ArticlePDF Available

Biological Network Approach for the Identification of Regulatory Long Non-Coding RNAs Associated With Metabolic Efficiency in Cattle

Authors:
  • Saxon State Office for Environment, Agriculture and Geology
  • Research Institute for Farm Animal Biology
  • Institute for Farm Animal Biology

Abstract and Figures

Background: Genomic regions associated with divergent livestock feed efficiency have been found predominantly outside protein coding sequences. Long non-coding RNAs (lncRNA) can modulate chromatin accessibility, gene expression and act as important metabolic regulators in mammals. By integrating phenotypic, transcriptomic, and metabolomic data with quantitative trait locus data in prioritizing co-expression network analyses, we aimed to identify and functionally characterize lncRNAs with a potential key regulatory role in metabolic efficiency in cattle. Materials and Methods: Crossbred animals (n = 48) of a Charolais x Holstein F2-population were allocated to groups of high or low metabolic efficiency based on residual feed intake in bulls, energy corrected milk in cows and intramuscular fat content in both genders. Tissue samples from jejunum, liver, skeletal muscle and rumen were subjected to global transcriptomic analysis via stranded total RNA sequencing (RNAseq) and blood plasma samples were used for profiling of 640 metabolites. To identify lncRNAs within the indicated tissues, a project-specific transcriptome annotation was established. Subsequently, novel transcripts were categorized for potential lncRNA status, yielding a total of 7,646 predicted lncRNA transcripts belonging to 3,287 loci. A regulatory impact factor approach highlighted 92, 55, 35, and 73 lncRNAs in jejunum, liver, muscle, and rumen, respectively. Their ensuing high regulatory impact factor scores indicated a potential regulatory key function in a gene set comprising loci displaying differential expression, tissue specificity and loci overlapping with quantitative trait locus regions for residual feed intake or milk production. These were subjected to a partial correlation and information theory analysis with the prioritized gene set. Results and Conclusions: Independent, significant and group-specific correlations (|r| > 0.8) were used to build a network for the high and the low metabolic efficiency group resulting in 1,522 and 1,732 nodes, respectively. Eight lncRNAs displayed a particularly high connectivity (>100 nodes). Metabolites and genes from the partial correlation and information theory networks, which each correlated significantly with the respective lncRNA, were included in an enrichment analysis indicating distinct affected pathways for the eight lncRNAs. LncRNAs associated with metabolic efficiency were classified to be functionally involved in hepatic amino acid metabolism and protein synthesis and in calcium signaling and neuronal nitric oxide synthase signaling in skeletal muscle cells.
Content may be subject to copyright.
1
Edited by:
David E. MacHugh,
University College Dublin, Ireland
Reviewed by:
James Reecy,
Iowa State University,
United States
Kieran G. Meade,
The Irish Agriculture and Food
Development Authority, Ireland
*Correspondence:
Christa Kühn
kuehn@fbn-dummerstorf.de
Specialty section:
This article was submitted to
Livestock Genomics,
a section of the journal
Frontiers in Genetics
Received: 25 June 2019
Accepted: 17 October 2019
Published: 22 November 2019
Citation:
NolteW, WeikardR, BrunnerRM,
AlbrechtE, HammonHM,
ReverterA and KühnC (2019)
Biological Network Approach for
the Identification of Regulatory Long
Non-Coding RNAs Associated With
Metabolic Efficiency in Cattle.
Front. Genet. 10:1130.
doi: 10.3389/fgene.2019.01130
Biological Network Approach for the
Identification of Regulatory Long
Non-Coding RNAs Associated With
Metabolic Efficiency in Cattle
Wietje Nolte 1, Rosemarie Weikard 1, Ronald M. Brunner 1, Elke Albrecht 2,
Harald M. Hammon 3, Antonio Reverter 4 and Christa Kühn 1,5*
1 Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany, 2 Institute of Muscle
Biology and Growth, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany, 3 Institute of Nutritional
Physiology “Oskar Kellner,” Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany, 4 Commonwealth
Scientific and Industrial Research Organisation (CSIRO) Agriculture and Food, Queensland Bioscience Precinct, St Lucia,
QLD, Australia, 5 Faculty of Agricultural and Environmental Sciences, University Rostock, Rostock, Germany
Background: Genomic regions associated with divergent livestock feed efficiency have
been found predominantly outside protein coding sequences. Long non-coding RNAs
(lncRNA) can modulate chromatin accessibility, gene expression and act as important
metabolic regulators in mammals. By integrating phenotypic, transcriptomic, and
metabolomic data with quantitative trait locus data in prioritizing co-expression network
analyses, we aimed to identify and functionally characterize lncRNAs with a potential key
regulatory role in metabolic efficiency in cattle.
Materials and Methods: Crossbred animals (n = 48) of a Charolais x Holstein F2-population
were allocated to groups of high or low metabolic efficiency based on residual feed intake in
bulls, energy corrected milk in cows and intramuscular fat content in both genders. Tissue
samples from jejunum, liver, skeletal muscle and rumen were subjected to global transcriptomic
analysis via stranded total RNA sequencing (RNAseq) and blood plasma samples were used
for profiling of 640 metabolites. To identify lncRNAs within the indicated tissues, a project-
specific transcriptome annotation was established. Subsequently, novel transcripts were
categorized for potential lncRNA status, yielding a total of 7,646 predicted lncRNA transcripts
belonging to 3,287 loci. A regulatory impact factor approach highlighted 92, 55, 35, and
73 lncRNAs in jejunum, liver, muscle, and rumen, respectively. Their ensuing high regulatory
impact factor scores indicated a potential regulatory key function in a gene set comprising loci
displaying differential expression, tissue specificity and loci overlapping with quantitative trait
locus regions for residual feed intake or milk production. These were subjected to a partial
correlation and information theory analysis with the prioritized gene set.
Results and Conclusions: Independent, significant and group-specific correlations
(|r|> 0.8) were used to build a network for the high and the low metabolic efficiency group
resulting in 1,522 and 1,732 nodes, respectively. Eight lncRNAs displayed a particularly
high connectivity (>100 nodes). Metabolites and genes from the partial correlation and
information theory networks, which each correlated significantly with the respective
lncRNA, were included in an enrichment analysis indicating distinct affected pathways
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
ORIGINAL RESEARCH
doi: 10.3389/fgene.2019.01130
published: 22 November 2019
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
2
INTRODUCTION
In recent years the focus of livestock production and farming
has shied in developed countries towards a stronger emphasis
on resource efficiency and sustainability (ornton, 2010). In
cattle, energy metabolism, nutrient conversion and efficient use
of primary resources are of increasing economic and ecological
importance to breeders and consumers. Genomic selection
and the use of biomarkers greatly facilitate the improvement of
complex phenotypes, e.g. feed efficiency, which remain cost- and
time-consuming to measure (Kenny et al., 2018).
Some pivotal gene mutations are known in major livestock
production traits, e.g. a meta-analysis on stature in cattle identied
PLAG1 as a major regulator and pointed towards putative causal
mutations (Bouwman et al., 2018). In pigs, the scavenger receptor
cysteine-rich domain 5 in gene CD163, when not being translated,
led to resistance to porcine reproductive and respiratory syndrome
virus 1 infection (Burkard et al., 2018). Pigs that did not express
the receptor protein were susceptible to the infection. For the
region between LCORL and NCAPG, which has been associated
with growth or feed efficiency in a number of species (cattle, horse,
human), multiple mappings have narrowed down the region of
interest but the causal mutation remains unknown (Widmann
etal., 2015; Bouwman et al., 2018). A large part of the variation
in traits like feed efficiency, growth and carcass traits remains still
unexplained (Hardie etal., 2017; Medeiros de Oliveira Silva et al.,
2017; Seabury et al., 2017) and genome-wide association studies
repeatedly pointed towards quantitative trait loci (QTL) outside
protein-coding genes (Ibeagha-Awemu et al., 2016; Seabury et al.,
2017; Higgins et al., 2018).
Due to their gene expression regulatory potential, long non-
coding RNAs (lncRNAs) have emerged as potential key regulators
for diverse biological processes, such as X-chromosomal
inactivation and dosage compensation (Brown et al., 1992;
Clemson et al., 1996), vernalization/ owering in plants (Csorba
et al., 2014), as well as human cancer biology as reviewed by
Serviss et al. (2014).
Recently, lncRNAs have been suggested as therapeutic
targets for diabetes and other metabolic diseases because of
their involvement in lipid metabolism, adipogenesis and fat
deposition (Chen et al., 2018a; Liu et al., 2018; Zeng et al., 2018).
In mammals, lncRNAs were further identied as key regulators
of energy metabolism and lipogenesis (Yang et al., 2016). In
adipocytes, these genomic elements also play an integral part
in the insulin-signaling pathway (Degirmenci et al., 2019). A
central regulatory role of lncRNAs was furthermore observed
in skeletal muscle in myogenesis and muscle cell differentiation:
SYISL has been shown to regulate myoblast proliferation and
fusion and acts in an inhibitory way in myogenic differentiation
(Jin et al., 2018), Irm enhances myogenic differentiation
during myogenesis through the binding to MEF2D (Sui etal.,
2019), and lnc-mg overexpression has directly been linked
to muscle hypertrophy in mice, whereas a knock-out led to
dystrophy (Zhu et al., 2017). It is likely that lncRNAs contribute
signicantly to economically important production traits and
divergent phenotypes in livestock as well. Since they show
little sequence conservation across species and their expression
appears to be mainly species specic and spatiotemporal
(Ulitsky et al., 2011; Ulitsky and Bartel, 2013), knowledge
transfer remains a challenging issue. e identication and
functional characterization of lncRNAs needs to be performed
for each species, and this ts into one of the major goals of the
consortium for the Functional Annotation of Animal Genomes
(FAANG, https://www.animalgenome.org/community/FAANG/)
that strives to identify and annotate functionally relevant elements
in livestock genomes.
Another key feature of lncRNAs is their low expression level
compared to protein-coding genes (Derrien et al., 2012), which
makes their detection challenging. From transcription factors
it is known, that little changes in abundance can however have
tremendous consequences if these have high regulatory potential
in terms of gene expression (Vaquerizas et al., 2009) and we
postulated an analogous phenomenon for lncRNAs. For instance,
the knockout of the lowly expressed lncRNA ßlinc in mice impaired
the correct formation of pancreatic islets and severely changed the
glucose homeostasis in adult animals (Arnes et al., 2016). A low and
tightly regulated gene expression has implications for differential
expression (DE) analyses, because little changes in expression are
oen not recognized as signicant due to lack of power in standard
experimental designs. erefore, other approaches are necessary
when aiming to identify and functionally annotate key regulatory
lncRNAs. A tested and proven method in the screening for critical
transcription factors from gene expression data, which are typically
low in abundance but have high regulatory power as reviewed by
Vaquerizas et al. (2009), is network co-expression analysis that
incorporates the regulatory impact factor (RIF) metrics and a
partial correlation and information theory (PCIT) (Reverter et al.,
2010; Perez-Montarelo et al., 2012). is approach has previously
also led to the identication of regulatory elements associated
with puberty (Canovas et al., 2014; Nguyen et al., 2018) and feed
efficiency in cattle (Alexandre et al., 2019). We assumed that this
rational network approach could also be used as a hypothetical
generation tool for the systematic detection of lncRNAs with
important regulatory potential.
for the eight lncRNAs. LncRNAs associated with metabolic efficiency were classified to
be functionally involved in hepatic amino acid metabolism and protein synthesis and in
calcium signaling and neuronal nitric oxide synthase signaling in skeletal muscle cells.
Keywords: Bos taurus, metabolic efficiency, co-expression network analysis, long non-coding RNA, Functional
Annotation of Animal Genomes
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
3
In this study, we took advantage of a unique F2 cross-population
of meat and dairy cattle breeds (Charolais x Holstein) (Kühn
etal., 2002) that has been deeply phenotyped and genotyped.
Earlier studies have shown that in this cross population a gene
variant of the NCAPG gene is associated with fetal and pubertal
growth (Eberlein et al., 2009; Weikard et al., 2010). By integrating
quantitative metabolite data with genotype information, this
NCAPG genotype was found to be associated with plasma
arginine levels (Weikard et al., 2010). A systems biology
approach, which combined metabolome data, growth-associated
phenotypic and genetic information, revealed a functional gene
interaction network characterizing the intensive growth phase
at the beginning of the pubertal growth interval (Widmann
etal., 2013). Potential interaction partners of the NCAPG gene
were predicted and the functional role of the NCAPG gene as
a growth regulator linked to the arginine NO metabolism was
concluded. A combined phenotype–metabolome–genome
analysis was also used to identify genetic switches of associated
molecular signaling pathways linked to variance in efficiency of
feed conversion (Widmann et al., 2015).
is current study on the regulatory role of lncRNAs for
metabolic efficiency was aimed to contribute to a more detailed
elucidation of the molecular background of this complex
physiological trait and help to characterize divergent metabolic
types with respect to nutrient partitioning. erefore, phenotypic
information, transcriptomic data from four metabolically relevant
tissues and QTL information were used to establish a prioritized
gene set that was submitted to the combinational RIF metrics and
subsequently to the PCIT algorithm for co-expression network
creation. e integration of metabolomic proles through
correlation with transcriptomic data added valuable information
for the interpretation of biological functions.
MATERIALS AND METHODS
Design of the Study
For this study, we made use of 48 animals (24 bulls, 24 cows) of a
F2-population [SEGFAM (Kühn et al., 2002)] from a Charolais×
Holstein cross. e cross population was bred at the Leibniz
Institute for Farm Animal Biology in Dummerstorf (Germany)
and kept under standardized housing and feeding conditions as
previously described (Eberlein et al., 2009; Weikard et al., 2010;
Widmann et al., 2011). Males were slaughtered at 18 months
of age and females were slaughtered aer their second parity
at 30 days postpartum. Based on residual feed intake (RFI) in
bulls and energy corrected milk yield (ECMw) in cows as well as
intramuscular fat content (IMF) of M. longissimus dorsi in both
genders, animals were assigned to either of the two groups: high
or low metabolic efficiency (Table 1). In this study we dened
high metabolic efficiency in cattle as the preference to accrete or
secrete protein while receiving the same diet as their inefficient
conspecics, which were characterized by a clear tendency to
accrete fat instead of protein. In European production systems,
those animals are most sustainable and economically efficient
producers, which build up protein mass (muscle) with little fat
content or, in case of females, secrete high amounts of milk.
Cows were categorized as highly efficient if their milk yield
within the 7 days prior to slaughter was above 140 kg energy
correct milk (ECMw) and the carcass fat content (CFC) was less
than the average CFC of all cows plus one standard deviation. In
contrast, cows were classied as lowly efficient if their milk yield
within the last week was between 14 and 40 kg ECMw and the
CFC was above the average CFC of all cows minus one standard
deviation. For all cows, the calving interval had to be less than
540 days, the maximum age was 1,510 days and they had to be
free of pathological ndings with metabolic implications noted
aer slaughter. Cows that were categorized as highly efficient
(high ECMw) on average had a lower CFC (mean 17.1%, SD
2.7%) and lowly efficient cows (low ECMw) had a higher CFC
(mean 25.9%, SD 3.6%) than the mean of the population (21.8%,
SD 5.3%, n = 242). In addition, highly efficient cows had a lower
IMF (mean 4.16%, SD 1.60%) and the lowly efficient cows had
a higher IMF (mean 6.46%, SD 2.53%) than the mean of the
population (5.21%, SD 2.21%, n = 242).
e individual milk volume yield per cow was measured on
a daily basis and the milk composition was determined once
per week. e trait included in cow selection for this study
corresponded to the weekly ECM determined for the 7 days
before slaughter (ECMw). e formula presented by Kirchgeßner
(1997) was modied accordingly for the one week interval (F% =
milk fat percentage, P% = milk protein percentage):
ECMFP MY d
w=++
×−
037021 095
31
7
.%.% .
