Transcriptional analysis of adipose tissue during development reveals depot-specific responsiveness to maternal dietary supplementation

Abstract

Brown adipose tissue (BAT) undergoes pronounced changes after birth coincident with the loss of the BAT-specific uncoupling protein (UCP)1 and rapid fat growth. The extent to which this adaptation may vary between anatomical locations remains unknown, or whether the process is sensitive to maternal dietary supplementation. We, therefore, conducted a data mining based study on the major fat depots (i.e. epicardial, perirenal, sternal (which possess UCP1 at 7 days), subcutaneous and omental) (that do not possess UCP1) of young sheep during the first month of life. Initially we determined what effect adding 3% canola oil to the maternal diet has on mitochondrial protein abundance in those depots which possessed UCP1. This demonstrated that maternal dietary supplementation delayed the loss of mitochondrial proteins, with the amount of cytochrome C actually being increased. Using machine learning algorithms followed by weighted gene co-expression network analysis, we demonstrated that each depot could be segregated into a unique and concise set of modules containing co-expressed genes involved in adipose function. Finally using lipidomic analysis following the maternal dietary intervention, we confirmed the perirenal depot to be most responsive. These insights point at new research avenues for examining interventions to modulate fat development in early life.

Full text

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SCIENTIfIC REPORTS | (2018) 8:9628 | DOI:10.1038/s41598-018-27376-3
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Transcriptional analysis of adipose
tissue during development reveals
depot-specic responsiveness to
maternal dietary supplementation
Hernan P. Fainberg1, Mark Birtwistle
1, Reham Alagal1,5, Ahmad Alhaddad1, Mark Pope1,
Graeme Davies1, Rachel Woods
1, Marcos Castellanos3, Sean T. May
3, Catharine A. Ortori4,
David A. Barrett
4, Viv Perry6, Frank Wiens7, Bernd Stahl7, Eline van der Beek7,8,
Harold Sacks9, Helen Budge1 & Michael E. Symonds1,2
Brown adipose tissue (BAT) undergoes pronounced changes after birth coincident with the loss of the
BAT-specic uncoupling protein (UCP)1 and rapid fat growth. The extent to which this adaptation may
vary between anatomical locations remains unknown, or whether the process is sensitive to maternal
dietary supplementation. We, therefore, conducted a data mining based study on the major fat depots
(i.e. epicardial, perirenal, sternal (which possess UCP1 at 7 days), subcutaneous and omental) (that do
not possess UCP1) of young sheep during the rst month of life. Initially we determined what eect
adding 3% canola oil to the maternal diet has on mitochondrial protein abundance in those depots
which possessed UCP1. This demonstrated that maternal dietary supplementation delayed the loss
of mitochondrial proteins, with the amount of cytochrome C actually being increased. Using machine
learning algorithms followed by weighted gene co-expression network analysis, we demonstrated
that each depot could be segregated into a unique and concise set of modules containing co-expressed
genes involved in adipose function. Finally using lipidomic analysis following the maternal dietary
intervention, we conrmed the perirenal depot to be most responsive. These insights point at new
research avenues for examining interventions to modulate fat development in early life.
e association between excessive fat storage (i.e. obesity) and increased risk of metabolic disease, which leads
to a reduction in life quality and expectancy, is well documented1. To identify modiable factors that drive
unhealthy fat deposition it could be informative to better understand the pronounced developmental changes in
adipose tissue that occur soon aer birth2. is age period is characterised by the rapid activation of nonshivering
thermogenesis in brown adipose, an energy-using process involving tissue-specic uncoupling protein (UCP)13,
which helps the newborn to achieve a physiological body temperature. Subsequently, brown fat is gradually lost
and replaced with white adipose tissue4, which contains cells that store energy for later usage as cellular fuel.
Over the last decade, high-throughput genome-wide association studies (GWAS), together with gene expression
proling, epigenetic and integrative genomic analysis, have all contributed to a better understanding of adipose
1Division of Child Health, Obstetrics & Gynaecology, The University of Nottingham, Nottingham, United Kingdom.
2Nottingham Digestive Disease Centre and Biomedical Research Centre, School of Medicine, Queen’s Medical
Centre, The University of Nottingham, Nottingham, United Kingdom. 3Nottingham Arabidopsis Stock Centre,
School of Biosciences, The University of Nottingham, Nottingham, United Kingdom. 4Centre for Analytical
Bioscience, School of Pharmacy, The University of Nottingham, Nottingham, United Kingdom. 5Princess Nourah
Bint Abdulrahman University, Department of Nutrition and food science, College of Home Economics, Riyadh, BOX:
84428, Saudi Arabia. 6Robinson Research Institute, Medical School, University of Adelaide, Adelaide, Australia.
