Abstract
Fatty liver (FL) is one of the risk factors for acute pancreatitis and
is also indicative of a worse prognosis as compared to acute
pancreatitis without fatty liver (AP). The aim of the present study was
to analyze, at the hepatic level, the differentially expressed genes
(DEGs) between acute pancreatitis with fatty liver (APFL) rats and AP
rats. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and
Genomes) pathway analyses of these DEGs indicated that PPARα signalling
pathway and fatty acid degradation pathway may be involved in the
pathological process of APFL, which indicated that fatty liver may
aggravate pancreatitis through these pathways. Moreover, the excessive
activation of JAK/STAT signaling pathway and toll-like receptor
signaling pathway was also found in APFL group as shown in heat map. In
conclusion, the inhibition of PPARα signaling pathway and the fatty
acid degradation pathway may lead to the further disorder of lipid
metabolism, which can aggravate pancreatitis.
Introduction
Acute pancreatitis (AP) is an acute inflammatory process of the
pancreas, with wide clinical variation, ranging from mild discomfort to
severe systemic complications^[44]1. Mild AP has a self-limiting course
with a low mortality rate. It responds to conservative treatment and
patients recover within a few days. In contrast, severe AP may lead to
a mortality rate of 10–24%^[45]2–[46]4.
Several risk factors for AP have been reported, including
alcohol^[47]5, gallstones, smoking^[48]6, [49]7, obesity^[50]8, and
non-alcoholic fatty liver disease (NAFLD)^[51]9. NAFLD is a clinical
term that refers to excess fat deposition in the liver without
excessive alcohol intake^[52]10. This is an increasing worldwide
disease, which encompasses a wide spectrum of complications, ranging
from simple steatosis to cirrhosis and hepatocellular carcinoma^[53]11.
Our previous studies have shown that the prognosis of pancreatitis
patients with a fatty liver disease was more severe than those with
non-fatty liver disease^[54]12. Although much effort has been made to
understand the underlying mechanism, the current knowledge remains
limited. Therefore, the primary purpose of this study was to explore
the differences between APFL and AP and make a primary exploration on
the possible effects of fatty liver on pancreatitis.
Recently, next-generation sequencing technology has had a profound
impact on a broad range of biological applications. RNA sequencing
(RNA-seq) is a promising and widely used technology for analysis of the
complete characterization of RNA transcripts, including gene fusion
detection and transcription start site mapping^[55]13. In the present
study, we used, for the first time, RNA-seq method to analyze the DEGs
between APFL and AP rats. Next, the DEGs were mapped to terms in the GO
database and were subjected to KEGG pathways enrichment analysis to
determine the functions of these dysregulated genes. Hence, this study
would enhance our understanding of the different mechanism between APFL
and AP, which may provide some clues to the identification of potential
therapeutic targets.
Results
Overall experimental procedure
Healthy SD rats were randomly divided into two groups. One group of 5%
sodium taurocholate retrograde pancreatic duct injection was as acute
pancreatitis group (AP), another group of intraperitoneal injection of
0.9% saline was as control group (NC); Fatty liver SD rats were
randomly divided into two groups. One group of 5% sodium taurocholate
retrograde pancreatic duct injection was as acute pancreatitis with
fatty liver group (APFL), another group of intraperitoneal injection of
0.9% saline was as fatty liver group (FL). Eight hours later, the liver
of each rat was collected for RNA-seq analysis. In order to explore the
difference in pathogenesis between APFL and AP, we compared the
differentially expressed genes between them. The specific experimental
design is shown in Fig. [56]1.
Figure 1.
Figure 1
[57]Open in a new tab
Experimental flow graph. Step 1: Model induction and liver collection;
Step 2: RNA-seq analysis of DEGs between APFL and AP group; Step 3: GO
and KEGG analysis of DEGs between APFL and AP.
