated metabolites are 2, meaning
they have matches to a search database [[68]26].
Metabolomics data analysis
Univariate and multivariate analyses were carried out using
Metaboanalyst 4.0 Web Server [[69]27]. Glog transformation [[70]28] and
auto-scaling [[71]29] were applied. Differences between the groups were
investigated using univariate (UA) and multivariate analysis (MA). For
MA, principal component analysis (PCA) and partial least-square
discriminant analysis (PLS-DA) were used for detection of outliers and
to identify features potentially responsible for variation between the
groups [[72]29]. PLS-DA model quality was assessed using the goodness
of fit (R^2) and goodness of prediction (Q^2) in cross-validation and
using a permutation test with 2000 permutations [[73]29]. For UA,
t-test was used to identify differentially expressed features, then the
p-values were corrected for multiple tests by Significance Analysis of
Microarrays (SAM-FDR) method [[74]30]. Features with SAM-FDR
q-value < 0.05 were considered different between groups.
Network analysis
Network and clustering analysis were performed using the Weighted Gene
Co-expression Network Analysis (WGCNA) R package [[75]31, [76]32].
Normalized data from positive and negative acquisition modes were used
separately as described by Fukushima et al. [[77]33], with a soft
threshold of 3, chosen using a scale-free topology criterion
(R^2 = 0.9). Modules containing at least 20 features were retained.
To select modules associated with FE, Pearson correlations between each
module’s “eigengene” and the RFI were calculated. The “eigengene” is
the first principal component of a given module and a representative
measure of its metabolic profile. (The term “gene” is used even for
other data types, due to the development of WGCNA originally for the
analysis of transcriptional data.) Modules with a module-trait
relationship magnitude (correlation) > 0.5 for RFI (P ≤ 0.05) were
considered significant. Individual features were considered for further
analysis only if they had module membership (MM) > 0.6 (P < 0.01) and
gene significance (GS) > 0.5 (P < 0.05). GS is defined as the
association of features with RFI, and MM is defined as the correlation
of the features with the module eigengene. High GS and MM scores
indicate a feature is a central element of a module and is
significantly associated with the trait [[78]34].
Metabolic pathway analysis
Metabolic pathway analysis was performed using Mummichog software 1.0.9
with Bos taurus species (KEGG database) as reference [[79]35]. Using
default parameters for analyte prediction (mass accuracy 10 ppm) and
for pathway enrichment analysis (1000 permutations). Features from UA
with P < 0.01 were used as input to mummichog to test for pathway
enrichment compared to random data resampled from the reference list,
yielding an empirical p-value per pathway. Pathways with corrected
q-value < 0.05 were considered significant.
Results
We performed a 70-day feeding trial on 98 Nellore young bulls to
evaluate their feed efficiency [[80]9]. Based on the linear model (see
Methods), there was no significant sire effect on RFI and the high feed
efficient (HFE) and the low feed efficient (LFE) groups were
statistically different (P ≤ 0.05) for all FE traits (feed conversion
ratio (FCR), RFI, residual weight gain (RWG) and residual intake and
weight gain (RIG), dry matter intake (DMI)) and also for average daily
gain (ADG). There was also a significant difference for backfat
thickness at the end of the experiment (BFTf, P ≤ 0.05), which were
greater in the LFE group (Table [81]1). Therefore, HFE animals in this
experiment are more sustainable since they eat less, are leaner and
have a better ADG than LFE animals.
Table 1.