7Nutricia Research, Utrecht, The Netherlands. 8Department of Pediatrics, University Medical Centre Groningen,
University of Groningen, Groningen, The Netherlands. 9VA Endocrinology and Diabetes Division, VA Greater Los
Angeles Healthcare System, and Department of Medicine, David Geen School of Medicine, University of California
Los Angeles, California, USA. Correspondence and requests for materials should be addressed to M.E.S. (email:
michael.symonds@nottingham.ac.uk)
Received: 2 November 2017
Accepted: 30 May 2018
Published: xx xx xxxx
OPEN
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tissue biology and its crucial role in the metabolic syndrome5,6. Scientic break-through, however, may have been
hampered by wrongly assuming that adipose tissue at various anatomical sites is regulated in an identical manner
for coping with metabolic challenges during current and later life2. To test for site-specic regulation and develop-
mental plasticity of adipose tissue we used sheep, based on the rapid transformation from brown to white adipose
tissue characteristics over the rst month of life3.
e function and development of adipose tissue depots are oen studied in isolation7, so, potential dierences
between depots and inuence of adjacent organs are not well established3,6,7. With recent advances in computer
power and functional annotation of transcriptome data, primary genes can be identied based on their pattern of
expression across the genome5,6. Furthermore, genes with similar co-expression patterns innately cluster together
and/or form distinct modules representing pathways involved in the regulation of interdependent biological
functions8. Dierences in the changes in the transcriptome with age between similar tissues may reect variations
in cell type, but also in their function, transcriptional regulation, and responsiveness to external cues8. In the pres-
ent study, we employed a machine learning (ML) algorithm followed by a weighted gene co-expression network
analysis in order to nd biologically meaningful associations in microarray datasets from the ve major fat depots
in sheep at 7 (when brown characteristics can dominate) and 28 (when brown fat is scarce) days of age. ese
measures enabled us to elucidate the distribution of cellular plasticity in response to a nutritional intervention
between depots. e maternal diet was modied to cause a shi in the fatty acid (FA) composition of maternal
milk achieved through feeding the mothers a supplement of canola oil. e inclusion of canola in dairy ruminants
feed has been reported to reduce the omega-6/omega-3 FA ratio and conjugated linoleic acid (CLA) in milk, both
which are properties that have been found to up-regulate UCP genes in adipose tissue9.
Methods
Animal Model. All of the procedures were performed with full institutional ethical approval from the
University of Nottingham as designated under the United Kingdom Animals (Scientic Procedures) Act, 1986.
All laboratory procedures were carried out at e University of Nottingham under the United Kingdom code of
laboratory practice (COSHH: SI NO 1657, 1988). For this study, thirteen twin-bearing (non-identical) Bluefaced
Leicester cross Swaledale ewes were randomly assigned immediately aer giving birth to receive their a standard
diet of roughage and concentrate throughout lactation (control, n = 5) or received the same diet supplemented
with 3% canola oil (i.e. 45 g in 1500 g of concentrate) (n = 8). All mothers delivered spontaneously at term (~147
d). One (sex-matched) twin from each mother was randomly assigned to be humanely euthanased at 7 days and
adipose tissues sampled from the epicardial, perirenal, sternal, subcutaneous and omental depots. Tissues were
quickly dissected and weighed before being sectioned and snap frozen in liquid nitrogen for storage at −80 °C.
Additional representative sections were xed in 10% v/v formalin and embedded in paran wax for histolog-
ical analysis. Each remaining twin was reared naturally with their mother until 28 days of age when they were
humanely euthanased, and adipose tissue sampled. Samples of the mother’s milk were also taken manually from
each mother on days 7 and 28 at ~08.00 h prior to euthanasia of the ospring. e milk was transferred into two
sterile 15 ml tubes (Greiner Bio-One, Gloucester, UK) and stored at 80 °C until being shipped on dry ice for anal-
ysis of milk FA composition.
Immunohistochemistry. Tissue sections were prepared as previously published10 and stained using hae-
matoxylin and eosin and for UCP1. At least 20 slides per animal alongside a negative control were labelled with a
random identier, loaded into the Leica BondMax IHC slide processor (Leica Microsystem), and run on an auto-
mated soware program (Vision Biosystems Bond version 3.4A) using bond polymer rene detection reagents
(Leica Microsystem) and a 1:500 dilution of primary rabbit polyclonal antibody to UCP1 (ab10983, Abcam).
Western blotting. The relative abundance of UCP1, voltage dependent anion channel 1 (VDAC) and
cytochrome c were determined in the perineal and sternal adipose tissue samples as previously described11. is
analysis was not performed on epicardial fat as insucient sample was available. All data was corrected against
the density of staining for total protein. Each antibody gave a signal at the correct molecular and the specicity of
binding for each anti-body was conrmed using non-immune rabbit serum.
RNA isolation, quantication, and quality control. For RNA isolation from each fat depot from 5 ani-
mals within each nutritional group, and a 100 mg was used from samples collected at 7, and 1000 mg from taken at
28 days of age, respectively. ese were mixed with 2 ml of TRI reagent (Sigma-Aldrich). Total RNA was extracted
using the RNeasy Plus kit (Qiagen) according to the manufacturer’s instructions and its quantity measured
with a NanoDrop ND-1000 Spectrophotometer (ermo Scientic). Optical density ratios (260/280 nm) were
>1.9 for all samples. Total RNA quality was assayed by the Agilent BioAnalyzer RNA 6000 Nano Kit (Agilent
Technologies) and only used if distinct ribosomal peaks measured (i.e. RIN > 7).