Diet-induced fatty liver and acute pancreatitis induction
According to the percentage of fatty degeneration of hepatic
parenchymal cells, simple fatty liver can be divided into the following
4 degrees: F0: <5% fatty degeneration of liver cells; F1: 5–30% fatty
degeneration of liver cells; F2: 30–50% fatty degeneration of liver
cells; F3: 50–75% fatty degeneration of liver cells; F4: above 75%
fatty degeneration of liver cells. As shown on H&E staining slides, the
liver cells of rats in high-fat diet group were obviously swollen, and
vacuolar lipid droplets were observed in the cytoplasm of hepatocytes,
indicating the establishment of fatty liver. The rate of fatty
degeneration of liver cells was above 75 percent and there was no
significant inflammatory cell infiltration and fibrosis in the liver
lobule. Therefore, the stage of the fatty liver in this model was a
simple steatosis (F4 degree) (Fig. [58]2b). The structure of normal
liver was clear and complete (Fig. [59]2a). Compared with AP group, the
pancreas of APFL exhibited more severe edema, inflammatory infiltration
and acinar necrosis after establishment of acute pancreatitis
(Fig. [60]2c,d).
Figure 2.
Figure 2
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Tissue morphology in APFL and AP models. H&E staining: liver structure
of rats in AP group (a) and APFL group (b); pancreas structure in AP
(c) and APFL (d) group.
Sequencing evaluation
The RNA quality reached the sequencing requirement. The percentages of
reads containing N, adaptors, clean reads and low-quality reads were
calculated, and more than 97% of the raw reads passed the filter in
each sample (Supplementary Fig. [62]S1a). To confirm whether the number
of detected genes increases proportionally to sequencing amount (total
clean reads number), saturation analysis was performed. The results
showed that the number of detected genes tended to saturation
(Supplementary Fig. [63]S1b). During preparation of the cDNA sequencing
libraries, the mRNA was first fragmented into short segments by
chemical methods and then sequenced. We used the distribution of read
location on the genes to evaluate the randomness of breaking. In this
study, the evenly distributed reads in every position of the genes
indicated that the randomness of breaking of these samples was good
(Supplementary Fig. [64]S1c). Gene coverage was calculated as the
percentage of a gene covered by reads from each sample. Supplementary
Fig. [65]S1d showed the distribution gene coverage of all the samples.
Approximately 50% of total genes had coverage between 90–100%. To
evaluate the result reliability, the correlation between replicates was
calculated. Supplementary Fig. [66]S1e shows the correlation of two
replicates; the pearson correlation, which was closed to 1, indicated
the good repeatability of the experiments. These results provide a
favorable reliability for the quantitative analysis of DEGs.
Analysis of differential gene expression
The scatter diagram results showed significantly differentially
expressed genes (DEGs) between APFL and AP. A total of 2177 unigenes
showed significant differential expression (false discovery rate
[FDR] ≤ 0.001, |log2 ratio| ≥ 1). Among these unigenes, 490 genes were
up-regulated and 1687 genes were down-regulated (Fig. [67]3). DEGs are
partly shown in shown in Table [68]1.
Figure 3.
Figure 3
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Differentially expressed genes between APFL and AP. Red spots
represented up-regulated genes, and green spots down-regulated genes.
Black spots indicate genes that were not differentially expressed
between the two samples.
Table 1.
List of representative dysregulated genes between APFL and AP.
Gene name Description Log[2] (Fold change) Functions Significance
(P-value)
Log[2] (APFL/AP)
RT1-A3 Uncharacterized protein 18.07504 Positive regulation of T cell
mediated cytotoxicity Yes (1.32E-05)
MSC Musculin 12.34538 Negative regulation of transcription from RNA
polymerase II promoter Yes (9.45E-04)
FBXO27 F-box protein 27 11.24744 Glycoprotein binding Yes (1.76E-07)
MSX1 Msh homeobox 1 10.72489 Negative regulation of cell growth Yes
(3.39E-04)