.
cows, the ECMw was used as a substitute feature for feed efficiency,
because the facilities did not allow for RFI measurement in cows
during the time of the experiment.
TABLE 1 | Sample characteristics.
Metabolic
efficiency
group
Number of
animals Sex RFI1 in last month
of life (bulls) ECM2w (cows) IMF3 (both sexes) CFC4 (both sexes)
µ5 (SD6) µ (SD) µ (SD) µ (SD)
High 25 12 males 13 females -21.30 (4.44) 190.87 (22.02) 3.46 (1.30) 15.93 (3.16)
Low 23 12 males 11 females 20.83 (4.41) 30.97 (9.18) 5.51 (2.34) 22.93 (4.88)
1RFI, residual feed intake; 2ECMw, energy corrected milk 7 days before slaughter; 3IMF, intramuscular fat content (given in percent, measured in M. longissimus dorsi);
4CFC, carcass fat content; 5µ, mean; 6SD, standard deviation.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
4
For bulls, the decisive factor for animal selection was RFI
calculated for the last month prior to slaughter. e RFI equals
the animals' energy intake while considering the average daily
gain and metabolic mid-weight (average body weight of months
of life 17 to 18 raised to the power of 0.75) (Archer et al., 1997).
Bulls with a low RFI (at least 1 standard deviation below
average) were assigned to the high metabolic efficiency group
and bulls with a high RFI (at least one standard deviation above
average) were assigned to the low metabolic efficiency group.
In their last month of life, all bulls had to have a positive daily
weight gain and no less than the population average minus one
standard deviation. Bulls that were categorized as highly efficient
(negative RFI) on average had a lower CFC (mean 14.2%, SD
3.0%) and lowly efficient bulls (positive RFI) had a higher CFC
(mean 20.2%, SD 4.4%) than the population mean (mean 16.5%,
SD 4.0%, n = 246). Analogously to cows, highly efficient bulls had
a lower IMF (mean 1.71%, SD 1.00%) and the lowly efficient bulls
had a higher IMF (mean 4.64%, SD 1.84%) than the population
mean (mean 3.67%, SD 1.76%, n = 246).
Plasma Metabolic Profiles
Blood samples were collected from all individuals (n =
48) at slaughter. Plasma samples were sent to Metabolon
Inc. (Durham/NC, USA) for the establishment of holistic
metabolite proles that included 640 biochemical compounds
and molecules. Metabolites with more than ve animals with
missing data were excluded. Aer this ltering step, 490
metabolites remained and missing values were imputed with
the minimum measurement, assuming that missing values were
due to concentrations below the detection limit. Values were
then scaled without centering for each metabolite in R (Core
Team 2018) with the scale-function.
All experimental procedures were carried out according
to the German animal care guidelines and were approved and
supervised by the relevant authorities of the State Mecklenburg-
Vorpommern, Germany (State Office for Agriculture, Food
Safety and Fishery; LALLF M-V/TSD/7221.3-2.1-010/03).
Sampling, RNA Isolation, Library
Preparation, and Sequencing
Tissue samples were collected from jejunum mucosa, liver (Lobus
caudatus), skeletal muscle (M. longissimus dorsi), and rumen
(Saccus ventralis, papillary base) directly aer slaughtering and
dissection, shock frozen in liquid nitrogen and subsequently
stored at -80°C.
For RNA extraction from muscle and rumen, frozen samples
(100 mg) were treated with 1 ml TRIzol reagent (Invitrogen,
Darmstadt, Germany) and subjected to the Precellys-24
homogenizer (5,500 rpm, 2 × 15 s, lysing kit containing 1.4 mm
ceramic beads). For RNA extraction from liver and jejunum,
frozen tissue samples were grinded in liquid nitrogen and 30
mg were used for further purication steps. No TRIzol was used
for liver and jejunum samples. All samples were then subjected
to an on-column-purication step with the NucleoSpin RNA II
kit (Macherey & Nagel, Düren, Germany) including a DNase
digestion to remove genomic DNA. In addition, the RNA was
tested for remaining traces of DNA contamination and, in case of
remaining DNA residues, further cleansed according to Weikard
et al. (2012).
e RNA concentration and integrity were measured
with a Qubit Fluorometer (Invitrogen, Germany) and a 2100
Bioanalyzer Instrument (Agilent Technologies, Germany).
Stranded, ribodepleted and indexed libraries were prepared from
1 µg total RNA using the TruSeq Stranded Total RNA Ribo-Zero
H/M/R Gold Kit (Illumina, San Diego, USA) and subjected to
paired-end sequencing (2 × 100 bp) in a multiplexed design on a
HiSeq 2500 Sequencing System (Illumina).
Alignment and Assembly
Aer quality control of raw sequencing reads with FastQC
(Andrew, 2010), adapter and quality trimming were performed
with Cutadapt v. 1.16 (Martin, 2011) and Quality Trim v. 1.6.0
(Robinson, 2015), respectively. In Quality Trim the start of
sequences was also trimmed (option -s) and the maximum
number of N bases was set to 3, while the minimum base quality
was set to 15. Reads were then mapped in a guided alignment
with HISAT2 v.2.1.0 (Kim et al., 2015) to the bovine reference
genome UMD.3.1 [Ensembl annotation release 92 (Frankish
et al., 2017)]. Aer sorting and indexing of BAM les with
samtools v.1.6 (Li et al., 2009), samples were individually
assembled with Stringtie v.1.3.4d (Pertea et al., 2015) based
on the reference genome and annotation used for alignment.
Using the individually assembled samples (n = 204) from all
four tissues and the bovine reference genome, we built a new
merged annotation in Stringtie across tissues, while specifying
for minimal transcript coverage across samples of 15 read
alignments per exonic base. In addition to the 192 samples (48
animals, four tissues) included in the subsequent steps for DE
and network analyses, we also took benet from rumen, liver
and muscle samples of further four individuals from the same
experimental herd. ese samples were subjected to exactly the
same processing steps as the 192. e new merged annotation
was used for fragment counting with featureCounts (subread
v.1.6.1) (Liao et al., 2014), while allowing for fractional counting
and specifying for reverse strandedness.
Long Non-Coding RNA Prediction and
Fragment Counting
LncRNAs were identied in-situ with FEELnc (Wucher et al.,
2017), a bioinformatics tool for lncRNA prediction and
annotation, using the merged transcript annotation and the
bovine reference genome and annotation UMD3.1 release
92. FEELnc excludes transcripts annotated as protein coding
and subsequently keeps transcripts with a minimum length of
200 nt and at least two exons and only monoexonic transcripts
with antisense localization. Other monoexonic transcripts were
excluded to reduce the number of false positives, which might
arise from the mapping of repetitive sequences (Wucher et al.,
2017), DNA contamination (Haerty and Ponting, 2015) and
in general transcriptional noise (Kern et al., 2018). For those
transcripts matching the requirements, the coding potential of
remaining transcripts was determined in shuffling mode.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
5
Fragment Count Normalization
For further pipeline steps, except for the DE analysis, fragments per
kilobase million (FPKM) were calculated from the featureCounts
derived fragment counts. Genes were ltered for a minimal
average expression value of 0.2 FPKM in at least one of the four
tissues and ribosomal and spliceosomal RNA genes were excluded
(Metazoan signal recognition particle RNA, U6 spliceosomal
RNA, small nucleolar RNA U6-53). For further analyses of FPKM
values performed in this study, a log2-scale of the data was used
(for log transformation a pseudo-count of 0.001 was added).
Prioritized Gene List
Gene co-expression networks are a useful tool when trying to
deduce the potential biological function of genes, novel loci and
non-coding elements (van Dam et al., 2017), assuming the guilt-
by-association principle. In order to create meaningful networks
that have a targeted focus on our phenotype (metabolic efficiency),
we created a set of prioritized genes where genes had to belong
to at least one of these four categories: differentially expressed
(DE) genes in at least one of the four investigated tissues, tissue-
specic (TS) genes, genes harboring a QTL for milk production
or RFI (QTL) according to the literature, and predicted lncRNAs.
Small nucleolar RNAs (snoRNAs), ribosomal RNAs, spliceosomal
RNAs, and Y-RNAs were excluded from the set.
Differential Expression Analysis
A DE analysis for the high and low metabolic efficiency group was
performed within tissues and across sexes in R with the package
DEseq2 (Love et al., 2014). Fragment counts from featureCounts
were used as input and normalization was performed within
DEseq2. To exclude very lowly expressed transcripts within a
tissue, the minimal fragment count threshold was set to at least
10 fragments for 10 out of 48 individuals. Ribosomal genes were
excluded from the analysis and year of slaughter and sex were
used as factors in the model. e signicance threshold was set
to q < 0.05 [Benjamini–Hochberg (BH) test].
Tissue Enriched Genes
e expression (log2-transformed FPKM) of a gene was dened as
enriched in a particular tissue, if the abundance in the other three
tissues was less than half the average across all tissues and above the
average plus one standard deviation in the tissue at hand. roughout
the further course of this study, we refer to these genes as TS.
Genes Harboring a Quantitative Trait Locus
We extracted QTL for milk production traits (MY) and RFI in
cattle from the Animal QTL database (Park et al., 2018) and then
screened our dataset in Ensembl Biomart (http://asia.ensembl.
org/biomart/martview, accession date 28 March 2019) for genes
that overlapped with these QTL regions. A physical overlap
of the QTL and the gene is needed for a gene hit, while close
neighborhood is not sufficient.
Regulatory Impact Factor Analysis
e RIF (Reverter et al., 2010) analysis makes use of two
alternative metrics (RIF1 and RIF2) that attribute scores to
potential key regulators. e strength of the score depends on
the change in correlation between the regulator and its target in
two groups or treatments, the level of DE of the target gene, and
the general expression level of the target gene. We conducted RIF
analyses within tissues and across metabolic efficiency groups to
assess the regulatory capacity of lncRNAs in a set of prioritized
genes (lncRNA, DE, TS, QTL harboring). erefore, RIF metrics
were calculated within each tissue for a prioritized gene set
(including log2(FPKM) data) that comprised genes which were
DE or TS in that tissue, harbored a QTL or were characterized
as a lncRNA. Naturally, some of the QTL-genes might have zero
expression in one or more of the tissues. To prevent erroneously
high RIF scores stemming from low variation in gene expression,
an additional lter for expression level was applied (on top of
minimal average expression of 0.2 FPKM in at least one tissue).
Only genes with abundance above tissue average were kept for
the RIF analysis.
A high RIF1 score was assigned to lncRNAs that were
consistently co-expressed with abundant target genes in both
metabolic efficiency groups. A high RIF2 score was attributed
to lncRNAs that displayed the most altered ability to predict
the abundance of target genes between groups, meaning that a
lncRNA exhibited strong correlation to a target on one condition
but none or a reverse correlation in the other. RIF scores were
standardized with a z-score. Key regulators (lncRNA) were
considered of signicant importance and were included in further
analyses if they had an absolute RIF1 or RIF2 z-score of≥1.96,
meaning that these lncRNAs and their scores were outside the
95% condence interval, corresponding to a signicance level of
p = 0.05 in a t-test.
Partial Correlation and Information Theory
e PCIT (Reverter and Chan, 2008) tests for signicant pairwise
correlations between two elements while accounting for all
possible three-way combinations in the dataset that include
either of the pair elements. Importantly, the PCIT recognizes
independent, signicant correlations regardless of the strength of
correlation. Within the high and low metabolic efficiency groups,
the PCIT approach across all tissues was used to investigate for
independent correlations of lncRNAs that had signicant RIF
scores with DE genes, TS genes, and QTL harboring genes.
Results were ltered for signicant correlations (minimal
correlation strength |r| > 0.8) between a lncRNA and another
gene that were exclusive for the high or low metabolic efficiency
group, meaning that the correlation was signicant in one group
but not in the other. e visualization was realized in Cytoscape
3.6.1 (Shannon et al., 2003).
Characterization of Key Regulatory Long
Non-Coding RNAs
Blast Search Against New Bovine Assembly
Highly connected lncRNAs with more than 100 directly linked
nodes (genes) were selected from each network for further
scrutiny. Since the prediction of lncRNAs was based on a merged
annotation, which was reference guided by UMD3.1, Ensembl
release 92, we wanted to investigate the sequence homology
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
6
and annotation status of key lncRNAs in the new bovine
assembly ARS1.2 annotated in Ensembl release 95. e lncRNA
sequences were blasted online with the blastn suite using the
MegaBlast algorithm, specifying for high sequence similarity
and otherwise default parameters (Altschul et al., 1990) (https://
blast.ncbi.nlm.nih.gov/Blast.cgi, accessed Mai 2019) against
the new bovine assembly (ARS-UCD1.2, https://www.ncbi.
nlm.nih.gov/assembly/GCA_002263795.2; GenBank accession
NKLS00000000.2; https://www.ensembl.org/Bos_taurus/Info/
Index). We considered blast hits to indicate high homology if the
sequence identity was at least 98% in a region covering at least
200 nucleotides.
Pathway Enrichment Analysis
To assess the possible biological function of high connectivity
lncRNAs, we performed a pathway enrichment analysis based on
genes identied as correlated (|r| > 0.8) in the PCIT analyses and
also including blood plasma metabolites that were signicantly
(p ≤ 0.05) correlated with the high connectivity lncRNAs. To
this end, a pairwise Pearson correlation analysis between blood-
plasma metabolites and lncRNA expression in the tissue, where
the lncRNA was most abundant, was performed in R with the
function rcorr of the Hmisc package (Harrell and Frank, 2019).
e list of signicantly correlated metabolites (p ≤ 0.05) and
genes (adjacent network nodes with |r| > 0.8) were analysed
using the Ingenuity Pathway Analysis (IPA: QIAGEN Inc.,
https://www.qiagenbioinformatics.com/products/ingenuity-
pathway-analysis) (Kramer et al., 2014). e workow from
group formation and tissue sampling up to the functional
characterization of key lncRNAs is visualized for better
comprehensibility and clarity in Figure 1.
RESULTS
RNA Preparation, Sequencing, Alignment,
and Mapping
e average RNA integrity (RIN) across the four tissues was
8.22 ± 0.81 (Table 2). Aer quality trimming the average RNA
sequencing depth was at 48 million read pairs per sample. A total
of 9 out of 192 samples reached less than a 40 million read pair
coverage. e alignment of reads with HISAT2 to the bovine
reference genome UMD.3.1 (Ensembl release 92) resulted
in an average alignment rate of 92.98 ± 9.50%. Compared
with the other tissues, rumen scored a distinctly lower rate
(78.00± 7.75%). e average mapping rate across all samples
to the customized annotation, which contained 30,072 loci, was
81.89%. e tissue specic average mapping rate was lowest in
rumen, of comparable dimension in jejunum and muscle, and
highest in liver.
Long Non-Coding RNA Prediction
Based on the merged annotation, FEELnc predicted 26,740
mRNAs and 7,646 lncRNA transcripts (3,287 loci), out of
which 544 were without potential positional interaction
partner gene within the default window size of 10,000 to
100,000 nucleotides. ose 7,102 lncRNA transcripts with
an assigned potential positional interaction partner were
generated by 3,051 loci (Table 3, Supplementary Table 1).