Transcriptome proling with Aymetrix GeneChip. Representative samples of mRNA were labelled
and hybridized onto Human Genome U133A plus 2 arrays according to manufacturer’s recommendations using
the GeneChip 3′ IVT Express kit (Aymetrix). e Aymetrix Human U133 + 2 gene chip array can be used to
study ovine tissues11 and detection was performed using a GeneChip Scanner 3000 7G.
Gene expression array analysis. Normalization and network analyses were undertaken using free and
open source packages from the R project (http://cran.r-project.org/) unless otherwise stated. We used the open
source Bioconductor community (http://www.bioconductor.org/) with the function “gcrma” embedded in
the “ay” package for pre-processing data, including background correction, normalization, and probe match
verication. For statistical analysis of gene expression, we used the “limma” library, which enabled us to per-
form empirical Bayesian statistical modelling between the 5 adipose tissue depots and the eect of the maternal
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nutritional intervention. For all other statistical analysis, unless otherwise stated, we applied the FDR approach
and considered q ≤ 0.05 as signicant12. e R-package “gplots” was used to assess fold changes, and make heat
maps, and expression plots.
Accession numbers. All original microarray data were deposited in the NCBI’s Gene Expression Omnibus
(accession GSE115799).
Selection of informative genes using the RF-PSOL algorithm. e ML algorithm was prepared
using the R package ml-DNA, of which the PSOL algorithm was used to correctly identify ~95–98% of “inform-
ative” genes13. It employed a “training dataset” comprising the top 100 genes selected through empirical Bayesian
statistical modelling between each dierent tissue at each age. Each result from the classier or the prediction
accuracy of the RF was tested using the 5-fold cross-validation method14. e validity for each selection cycle
of interactions was assessed by values of the area under the curve (AUC) received from operating characteristic
analysis or ROC (i.e. the two-dimensional plot between the false (x axis) versus the true positives rate (y axis)
at all possible thresholds). e AUC values ranged from 0 to 1, with a higher AUC indicating better prediction
accuracy for the random forest model.
Gene Co-expression Network Construction. Datasets from both age groups (i.e. 25 samples, compris-
ing 5 dierent adipose tissues depots from 5 animals) were constructed separately using a standard workow
as recommended by weighted correlation network analysis (WGCNA)15. We used a signed weighted correla-
tion network for both age groups and the resulting Pearson correlation matrix was transformed into a matrix of
connection strengths (e.g., an adjacency matrix) using a power of 19. For the dynamic tree-cutting algorithms,
a merging function was set at 0.25, which identied 11 modules at 7 days and 14 modules at 28 days of age. To
better describe molecular outgoing events in each age group dataset, our analysis was restricted to genes selected
as informative by the ML-based ltering process13.
e function ‘module Preservation’ added into the WGCNA R package provides a reliable module preser-
vation statistics base on the generation of 200 random permutations between two independent datasets. ese
permutations allow the calculation of a series of network Z-scores for statistical properties such as network con-
nectivity and density. By averaging these Z-scores in a single Z-score or Z-summary, this value can be used as an
indication of relationship preservation between genes in two independent networks. For example, a Z-summary
for a module that scores >10 could be interpreted as strongly preserved (i.e. no changes in the topology), or
scores between 2 and 10 are considered to be moderately preserved, or <2 indicates that the relationship between
the genes are not preserved15.
Gene ontogeny analysis. Functional annotation was performed with the WEB-based GEneSeTAnaLysis
Toolkit (or WebGestalt) and all genes within each module were analysed. e GO terms with a FDR < 0.05 and
enrich >5 genes per classication16.
Determination of milk fat content and composition. Milk fat concentration was determined by meth-
ods following Bligh & Dyer17. High-resolution capillary gas-liquid chromatography was used to determine the
composition of short- to long chain FAs in milk fat as previously described18. A total of 46 FAs were analyzed this
way and included saturated FAs: C4:0, C6:0, C8:0, C10:0, C11:0, C12:0, C13:0, C13ai, C14:0, C14ai, C15:0, C15ai,
C16:0, C16ai, C17:0, C18:0, C18i, C20:0, C20i, C21:0, C22:0, C23:0, monounsaturated FAs: C14:1n-5, C15:1n-5,
C16:1n-7, polysaturated FAs, omega (n)-3: C18:3n3, C18:4n-3, C20:3n3, C20:5n-3, C22:5n3, C22:6n3. Omega
(n)-6: C18:2n6tr, C18:2n6, C18:3n-6, C20:2n-6 C20:3n6, C20:4n6, C22:2n-6, C22:4n-6, C22:5n-6 and omega
(n)-9: C16:1n-9/7t, C18:1n-9, C20:1n-9, C22:1n-9, C24:1n-9. Results are expressed as % milk fat. All data were
evaluated using the “Limma” and “gplots” in a similar fashion as described above for with the microarray analysis.