RGD1561212 Similar to RIKEN cDNA 10.69475 Exhibits retinoic acid
receptor binding Yes (1.46E-07)
SIGLEC8 Sialic acid binding Ig-like lectin 8 9.65489 Intracellular
signal transduction Yes (2.15E-05)
RAMP3 Receptor (G protein-coupled) activity modifying protein 3 9.49772
Regulation of G-protein coupled receptor protein signaling pathway Yes
(3.41E-18)
ZFYVE28 Zinc finger, FYVE domain containing 28 9.45763 Negative
regulation of epidermal growth factor Yes (7.88E-09)
TNFAIP6 Tumor necrosis factor alpha induced protein 6 9.33946 Negative
regulation of inflammatory response Yes (1.36E-06)
AOC1 Amine oxidase, copper containing 1 9.18586 Amine metabolic process
Yes (1.03E-04)
TNN Tenascin N 8.82202 Cell-matrix adhesion Yes (4.54E-07)
PTX3 Pentraxin 3 8.80702 Innate immune response Yes (1.73E-06)
LMX1A LIM homeobox transcription factor 1 alpha 8.36729 Regulation of
transcription, DNA-templated Yes (1.65E-04)
RASEF RAS and EF hand domain containing 7.90165 Rab protein signal
transduction Yes (1.77E-04)
LOC497963 Similar to Nitric oxide synthase 7.78913 Nitric oxide
biosynthetic process Yes (2.15E-03)
DGKH Diacylglycerol kinase 7.55722 Protein oligomerization Yes
(4.43E-06)
SLC7AL1 Solute carrier family 7, member 11 7.18928 Response to
oxidative stress Yes (7.32E-06)
DNM3 Dynamin 3 5.22016 Anatomical structure development Yes (1.46E-07)
LRRC8E Leucine rich repeat containing 8 family 5.07676 Ion transport
Yes (2.36E-06)
VWA2 Von Willebrand factor A domain containing 2 4.13783 Regulation of
insulin receptor signaling pathway Yes (2.53 E-03)
EGR2 Early growth response 2 4.11453 Cellular protein modification
process Yes (5.49 E-06)
FOSL2 Fos-like antigen 2 4.03226 Positive regulation of fibroblast
proliferation Yes (1.49 E-03)
TREM1 Triggering receptor expressed on myeloid cells 1 3.94884
Neutrophil chemotaxis Yes (3.92 E-03)
FOSL1 Fos-like antigen 1 3.30191 Neurological system process Yes (1.80
E-05)
GAS1 Growth arrest-specific 1 2.79755 Negative regulation of mitotic
cell cycle Yes (1.10E-20)
WDR4 WD repeat domain 4 1.30914 tRNA methylation Yes (8.61E-03)
PTPRM Protein tyrosine phosphatase, receptor type, M −1.0036
Peptidyl-tyrosine dephosphorylation Yes (1.55 E-04)
CDKN1C CDKI protein long isoform −1.71620 Cell cycle arrest Yes (6.87
E-03)
UBD Ubiquitin D −2.32347 Protein ubiquitination Yes (2.83E-06)
RGMA Repulsive guidance molecule family member A −2.87523 Negative
regulation of collateral sprouting Yes (8.81E-19)
BRDT Bromodomain, testis-specific −2.90388 Chromatin remodeling Yes
(4.13 E-03)
CD8A CD8a molecule −3.37913 Response to stress Yes (6.91E-06)
FOXH1 Forkhead box H1 −3.95036 Anatomical structure development Yes
(9.49E-23)
PDE3A Phosphodiesterase 3A −4.67631 Small molecule metabolic process
Yes (4.37E-06)
TFF3 Tff3 molecule −4.82677 Regulation of glucose metabolic process Yes
(5.44E-04)
OTOGL Otogelin-like −6.85145 Sensory perception of sound Yes (6.46
E-04)
NPR3 Natriuretic peptide receptor 3 −8.79741 Negative regulation of
adenylate cyclase activity Yes (1.81E-03)
LOC100360055 Cytochrome P450 2B15-like −9.82802 Xenobiotic metabolic
process Yes (8.93E-05)
MMD2 Monocyte to macrophage differentiation -associated 2 −10.26003
Protein phosphorylation Yes (2.30E-08)
CML3 Camello-like 3 −10.90368 Gastrulation with mouth forming second
Yes (1.13E-13)
ALDH1A7 Aldehyde dehydrogenase family 1 −13.13943 Oxidation-reduction
process Yes (3.74E-28)
[70]Open in a new tab
Gene ontology (GO) classification of DEGs
To determine the function of differentially expressed genes, all DEGs
were mapped to terms in the GO database. A total of 2177 DEGs between
APFL and AP samples were categorized into the three main categories of
GO classification (e.g., biological process, cellular component and
molecular function). For molecular function category, metabolic
process, catalytic activity, and cofactor binding were the top abundant
subcategories. Under the cellular component category, a large number of
up-regulated, as well as down-regulated DEGs were categorized as cell
part, cell and organelle. For biological processes, most of those were
classified into cellular process and single-organism process
(Fig. [71]4).
Figure 4.