FEELnc distinguishes between intergenic and genic lncRNA
with different subtypes (see Wucher et al. (2017) for a graphical
explanation). LncRNAs are also classied according to their
position to neighboring protein coding genes (interaction
partner gene). For intergenic lncRNAs, the best partner gene is
closest in terms of distance in base pairs and for genic lncRNAs
the best partner gene directly overlaps with it, preferably at an
exon. All predicted 7,646 lncRNA transcripts were considered
for further computational analyses.
e total of 3,287 lncRNA loci are equally distributed in
terms of strandedness (50.6% on the plus strand, 49.41% on the
minus strand), and in a locus-based approach (considering the
transcript with highest exon number for each locus) the median
number of exons per transcript was 3 (average number of exons
per transcript: 4.9 ± 8.2). e total exon length geometric mean
of the lncRNA loci amounted to 2,201.0 bp.
Prioritized Gene List for Co-Expression
Analysis
Aer ltering the 30,072 genes in the merged annotation for
minimal expression (average FPKM across all samples >0.2 in
at least one tissue) and exclusion of ribosomal and spliceosomal
RNA genes, the dataset contained 22,625 genes out of which
2,886 were lncRNAs, meaning that 401 lncRNAs were removed
from RIF and subsequent PCIT co-expression analysis due to
very low abundance.
Differential Expression Analysis
e DE analysis yielded a total of 2,154 unique signicantly
(q < 0.05) DE genes between the high and low metabolic
efficiency group with 496 DE genes in jejunum, 1,286 DE genes
in liver, 479 DE genes in muscle, and no signicant differences
in rumen (Figure 2A). Generally, we observed little overlap of
differentially expressed loci between tissues. Out of these unique
2,154 DE genes, 238 were predicted to be lncRNAs corresponding
to 11.05%. We observed 40 DE lncRNAs in jejunum, 173 DE
lncRNAs in liver, 40 DE lncRNAs in muscle, and none in rumen
(Figure 2B).
Tissue Enriched Genes
We found a total of 930 genes to be tissue-specically expressed
out of the 22,625 genes, which had passed the initial minimal
expression threshold (average expression > 0.2 FPKM in at least
on tissue). Out of those 930 genes, 279 were TS in jejunum, 283
in liver, 204 in muscle, and 164 in rumen. ereof, 21.9% were
lncRNAs with 42 in jejunum, 65 in liver, 48 in muscle, and 49
in rumen.
Quantitative Trait Locus Harboring Genes
e database AnimalQTL listed 278 QTL for RFI and 1,881
QTL for milk production traits, which were distributed across
1,615 genes out of which 1,064 passed the minimal expression
threshold (average expression > 0.2 FPKM in at least one tissue)
in our dataset.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
7
FIGURE 1 | Workflow for the identification and functional characterization of key lncRNAs with regulatory potential in two contrasting biological conditions. The
phenotypes under investigation were high and low metabolic efficiency in a Charolais x Holstein cross-population. lncRNA, long non-coding RNA; FPKM, fragments
per kilobase transcript length per million reads; TS, tissue specific; DE, differentially expressed; QTL, quantitative trait locus; RFI, residual feed intake; MY, milk
production; RIF, regulatory impact factor; PCIT, partial correlation and information theory.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
8
Regulatory Impact Factor to Select
Long Non-Coding RNAs With a Potential
Regulatory Effect on Metabolic Efficiency
e input prioritized gene lists ltered for expression level for the
tissue specic RIF analysis contained 2,097 loci for jejunum (880
lncRNAs), 1,890 loci for liver (614 lncRNAs), 961 loci for muscle
(363 lncRNAs), and 1,458 loci for rumen (755 lncRNAs). RIF
scores were then calculated for the lncRNAs in these gene sets.
With a signicance threshold of a RIF1 or RIF2 score ≥
1.96, the tissue specic RIF analyses identied 92 potential key
lncRNAs in jejunum, 55 in liver, 35 in muscle, and 73 in rumen.
In total 240 unique lncRNAs had a RIF score ≥ 1.96 in at least one
tissue and were considered for subsequent PCIT analysis.
Partial Correlation and Information Theory
Approach to Identify Long Non-Coding
RNA-Associated Co-Expression Networks
For the within-tissue RIF analysis, the sets of DE genes, TS
genes, QTL harboring genes and lncRNAs had been ltered for
a seizable expression level (abundance above average expression
in the respective tissue) to facilitate a reliable calculation of
correlation. For the PCIT analysis, a similar lter for minimal
expression was applied: abundance above average expression
across all samples in at least one tissue when combining DE genes
and TS genes from all tissues with the QTL genes and lncRNAs
with signicant RIF scores. A total of 295 of the 4,049 prioritized
loci were excluded due to not meeting this expression limit. e
set of prioritized genes that was used for the nal PCIT network
analysis contained 3,754 unique genes in total. ereof, 1,990
were DE genes, 895 QTL containing genes, 926 TS genes, and
583 lncRNAs, though some genes belonged to several categories
(Figure 3, Supplementary Table 2).
e PCIT analysis was performed across tissues and results
were ltered for signicant correlations with a correlation
strength |r| ≥ 0.8, between a lncRNA with signicant RIF score
and all genes from the prioritized gene list already used for
RIF calculation. Furthermore, correlations had to be exclusive
to either the high or low metabolic efficiency group. e high
and low network contained 1,522 and 1,732 nodes (genes)
respectively (Supplementary Figure 1, Supplementary Figure 2,
Supplementary Table 3). Six and two lncRNAs showed a
high connectivity (>100 nodes) exclusively in one of the two
networks, which represent high and low metabolic efficiency,
respectively. us, these eight lncRNAs stand out as potential
regulatory keys for lncRNAs with respect to metabolic
efficiency.
Characterization of Key Regulatory Long
Non-Coding RNAs in the Networks
Blast Against New Bovine Assembly
e eight lncRNAs characterized by high connectivity for high
and low metabolic efficiency in the PCIT analysis were blasted
against the new bovine assembly and annotation [ARS-UCD.1.2,
National Center for Biotechnology Information (NCBI) release
106] (Tabl e 4 ). If lncRNAs completely overlapped with annotated
genes, the respective lncRNA was located on the opposite strand
to the annotated gene (e.g. MSTRG.4926 overlapped with CDH17
on the opposite strand). None of the eight lncRNA loci had yet
been annotated as non-coding in the NCBI or the Ensembl
genome annotation (ARS-UCD1.2, release 95).
Pathway Enrichment Analysis
e Pearson correlation analysis between blood plasma
metabolites and lncRNA expression, which was calculated
prior to the pathway enrichment analysis, showed that the eight
key lncRNAs were signicantly (p < 0.05) correlated to very
different numbers of metabolites. Correlations ranged from one
(MSTRG.18433) to 117 (MSTRG.4740) metabolites, out of which
an average of 75% was successfully mapped in the IPA database
and used in the subsequent enrichment analyses (Supplementary
Table 4). e correlation strength ranged from-0.53 to + 0.48
with an average of |0.35|.
Pathway enrichment analysis for each of the eight key
lncRNAs with their respective correlated metabolites and genes
showed that calcium signaling was the most strongly enriched
canonical pathway for half of the key lncRNAs (MSTRG.9051,
MSTRG.10337, MSTRG.18433, and MSTRG.19312). e other
high ranking canonical pathway hits, i.e. hits with the lowest
p-value, were tRNA charging, leukocyte extravasation signaling,
caveolar-mediated endocytosis signaling, and T cell receptor
signaling (data not shown).
Within the eight lncRNAs with a high connectivity in the
PCIT analysis, three loci showed distinct pattern in the pathway
enrichment analysis suggesting divergent molecular functions.
Inspection of the results showed that the enriched canonical
pathways for MSTRG.4740, which was differentially expressed in
TABLE 2 | Overall and tissue-specific RNA sequencing, alignment, and mapping statistics.
RIN1Sequencing depth [read
pairs] Alignment to UMD.3.1 [%] Mapping to project-
specific annotation [%]
µ2SD3µ SD µ SD µ SD
All 8.22 0.81 48,041,209 5,601,638 92.98 9.50 81.89 8.67
Jejunum 8.73 0.44 48,954,376 3,993,201 96.91 0.31 84.99 2.20
Liver 8.00 0.62 50,093,826 5,869,833 98.43 0.20 91.36 1.21
Muscle 7.55 0.85 47,117,156 5,815,843 98.59 0.13 82.42 1.79
Rumen 8.41 0.86 45,999,477 5,587,407 78.00 7.75 69.05 4.67
1RIN, RNA integrity number, 2µ, mean, 3SD, standard deviation.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
9
liver (Figure 4, Table 3, Supplementary Table 5), were related
to amino acid biosynthesis and metabolism, as well as protein
synthesis (Table 5). MSTRG.17681 (Figure 5, Supplementary
Table 5) which was also differentially expressed in liver,
seemed to act very locally in the coatomer subunit of the coat
protein I (COPI) in the caveosome. MSTRG.10337, (Figure 6,
Supplementary Table 5) apparently acts specically in muscle
where it was related to several signaling pathways, most strongly
to calcium, protein kinase A, neuronal nitric oxide synthase
(nNOS), and RhoA signaling (Tab le 5).
DISCUSSION
A major goal of this study was the identication of lncRNAs
that hold a potential key regulatory role in metabolic efficiency,
TABLE 3 | Characterization of high connectivity long non-coding RNAs from networks specific for high or low metabolic efficiency in cattle.
Identifier FEElnc Prediction (based on UMD3.1 release 92) FPKM Position & Structure Differential Expression Analysis
MSTRG TN Closest partner gene (gene symbol) Direction, type, location Distance
bp Mean BTA Exons Start bp Strand Exonic
length Log2
FC p-value q-value
(BH) Tissue
MSTRG.4740 1 ENSBTAG00000002062 (TRPA1) Anti-sense, genic, intronic 0 6.74 14 2 37760114 + 437 0.77 2.10E-04 9.13E-03 Liver
MSTRG.4926 1 ENSBTAG00000021964 (CDH17) Anti-sense, genic, exonic 0 0.78 14 18 72437085 - 3,321 NA NA NA None
MSTRG.9051 1 ENSBTAG00000004651 (NME1) Anti-sense, intergenic,
downstream
358 2.57 19 2 36225984 - 1,866 NA NA NA None
2 1,587 36227213
MSTRG.10337 1 ENSBTAG00000005353 (DES) Anti-sense, genic, exonic 0 5.37 2 9 108075380 - 2,989 NA NA NA None
MSTRG.17681 1 ENSBTAG00000005726 (HNRNPA2B1) Anti-sense, intergenic, upstream 9,170 19.80 4 3 70184682 - 1,552 -0.38 8.45E-05 5.04E-03 Liver
MSTRG.18433 1 ENSBTAG00000015828 (FKBP11) Sense, intergenic, upstream 5,808 8.94 5 2 31038376 + 2,683 NA NA NA None
MSTRG.19098 1 ENSBTAG00000046324 (C-type lectin
domain family 2 member D11)
Anti-sense, genic, exonic 0 1.72 5 5 100926473 + 1,570 -0.63 1.27E-06 3.04E-04 Liver
MSTRG.19312 1 ENSBTAG00000009886 (KDELR3 ) Anti-sense, genic, exonic 0 25.84 5 3 110665971 - 5,768 NA NA NA None
3 110670286
*MSTRG, identifier from project-specific bovine transcriptome annotation; TN, transcript number; FPKM, fragments per kilobase million; BTA, bovine chromosome, bp, base pair; FC, foldchange; BH, Benjamini–
Hochberg, NA, not available.
FIGURE 2 | Venn diagrams of (A) all loci (B) exclusively lncRNAs with differential
expression (DE) between high and low metabolic efficiency in cattle. DE analysis
was performed within the tissues jejunum, liver, muscle, and rumen. No loci
were significantly [q-value (Benjamini–Hochberg) < 0.05] DE in rumen.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
10
which was roughly dened as the animal's ability to direct
the energy adsorbed into protein synthesis and use it for
muscle mass accumulation or milk secretion. We integrated
phenotypic, metabolomics and transcriptomics data from a
cattle F2-population (Charolais × Holstein) in a co-expression
network approach to mine for lncRNAs with a regulatory role
in metabolic processes. By contrasting animals of high and low
metabolic efficiency and by including RNAseq data from four
key metabolic tissues in a combined analysis, we identied highly
connected hub lncRNAs. Finally, we subjected metabolites and
genes, whose plasma levels or transcript abundance signicantly
correlated with expression levels of the specic, highly connected
lncRNA, to the integrative approach for metabolomics and
transcriptomics data as offered by the cross-platform IPA
(Kramer et al., 2014).
Establishment of a Pipeline Based on
Regulatory Impact Factor and Partial
Correlation and Information Theory to
Establish Co-Expression Networks for
Long Non-Coding RNAs and Genes to
Predict Their Role in Metabolic Efficiency
Weighted gene co-expression network analysis (WGCNA)
(Langfelder and Horvath, 2008) is a frequently applied method
to identify co-expression pattern at whole transcriptome level.
Recently, Sun et al. (2019) applied this method for mining
regulatory signatures of divergent feed efficiency in beef cattle
investigating a multi-tissue transcriptome data set. WGCNA
has also been used to nd hub lncRNAs in a transcriptomic
landscape in multiple studies in humans as well as animals (Miao
et al., 2016; Tang et al., 2017; Li et al., 2018; Weikard etal., 2018;
Wang et al., 2019). To mine for the functional role of lncRNAs
of interest via WGCNA, one might select lncRNAs that are
strongly correlated with coding neighbor genes (Li etal., 2018) or
lncRNAs that were differentially expressed between conditions
or phenotypes (Weikard et al., 2018; Wang et al., 2019).
FIGURE 3 | Venn diagram of 3,754 loci selected for co-expression network
construction. Loci belonging to at least one of these four categories were
considered: differential expression (DE) in at least one tissue, tissue specific
(TS) expression, harboring a QTL for residual feed intake and or milk
production (QTL) and key regulatory long non-coding (lnc) RNAs [significant
(p < 0.05) regulatory impact factor score].