Determination of adipose tissue lipid composition. Adipose tissue samples (100–200 mg) were
ground in Retsch ball mill for 3 min using 6 mm stainless steel balls, all pre-chilled to −18 °C. Ice cold chloroform:
methanol (2:1) was added (0.5 mL) and the slurry was agitated at room temperature for 20 min, and centrifuged
for 10 min at 10,000 × g at 4 °C. e lower layer was removed and dried in a centrifugal evaporator. e sam-
ples were reconstituted in 100 µL chloroform:methanol 1:2 centrifuged, then decanted into amber glass LC vials
with inserts and stored at −80 °C until LC-MS analysis. Lipidomic QC samples were created from pooling equal
volumes of all sample extracts. e extracted lipids were injected (5 µL) onto an Agilent Poroshell 120 SB-C18
50 × 2.1 mm (2.7 µm particle size) with guard, held at 45 °C, and eluted using 0.1% aqueous ammonium acetate
(A), to 0.1% aqueous ammonium acetate /acetonitrile/isopropanol gradient (1:1:8) (B) using gradient elution. A
ermoScientic Accela modular HPLC system (Hemel Hempstead, UK) was used at a ow rate of 0.45 mL/min.
Ions in the range m/z 100 to 1900 were detected using an Exactive series mass spectrometer (ermoScientic
Hemel Hempstead, UK) in electrospray mode with +/−ve switching at a resolution setting of 25000. Data was
normalised to the total ion count, pre-processed and exported to Excel for further processing using Progenesis QI
soware (Progenesis, Newcastle on Tyne, UK). A product ion prediction tool from Lipidmaps (Lipid MS Predict)
was use to assist spectral interpretation. e identities of selected isobaric lipid species were subsequently eluci-
dated by generating MS/MS spectra (nominal mass) using the same LC method with a ermo Scientic LTQ
Velos ion trap mass spectrometer using equivalent electrospray source settings and with a collision energy of 4019.
Results
Changes in the cellular landscape of brown and white adipose tissue during the rst four weeks
of postnatal life. To establish which fat depots could be classied as being brown and whether the rate of
loss of brown adipocytes occurs at similar rates in those depots, we rst examined the distribution of UCP1 by
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immunohistochemistry. At 7 days of age, UCP1 was more abundant in perirenal and sternal than in epicardial
adipose tissue (Fig.1A) and was not present in subcutaneous or omental fat (not shown). UCP1 abundance was
substantially reduced by 28 days of age, although more UCP1 signal remained in the epicardial compared to other
depots. In addition, the rate of loss of UCP1 appeared to be delayed by the addition CLA to the mother’s diet.
We then further determined whether this response was specic to UCP1 using western blotting, in those brown
fat depots of which we had sucient tissue i.e. perirenal and sternal (Fig.1B). is demonstrated that the rate of
loss of both UCP1 and VDAC were delayed in both depots at 7 and 28 days of age (Fig.1B), whilst the amount of
cytochrome C was enhanced.
In order to identify changes in gene regulation with age, we performed a transcriptomic comparison between
all 5 depots. At 7 days of age, 839 genes were signicantly upregulated between depots (false discovery rate
(FDR) ≤ 0.05; Supplement: Dataset1) and the greatest dierence was between epicardial and omental fat (429
signicant genes, FDR ≤ 0.05; Fig.2A). Only perirenal adipose tissue, however, exhibited a consistent upregu-
lation of UCP1 and other thermogenesis-related genes compared with the omental and subcutaneous adipose
depots, which were populated primarily by white adipocytes (Supplement: Dataset1). ese results indicate local
dierences in transcriptional regulation, which is indicative of substantial changes in cell population between fat
depots during postnatal life. is is in accord with their divergent developmental ontogeny between depots. In
this regard epicardial, perirenal and sternal are all present in the fetus20, whereas omental and to a lesser extent
subcutaneous fat only appears aer birth21.
Next, we performed a clustering analysis of the transcriptomic data at 7 days of age. Unsupervised hierarchical
clustering and principal component analysis (PCA) revealed that each depot forming a distinct cluster (Fig.2),
with the rst two components explaining 80.2% of the total variance in the data set. is suggests a strong t
between the computational model and the existence of intrinsic dierences in gene expression between depots.
PCA cluster analysis further showed that the pro-thermogenic perirenal and sternal fat depots clustered together
as did subcutaneous and omental depots (white fat depots) whereas epicardial adipose tissue was separate from
other depots (Fig.2C).
We repeated these statistical analyses at 28 days of age to identify the primary changes in gene expression,
and identied 2059 dierentially expressed genes between depots (FDR ≤ 0.05; Supplement: Dataset2). At this
time point, sternal adipose tissue showed a more pronounced downregulation of the transcriptional architecture
compared with the other fat depots (Fig.2B). Similar to our observation at 7 days of age, each depot had a distinc-
tive pattern of gene expression. Genes within the perirenal depot clustered together with those from omental fat
and close to those from the subcutaneous depot, but away from the epicardial and sternal depots. Overall these
samples formed three stable clusters and the rst two components of the PCA analysis accounted for 73.1% of the
variance observed in gene expression between depots (Fig.2D). In summary, clustering analyses demonstrated
that anatomical location determines transcriptome dierences which are modied with age. e sternal and per-
irenal depots exhibited the greatest transcriptomic remodeling, reecting changes in their biological/metabolic
functions with age4.