Descriptive statistics of high feed efficiency (HFE) and low feed
efficiency (LFE) for phenotypic traits
Trait HFE (±SEM) LFE (±SEM) P value
BWi (kg) ■ 410 ± 16.03 404.3 ± 7.97 0.64
BWf (kg) ○ 563.5 ± 17.35 525.8 ± 9.87 0.07
DMI (kg/d) ■ 10.38 ± 0.39 12.35 ± 0.33 < 0.0001*
ADG (kg/d) ■ 2.194 ± 0.15 1.734 ± 0.08 0.0497*
FCR ■ 4.763 ± 0.17 7.3 ± 0.29 < 0.0001*
RFI (kg/d) ○ −1.384 ± 0.12 1.791 ± 0.12 < 0.0001*
RWG (kg/d) ■ 0.4325 ± 0.07 − 0.3988 ± 0.06 < 0.0001*
RIG ○ 1.815 ± 0.10 −2.188 ± 0.13 < 0.0001*
REAi (cm2) ■ 68.26 ± 2.22 67.23 ± 1.95 0.63
REAf (cm2) ■ 84.94 ± 2.58 82.91 ± 1.65 0.64
REAg (cm2) ■ 19.34 ± 2.83 15.69 ± 1.77 0.99
BFTi (mm) ○ 0.775 ± 0.38 1.975 ± 0.46 0.07
BFTf (mm) ■ 2.975 ± 0.67 5.713 ± 0.64 0.0096*
BFTg (mm) ■ 2.2 ± 0.67 3.738 ± 0.37 0.063
[82]Open in a new tab
BWi initial body weight, BWF final body weight, DMI dry matter intake,
ADG average daily gain, FCR feed conversion ratio, RFI residual feed
intake, RWG residual body weight gain, RIG residual intake and body
weight gain, REAi initial ribeye area, REAf final ribeye area, REAg
gain of ribeye area. *P < 0.05. ■ Student’s t-test.
○Mann-Whitney-Wilcoxon Test
Metabolome profile and differential analysis
After quality assurance processing, a total of 3598 and 4210 m/z in
negative and positive ionization modes, respectively, were used for
parallel analyses. For Principal component analysis (PCA), no
separation was observed for high and low FE animals in the first five
principal components (Fig. [83]1), which explained 64.5 and 57% of
total variance for negative and positive modes, respectively. PLS-DA
was able to distinguish the two groups, but permutation and
cross-validation analyses indicated the model was overfitted and thus
not predictive (Fig. [84]1). The univariate analysis yielded one
feature with different abundance between groups in positive mode. The
spectra of mass-charge 183.1670 m/z and retention time v4.00 min on
chromatography column (Fig. [85]2) has a P < 0.001 (SAM-FDR = 0.03)
which is greater on HFE group. No significantly different m/z were
identified in negative mode.
Fig. 1.
[86]Fig. 1
[87]Open in a new tab
PCA (a and c, in negative and positive mode, respectively) and PLS-DA
(b and d, negative and positive mode, respectively) scores plots based
on LC/MS data of serum samples from HFE (red) and LFE (green). The
PLS-DA models discriminated between HFE and LFE groups (R^2 of 0.87 and
0.98 in negative and positive mode, respectively) but were not
predictive (Q^2 of 0.08 and 0.15). Considering a common heuristic for
metabolomics data: R^2 > 0.8 and Q^2 > 0.5, the model was not
overfitted. Consistent with this, a permutation test (2000
permutations) yielded P-values > 0.9 in both modes
Fig. 2.
[88]Fig. 2
[89]Open in a new tab
Univariate differential analysis of features from bovine metabolome. a
Univariate analysis corrected by multiple tests (SAM-FDR) results for
positive mode features. b The difference of abundance between the HFE
and LFE groups for the m/z 183.1670 peak with a retention time of
4.00 min (positive mode; SAM-FDR ≤ 0.05)
Pathway enrichment analysis
Pathway enrichment analysis was performed to explore possible pathways
involved in RFI phenotypic variation prior to the feedlot. Mummichog
software identified the enrichment of retinol metabolic pathway
(P < 0.05; Table [90]2), as being associated with FE in positive mode
with 2 pathway metabolites annotated in the data. The putative
compounds hit included retinoate ([91]C00777) and, either the isobaric
compounds (molecular weight 284.4357): all-trans-Retinal ([92]C00376)
or 11-cis-Retinal ([93]C02110) (Table [94]3).