TABLE 4 | BLAST results for eight high connectivity long non-coding RNAs (>100 nodes) in partial correlation and information theory networks with connections
exclusive for high or low metabolic efficiency.
lncRNA BLAST against bovine reference genome (ARS-UCD1.2, release 106)
Identifier Network
(connectivity
in nodes)
Annotated gene with highest sequence
homology Identity [%] Query cover
[%] E-Value Position of
lncRNA relative to
homologous gene
in ARS-UCD1.2
MSTRG.4740 Low (147) mRNA-transient receptor potential cation
channel subfamily A member 1 (TRPA1)
100.00 100.00 9.00E-116 Intronic, anti-sense
ADP-ribosylation factor 4 (ARF4) 98.57 91.00 3.00E-100 Exonic, sense
MSTRG.4926 High (144) Cadherin-17 precursor (CDH17) 100.00 100.00 0.00E+00 Anti-sense
MSTRG.9051 High (170) Nucleoside diphosphate kinase A 1
isoform X1 (NME1)
99.72 100.00 0.00E+00 Sense, genic
MSTRG.10337 Low (239) Desmin (DES) 99.93 100.00 0.00E+00 Exonic, anti-sense
MSTRG.17681 High (120) 39,201 bp at 5' side: alpha-aminoadipic
semialdehyde synthase, mitochondrial
precursor 88559 bp at 3' side: fez family
zinc finger protein 1
98.40 99.00 0.00E+00 Sense, genic
Chromobox protein homolog 3 isoform X1
(CBX3)
99.00 89.00 0.00E+00 Sense, genic
MSTRG.18433 High (268) 364 bp at 5' side: ADP-ribosylation factor
3; 37831 bp at 3' side: peptidyl-prolyl cis-
trans isomerase FKBP11 precursor
99.96 100.00 0.00E+00 Sense, intergenic
MSTRG.19098 High (184) C-type lectin domain family 2 member D11 100.00 100.00 0.00E+00 Anti-sense, genic
MSTRG.19312 High (212) ER lumen protein-retaining receptor 3
(KDELR3)
100.00 99.00 0.00E+00 Anti-sense, genic
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
11
e connectivity within a network and the differential wiring
between two networks can also serve as a selection criterion
(Pellegrina et al., 2017). In our study we present an alternative
approach for the selection of lncRNAs of interest, the RIF
(Reverter et al., 2010), which has already successfully been
applied to transcription factors (TF). In combination with a
PCIT (Reverter and Chan, 2008), key regulatory TFs during
puberty could be identied in cattle (Cánovas et al., 2014), as well
as critical TFs in porcine muscle (Perez-Montarelo et al., 2012).
is approach seemed to be particularly applicable for lncRNAs
with regard to the expression level as they generally exhibit lower
transcript abundance compared with mRNAs (Derrien et al.,
2012), as do TFs compared with other coding genes (reviewed
by Vaquerizas et al., 2009). We indeed found that only 10% of
the unique lncRNAs with a signicant RIF-score (n = 240) were
also differentially expressed, including three of the eight key hub
lncRNAs. LncRNAs were signicantly underrepresented in the
list of DE loci across all tissues (Χ2 test, p = 1.2E-06): while they
accounted for 14.85% of all loci in the DE analyses, only 11.05%
of the DE loci were classied as lncRNAs. In contrast, the other
FIGURE 4 | Co-expression network for the novel long non-coding (lnc) RNA MSTRG.4740 with key regulatory potential for metabolic efficiency in cattle and
significantly (p < 0.05) correlated genes with a minimal correlation coefficient of |r| > 0.8. Correlations are exclusive for animals with low metabolic efficiency.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
12
loci accounted for 85.25% of all loci in the DE analyses, but had
a share of 88.95% in the total of 2,154 differentially expressed
unique loci.
In a recent publication, van Dam et al. (2017) reviewed and
highlighted the usefulness of gene co-expression networks for
the functional classication of genes and novel loci, such as non-
coding elements without any known function. Correspondingly
Oliveira et al. (2018) successfully applied a co-expression network
concept to identify genes and miRNAs regulating IMF in Nellore
steers. Besides the preselection of lncRNAs for co-expression
networks, it might be advisable to make a knowledge-based
preselection also for other genes to be included instead of
simply using all expressed genes. e combination of RNA-Seq
results with GWAS hits (gene regions associated with QTL for
milk performance traits or RFI) is an acknowledged procedure
to integrate multiple layers of knowledge into a prioritized gene
set for co-expression network analysis (Schaefer et al., 2018).
In our PCIT analysis, we prioritized genes that appeared to be
functionally important from the RNA-Seq analysis [DE loci
(2,154) or TS loci (930)] and published GWAS data and selected
those for our prioritized gene set to create a stronger focus
on bovine metabolic efficiency, accepting however that still
unknown, yet important elements might be overlooked. When
preparing the prioritized gene set, we noted that the key role of
liver in metabolic processes was clearly reected by the by far
highest number of DE loci (1,286) between the two metabolic
TABLE 5 | Top 10 enriched pathways derived from genes and metabolites significantly correlated with key long non-coding RNAs associated with metabolic efficiency
ID Ingenuity Canonical Pathways log(p) Ratio Molecules
MSTRG.4740 tRNA Charging 5.56E00 8.54E-02 L-valine, L-phenylalanine, L-tryptophan, glycine,
L-arginine, L-tyrosine, L-lysine
EIF2 Signaling 4.13E00 3.83E-02 MYC, RPS7, RPL27A, RPL35, RPL23A, RPL37,
RPL26, EIF3E, RPL31
Glucose and Glucose-1-phosphate
Degradation
3.18E00 1.3E-01 D-glucose, PGM3, phosphate
Tyrosine Biosynthesis IV 2.94E00 2.86E-01 L-phenylalanine, L-tyrosine
Acetyl-CoA Biosynthesis III (from Citrate) 2.82E00 2.5E-01 phosphate, citric acid
Glycine Degradation (Creatine
Biosynthesis)
2.71E00 2.22E-01 glycine, L-arginine
Phenylalanine Degradation IV
(Mammalian, via Side Chain)
2.68E00 8.82E-02 L-phenylalanine, phenylpyruvic acid, glycine
Glutathione Biosynthesis 2.53E00 1.82E-01 phosphate, glycine
Thymine Degradation 2.53E00 1.82E-01 5, 6-dihydrothymine, beta-ureidoisobutyric acid
MSTRG.10337 Calcium Signaling 1.63E01 9.35E-02 TNNT1, CHRNA1, CACNB1, CACNG1,
CACNA1S, MYL2, TNNI2, TNNT3,T NNC2,
TNNC1, MYL1, ATP2A1, CAMK2A, CASQ1,
RYR1, TNNI1, CASQ2, MYL3, ACTA1, CAMK2B
Protein Kinase A Signaling 7.45E00 3.88E-02 TNNI2, MYL2, MYLPF, MYLK2, PPP1R3A, TTN,
MYL1, EPM2A, CAMK2A, PLCB1, RYR1, TNNI1,
EYA1, MYL3, CAMK2B, PHKG1
nNOS Signaling in Skeletal Muscle Cells 6.1E00 1.3E-01 CACNG1, CACNB1, CACNA1S, CHRNA1, RYR1,
L-arginine
Cellular Effects of Sildenafil (Viagra) 6.09E00 6.25E-02 CACNA1S, CACNG1, MYL2, MYLPF, PLCB1,
L-arginine, MYL1, MYL3, ACTA1
RhoA Signaling 4.55E00 5.6E-02 MYL2, MYLPF, MYLK2, TTN, MYL1, MYL3,
ACTA1
Apelin Cardiomyocyte Signaling Pathway 3.7E00 5.00E-02 MYL2, MYLPF, PLCB1, MYL3, MYL1, ATP2A1
Actin Cytoskeleton Signaling 3.55E00 3.36E-02 MYL2, MYLPF, ACTN3, MYLK2, TTN, ACTA1,
MYL3, MYL1
Regulation of Actin-based Motility by Rho 3.24E00 5.21E-02 MYL2, MYLPF, MYL3, ACTA1, MYL1
ILK Signaling 3.19E00 3.38E-02 PARVB, MYL2, TNFRSF1A, ACTN3, MYL1,
MYL3, ACTA1
Thrombin Signaling 2.93E00 3.06E-02 CAMK2A, MYL2, MYLPF, PLCB1, MYL1, MYL3,
CAMK2B
MSTRG.17681 Caveolar-mediated Endocytosis Signaling 3.56E00 5.48E-02 ARCN1, COPA, COPE, COPB2
Fatty Acid α-oxidation 2.29E00 8.00E-02 ALDH3A2, ALDH9A1
Death Receptor Signaling 2.15E00 3.3E-02 PARP10, PARP4, HTRA2
Histamine Degradation 2.05E00 6.06E-02 ALDH3A2, ALDH9A1
Oxidative Ethanol Degradation III 2.05E00 6.06E-02 ALDH3A2, ALDH9A1
G Protein Signaling Mediated by Tubby 2.03E00 5.88E-02 GNG2, GNAQ
Tryptophan Degradation X (Mammalian,
via Tryptamine)
2.00E00 5.71E-02 ALDH3A2, ALDH9A1
Putrescine Degradation III 2.00E00 5.71E-02 ALDH3A2, ALDH9A1
Ethanol Degradation IV 1.98E00 5.56E-02 ALDH3A2, ALDH9A1
NER Pathway 1.96E00 2.8E-02 HIST2H4B, XAB2, RAD23B
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
13
efficiency groups, which was 2.6 fold higher than in jejunum or
rumen. For DE loci in the prioritized gene set that was used for
the PCIT, we noted that these predominantly (65%) had their
highest expression in a different tissue than where they were
differently expressed. is underlines that tissue specicity or
tissue of highest abundance and DE of loci are indeed different,
non-redundant features and that it is recommendable to follow a
TS perspective in the beginning of the analysis.
One way to deduce a biological function of lncRNAs is to take
a close look at coding genes in their immediate vicinity. is idea
has also been implemented in the bioinformatics tool FEELnc
for lncRNA prediction and annotation (Wucher et al., 2017),
where the potential partner gene is generally assumed to be the
closest annotated gene. However, this exclusively focusses on
in-cis interaction with a narrow frame of impact. However, it has
been reported that some lncRNAs execute in-trans regulatory
tasks by binding directly to distant DNA sites or via RNA-
protein interactions (Long et al., 2017) or a direct effect on RNA
polymerase II activity (Kornienko et al., 2013).
Another way to infer functionality of unknown genomic
elements subsequent to the network construction is to submit
correlated coding genes to an enrichment analysis (Chen et al.,
2018b), thereby assuming the guilt-by-association principle.
Following this approach, we took genes from the prioritized
FIGURE 5 | Co-expression network for the novel long non-coding (lnc) RNA MSTRG.17681 with key regulatory potential for metabolic efficiency in cattle and
significantly (p < 0.05) correlated genes with a minimal correlation coefficient of |r| > 0.8. Correlations are exclusive for animals with high metabolic efficiency.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
14
gene set that were correlated with high connectivity lncRNAs
of interest. LncRNA partner genes predicted by FEELnc could
also be part of the prioritized gene set if they fell into one of the
categories (DE, tissue-specicity, QTL-harboring). is was the
case for 473 out of 2,741 unique predicted lncRNA interaction
partner genes. us, 12.6% of the genes that were used as PCIT
input (3,754) were very close to or overlapped with a lncRNA.
In addition, we aimed to add a supplementary layer of
information to the pathway enrichment analysis and thereby to
create further biological depth by using the option to integrate
gene expression and metabolic proles. In a single step this
approach facilitates to predict a link between transcriptome
activity, the direct functional readout of metabolic activity or
physiological status and the functional analysis of lncRNAs.
MSTRG.4740, e.g., correlated with plasma levels of 117
metabolites—valuable information that would otherwise be
missing from the enrichment analysis. To our knowledge, we
here present the rst study that integrates metabolomics and
FIGURE 6 | Co-expression network for the novel long non-coding (lnc) RNA MSTRG.10337 with key regulatory potential for metabolic efficiency in cattle and
significantly (p < 0.05) correlated genes with a minimal correlation coefficient of |r| > 0.8. Correlations are exclusive for animals with low metabolic efficiency.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
15
transcriptomic data in an enrichment analysis to predict the
functional role of lncRNAs.
Across-Tissue Candidate Long Non-
Coding RNAs for Metabolic Efficiency
LncRNAs were dened as hubs when they were connected
to at least 100 other nodes in the high or low efficiency PCIT
network. ree of the identied eight hub lncRNAs were
exemplarily chosen for a more detailed description of their
biological functionality predicted with IPA. ese lncRNAs—
namely MSTRG.4740, MSTRG.10337, and MSTRG.17681—
were hubs in gene groups that showed enrichment for transfer
RNA (tRNA) charging (p = 2.78E-06) and EIF2 signaling (p =
7.34E-05), calcium signaling (p = 4.98E-17) and nNOS signaling
in skeletal muscle cells (p = 7.88E-07), and calveolar-mediated
endocytosis signaling (p = 2.77E-04) and fatty acid oxidation
(p= 5.13E-03), respectively.
For MSTRG.4740 an encompassing look at the enriched
pathways clearly pointed towards amino acid metabolism
and protein synthesis. is lncRNA was DE in liver (adjusted
p-value (BH) = 9.13E-03, log2FC = 1.70) but displayed highest
abundance (average FPKM) in jejunum (10.68) and rumen
(8.41) and lowest in muscle (1.66) compared to liver (6.23). e
DE status in liver suggested biological relevance there. However,
the RIF analysis attributed a signicant score to MSTRG.4740
in jejunum. e strongest enrichment was for tRNA charging
(p = 2.78E-06), which describes the attachment of amino acids
to a tRNA before incorporation into a growing polypeptide.
According to IPA, the enrichment of this pathway was due to
the correlation of MSTRG.4740 expression level with the blood
plasma content of six essential or semi-essential amino acids
(L-valine, L-phenylalanine, L-tryptophan, L-arginine, L-tyrosine,
L-lysine). No non-essential amino acid showed a signicant
correlation with this lncRNA. e signicantly correlated amino
acids play integral roles as regulators of metabolism and key body
functions, but cannot or only partially be synthesized by bovine
animals themselves. Plasma concentration of essential amino
acids depends on uptake from the diet, the balance between
protein synthesis and degradation in peripheral tissues as well as
on the efficiency of transport processes. e enrichment of the
tRNA Charging pathway was not backed up by other components
in addition to the indicated amino acids (e.g., charged tRNAs
themselves). us, we restrict our conclusion and suggest that
the lncRNA has a close relationship with (semi-) essential amino
acid levels, but rather not to tRNA Charging per se. Widmann
et al. (2015) reported no signicant correlation between plasma
amino acids and RFI at the onset of puberty in bulls in the same
resource population. However, in the current study we employed
adult animals.
Endogenous metabolism and also supply of amino acid have
been demonstrated to limit growth or lactation in pigs, cattle and
sh as reviewed by Hou et al. (2016). Furthermore, Doelman et al.
(2015) showed that an abomasal infusion with essential amino
acids leads to increased protein levels of eIF2α and eIF2Bε in the
mammary gland in dairy cows. e authors proclaimed a direct
link between the eIF2 factor, which is essential for eukaryotic
translation initiation and milk protein yield. Interestingly, we
found eIF2Bε to be DE [q-value (BH)= 0.022, log2FC = 0.204]
in liver and to be one of the genes underlying the signicant
enrichment of the EIF2 Signaling pathway (p = 7.34E-05),
which is tightly linked to protein synthesis. Genes encoding
for ribosomal proteins of 40S (RPS7) or 60S subunits (e.g.
RPL26, RPL31) were signicantly correlated with MSTRG.4740,
as well as the before mentioned eIF2Bε. EIF2 signaling and
subsequently EIF3E are required for the correct initiation of
mRNA translation (Kimball 1999; Walsh and Mohr, 2014).
Considering the presented correlations of MSTRG.4740 with
other genes and plasma metabolites, this hub lncRNA seems to be
an excellent example of a potential new key regulator in metabolic
efficiency through the modulation of translational processes.
In contrast to MSTRG.4740 that seems to act on the broader
forefront of translation, MSTRG.17681 appears to have a rather
narrow and more targeted function. e rst hit in pathway
enrichment was calveolar-mediated endocytosis signaling
(p = 2.77E-04). Four genes (COPA, COPE, COPB2, ARCN1)
belonging to this pathway were highly correlated (|r| > 0.8) with
this hub lncRNA. We observed signicant DE in the liver of
divergently efficient animals for MSTRG.17681 (q-value (BH) =
0.0050, log2FC = 0.766) as well as the respective quartet of genes.
COPA, COPE and COPB2 are transporters and ARCN1 encodes
the coatomer subunit of the coat protein I (COPI) complex
(Tunnacliffe et al., 1996). All genes are allocated to a subunit
in the cellular calveolar-mediated endocytosis signaling: the
COPI vesicle, which plays a role in intracellular lipid transport
(Popoff et al., 2011) and regulates lipid homeostasis (Beller etal.,
2008). COPI-vesicle biogenesis is ARF1-dependent (Beck etal.,
2009), which we found to be DE in liver and to be positively
correlated with MSTRG.17681. e Arf1 GTPase-activating
protein 3 (ArfGAP3) that subsequently allows the vesicle to fuse
with a target membrane (Beck et al., 2009), was also correlated to
MSTRG.17681 and DE in liver.