Figure 1. (A) Representative immunohistochemical detection of uncoupling protein (UCP)1 from sternal,
perirenal and epicardial, sampled from 7 and 28 day old sheep. Rectangular outlines indicate clusters of
uncoupling protein 1 (UCP) positive cells found in the epicardial adipose tissue at 28 days (scale bar = 50 μm;
Magnication 40x) and (B) mean mitochondrial protein abundance as determined by western blotting
in adipose tissue sampled from 7 and 28 day old ospring born to mothers fed a control diet (n = 5) or
supplemented with 3% canola oil (n = 8). Values are means with their standard errors signicant dierences
between dietary groups at the same age denoted by *p < 0.05; **p < 0.01. UCP, Voltage dependent anion
channel 1, VDAC.
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Use of ML and gene network analyses for identifying the transcriptional architecture changes
between adipose tissue depots with age. To determine the magnitude of potential changes in gene
function between depots during early life, we combined computer-assisted learning algorithms with weighted
gene co-expression network analysis. Through a reiterative process of error minimisation and supervised
learning algorithms (i.e. random forest (RF)), the optimal gene expression pattern for each adipose depot was
established14. Tissue gene expression datasets were submitted for each age group, the RF approach enabled iden-
tication beyond a 98% certainty of the informative genes with an overall accuracy of 95–100%.
Figure 2. Comparison in gene expression of ve adipose tissue depots at (A) 7 and (B) 28 days of age. Heat
map and unsupervised hierarchical clustering dendrograms are shown for the top 100 dierentially-expressed
gene transcript comparisons identied by microarray analysis (average linkage, Euclidean distance metric) as
selected by eBayes moderated t-statistics (FDR < 0.05). Gene expression was transformed to a Z-score, and blue
represents a relative is a decrease and red an increase in gene expression between each depot at the same age.
Principal component analysis (PCA) of gene expression data from ve dierent adipose tissue depots from each
animal was performed using 10055 data sets that passed the variance test QC at (C) 7 and (D) 28 days of age.
Each sample is represented by a sphere (7 days) or rectangle (28 days) and color-coded to indicate the age and
tissue to which it belongs.
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e number of informative genes varied with age indicating dierent response patterns of the transcriptomes
between depots, with 2274 and 3339 genes identied as informative at 7 and 28 days of age, respectively. Next, we
used both data sets of informative genes to generate two independent weighted co-expressed networks for each
age. We identied 11 distinct modules (designated as C1.1–11; Fig.3A and Supplement: Dataset3) at 7 days of
age and 14 modules of co-expressed genes at 28 days (designated as C2.1–14; Fig.3B and Supplement: Dataset4).
Further analyses demonstrated that most gene modules corresponded to dierent transcriptional or metabolic
functions performed in each depot, whereas the intensity of these events varied with age.
Functional enrichment of gene clusters obtained at 7 days of age. e gene network analyses
demonstrated that the sternal and perirenal depots shared common transcriptional functionalities, although they
were more pronounced in perirenal fat (Fig.4A and B). Many of these modules (i.e. C1.1, C1.2 and C1.3) con-
tained genes involved in regulating mitochondrial biogenesis and aerobic respiration. e C1.3 and C1.2 modules
showed several gene ontology enriched terms related to oxidation-reduction processes and mechanisms of mito-
chondrial regulation of mRNA translation, respectively (Supplement: Dataset5). However, the separation of both
co-expressed modules suggests the potential existence of multiple transcriptional loops regulating mitochondrial
genes5,22. ese transcriptional separations could potentially allow brown adipocytes to regulate the expression of
mitochondrial genes independent of UCP1 expression23.
e majority of gene modules aligning within epicardial adipose tissue coincided with specic stages of
changes in transcriptional cardiomyocyte cell dierentiation (Fig.5A)24. For example, the C1.4 module showed
hallmarks of mRNA and DNA processes associated with the maintenance of stem cell pluripotency24. Module
C1.6 had a large transcriptional signature usually found in mature cardiomyocytes, suggesting that these tran-
scripts reect an advanced stage in cell dierentiation25. All these ndings are in agreement with the known
pluripotency of adipose tissue-derived stem cells towards dierentiation into cardiac myocyte-like cells or brown
adipocytes26.
Modules, C1.7 and C1.8, both describe transcription events occurring mainly within subcutaneous adipose
tissue (Fig.4A), and contain genes functionally linked to myocyte precursors26. ese are considered to give
rise to brown adipocytes and a subset of white adipocytes populating subcutaneous fat (Supplement: Dataset5).
Figure 3. Co-expression dendrogram analysis from the ve adipose tissue depots sampled at either (A) 7 or (B)
28 days of age. In each dendrogram, the rst row is subdivided into co-expressed modules founded in each age
group. Rows 2 to 6 show the dierential expression relationships between module genes and the adipose depot.
e relationship of each gene with the assigned module is colour coded from blue (negative co-expression) to
red (positive co-expression).