Table 2.
Metabolic pathways for RFI prior to the feedlot and their size on the
positive mode of acquisition
Pathway Pathway size Total Hits Significant Hits Fisher’s P value
Retinol metabolism 17 6 2 0.0237*
Steroid hormone biosynthesis 67 8 1 0.3055
Arachidonic acid metabolism 36 7 1 0.2725
[95]Open in a new tab
Table 3.
Significant analytes predicted by mummichog
m/z Compound adduct mass diff P value HFE/LFE
267.2105 all-trans-Retinal / 11-cis-Retinal M-H2O + H[1+] 0.00017686
0.0075* Down
273.2233 Retinoate M-CO + H[1+] 0.00213163 0.0019* Up
[96]Open in a new tab
The mass-charge (m/z), compounds hit, mass difference, analyte p-value
and FE group association
Weighted correlation network analysis
We then used WGCNA co-expression analysis to identify clusters of
analytes that may have a relationship with the feed efficiency. WGCNA
identified 19 and 20 modules of highly correlated features in negative
and positive mode, respectively.
One of these modules was significantly positively correlated with RFI
(blue module from the negative mode, r = 0.55, and P = 0.033),
indicating higher levels in LFE animals. The blue module contains 196
features (Fig. [97]3a), of which 65 were identified as important
contributors to this module (Additional file [98]2). Using mummichog,
three of these features were putatively annotated: (i) 6S,9R-Vomifoliol
(compound KEGG [99]C01760) (ii) 2,3, Dihydroflavone (compound
[100]C00766); (iii) Limonoate (compound [101]C01593). The additional
file [102]2 has information of the important features of blue module on
negative mode, including mass-charge (m/z), retention time (rt),
feature significance to trait (GS), feature significance to module
(FM), group with highest abundance, putative matched compound in KEGG
and mass difference between feature and putative compound.
Fig. 3.
[103]Fig. 3
[104]Open in a new tab
Network analysis of co-expressed features in the negative and positive
mode of acquisition. Pearson correlation between residual feed intake
(RFI) and the module eigengenes in the negative (a) and positive (b)
mode. In each line the color name of modules (ME). The number in each
module is the Pearson correlation between the module and RFI; In
brackets the p-value of the correlation
A second module was associated with RFI (blue module from the positive
mode, r = 0.55, and p-value = 0.033), also indicating higher levels in
LFE animals. This module contains 112 features (Fig. [105]3b), of which
39 were identified as highly important contributors to this module
(Additional file [106]3). Using mummichog, 5 compounds were annotated:
(i) Phytanic acid (compound [107]C01607); (ii) all-trans-Retinal
(compound [108]C00376); (iii) Progesterone (compound [109]C00410); (iv)
Limonoate (compound [110]C01593); (v) Stearic acid (compound
[111]C01530). The additional file [112]3 has information of the
important features of blue module on positive mode, including
mass-charge (m/z), retention time (rt), feature significance to trait
(GS), feature significance to module (FM), group with highest
abundance, putative matched compound in KEGG and mass difference
between feature and putative compound.
Discussion
Brazilian cattle are mainly raised in pastures, but can also be kept in
feedlot systems with diets composed of silage or other feedstuffs, such
as high-fiber crop residues or grains (corn and soybean), to improve
body weight gain before slaughter. We used serum samples collected
before the feedlot period to search for a potential early metabolomic
signature for FE, with the intent to support nutritional management
decisions to improve productivity and sustainability of livestock.
Thus, we performed an exploratory analysis using untargeted
metabolomics coupled with bioinformatics and interpretation tools
including Mummichog and WGCNA. We found one differentially expressed
feature between HFE and LFE animals in these conditions, but most
importantly, we also found one enriched pathway and two sets of highly
correlated features significantly associated with FE, which could be
considered a potential molecular signature of FE in Nellore cattle
before they enter the feedlot period.