Considering that COPI-vesicles assist in lipid transport, it
seems tting that we found signicant correlations between
MSTRG.17681 expression and plasma levels of two saturated
fatty acids: caprylate (p = 0.013, r = 0.357) and heptanoate (p=
0.047, r = 0.289). Caprylic acid supplementation in the diet of
weaned piglets was observed to lead to a signicant increase body
weight gain (Marounek et al., 2004). MSTRG.17681 most likely
acts predominantly in jejunum, liver, and rumen, where average
expression was much higher (31.83, 25.26, and 18.74 FPKM,
respectively) compared with the expression in skeletal muscle (3.36
FPKM). We infer that MSTRG.17681 is a key regulator in COPI-
vesicle functioning and thereby presumably affects lipid levels.
MSTRG.10337 was the third key hub lncRNA with a distinct
prediction of biological function. In the network specic
for animals of low metabolic efficiency, MSTRG.10337 was
co-expressed with 39 genes that were DE in liver, 4 of which were
also DE in muscle. Interestingly, the hub lncRNA MSTRG.10337
correlated with RORA (RAR related orphan receptor A), which
was DE in liver. RORA is a transcriptional regulator of genes
related to lipid metabolism, e.g. APOA1, APOA5, APOC3, and
PRAPRG (Vu-Dac et al., 1997; Raspe et al., 2001; Sundvold and
Lien, 2001; Lind et al., 2005). Although not meeting the threshold
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
16
for entering the PCIT network with respect to correlation to
MSTRG.10337, we found APOA1 to be DE in the liver, providing
consistency in gene expression and biological interplay with
regard to RORA. Previously, Krappmann et al. (2012) has attested
an association of a RORC (RAR Related Orphan Receptor C)
variant with milk yield, as well as milk fat and protein percentage
in our SEGFAM resource population. Furthermore, Zhang et al.
(2017) linked both nuclear receptors RORA and RORC to hepatic
lipid and fatty acid metabolism as well as circadian rhythm
pathways in a liver-specic depletion experiment in mice.
e most enriched pathways related to MSTRG.10337 are
Calcium signaling (p = 4.98E-17) Protein Kinase A (PKA)
signaling (p = 3.51E-08), and nNOS signaling in skeletal muscle
cells (p = 7.88E-07). ese data conrmed ndings from an
alternative previous network analysis in our resource population,
where GWAS results for RFI and metabolomics proles were
merged for bulls in puberty. Widmann et al. (2015) also has
identied Protein Kinase A (PKA) signaling and Nitric Oxide
signaling to be signicantly enriched pathways in IPA analyses.
Calcium signaling, Protein Kinase A (PKA) signaling and
nNOS signaling in skeletal muscle cells are in biological interplay.
Protein kinases are in charge of nNOS phosphorylation on
different serine residues and catalyze the hydroxylation of
L-arginine (Fleming, 2008). In turn, L-arginine plasma levels were
negatively correlated with expression levels of MSTRG.10337
(p=0.038, r=-0.323) in our study. is would t an inhibitory role
of MSTRG.10337 in metabolic efficiency, because of unfavorable
effects of arginine depletion in the diet on milk protein synthesis
in dairy cows (Tian et al., 2017). e inhibitory effect is underlined
by numerous negative correlations of MSTRG.10337 to genes with
DE in liver (e.g. LGR4, FIG4, ESD), muscle (e.g. PON2, IDH1,
NUP54) and jejunum (e.g. LINGO1, MPDU1, UFC1), as well as
QTL harboring genes (e.g. GAPDH, MAFA, MYBPC1), although
the exact mode of operation is unclear. e supplementation of
arginine has been reported to reduce body fat deposition, improve
muscle gain and improve insulin sensitivity and the metabolic
prole (Wu et al., 2009), and its availability in the organism is
therefore particularly interesting for beef production. In chicken,
L-arginine supplementation enhanced lean muscle growth (Castro
et al., 2018). However, protein anabolic effects in muscle via
dietary arginine supplementation are controversially discussed in
other species (Tang et al., 2011). In addition to Calcium and PKA
signaling, a third highly enriched pathway for MSTRG.10337
was nNOS signaling. In terms of gene expression, nNOS is not
restricted to neuronal cells but is commonly expressed in skeletal
muscle and certain vascular smooth muscle cells as well (Fleming
2008), where it is important for tissue integrity and contractile
performance (Percival, 2011). Aer Ca2+-activation, nNOS
enzymes produce NO, which affects the autoregulation of blood
ow, myocyte differentiation and glucose homeostasis in skeletal
muscle cells (Stamler and Meissner, 2001). In a previous study we
already suspected a relationship between NO signaling, arginine
and growth in cattle (Widmann et al., 2013).
We assume that MSTRG.10337 inuences the onset of nNOS
activation, because of its correlation to calcium voltage-gated
channel genes and RYR1 (ryanodine receptor 1) that encodes a
calcium release channel protein (Loy et al., 2011). Co-expression
with a large number of muscle specic genes (e.g. CACNG1,
MYLK2, TNNT1, MYL2) or genes that are DE in muscle
(CAMK2B) related this hub lncRNA to PKA and nNOS signaling.
It might thereby inuence phosphorylation, degradation and
availability of L-arginine in the muscle cells, but simultaneously
perform some regulatory tasks in hepatic lipid metabolism.
CONCLUSIONS
In this study, we were able to identify novel lncRNAs with potential
key regulatory function in metabolic efficiency in cattle. Although
usually low expression levels of lncRNAs entail difficulties in DE and
co-expression analyses, the careful setting of expression thresholds,
the use of a-priori knowledge in gene prioritization and the integrated
use of RIF metrics and PCIT based co-expression networks have
proven to be a valid method for the identication of regulatory hub
lncRNAs. e enrichment analysis based on metabolites and gene
expression data provided valuable insight into the putative biological
functions of yet uncharacterized lncRNAs.
We focused on phenotypic differences and looked at
mechanisms or correlations that were exclusive to either metabolic
efficiency group. Still, other correlations between lncRNAs and
mRNAs might exist simultaneously in both groups, and we
propose to take a group transcending approach in a follow-up
study. For future work, we suggest to proceed within tissues to
get a clearer picture of gene-gene interactions within a tissue,
also because we noted that a multi-tissue approach presents its
challenges when interpreting pathway enrichment results. e hub
lncRNAs, which we identied, can be considered as candidates for
further validation studies, in vitro or in vivo. Kashi et al. (2016)
neatly described modern methods to determine where and how
lncRNAs act in the cell or organism, such as chromatin isolation
by RNA purication (ChIRP) sequencing (Chu et al., 2011).
In conclusion, our study demonstrates that the method we
presented is suitable for the identication for key regulatory lncRNAs
in a complex phenotype. By carefully adjusting different elements of
the procedure, e.g. the tissue under consideration or the choice of
priority categories for genes to include in the network analysis, this
pipeline allows us to answer targeted biological questions.
DATA AVAILABILITY STATEMENT
e datasets generated for this study have been submitted to the
“Functional Annotation of Animal Genomes” (FAANG) initiative
database, accession PRJEB34570, and are also available via the
European Nucleotide Archive (ENA).
ETHICS STATEMENT
e animal study was reviewed and approved by Animal care and
experimental procedures following the guidelines of the German
Law of Animal Protection. e protocols were approved by
the Animal Protection Board of the Leibniz Institute for Farm
Animal Biology as well as by the Animal Care Committee of the
State Mecklenburg-Western Pomerania, Germany (State Office
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
17
for Agriculture, Food Safety and Fishery; LALLF M-V/ Rostock,
Germany, TSD/7221.3-2.1-010/03).
AUTHOR CONTRIBUTIONS
WN performed the statistical analyses and investigations, created the
visualizations and wrote the original dra. RW and CK performed
data collection, generated transcriptomic data, contributed to
data analysis and conceptualized and administered the project
and supervised WN. AR coded and performed bioinformatics
analyses and supervised WN. RB, EA, and HH provided support
with sampling and phenotyping of the test animals. All authors
contributed to reviewing and editing the manuscript.
FUNDING
is study was funded by the German Research Foundation
(DFG–grant numbers: KU 771/8-1 and WE 1786/5-1). WN
received a scholarship for doctoral candidates from the German
Academic Exchange Service (DAAD) and travel funds from the
Graduate Academy of the University of Rostock. e publication
of this article was funded by the Open Access Fund of the Leibniz
Institute for Farm Animal Biology (FBN).
ACKNOWLEDGMENTS
e authors thank Frieder Hadlich for his support with
bioinformatics obstacles of all kind, Marina Naval-Sanchez for
her insightful ideas in network analysis, and Simone Wöhl and
Bärbel Pletz for their excellent technical work in the lab.
SUPPLEMENTARY MATERIAL
e Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fgene.2019.01130/
full#supplementary-material
REFERENCES
Alexandre, P. A., Naval-Sanchez, M., Porto-Neto, L. R., Ferraz, J. B. S., Reverter, A., and
Fukumasu, H. (2019). Systems biology reveals NR2F6 and TGFB1 as key regulators
of feed efficiency in beef cattle. Front. Genet. 10, 230. doi: 10.3389/fgene.2019.00230
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990).
Basic local alignment search tool. J. Mol. Biol. 215, 403–410. doi: 10.1016/
S0022-2836(05)80360-2
Andrew, S. (2010). FastQC: a quality control tool for high throughput sequence data.
https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Archer, J. A., Arthur, P. F., Herd, R. M., Parnell, P. F., and Pitchford, W. S. (1997).
Optimum postweaning test for measurement of growth rate, feed intake,
and feed efficiency in British breed cattle. J. Anim. Sci. 75, 2024–2032. doi:
10.2527/1997.7582024x
Arnes, L., Akerman, I., Balderes, D. A., Ferrer, J., and Sussel, L. (2016). Betalinc1
encodes a long noncoding RNA that regulates islet beta-cell formation and
function. Genes Dev. 30, 502–507. doi: 10.1101/gad.273821.115
Beck, R., Ravet, M., Wieland, F. T., and Cassel, D. (2009). e COPI system:
molecular mechanisms and function. FEBS Lett. 583, 2701–2709. doi:
10.1016/j.febslet.2009.07.032
Beller, M., Sztalryd, C., Southall, N., Bell, M., Jäckle, H., Auld, D. S., et al. (2008).
COPI Complex is a regulator of lipid homeostasis. PloS Biol. 6, e292. doi:
10.1371/journal.pbio.0060292
Bouwman, A. C., Daetwyler, H. D., Chamberlain, A. J., Ponce, C. H., Sargolzaei,M.,
Schenkel, F. S., et al. (2018). Meta-analysis of genome-wide association studies
for cattle stature identies common genes that regulate body size in mammals.
Nat. Genet. 50, 362–367. doi: 10.1038/s41588-018-0056-5
Brown, C. J., Hendrich, B. D., Rupert, J. L., Lafrenière, R. G., Xing, Y.,
Lawrence,J.B., et al. (1992). e human XIST gene: Analysis of a 17 kb inactive
X-specic RNA that contains conserved repeats and is highly localized within
the nucleus. Cell 71, 527–542. doi: 10.1016/0092-8674(92)90520-M
Burkard, C., Opriessnig, T., Mileham, A. J., Stadejek, T., Ait-Ali, T., Lillico, S. G.,
et al. (2018). Pigs lacking the scavenger receptor cysteine-rich domain 5 of
CD163 are resistant to porcine reproductive and respiratory syndrome virus 1
infection. J. Virol. 92, e00415–e00418. doi: 10.1128/JVI.00415-18
Canovas, A., Reverter, A., DeAtley, K. L., Ashley, R. L., Colgrave, M. L., Fortes,
M. R. S., et al. (2014). Multi-tissue omics analyses reveal molecular regulatory
networks for puberty in composite beef cattle. PloS One 9, 17. doi: 10.1371/
journal.pone.0102551
Cánovas, A., Reverter, A., Kasey, L., DeAtley, K. L., Ashley, R. L., Colgrave, M.L.,
et al. (2014). Multi-tissue omics analyses reveal molecular regulatory networks
for puberty in composite beef cattle. PloS One 9, e102551. doi: 10.1371/journal.
pone.0102551
Castro, F. L. S., Su, S., Choi, H., Koo, E., and Kim, W. K. (2018). L-Arginine
supplementation enhances growth performance, lean muscle, and bone density
but not fat in broiler chickens. Poultry Sci. 98, 1716–1722. doi: 10.3382/ps/
pey504
Chen, C., Cui, Q. M., Zhang, X., Luo, X., Liu, Y. Y., Zuo, J. B., et al. (2018a). Long non-
coding RNAs regulation in adipogenesis and lipid metabolism: emerging insights
in obesity. Cell. Signalling 51, 47–58. doi: 10.1016/j.cellsig.2018.07.012
Chen, W., Zhang, X., Li, J., Huang, S., Xiang, S., Hu, X., et al. (2018b).
Comprehensive analysis of coding-lncRNA gene co-expression network
uncovers conserved functional lncRNAs in zebrash. BMC Genomics 19, 112.
doi: 10.1186/s12864-018-4458-7
Chu, C., Qu, K., Zhong, F. L., Artandi, S. E., and Chang, H. Y. (2011). Genomic
maps of long noncoding RNA occupancy reveal principles of RNA-chromatin
interactions. Mol. Cell 44, 667–678. doi: 10.1016/j.molcel.2011.08.027
Clemson, C. M., McNeil, J. A., Willard, H. F., and Lawrence, J. B. (1996). XIST RNA
paints the inactive X chromosome at interphase: evidence for a novel RNA
involved in nuclear/chromosome structure. J. Cell Biol. 132, 259–275. doi: 10.1083/
jcb.132.3.259
Core Team, R. (2018). R: A language and environment for statistical computing. R
Foundation for Statistical Computing. https://www.r-project.org/
Csorba, T., Questa, J. I., Sun, Q., and Dean, C. (2014). Antisense COOLAIR mediates
the coordinated switching of chromatin states at FLC during vernalization.
Proc. Natl. Acad. Sci. 111, 16160–16165. doi: 10.1073/pnas.1419030111
Degirmenci, U., Li, J., Lim, Y. C., Siang, D. T. C., Lin, S., Liang, H., et al. (2019).
Silencing an insulin-induced lncRNA, LncASIR, impairs the transcriptional
response to insulin signalling in adipocytes. Sci. Rep. 9, 5608. doi: 10.1038/
s41598-019-42162-5
Derrien, T., Johnson, R., Bussotti, G., Tanzer, A., Djebali, S., Tilgner, H., et al.
(2012). e GENCODE v7 catalog of human long noncoding RNAs: analysis
of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789.
doi: 10.1101/gr.132159.111
Doelman, J., Curtis, R. V., Carson, M., Kim, J. J. M., Metcalf, J. A., and Cant, J.P.
(2015). Essential amino acid infusions stimulate mammary expression of
eukaryotic initiation factor 2Bε but milk protein yield is not increased during
an imbalance. J. Dairy Sci. 98, 4499–4508. doi: 10.3168/jds.2014-9051
Eberlein, A., Takasuga, A., Setoguchi, K., Pfuhl, R., Flisikowski, K., Fries, R., et al.
(2009). Dissection of genetic factors modulating fetal growth in cattle indicates
a substantial role of the non-SMC condensin I complex, subunit G (NCAPG)
gene. Genetics 183, 951–964. doi: 10.1534/genetics.109.106476
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
18
Fleming, Ingrid. (2008). Chapter 3 “Biology of Nitric Oxide Synthases,” in
Microcirculation (Second Edition). Eds. Tuma, R . F., Durán, W. N., and Ley, K. (San
Diego, USA: Academic Press), 56–80. doi: 10.1016/B978-0-12-374530-9.00003-6
Frankish, A., Vullo, A., Zadissa, A., Yates, A., ormann, A., Parker, A., et al. (2017).