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Finally, we identied modules C1.9, C1.10 and C1.11 that were enriched within omental fat and contained genes
linked to carbohydrate metabolism, cytoskeleton composition and tissue remodeling mediated through activa-
tion of the immune system (Supplement: Dataset5)27.
Changes in transcriptional control and its consequences for adipocyte function with age. To
address whether the dierences in transcriptome regulation identied between depots at 7 days of age had any
phenotypical consequences, we generated a second gene co-expression network at 28 days of age. As observed
from the PCA analysis, the perirenal depot underwent the greatest shi in gene expression. Four co-expression
modules with selective enrichment in that depot were detected, of which the C2.7 module was also enriched in
omental fat (Fig.4C,D). is common module indicates a major metabolic change as perirenal adipose tissue
adapts from a heat production depot populated by brown adipocytes to a white fat depot, storing energy as
lipid4,22. is module contained many genes associated with FA metabolism, including GHR, ABHD5, DECR1 and
PPARG that also regulates adipocyte dierentiation (see Supplement: Datasets4 and 6)28. In addition, this mod-
ule revealed that genes associated with white adipose tissue expansion were co-expressed, including angiogenic
genes (HOXA5, HIF1A) and pre-adipocyte precursor genes (HOXB6, HOXB8, HOXB5; Supplement: Datasets4
and 6)29,30. Other genes enriched in perirenal fat were those in module C2.5, and included transcripts associated
with activation of inammatory responses and endoplasmic reticulum, indicating cellular stress (Supplement:
Dataset6)27. e last two modules aligned within this depot, C2.9 and C2.8, had large signatures associated with
cell division and mRNA transcriptional regulation, suggesting an increase in cell diversication or dierentiation
(Fig.4C and Supplement: Dataset6)3,31,32. Conversely, in sternal fat, these cellular events appeared to be repressed
(Supplement: Dataset2), and the only cluster positively aligned was C2.1, which was enriched with genes indi-
cating chemical or hormonal responsiveness and those leading to tissue angiogenesis (Fig.5B and Supplement:
Dataset6)30,33. As observed at 7 days, the epicardial adipose tissue also exhibited modules enriched with genes
with a large genetic signature associated with cardiomyocyte cell dierentiation such as module C2.2 (Fig.5B)26.
Modules C2.10 and C2.11 showed signicant relationships in omental adipose tissue (Fig.4C,D)32, and exhib-
ited over-representation of transcripts linked to the balance between anabolic and catabolic pathways occurring
in mature adipocytes (Supplement: Dataset6)34. Finally, in subcutaneous adipose tissue, most genes were assigned
to modules C2.12, C2.13 and C2.14 and these were associated with cell dierentiation, growth and remodelling30.
Changes in gene networks represent functional adaptations of dierent adipose depots during
development. To explore the biological relevance of each module in more detail, we applied a statistical
approach based on a series of random permutations between datasets. is enabled us to nd evidence of similar-
ities and dierences in the network topology at both ages. We found that the majority of genetic interactions per-
sisted at both ages (Fig.5A,B). At 7 days, 86.4% of all the genes were allocated to a conserved module, including
module C1.2 which was enriched with mitochondrial genes such UCP1 (Fig.5A). Gene ontology analysis of the
Figure 4. Summary of adipose tissue depot- and age-specic functional organisation of modules within each
gene network. ey were related individually by their rst principal component, referred to as the module
eigengene (ME). Each dendrogram illustrates the modules of co-expressed genes and their positive alignment
within the ME at (A) 7 and (C) 28 days of age. e height (X-axis) indicates the magnitude of correlation
expressed as Euclidean distances. Heat maps represent the correlation (and corresponding p-values) between
co-expressed modules for each fat depot at (B) 7 and (D) 28 days of age. e colour scheme, from blue to red,
indicates the magnitude of correlation, from low to high. Regional-specic modules identied as being highly
correlated (i.e. over-expressed) for each adipose depot are shown in the columns.
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four non-conserved modules showed that they all shared functional similarities associated with early stages of cell
dierentiation and adipose tissue remodelling30. At 28 days, 71.4% of all the genes were allocated to a conserved
module within the network. e gene ontogeny enrichment analysis from these conserved modules revealed
metabolic and transcriptomic processes associated with mature adipocyte function which mainly involve lipid
metabolism, immune responses and tissue remodeling (Fig.5B).
Functional assessment of changes in transcriptional architecture. In order to evaluate the potential
impact of the transcriptional dierences between adipose depots, we performed lipidomic and gene expression
analyses on perirenal and sternal fat at 28 days of age in ospring of mothers who consumed a diet supplemented
with 3% canola oil. By this point of lactation milk from supplemented mothers had developed an altered FA
prole without changes in the total fat content. Besides, the maternal nutritional supplementation had no eect
on growth or body weight and of the ospring. Features of the FA prole that had changed in relation to the
control group according to predictions included the statistically signicantly lowered proportion of linoleic acid
and a lowered omega-6/omega-3 FA ratio (Supplement: Dataset7 and Supplement: Fig.1). e supplemented
milk exhibited similar omega 3 FAs but a lower arachidonic (C20:4n6) and γ-linolenic FA (C18:3n6) content.