A co-expression module associated with a phenotype provides significant
promise for the development of a molecular signature, clearly more than
a single statistically different feature between two conditions
[[113]36]. In our previous work, the co-expression gene modules and
their gene ontology were far more important results than the
differentially expressed genes [[114]9]. In this context, the hepatic
inflammatory response was associated with feed efficiency in cattle.
Here, the WGCNA analysis indicated two modules of co-expressed features
positively associated with RFI, with equal correlation, p-values (Fig.
[115]3) and common features, suggesting that both networks belong to
the same molecular signature.
We were able to identify 7 molecules from the co-expressed modules
through Mummichog prediction: Retinal, Progesterone, Stearic acid,
Vomifoliol, 2,3 Dihydroflavone, Limonoate and Phytanic acid.
Interestingly, all these molecules have higher levels in LFE animals
which are in accordance with the modules being positively associated
with RFI. In addition, mummichog software predicted two molecules from
the retinol pathway significantly associated with FE: a higher level of
Retinal and lower level of Retinoate ([116]C00777) in LFE, which
implies the enzymes aldehyde oxidase and retinal dehydrogenase (that
convert Retinal to Retinoate) as probably less active/expressed in LFE
animals. This result is in accordance with Zhao and colleagues
[[117]37] who demonstrate vitamin A (VA) metabolism is important for
feed efficiency in pigs as key genes of VA metabolism such as ALDH1A2
and CYP1A1 are upregulated in the liver of HFE animals. Also, in two
transcriptome studies, the retinol pathway was upregulated in the liver
of high-RFI Jersey steers [[118]38] and over-represented in the small
intestine from high intake beef steers [[119]39]. A GWAS-study using
CNV markers evidenced the RDH5 (an important gene of the retinol
metabolism pathway) as a candidate gene associated with feed conversion
rate in Nellore cattle [[120]40]. Therefore, our results agreed with
the literature regarding the importance of the retinol metabolism
pathway for feed efficiency in livestock animals.
Progesterone (P4) was another feature predicted in the molecular
signature of FE being more present in the blood of LFE animals. Steroid
hormone biosynthesis was overrepresented in the set of genes in the
liver that were upregulated in the high-RFI (low FE) group of Jersey
cows [[121]38], which is in accordance with our results. Recently, P4
signaling in broiler skeletal muscle was associated with divergent feed
efficiency [[122]41]. So far, there is no consensus on the role of P4
on feed efficiency in livestock and further studies should be
performed.
The stearic acid is a saturated acid (C18:0) and one of the end
products of the fatty acid biosynthesis pathway in animals. This fatty
acid was found increased in plasma of steers with least ADG in
comparison with greatest ADG [[123]42], and this result corroborates
our finding of a higher level of stearic acid in the molecular
signature associated with LFE animals since they had less ADG than HFE
in our experiment.
From all predicted molecules, Vomifoliol, 2,3 Dihydroflavone, Limonoate
and Phytanic acid are molecules produced exclusively by bacteria or
plants and not mammals. The higher presence of these molecules in the
blood of LFE animals could be due to higher DMI of these animals in
comparison with HFE animals, allowing the higher presence of these
metabolites in the blood. However, this possibility lacks further
evidence since we did not evaluate the pasture DMI of these animals,
i.e. feed intake before they arrive at the feedlot. From these 4
molecules, the Phytanic Acid could have a role on feed efficiency.
Phytanic acid is a branched-chain fatty acid formed during the
metabolism of phytol [[124]43] by ruminal bacteria and is a known
agonist for the nuclear-receptor-retinoid-X-receptor [[125]44] and the
peroxisome proliferated-activated receptor-α (PPAR-α) [[126]45]. These
two proteins are important nuclear receptors regulating the expression
of several genes in response to environmental factors (i.e. diet) and
endogenous molecules. Interestingly, in rats, agonists of PPAR-α
decreased feed efficiency [[127]46], and the PPAR signalling pathway
was enriched in the small intestine transcriptome analysis of high vs.
low feed intake cattle [[128]39]. Therefore, agonists of PPAR-α could
reasonably be associated with feed efficiency in cattle, but new
evidence should be provided to confirm this hypothesis.