Ensembl 2018. Nucleic Acids Res. 46, D754–DD61. doi: 10.1093/nar/gkx1098
Haerty, W., and Ponting, C. P. (2015). Unexpected selection to retain high GC
content and splicing enhancers within exons of multiexonic lncRNA loci. RNA
21, 333–346. doi: 10.1261/rna.047324.114
Hardie, L. C., VandeHaar, M. J., Tempelman, R. J., Weigel, K. A., Armentano,
L. E., Wiggans, G. R., et al. (2017). e genetic and biological basis of feed
efficiency in mid-lactation Holstein dairy cows. J. Dairy Sci. 100, 9061–9075.
doi: 10.3168/jds.2017-12604
Harrell, and Frank, E. (2019). Hmisc: Harrell Miscellaneous, R-package version 4.2-0.
https://cran.r-project.org/web/packages/Hmisc/index.html
Higgins, M. G., Fitzsimons, C., McClure, M. C., McKenna, C., Conroy, S.,
Kenny,D.A. et al. (2018). GWAS and eQTL analysis identies a SNP associated
with both residual feed intake and GFRA2 expression in beef cattle. Sci. Rep. 8,
14301–14301. doi: 10.1038/s41598-018-32374-6
Hou, Y. Q., Yao, K., Yin, Y. L., and Wu, G. Y. (2016). Endogenous synthesis
of amino acids limits growth, lactation, and reproduction in animals. Adv.
Nutr. 7, 331–342. doi: 10.3945/an.115.010850
Ibeagha-Awemu, E. M., Peters, S. O., Akwanji, K. A., Imumorin, I. G., and Zhao, X.
(2016). High density genome wide genotyping-by-sequencing and association
identies common and low frequency SNPs, and novel candidate genes
inuencing cow milk traits. Sci. Rep. 6, 31109. doi: 10.1038/srep31109
Jin, J. J., Lv, W., Xia, P., Xu, Z. Y., Zheng, A. D., Wang, X. J., et al. (2018). Long
noncoding RNA SYISL regulates myogenesis by interacting with polycomb
repressive complex 2. Proc. Natl. Acad. Sci. 115, E9802–E9E11. doi: 10.1073/
pnas.1801471115
Kashi, K., Henderson, L., Bonetti, A., Carninci, P. (2016). Discovery and functional
analysis of lncRNAs: Methodologies to investigate an uncharacterized
transcriptome. Biochim. Biophys. Acta Gene Regul. Mech. 1859, 3–15. doi:
10.1016/j.bbagrm.2015.10.010
Kenny, D. A., Fitzsimons, C., Waters, S. M., and McGee, M. (2018). Invited review:
improving feed efficiency of beef cattle - the current state of the art and future
challenges. Animal 12, 1815–1826. doi: 10.1017/S1751731118000976
Kern, C., Wang, Y., Chitwood, J., Korf, I., Delany, M., Cheng, H., et al. (2018).
Genome-wide identication of tissue-specic long non-coding RNA in three
farm animal species. BMC Genomics 19, 684. doi: 10.1186/s12864-018-5037-7
Kim, D., Langmead, B., and Salzberg, S. L. (2015). HISAT: a fast spliced aligner with
low memory requirements. Nat. Methods 12, 357. doi: 10.1038/nmeth.3317
Kimball, S. R. (1999). Eukaryotic initiation factor eIF2. Int. J. Biochem. Cell Biol.
31, 25–29. doi: 10.1016/S1357-2725(98)00128-9
Kirchgeßner, M. (1997). Tierernährung. Frankfurt a.M., Germany: Verlags Union
Agrar DLG-Verlag, 574.
Kornienko, A. E., Guenzl, P. M., Barlow, D. P., and Pauler, F. M. (2013). Gene
regulation by the act of long non-coding RNA transcription. BMC Biol. 11, 59.
doi: 10.1186/1741-7007-11-59
Kramer, A., Green, J., Pollard, J. Jr., and Tugendreich, S. (2014). Causal analysis
approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530. doi:
10.1093/bioinformatics/btt703
Krappmann, K., Widmann, P., Weikard, R., and Kühn, C. (2012). Variants of the
bovine retinoic acid receptor-related orphan receptor C gene are in linkage
disequilibrium with QTL for milk production traits on chromosome 3 in a beef
× dairy crossbreed population. Arch. Anim. Breed. 55, 346–355. doi: 10.5194/
aab-55-346-2012
Kühn, C., Bellmann, O., Voigt, J., Wegner, J., Guiard, V., and Ender, K. (2002). An
experimental approach for studying the genetic and physiological background
of nutrient transformation in cattle with respect to nutrient secretion and
accretion type. Arch. Anim. Breed. 45, 14. doi: 10.5194/aab-45-317-2002
Langfelder, P., and Horvath, S. (2008). WGCNA: an R package for weighted
correlation network analysis. BMC Bioinf. 9, 559. doi: 10.1186/1471-2105-9-559
Li, B. J., Jiang, D. L., Meng, Z. N., Zhang, Y., Zhu, Z. X., Lin, H. R., et al. (2018).
Genome-wide identication and differentially expression analysis of lncRNAs
in tilapia. BMC Genomics 19, 729. doi: 10.1186/s12864-018-5115-x
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., et al. (2009).
e sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–
2079. doi: 10.1093/bioinformatics/btp352
Liao, Y., Smyth, G. K., and Shi, W. (2014). Featurecounts: an efficient general
purpose program for assigning sequence reads to genomic features.
Bioinformatics 30, 923–930. doi: 10.1093/bioinformatics/btt656
Lind, U., Nilsson, T., McPheat, J., Stromstedt, P. E., Bamberg, K., Balendran, C., et al.
(2005). Identication of the human ApoAV gene as a novel RORalpha target gene.
Biochem. Biophys. Res. Commun. 330, 233–241. doi: 10.1016/j.bbrc.2005.02.151
Liu, P., Jin, L., Zhao, L., Long, K., Song, Y., Tang, Q., et al. (2018). Identication
of a novel antisense long non-coding RNA PLA2G16-AS that regulates
the expression of PLA2G16 in pigs. Gene. 671, 78–84. doi: 10.1016/j.
gene.2018.05.114
Long, Y., Wang, X., Youmans, D. T., and Cech, T. R. (2017). How do lncRNAs
regulate transcription. Sci. Adv. 3, eaao2110. doi: 10.1126/sciadv.aao2110
Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change
and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. doi:
10.1186/s13059-014-0550-8
Loy, R. E., Orynbayev, M., Xu, L., Andronache, Z., Apostol, S., Zvaritch, E.,
etal. (2011). Muscle weakness in Ryr1I4895T/WT knock-in mice as a result
of reduced ryanodine receptor Ca2+ ion permeation and release from the
sarcoplasmic reticulum. J. Gen. Physiol. 137, 43–57. doi: 10.1085/jgp.201010523
Marounek, M., Skřivanová, E., and Skřivanová, V. (2004). A note on the effect of
caprylic acid and triacylglycerols of caprylic and capric acid on growth rate and
shedding of coccidia oocysts in weaned piglets. J. Anim. Feed Sci. 13, 269–274.
doi: 10.22358/jafs/67411/2004
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput
sequencing reads. EMBnet.journal 17, 10–12. doi: 10.14806/ej.17.1.200
Medeiros de Oliveira Silva, R., Bonvino Stafuzza, N., de Oliveira Fragomeni,B.,
Miguel Ferreira de Camargo, G., Matos Ceacero, T., Noely dos Santos
Gonçalves Cyrillo, J., et al. (2017). Genome-wide association study for carcass
traits in an experimental nelore cattle population. PloS One 12, e0169860. doi:
10.1371/journal.pone.0169860
Miao, X., Luo, Q., Zhao, H., and Qin, X. (2016). Co-expression analysis and
identication of fecundity-related long non-coding RNAs in sheep ovaries. Sci.
Rep. 6, 39398. doi: 10.1038/srep39398
Nguyen, L. T., Reverter, A., Cánovas, A., Venus, B., Anderson, S. T., Islas-Trejo,A.,
et al. (2018). STAT6, PBX2, and PBRM1 emerge as predicted regulators of 452
differentially expressed genes associated with puberty in brahman heifers.
Front. Genet. 9, 87. doi: 10.3389/fgene.2018.00087
Oliveira, G. B., Regitano, L. C. A., Cesar, A. S. M., Reecy, J. M., Degaki, K. Y., Poleti,
M. D., et al. (2018). Integrative analysis of microRNAs and mRNAs revealed
regulation of composition and metabolism in Nelore cattle. BMC Genomics 19,
126. doi: 10.1186/s12864-018-4514-3
Park, C. A., Reecy, J. M., and Hu, Z.-L. (2018). Building a livestock genetic and
genomic information knowledgebase through integrative developments of
Animal QTLdb and CorrDB. Nucleic Acids Res. 47, D701–DD10. doi: 10.1093/
nar/gky1084
Pellegrina, D.V.da Silva, Severino, P., Barbeiro, H. V., de Souza, H. P., Machado,
M. C. C., Pinheiro-da-Silva, F., et al. (2017). Insights into the function of long
noncoding rnas in sepsis revealed by gene co-expression network analysis.
Non-Coding RNA 3, 5. doi: 10.3390/ncrna3010005
Percival, J. M. (2011). nNOS regulation of skeletal muscle fatigue and exercise
performance. Biophys. Rev. 3, 209–217. doi: 10.1007/s12551-011-0060-9
Perez-Montarelo, D., Hudson, N. J., Fernandez, A. I., Ramayo-Caldas, Y.,
Dalrymple, B. P., and Reverter, A. (2012). Porcine tissue-specic regulatory
networks derived from meta-analysis of the transcriptome. PloS One 7, e46159.
doi: 10.1371/journal.pone.0046159
Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T.-C., Mendell, J. T., and Salzberg,
S. L. (2015). StringTie enables improved reconstruction of a transcriptome from
RNA-seq reads. Nat. Biotechnol. 33, 290–295. doi: 10.1038/nbt.3122
Popoff, V., Adolf, F., Brugger, B., and Wieland, F. (2011). COPI budding within
the Golgi stack, Cold Spring Harbor. Perspect. Biol. 3, a005231. doi: 10.1101/
cshperspect.a005231
Raspe, E., Duez, H., Gervois, P., Fievet, C., Fruchart, J. C., Besnard, S., et al.
(2001). Transcriptional regulation of apolipoprotein C-III gene expression by
the orphan nuclear receptor RORalpha. J. Biol. Chem. 276, 2865–2871. doi:
10.1074/jbc.M004982200
Reverter, A., and Chan, E. K. (2008). Combining partial correlation and an
information theory approach to the reversed engineering of gene co-expression
networks. Bioinformatics 24, 2491–2497. doi: 10.1093/bioinformatics/btn482
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
LncRNAs Regulating Bovine Metabolic EfficiencyNolte et al.
19
Reverter, A., Hudson, N. J., Nagaraj, S. H., Perez-Enciso, M., and Dalrymple, B. P.
(2010). Regulatory impact factors: unraveling the transcriptional regulation of
complex traits from expression data. Bioinformatics 26, 896–904. doi: 10.1093/
bioinformatics/btq051
Robinson, A. (2015). Quality Trim version 1.6.0. https://bitbucket.org/arobinson/
qualitytrim/src/master/
Schaefer, R. J., Michno, J. M., Jeffers, J., Hoekenga, O., Dilkes, B., Baxter, I., et al.
(2018). Integrating coexpression networks with GWAS to prioritize causal
genes in maize. Plant Cell 30, 2922–2942. doi: 10.1105/tpc.18.00299
Seabury, C. M., Oldeschulte, D. L., Saatchi, M., Beever, J. E., Decker, J. E.,
Halley,Y. A., et al. (2017). Genome-wide association study for feed efficiency
and growth traits in U.S. beef cattle. BMC Genomics 18, 386–386. doi: 10.1186/
s12864-017-3754-y
Serviss, J. T., Johnsson, P., and Grandér, D. (2014). An emerging role for long
non-coding RNAs in cancer metastasis. Front. Genet. 5, 234. doi: 10.3389/
fgene.2014.00234
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D.,
et al. (2003). Cytoscape: a soware environment for integrated models of
biomolecular interaction networks. Genome Res. 13, 2498–2504. doi: 10.1101/
gr.1239303
Stamler, J. S., and Meissner, G. (2001). Physiology of nitric oxide in skeletal muscle.
Physiol. Rev. 81, 209–237. doi: 10.1152/physrev.2001.81.1.209
Sui, Yutong, Han, Yu, Zhao, Xingyu, Li, Dongsong, and Li, Guangyu (2019). Long
non-coding RNA Irm enhances myogenic differentiation by interacting with
MEF2D. Cell Death Dis. 10, 181–181. doi: 10.1038/s41419-019-1399-2
Sun, H. Z., Zhao, K., Zhou, M., Chen, Y., and Guan, L. L. (2019). Landscape
of multi-tissue global gene expression reveals the regulatory signatures of
feed efficiency in beef cattle. Bioinformatics 35, 1712–1719. doi: 10.1093/
bioinformatics/bty883 (Oxford, England).
Sundvold, H., and Lien, S. (2001). Identication of a novel peroxisome proliferator-
activated receptor (PPAR) gamma promoter in man and transactivation by the
nuclear receptor RORalpha1. Biochem. Biophys. Res. Commun. 287, 383–390.
doi: 10.1006/bbrc.2001.5602
Tang, J. E., Lysecki, P. J., Manolakos, J. J., MacDonald, M. J., Tarnopolsky, M. A.,
and Phillips, S. M. (2011). Bolus arginine supplementation affects neither
muscle blood ow nor muscle protein synthesis in young men at rest or aer
resistance exercise. J. Nu tr. 141, 195–200. doi: 10.3945/jn.110.130138
Tang, Z., Wu, Y., Yang, Y., Yang, Y.-C. T., Wang, Z., Yuan, J., et al. (2017).
Comprehensive analysis of long non-coding RNAs highlights their spatio-
temporal expression patterns and evolutional conservation in Sus scrofa. Sci.
Rep. 7, 43166. doi: 10.1038/srep43166
ornton, P. K. (2010). Livestock production: recent trends, future prospects,
Philosophical Transactions of the Royal Society of London. Ser. B Biol. Sci. 365,
2853–2867. doi: 10.1098/rstb.2010.0134
Tian, W., Wang, H. R., Wu, T. Y., Ding, L. Y., Zhao, R., Khas, E., et al. (2017).
Milk protein responses to balanced amino acid and removal of Leucine and
Arginine supplied from jugular-infused amino acid mixture in lactating dairy
cows. J. Anim. Physiol. Anim. Nutr. 101, e278–ee87. doi: 10.1111/jpn.12603
Tunnacliffe, A., Pensotti, V., and Radice, P. (1996). e coatomer protein delta-
COP, encoded by the archain gene, is conserved across diverse eukaryotes.
Mamm. Genome 7, 784–786. doi: 10.1007/s003359900234
Ulitsky, I., and Bartel, D. P. (2013). lincRNAs: genomics, evolution, and
mechanisms. Cell. 154, 26–46. doi: 10.1016/j.cell.2013.06.020
Ulitsky, I., Shkumatava, A., Jan, C. H., Sive, H., and Bartel, D. P. (2011). Conserved
function of lincRNAs in vertebrate embryonic development despite rapid
sequence evolution. Cell 147, 1537–1550. doi: 10.1016/j.cell.2011.11.055
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., and de Magalhães, J. P. (2017).