Figure 5. Summary of cross-adipose tissue depot module preservation with age. e test uses a Z score
summary of dierent network properties to determine gene connectivity at (A) 7 and (B) 28 days of age. Each
row represents a module and each column a unique feature of each module including positive alignment with
each ME and the number of genes per module. A Z summary value >2 represents a moderately preserved
module, and a value >10 provides strong evidence of module preservation.
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Dietary supplementation produced a localised change in the lipidome of perirenal fat (Supplement: Dataset8
and Supplement: Fig.2). Overall, the intervention led to dierences in the relative abundance of 28 day perirenal
adipose tissue spectrometry lipid masses. Amongst those that we were able to identify, we observed decreased
proportions of phosphatidylcholines (PC; a major constituent of cell membranes) and long chain FAs (carbon
chain length >20). In contrast, in sternal adipose tissue, there were no dierences in lipid composition between
the intervention and control groups at 28 days of age. Overall, microarray analyses of both fat depots revealed that
only the genome of perirenal fat responded to the nutritional intervention. Data mining (Supplement: Dataset9)
results thus showed a signicant increase in the expression of only 4 genes. Interestingly, when comparing the
most dierentially regulated genes, eects of the intervention on the down regulation of NR3C1 were highlighted.
is gene encodes for the glucocorticoid receptor and is directly associated with inammatory responses, cellular
proliferation, lipid metabolism, cell dierentiation and more importantly the modulation of thermogenesis in
brown fat35,36. Potentially, due to the relatively short duration of intervention and/or by modest amount of canola
oil added to the diet, we could not observe substantial changes in UCP1. Taken together, these results demon-
strate that the cellular architecture of perirenal fat is unique among fat depots in that it can undergo pronounced
remodeling and is sensitive even to a modest nutritional stimulus.
Discussion
We have shown profound dierences in gene expression proles between the major fat depots in sheep through
early postnatal life, coincident with the transition from brown to white fat depots3. e developmental changes
markedly diered between depots despite them showing a similar macroscopic morphology at each developmen-
tal age. For example, at 28 days when in sheep fat is considered primarily white3, each adipose depot kept a dis-
tinct expression prole. is appeared to determine its capacity to respond to modication of the composition of
the mother’s milk. Adipose tissue has been considered a metabolic organ with important functions beyond lipid
storage but the extent to which this varies between depots especially during development is largely unexplored.
Our new data support the concept that adipose tissue functions not as one metabolic organ, but as several auto-
nomic organs which appear to have distinct functions32.
By using a computer-assisted supervised learning algorithm, we demonstrate that during postnatal develop-
ment each fat depot contains a transcriptome which forms dynamic networks with unique sets of genes8. Over
time these gene networks can undergo profound reorganisation by accommodating novel members and/or losing
some of their original components8. Whilst ontogenic plasticity can be driven entirely by intrinsic factors, sur-
vival value in uctuating environments can be enhanced by responsiveness to extrinsic factors3. However, both
types of plasticity are prone to error and maladaptation which can ultimately lead to obesity, increasingly threat-
ening metabolic health1. Understanding how environmental factors, particularly during early life, interfere with
pathways of energy utilisation or storage is one of the most important intermediate goals in obesity prevention.
By examining the postnatal development of adipose tissue through gene network analysis, we have been able to
construct novel biological interpretations8 specic to each fat depot over the period when any large mammal
needs to respond to environmental, nutritional and physiological challenges3. We, therefore, explored dynamic
changes in gene regulation and identied the main regulatory relationships. ese have crucial regulatory roles so
each separate adipose tissue depot can dierentiate and adapt, potentially enabling the dierent genes involved to
modulate metabolic homeostasis32. Despite recent eorts to elucidate the cellular and transcriptome composition
of dierent fat depots4,23, the inuence of genetic, endocrine or environmental factors on fat development remains
largely unknown. However, in our study we observed that the co-expression modules within networks show a
depot-specic pattern enriched with genes performing specic functions.
Studies on adipose tissue function during early postnatal life have mostly focused on explaining the loss
of genes associated with cellular thermogenesis, especially UCP13. Our comparison between the three depots
enriched with brown adipocytes suggests the existence of dierent networks in the regulation of mitochondrial
activity5. e expression of mRNA is regulated by a balance between transcription and mRNA degradation, and
the C1.2 module captures this complexity in the control of UCP137. We found transcription factors that stimulate
a cell’s transition from myoblastic precursors to brown fat cells, including C/EBPβ and EP30022,38. Other mem-
bers of this module were EIF4B, EIF4G3, EIF3D and EIF3G, known transcriptional factors that downregulate
mRNA transcription of UCP1 in response to raised temperature39. We also found a large number of ribosomal
proteins co-expressed with UCP1 that are similarly regulated, including RPS5 and RPS9. ese comprise part of
the original mitochondrial protein assembly machinery40. UCP1 is also regulated by AU-rich elements, which
are mRNA binding proteins37. In humans, it has been estimated that less than 8% of genes are regulated in this
manner, raising the possibility that mRNA binding proteins such as ZFP36L1 and DHX32 co-expressed with
UCP1 could potentially degrade this gene37,41. ZFP36L1 specically binds at its 3′-UTR mRNA site and recruits
the Cnot7-Tob-BRF1 axis, resulting in mRNA destabilisation41. ese genes are co-regulated with multiple tran-
scripts involved in energy metabolism and mRNA transcription allocated to the C1.1 and C1.3 modules, which
are both related to the functional changes observed in perirenal fat up to 28 days of age.