Our integrated approach using data annotation, mummichog prediction and
WGCNA co-expression analyses indicated a molecular signature enriched
for biological processes previously associated with FE. The metabolites
in WGCNA modules were also predicted by mummichog, which supports the
validity of the in silico network analysis since the two different
analyses yielded consistent results. Therefore, we believe metabolomics
based modules associated with FE possibly represent a molecular
metabolic signature of FE. Although we have not yet been able to
identify the majority of the features in those modules, previous
studies on feed efficiency support the network analysis results.
Moreover, we noted it is possible to have a molecular signature
associated with a phenotype without knowing the function of the
components, just by (for metabolites) tracking m/z ratio and retention
time in a standardized assay. As an example, this is the case for
commercially available genomic selection in dairy cattle using DNA
markers, where the majority of the markers are not functional SNPs.
In our data, we found only one feature statistically different between
the FE groups: the feature with m/z 183.1670 and RT of 4.00 min
(positive mode) is upregulated in HFE animals. This result along with
the co-expressed module provides evidence of early serum metabolome
differences between high and low FE animals. Between both the positive
and negative ionization modes and after quality control-based
filtering, the serum metabolome of the animals in this experiment
consisted of approximately 8000 features. One may expect a priori to
identify more than just one different feature between high and low FE
animals using such a powerful tool. Possible explanations for this
result include, but are not limited to: (1) although the groups are
very distinct phenotypically at the end of the experiment, their
baseline metabolic profiles may have been more similar at the time when
samples were collected (21 days before the beginning of the feeding
trial) [[129]9]; (2) the FE was estimated for feedlot performance and
not for pasture grazing; at the time of sampling all animals were still
on pasture conditions, which may yield more similar metabolic
phenotypes than a high grain diet; (3) the animals were clinically
healthy over the whole experiment. Thus, no major physiological disturb
could lead to large metabolome difference between the FE groups; (4)
the number of sampled animals (8 animals per group) could limit the
statistical power [[130]47] for these outbred, genetically different
animals that may have high baseline diversity in metabolic profiles. To
address this last issue, one of our ongoing projects is to validate
these results in a cohort with more animals, to develop a future
technology help establish a framework for future for FE prediction.
Conclusion
The conclusion from this work is the detection of a molecular signature
for feed efficiency of beef cattle based on untargeted metabolomics.
This molecular signature indicated the vitamin A metabolism pathway as
one of the important pathways for this phenotype.
Additional files
[131]Additional file 1:^ (905B, csv)
Experiment information of animals including group, birth, days of life
at before feedlot (− 21 days), father, residual feed intake and
residual feed intake adjusted by age as a covariate. The FE groups had
different ages (P < 0.05). To perform the Network analysis, the
phenotype was adjusted by age, fitted as a covariate. (CSV 905 bytes)
[132]Additional file 2:^ (6.1KB, csv)
Important features in blue module in negative acquisition mode.
Mass/charge ratio (m/z); Gene significance (GS); Module Membership
(MM); Feature connectivity within the module (Kwithin); Adducts;
Highest abundance group; Adducts; Matched Compound (KEGG by mummichog);
Mass difference between m/z and matched compound. (CSV 6 kb)
[133]Additional file 3:^ (3.8KB, csv)
Important features in blue module in positive acquisition mode.
Mass/charge ratio (m/z); Gene significance (GS); Module Membership
(MM); Feature connectivity within the module (Kwithin); Adducts;
Highest abundance group; Adducts; Matched Compound (KEGG by mummichog);
Mass difference between m/z and matched compound. (CSV 3 kb)
Acknowledgments