Gene co-expression analysis for functional classication and gene-disease
predictions. Briengs Bioinf. 19, 575–592. doi: 10.1093/bib/bbw139
Vaquerizas, J. M., Kummerfeld, S. K., Teichmann, S. A., and Luscombe, N. M.
(2009). A census of human transcription factors: function, expression and
evolution. Nat. Rev. Genet. 10, 252–263. doi: 10.1038/nrg2538
Vu-Dac, N., Gervois, P., Grotzinger, T., De Vos, P., Schoonjans, K., Fruchart, J. C.,
et al. (1997). Transcriptional regulation of apolipoprotein A-I gene expression
by the nuclear receptor RORalpha. J. Biol. Chem. 272, 22401–22404. doi:
10.1074/jbc.272.36.22401
Walsh, D., and Mohr, I. (2014). Coupling 40S ribosome recruitment to modication
of a cap-binding initiation factor by eIF3 subunit e. Genes Dev. 28, 835–840.
doi: 10.1101/gad.236752.113
Wang, C.-H., Shi, H.-H., Chen, L.-H., Li, X.-L., Cao, G.-L., and Hu, X.-F. (2019).
Identication of key lncRNAs associated with atherosclerosis progression
based on public datasets. Front. Genet. 10, 123. doi: 10.3389/fgene.2019.00123
Weikard, R., Altmaier, E., Suhre, K., Weinberger, K. M., Hammon, H. M.,
Albrecht, E., et al. (2010). Metabolomic proles indicate distinct physiological
pathways affected by two loci with major divergent effect on Bos taurus
growth and lipid deposition. Physiol. Genomics 42a, 79–88. doi: 10.1152/
physiolgenomics.00120.2010
Weikard, R., Goldammer, T., Brunner, R. M., and Kuehn, C. (2012). Tissue-specic
mRNA expression patterns reveal a coordinated metabolic response associated
with genetic selection for milk production in cows. Physiol. Genomics 44, 728–
739. doi: 10.1152/physiolgenomics.00007.2012
Weikard, R., Hadlich, F., Hammon, H. M., Frieten, D., Gerbert, C., Koch, C., etal.
(2018). Long noncoding RNAs are associated with metabolic and cellular
processes in the jejunum mucosa of pre-weaning calves in response to different
diets. Oncotarget 9, 21052–21069. doi: 10.18632/oncotarget.24898
Widmann, P., Nuernberg, K., Kuehn, C., and Weikard, R. (2011). Association of an
ACSL1 gene variant with polyunsaturated fatty acids in bovine skeletal muscle.
BMC Genet. 12, 13. doi: 10.1186/1471-2156-12-96
Widmann, P., Reverter, A., Fortes, M. R. S., Weikard, R., Suhre, K., Hammon,H.,
et al. (2013). A systems biology approach using metabolomic data reveals
genes and pathways interacting to modulate divergent growth in cattle. BMC
Genomics 14, 798. doi: 10.1186/1471-2164-14-798
Widmann, P., Reverter, A., Weikard, R., Suhre, K., Hammon, H. M., Albrecht,E.,
et al. (2015). Systems biology analysis merging phenotype, metabolomic and
genomic data identies non-smc condensin i complex, subunit G (NCAPG)
and cellular maintenance processes as major contributors to genetic
variability in bovine feed efficiency. PloS One 10, 22. doi: 10.1371/journal.
pone.0124574
Wu, G., Bazer, F. W., Davis, T. A., Kim, S. W., Li, P., Marc Rhoads, J., et al. (2009).
Arginine metabolism and nutrition in growth, health and disease. Amin o Acids
37, 153–168. doi: 10.1007/s00726-008-0210-y
Wucher, V., Legeai, F., Hedan, B., Rizk, G., Lagoutte, L., Leeb, T., et al. (2017).
FEELnc: a tool for long non-coding RNA annotation and its application to the
dog transcriptome. Nucleic Acids Res. 45, e57. doi: 10.1093/nar/gkw1306
Yang, L., Li, P., Yang, W., Ruan, X., Kiesewetter, K., Zhu, J., et al. (2016). Integrative
transcriptome analyses of metabolic responses in mice dene pivotal LncRNA
metabolic regulators. Cell Me tab. 24, 627–639. doi: 10.1016/j.cmet.2016.08.019
Zeng, Y., Ren, K., Zhu, X., Zheng, Z., and Yi, G. (2018). “Chapter one long
noncoding RNAs,” in Advances in Lipid Metabolism. Ed. Gregory, S. (Makowski:
Advances in Clinical Chemistry (Elsevier)). doi: 10.1016/bs.acc.2018.07.001
Zhang, Y., Papazyan, R., Damle, M., Fang, B., Jager, J., Feng, D., et al. (2017). e
hepatic circadian clock ne-tunes the lipogenic response to feeding through
RORalpha/gamma. Genes Dev. 31, 1202–1211. doi: 10.1101/gad.302323.117
Zhu, M., Liu, J. F., Xiao, J., Yang, L., Cai, M. X., Shen, H. Y., et al. (2017). Lnc-mg is
a long non-coding RNA that promotes myogenesis. Nat. Commun. 8, 11. doi:
10.1038/ncomms14718
Conict of Interest: e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could be construed as a
potential conict of interest.
Copyright © 2019 Nolte, Weikard, Brunner, Albrecht, Hammon, Reverter and Kühn.
is is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). e use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner(s) are credited
and that the original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which does not
comply with these terms.
Frontiers in Genetics | www.frontiersin.org November 2019 | Volume 10 | Article 1130
... The primary selection criterion was the energy corrected milk (ECM) yield during seven days prior to slaughter at 30 days in lactation with the fat content in carcass (CFC) and the intramuscular fat content (IMF) in M. longissimus dorsi as accessory selection parameters. Phenotyping and sampling strategy have been explained in our previous study [29]. The cows were grouped into animals of nutrient partitioning predominantly directed to milk secretion (SEC, n = 13) or to body fat accretion (ACC, n = 12) type (see Supplementary Table 1). ...
... The transcriptomic data set examined in this study was already used in a previous study (Nolte et al. [29], aligned to UMD.3.1, Ensembl annotation release 92) and is stored in the Functional Annotation of Animal Genomes (FAANG) database (https://data.faang.org/dataset) ...
Article
Long non-coding RNAs (lncRNAs) hold gene regulatory potential, but require substantial further functional annotation in livestock. Applying two metabogenomic approaches by combining transcriptomic and metabolomic analyses, we aimed to identify lncRNAs with potential regulatory function for divergent nutrient partitioning of lactating crossbred cows and to establish metabogenomic interaction networks comprising metabolites, genes and lncRNAs. Through correlation analysis of lncRNA expression with transcriptomic and metabolomic data, we unraveled lncRNAs that have a putative regulatory role in energy and lipid metabolism, the urea and tricarboxylic acid cycles, and gluconeogenesis. Especially FGF21, which correlated with a plentitude of differentially expressed genes, differentially abundant metabolites, as well as lncRNAs, suggested itself as a key metabolic regulator. Notably, lncRNAs in close physical proximity to coding-genes as well as lncRNAs with natural antisense transcripts appear to perform a fine-tuning function in gene expression involved in metabolic pathways associated with different nutrient partitioning phenotypes.
... Recent research in cattle has revealed more lncRNA transcripts in many tissues across cattle breeds: over 4,000 in 6 different tissues between 2 Chinese cattle breeds, nearly 10,000 in 18 different bovine tissues, over 23,000 lncRNA in bovine testes tissue as they mature, and 1,535 lncRNAs in bovine oocytes [5][6][7]. Additionally, almost 8,000 lncRNAs were found to be associated with metabolic efficiency [8]. ...
Article
Full-text available
Background This study aimed to identify long non-coding RNA (lncRNA) from the rumen tissue in dairy cattle, explore their features including expression and conservation levels, and reveal potential links between lncRNA and complex traits that may indicate important functional impacts of rumen lncRNA during the transition to the weaning period. Results A total of six cattle rumen samples were taken with three replicates from before and after weaning periods, respectively. Total RNAs were extracted and sequenced with lncRNA discovered based on size, coding potential, sequence homology, and known protein domains. As a result, 404 and 234 rumen lncRNAs were identified before and after weaning, respectively. However, only nine of them were shared under two conditions, with 395 lncRNAs found only in pre-weaning tissues and 225 only in post-weaning samples. Interestingly, none of the nine common lncRNAs were differentially expressed between the two weaning conditions. LncRNA averaged shorter length, lower expression, and lower conservation scores than the genome overall, which is consistent with general lncRNA characteristics. By integrating rumen lncRNA before and after weaning with large-scale GWAS results in cattle, we reported significant enrichment of both pre- and after-weaning lncRNA with traits of economic importance including production, reproduction, health, and body conformation phenotypes. Conclusions The majority of rumen lncRNAs are uniquely expressed in one of the two weaning conditions, indicating a functional role of lncRNA in rumen development and transition of weaning. Notably, both pre- and post-weaning lncRNA showed significant enrichment with a variety of complex traits in dairy cattle, suggesting the importance of rumen lncRNA for cattle performance in the adult stage. These relationships should be further investigated to better understand the specific roles lncRNAs are playing in rumen development and cow performance.
... On chromosome BTA6, the CXCL11 gene was identified and has been associated with immune response during pathological processes, including inflammation and autoimmune and infectious diseases in different cattle populations [30]. Moreover, the NUP54 gene was related to body fat deposition and muscle development [31]. SCARB2 gene was associated with several functions in energy metabolism, including the synthesis and transport of energy, lipids, and activity in large organs [32]. ...
Article
Full-text available
The Caqueteño Creole (CAQ) is a native breed of cattle from the Caquetá department (Colombia), adapted to tropical conditions, which is extremely important to production systems in those regions. However, CAQ is poorly studied. In this sense, population structure studies associated with runs of homozygosity (ROH) analysis would allow for a better understanding of CAQ. Through ROH analysis, it is possible to reveal genetic relationships between individuals, measure genome inbreeding levels, and identify regions associated with traits of economic interest. Samples from a CAQ population (n = 127) were genotyped with the Bovine HD BeadChip (777,000 SNPs) and analyzed with the PLINK 1.9 program to estimate FROH and ROH islands. We highlighted a decrease in inbreeding frequency for FROH 4–8 Mb, 8–16 Mb, and >16 Mb classes, indicating inbreeding control in recent matings. We also found genomic hotspot regions on chromosomes 3, 5, 6, 8, 16, 20, and 22, where chromosome 20 harbored four hotspots. Genes in those regions were associated with fertility and immunity traits, muscle development, and environmental resistance, which may be present in the CAQ breed due to natural selection. This indicates potential for production systems in tropical regions. However, further studies are necessary to elucidate the CAQ production objective.
... For the current test run, the WGS include 19,590,389 bi-allelic, polymorphic (MAF > 0.005) variants across 29 autosomal chromosome, which were imputed using a stepwise strategy from 6k to 50k to HD to WGS (PAUSCH et al. 2016) with a mean imputation accuracy of 0.97 across different steps. The RNA-Seq samples consist of paired end libraries sequenced on the Illumina HiSeq 2500 system (NOLTE et al. 2019;HEIMES et al. 2020). The average number of input reads across 88 RNA samples were 56 million read pairs varying between 43 to 74 million read pairs. ...
Presentation
Full-text available
The in silico detection of expression quantitative trait loci (eQTL) demands high throughput processing from hundreds of samples, which is often a challenge to handle and run such large datasets. In order to focus on the core analysis, it is convenient to have simple coding and hassle-free installation of different software tools required for the bioinformatics workflow. In this context, the newly available technologies like workflow managers and software containers enabled to develop workflows with less complexity. In this study, we developed an eQTL bioinformatics pipeline with the workflow manager Nextflow and docker container software, for coding and installing the required software tools. This workflow can be portable to a different computer environment, and the results are reproducible. We tested the functionality of our workflow with a sample dataset and the runtime estimates from this demo run will provide important information in planning future analyses with much larger datasets.
... LncRNAs are relevant to the development of COAD, and the lncRNA LINC00114 is a potential target for the diagnosis of COAD [43]. Another study identified three COAD-related lncRNAs with prognostic values (LINC00114, LINC00261, and HOTAIR) [44]. In addition, LINC00114 may be associated with the OS of CRC patients [45]. ...
Article
Full-text available
Colorectal cancer (CRC) includes colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Competitive endogenous RNA (ceRNA) is crucial for cancer pathogenesis. Abnormal expression of MYC is generally associated with a poor colon adenocarcinoma prognosis. The present study aimed to identify a novel MYC-associated ceRNA regulatory network and identify potential prognostic markers associated with COAD. We obtained the transcriptome sequencing profiles of 462 COAD cases from the TCGA database and analyzed differentially expressed genes (DEGs) in MYC high expression (MYChigh) and MYC low expression (Myclow) tumors. We identified an important lncRNA, LINC00114, which effectively predicts overall survival and plays a protective role in COAD. Moreover, the LINC00114/miR-216a-5p axis was identified as a clinical prognostic model. The predicted target genes of the LINC00114/miR-216a-5p axis include uromodulin Like 1 (UMODL1) and oncoprotein induced transcript 3 (OIT3), which are closely related to the survival and prognosis of COAD patients. In summary, we constructed a novel ceRNA regulatory network and identified potential biomarkers for the targeted therapy and prognosis of COAD.
... For bovine liver and bovine muscle, we retained the 6 datasets already used in [34] from males (available on ENA [73] under the project PRJEB34570). Reference article including the protocol of the preparation of sequencing libraries (Reads: 2X100bp; Ribo-Zero): [82]. ...
Article
Full-text available
Circular intronic RNAs (ciRNAs) are still unexplored regarding mechanisms for their emergence. We considered the ATXN2L intron lariat-derived circular RNA (ciRNA-ATXN2L) as an opportunity to conduct a cross-species examination of ciRNA genesis. To this end, we investigated 207 datasets from 4 tissues and from 13 mammalian species. While in eight species, ciRNA-ATXN2L was never detected, in pigs and rabbits, ciRNA-ATXN2L was expressed in all tissues and sometimes at very high levels. Bovine tissues were an intermediate case and in macaques and cats, only ciRNA-ATXN2L traces were detected. The pattern of ciRNA-ATXN2L restricted to only five species is not related to a particular evolution of intronic sequences. To empower our analysis, we considered 221 additional introns including 80 introns where a lariat-derived ciRNA was previously described. The primary driver of micro-ciRNA genesis (< 155 nt as ciRNA-ATXN2L) appears to be the absence of a canonical “A” (i.e. a "tnA" located in the usual branching region) to build the lariat around this adenosine. The balance between available "non canonical-A" (no ciRNA genesis) and "non-A" (ciRNA genesis) for use as a branch point to build the lariat could modify the expression level of ciRNA-ATXN2L. In addition, the rare localization of the 2′-5′ bond in an open RNA secondary structure could also negatively affect the lifetime of ciRNAs (macaque ciRNA-ATXN2L). Our analyses suggest that ciRNA-ATXN2L is likely a functionless splice remnant. This study provides a better understanding of the ciRNAs origin, especially drivers for micro ciRNA genesis.
... An example is the investigation of ncRNAs in cattle considering that cattle and buffalo meat and milk are the sources of 45% of global animal protein supply [235]. Computational analysis of sequencing data has already found lncR-NAs associated with metabolism in cattle [236]. Furthermore, lncRNAs are responsible for differences in muscle characteristics between cattle and buffalo [237], which complements a previous study of skeletal-muscle related bovine lncRNAs [238]. ...
Article
Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.