Another novel nding is the relationship between epicardial adipose tissue and genes associated with cardi-
omyocyte cell dierentiation24. Cardiomyocytes rapidly dierentiate and proliferate during fetal life, but exit the
cell cycle soon aer birth, limiting the ability of the heart to restore function aer any signicant injury26. ere
are reservoirs of multipotent stem cells in most fat depots which can dierentiate in vitro into brown adipocytes
and also, as observed, into cardiomyocytes25. As shown in Fig.5, the highest biologically expressed genes were
those of cardiac myocytes in epicardial fat cells in module C1.6 at 7 and module C2.2 at 28 days, respectively. One
pathway to brown adipocyte dierentiation is through chronic adrenergic stress, as observed in rodents aer
prolonged cold exposure39 and in patients with severe skin burn injuries42. We have previously observed that
epicardial fat maintains a signicant number of brown adipocytes, in children with congenital heart disease43. It
is, therefore, possible that the fate of these multipotent stem cells, or their ability to dierentiate into other cell
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SCIENTIfIC REPORTS | (2018) 8:9628 | DOI:10.1038/s41598-018-27376-3
types, could be regulated by specic growth factors supporting normal physiological development or enabling
them to respond to disease24,43.
Our study conrms that the perirenal and sternal depots follow dierent development patterns20. Although
UCP1 expression ceases in both depots with age, only perirenal fat exhibits the major hallmarks of white adipose
tissue development7. is insight was further substantiated by the observation that, when ospring consumed
milk from mothers that had received a 3% supplement of canola oil, only the cellular lipidome of the perire-
nal adipose tissue was modied. is was accompanied with the retention of UCP1 up to 28 days of age, that
is likely to be mediated by dierences in milk lipid proles found between control and intervention groups.
Supplementation of canola oil in the maternal diet did not have a direct eect on omega 3, but limited the accre-
tion of two omega 6 FAs, γ-linolenic acid (C18:3n6) and more signicantly of arachidonic acid (C20:4n6). is
essential FA is metabolized by a transcellular process using cyclooxygenases to induce prostaglandin synthe-
sis, thus triggering a pro-inammatory response44. e changes in arachidonic acid in perirenal adipose tissue
may explain in part the dierences in membrane architecture and the up-regulation of genes related to inam-
mation between groups. Downstream, we observed that these changes in FA milk proles were accompanied
with reduced NR3C1 gene expression in perirenal fat. is is the transcript of the type 2 glucocorticoid receptor
mRNA, which is important in regulating the actions of glucocorticoids in most tissues3. Furthermore, glucocorti-
coids are not only necessary for adipocyte dierentiation they also modulate thermogenesis in a species and depot
specic manner35. Taken together, our observations suggest that dierences in the tissue microenvironment, pos-
sibly dictated by nearby endocrine organs such as the adrenals, determine changes in the metabolic/phenotypic
characteristics of existing fat cells6,32.
In conclusion, adipose tissue depots dier dramatically in terms of their gene expression signature, dieren-
tiation ability, cellular composition, and capacity to respond to local environmental stimuli45. Perirenal adipose
tissue shows the greatest propensity to dierentiate and respond to an external stimulus. In contrast, fat depots
such as sternal and epicardial do not exhibit an adipogenic prole and would therefore, complete their normal
programmed development. e data presented here suggest that microarray gene expression in combination with
advanced data analytic tools provide a robust and accurate approach for producing adipose depot-specic gene
signatures. Moreover, this approach could enhance our ability to identify and manipulate specic characteristics
of adipose tissue in dierent anatomical locations.
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Acknowledgements
This work was supported by the Biotechnology and Biological Sciences Research Council [grant number
FS/15/4/31184, BB/I016015/1], and e Cardiometabolic Disease Research Foundation (Los Angeles, USA).
Author Contributions
M.E.S. and F.W. designed the study. M.E.S., M.B., G.D. and V.P. conducted the in vivo studies. H.P.F., A.A., C.M.
and M.S.T. conducted the microarray analysis. R.A. conducted the histological and western blot analysis. M.P.
and R.W. conducted the additional laboratory analysis. C.A.O. and D.B. conducted the lipidomic analysis. H.B.,
H.S., B.S. and E.v.d.B. further developed the study. H.P.F. and M.E.S. led the manuscript writing and development.
H.P.F. prepared all the tables and gures. All authors edited, contributed and reviewed the nal manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-27376-3.
Competing Interests: e authors declare no competing interests.
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