Article
Full-text available
The insertion of an endogenous retroviral long terminal repeat (LTR) sequence into the bovine apolipoprotein B (APOB) gene is causal to the inherited genetic defect cholesterol deficiency (CD) observed in neonatal and young calves. Affected calves suffer from developmental abnormalities, symptoms of incurable diarrhoea and often die within weeks to a few months after birth. Neither the detailed effects of the LTR insertion on APOB expression profile nor the specific mode of inheritance nor detailed phenotypic consequences of the mutation are undisputed. In our study, we analysed German Holstein dairy heifers at the peak of hepatic metabolic load and exposed to an additional pathogen challenge for clinical, metabolic and hepatic transcriptome differences between wild type (CDF) and heterozygote carriers of the mutation (CDC). Our data revealed that a divergent allele-biased expression pattern of the APOB gene in heterozygous CDC animals leads to a tenfold higher expression of exons upstream and a decreased expression of exons downstream of the LTR insertion compared to expression levels of CDF animals. This expression pattern could be a result of enhancer activity induced by the LTR insertion, in addition to a previously reported artificial polyadenylation signal. Thus, our data support a regulatory potential of mobile element insertions. With regard to the phenotype generated by the LTR insertion, heterozygote CDC carriers display significantly differential hepatic expression of genes involved in cholesterol biosynthesis and lipid metabolism. Phenotypically, CDC carriers show a significantly affected lipomobilization compared to wild type animals. These results reject a completely recessive mode of inheritance for the CD defect, which should be considered for selection decisions in the affected population. Exemplarily, our results illustrate the regulatory impact of mobile element insertions not only on specific host target gene expression but also on global transcriptome profiles with subsequent biological, functional and phenotypic consequences in a natural in-vivo model of a non-model mammalian organism.
Article
Full-text available
In this study, actinin-3 ( ACTN3 ) gene expression was investigated in relation to the feed efficiency phenotype in Bos indicus - Bos taurus crossbred steers. A measure of relative feed efficiency based on residual feed intake relative to predictions from the NRC beef cattle model was analyzed by the use of a mixed linear model that included sire and family nested within sire as fixed effects and age, animal type, sex, condition, and breed as random effects for 173 F 2 Nellore-Angus steers. Based on these residual intake observations, individuals were ranked from most efficient to least efficient. Skeletal muscle samples were analyzed from 54 steers in three groups of 18 (high efficiency, low efficiency, and a statistically average group). ACTN3 , which encodes a muscle-specific structural protein, was previously identified as a candidate gene from a microarray analysis of RNA extracted from muscle samples obtained from a subset of steers from each of these three efficiency groups. The expression of ACTN3 was evaluated by quantitative reverse transcriptase PCR analysis. The expression of ACTN3 in skeletal muscle was 1.6-fold greater in the inefficient steer group than in the efficient group ( p = 0.007). In addition to expression measurements, blocks of SNP haplotypes were assessed for breed or parent of origin effects. A maternal effect was observed for ACTN3 inheritance, indicating that a maternal B. indicus block conferred improved residual feed efficiency relative to the B. taurus copy ( p = 0.03). A SNP haplotype analysis was also conducted for m-calpain ( CAPN2 ) and fibronectin 1 ( FN1 ), and a significant breed effect was observed for both genes, with B. indicus and B. taurus alleles each conferring favorable efficiency when inherited maternally ( p = 0.03 and p = 0.04). Because the ACTN3 structural protein is specific to fast-twitch (type II) muscle fibers and not present in slow-twitch muscle fibers (type I), muscle samples used for expression analysis were also assayed for fiber type ratio (type II/type I). Inefficient animals had a fast fiber type ratio 1.8-fold greater than the efficient animals ( p = 0.027). Because these fiber-types exhibit different metabolic profiles, we hypothesize that animals with a greater proportion of fast-twitch muscle fibers are also less feed efficient.
Article
Full-text available
Animal genomes are pervasively transcribed into multiple RNA molecules, of which many will not be translated into proteins. One major component of this transcribed non-coding genome is the long non-coding RNAs (lncRNAs), which are defined as transcripts longer than 200 nucleotides with low coding-potential capabilities. Domestic animals constitute a unique resource for studying the genetic and epigenetic basis of phenotypic variations involving protein-coding and non-coding RNAs, such as lncRNAs. This review presents the current knowledge regarding transcriptome-based catalogues of lncRNAs in major domesticated animals (pets and livestock species), covering a broad phylogenetic scale (from dogs to chicken), and in comparison with human and mouse lncRNA catalogues. Furthermore, we describe different methods to extract known or discover novel lncRNAs and explore comparative genomics approaches to strengthen the annotation of lncRNAs. We then detail different strategies contributing to a better understanding of lncRNA functions, from genetic studies such as GWAS to molecular biology experiments and give some case examples in domestic animals. Finally, we discuss the limitations of current lncRNA annotations and suggest research directions to improve them and their functional characterisation.
Article
Full-text available
Long noncoding RNA(lncRNA)s are new regulators governing the metabolism in adipose tissue. In this study, we aimed to understand how lncRNAs respond to insulin signalling and explore whether lncRNAs have a functional role in insulin signalling pathway. We treated primary adipocyte cultures with insulin and collected RNA for RNA-sequencing to profile the non-coding transcriptome changes, through which we identified a top Adipose Specific Insulin Responsive LncRNA (LncASIR). To determine its biological function, we knocked down LncASIR using dcas9-KRAB, followed by RNA-seq to examine the effect on insulin-induced gene expression program. We identified a set of lncRNAs regulated by insulin signalling pathway. LncASIR is transcribed from a super enhancer region and responds robustly to insulin treatment. Silencing LncASIR resulted in an impaired global insulin-responsive gene program. LncASIR is a novel and integral component in the insulin signalling pathway in adipocytes.
Article
Full-text available
Systems biology approaches are used as strategy to uncover tissue-specific perturbations and regulatory genes related to complex phenotypes. We applied this approach to study feed efficiency (FE) in beef cattle, an important trait both economically and environmentally. Poly-A selected RNA of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle and pituitary) of eighteen young bulls, selected for high and low FE, were sequenced (Illumina HiSeq 2500, 100 bp, pared-end). From the 17,354 expressed genes considering all tissues, 1,335 were prioritized by five selection categories (differentially expressed, harboring SNPs associated with FE, tissue-specific, secreted in plasma and key regulators) and used for network construction. NR2F6 and TGFB1 were identified and validated by motif discovery as key regulators of hepatic inflammatory response and muscle tissue development, respectively, two biological processes demonstrated to be associated with FE. Moreover, we indicated potential biomarkers of FE, which are related to hormonal control of metabolism and sexual maturity. By using robust methodologies and validation strategies, we confirmed the main biological processes related to FE in Bos indicus and indicated candidate genes as regulators or biomarkers of superior animals.
Article
Full-text available
Atherosclerosis is one of the most common type of cardiovascular disease and the prime cause of mortality in the aging population worldwide. However, the detail mechanisms and special biomarkers of atherosclerosis remain to be further investigated. Lately, long non-coding RNAs (lncRNAs) has attracted much more attention than other types of ncRNAs. In our work, we found and confirmed differently expressed lncRNAs and mRNAs in atherosclerosis by analyzing GSE28829. We performed the weighted gene co-expression network analysis (WGCNA) by analyzing GSE40231 to confirm highly correlated genes. Gene Ontology (GO) analysis were utilized to assess the potential functions of differential expressed lncRNAs in atherosclerosis. Co-expression networks were also constructed to confirm hub lncRNAs in atherosclerosis. A total of 5784 mRNAs and 654 lncRNAs were found to be dysregulated in the progression of atherosclerosis. A total of 15 lncRNA-mRNA co-expression modules were identified in this study based on WGCNA analysis. Moreover, a few lncRNAs, such as ZFAS1, LOC100506730, LOC100506691, DOCK9-AS2, RP11-6I2.3, LOC100130219, were confirmed as important lncRNAs in atherosclerosis. Taken together, bioinformatics analysis revealed these lncRNAs were involved in regulating the leukotriene biosynthetic process, gene expression, actin filament organization, t-circle formation, antigen processing, and presentation, interferon-gamma-mediated signaling pathway, and activation of GTPase activity. We believed that this study would provide potential novel therapeutic and prognostic targets for atherosclerosis.
Article
Full-text available
Recent studies suggest important roles for long non-coding RNAs as essential regulators of myogenic differentiation. Here, we report that lncRNA Irm is upregulated during myogenesis. Functional analyses show that the overexpression of Irm enhances myogenic differentiation, whereas the inhibition of Irm has completely opposite effects in vitro. Notably, the inhibition of Irm blocks damage-induced muscle regeneration in vivo. Mechanistically, Irm regulates the expression of myogenic genes by directly binding to MEF2D, which in turn promotes the assembly of MyoD/MEF2D on the regulatory elements of target genes. Collectively, we have identified a novel lncRNA that interacts with MEF2D to regulate myogenesis.
Article
Full-text available
This study evaluated the effects of crystalline arginine (Arg) on performance and body composition in male broilers. A total of 600 1-day-old Ross 308 broilers were distributed in a completely randomized design, with 5 treatments and 6 replicates of 20 birds. The treatments were given as a percentage of the Ross 308 requirement, and defined as 70, 80, 90, 100 (Ross 308 requirement), and 110% of Arg. Body weight gain (BWG), feed intake (FI), and feed conversion ratio (FCR) were evaluated at 10, 24, and 42 d. Bone growth was measured from 7 to 11 d using mineral apposition rate (MAR) technique. At 42 d, 2 birds per pen were euthanized for bone mineral density (BMD) and body composition measurement using dual-energy X-ray absorptiometry, and liver gene expression and muscle diameter size analysis. The means were subjected to ANOVA and, when significant (P ≤ 0.05), were compared by Dunnett's test. Regression analyses were performed to evaluate trends of Arg dose response. Birds fed 70 and 80% of Arg had lower BWG than the ones fed 100% of Arg (P < 0.001), with quadratic effects for all phases (P < 0.001, R2 = 0.94). The 70% of Arg group showed lower FI compared to 100% from 11 to 24 d and 1 to 42 d (P < 0.009), with quadratic and linear effects (P < 0.049, R2 > 0.72), respectively. The 70% of Arg group showed higher FCR compared to 100% (P < 0.0001) with quadratic effects (P < 0.002, R2 > 0.94) for all periods. At 42 d, the 70% of Arg group showed lower BMD, tissue, and lean muscle percentage than 100% of Arg. There was a quadratic effect of Arg levels on lean muscle (P = 0.046, R2 = 0.89). Therefore, the dietary supplementation with Arg is necessary as it leads to an overall body growth with increased lean deposition and BMD, without increasing fat accretion in Ross 308 broiler chickens.
Article
Full-text available
Successful development of biological databases requires accommodation of the burgeoning amounts of data from high-throughput genomics pipelines. As the volume of curated data in Animal QTLdb (https://www.animalgenome.org/QTLdb) increases exponentially, the resulting challenges must be met with rapid infrastructure development to effectively accommodate abundant data curation and make metadata analysis more powerful. The development of Animal QTLdb and CorrDB for the past 15 years has provided valuable tools for researchers to utilize a wealth of phenotype/genotype data to study the genetic architecture of livestock traits. We have focused our efforts on data curation, improved data quality maintenance, new tool developments, and database co-developments, in order to provide convenient platforms for users to query and analyze data. The database currently has 158 499 QTL/associations, 10 482 correlations and 1977 heritability data as a result of an average 32% data increase per year. In addition, we have made >14 functional improvements or new tool implementations since our last report. Our ultimate goals of database development are to provide infrastructure for data collection, curation, and annotation, and more importantly, to support innovated data structure for new types of data mining, data reanalysis, and networked genetic analysis that lead to the generation of new knowledge.
Article
Full-text available
Background: Long noncoding RNAs (LncRNAs) play important roles in fundamental biological processes. However, knowledge about the genome-wide distribution and stress-related expression of lncRNAs in tilapia is still limited. Results: Genome-wide identification of lncRNAs in the tilapia genome was carried out in this study using bioinformatics tools. 103 RNAseq datasets that generated in our laboratory or collected from NCBI database were analyzed. In total, 72,276 high-confidence lncRNAs were identified. The averaged positive correlation coefficient (r_mean = 0.286) between overlapped lncRNA and mRNA pairs showed significant differences with the values for all lncRNA-mRNA pairs (r_mean = 0.176, z statistics = - 2.45, p value = 0.00071) and mRNA-mRNA pairs (r_mean = 0.186, z statistics = - 2.23, p value = 0.0129). Weighted correlation network analysis of the lncRNA and mRNA datasets from 12 tissues identified 21 modules and many interesting mRNA genes that clustered with lncRNAs. Overrepresentation test indicated that these mRNAs enriched in many biological processes, such as meiosis (p = 0.00164), DNA replication (p = 0.00246), metabolic process (p = 0.000838) and in molecular function, e.g., helicase activity (p = 0.000102) and catalytic activity (p = 0.0000612). Differential expression (DE) analysis identified 99 stress-related lncRNA genes and 1955 tissue-specific DE lncRNA genes. MiRNA-lncRNA interaction analysis detected 72,267 lncRNAs containing motifs with sequence complementary to 458 miRNAs. Conclusions: This study provides an invaluable resource for further studies on molecular bases of lncRNAs in tilapia genomes. Further function analysis of the lncRNAs will help to elucidate their roles in regulating stress-related adaptation in tilapia.
Article
Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize. © 2018 American Society of Plant Biologists. All rights reserved.
Article
Motivation: Feed efficiency is an important trait for sustainable beef production that is regulated by the complex biological process, but the mode of action behinds it has not been clearly defined. Here, we aimed to elucidate the regulatory mechanisms of this trait through studying the landscape of the genome-wide gene expression of rumen, liver, muscle, and backfat tissues, the key ones involved in the energy metabolism. Results: The transcriptome of 189 samples across four tissues from 48 beef steers with varied feed efficiency were generated using Illumina HiSeq4000. The analysis of global gene expression profiles of four tissues, functional analysis of tissue shared and unique genes, co-expressed network construction of tissue-shared genes, weighted correlations analysis between gene modules and feed efficiency related traits in each tissue were performed. Among four tissues, the transcriptome of muscle tissue was distinctive from others, while those of rumen and backfat tissues were similar. The associations between co-expressed genes and feed efficiency related traits at single or all tissues level exhibited that the gene expression in the rumen, liver, muscle, and backfat were the most correlated with feed conversion ratio, dry matter intake, average daily gain, and residual feed intake respectively. The 19 overlapped genes identified from the strongest module-trait relationships in four tissues are potential generic gene markers for feed efficiency. Availability: The distribution of gene expression data can be accessed at https://www.cattleomics.com/transcriptome. Supplementary information: Supplementary data are available at Bioinformatics online.
Article
Although many long noncoding RNAs (lncRNAs) have been identified in muscle, their physiological function and regulatory mechanisms remain largely unexplored. In this study, we systematically characterized the expression profiles of lncRNAs during C2C12 myoblast differentiation and identified an intronic lncRNA, SYISL (SYNPO2 intron sense-overlapping lncRNA), that is highly expressed in muscle. Functionally, SYISL promotes myoblast proliferation and fusion but inhibits myogenic differentiation. SYISL knockout in mice results in significantly increased muscle fiber density and muscle mass. Mechanistically, SYISL recruits the enhancer of zeste homolog 2 (EZH2) protein, the core component of polycomb repressive complex 2 (PRC2), to the promoters of the cell-cycle inhibitor gene p21 and muscle-specific genes such as myogenin (MyoG), muscle creatine kinase (MCK), and myosin heavy chain 4 (Myh4), leading to H3K27 trimethylation and epigenetic silencing of target genes. Taken together, our results reveal that SYISL is a repressor of muscle development and plays a vital role in PRC2-mediated myogenesis.