Figure 4
[72]Open in a new tab
GO classification of DEGs between APFL and AP. The x-axis indicated the
subcategories, the left y-axis represented the percentage of a specific
category of DEGs and the right y-axis indicated the number of DEGs.
KEGG pathway analysis of DEGs between APFL and AP
In order to explore the mechanism of fatty liver aggravated acute
pancreatitis, we performed KEGG pathway analysis of the dysregulated
genes between APFL and AP. The results indicated that fatty acid
degradation pathway (ko00071) and PPARα signaling pathway (ko03320) may
be involved in the pathogenesis of APFL. It was recognized that the
disorder of lipid metabolism will aggravate the condition of the
pancreatitis, so we choose these pathways to further analyze. The KEGG
results of the top 10 pathways enrichment are shown in Fig. [73]5.
Figure 5.
Figure 5
[74]Open in a new tab
Scatter plot for KEGG enrichment results. The top 10 enrichment
pathways are shown in the senior bubble chart. The Rich factor is the
ratio of DEGs numbers annotated in this pathway term to all gene
numbers annotated in this pathway term. A Q value is the corrected p
value.
Dysregulated genes participated in fatty acid degradation and PPARα signaling
pathway
KEGG pathway analyses of the dysregulated genes between APFL and AP
indicated that fatty acid degradation and PPARα signaling pathway may
be involved in the pathological process of APFL. The detailed
information about these pathways in KEGG database is shown in
Fig. [75]6a,b. We found that most of the key genes involved in fatty
acid degradation were significantly down-regulated, a reflection of
lipid metabolic disorder. Meanwhile, some related genes in PPARα
signaling pathway were also down-regulated, which would further
aggravate lipid metabolism disorder. The physiological function of the
PPARα signaling pathway in fatty acid degradation is illustrated in
Fig. [76]6c.
Figure 6.
Figure 6
[77]Open in a new tab
DEGs related to fatty acid degradation and PPARα signaling pathway
between APFL and AP. KEGG pathway maps for (a) fatty acid degradation
pathway (ko00071) and (b) PPARα signaling pathway (ko03320)^[78]49.
Up-regulated genes are marked with red borders and down-regulated genes
with green borders. Non-change genes are marked with black borders.
Physiological function of the peroxisome proliferator activated
receptors (PPARs) is shown in (c).
Gene expression cluster
A hierarchical cluster of DEGs is partially shown in Fig. [79]7.
Compared with the AP group, a significant number of genes were
up-regulated in APFL group encoding proteins linked to inflammatory
processes, most prominently chemokines and chemokine receptors (e.g.,
CXCR2, CXCL1, CXCR4 and CCR1), and tumor necrosis factor receptor
superfamily (e.g., TNFRSF21, TNFRSF12a and TNFRSF11a). This suggests
that inflammatory reaction is more serious in the APFL group than in AP
group. A large number of genes involved in lipid metabolism (e.g.,
ACADL, ALDH1B1, CPT1A, PPARα, ACADSB, ACSL5, ACSL3, HADH, ACADM, and
ACSL1) were significantly down-regulated in APFL group compared with
the AP group. This reflects that the fatty liver rats after induction
of acute pancreatitis can appear more serious lipid metabolic disorder
than non-fatty liver rats.
Figure 7.
Figure 7
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Cluster analysis of DEGs annotated in pathways associated with lipid
metabolism and inflammation. The heatmap shows the expression levels of
DEGs between APFL group and AP group. Transcript levels of genes
encoding components involved in lipid metabolism were marked with an
asterisk (*), insulin signaling pathway were marked with an asterisk (
Inline graphic ), JAK/STAT signaling pathway were marked with an
asterisk ( Inline graphic ), endoplasmic reticulum stress were marked
with an asterisk ( Inline graphic ), chemokine receptors were marked
with an asterisk ( Inline graphic ), tumor necrosis factor receptor
superfamily were marked with an asterisk ( Inline graphic ),
interleukin were marked with an asterisk ( Inline graphic ) and
toll-like receptors were marked with an asterisk ( Inline graphic ).
Quantitative RT-PCR validation of the dysregulated genes that are involved in
PPARα and fatty acid degradation pathway
The RNA-seq results of selected genes were validated by real-time
RT-PCR. The dysregulated genes PPARα, ACSL1, CPT1A, EHHADH, ACAA1A,
ACADM, ACADSB, ALDH1B1, HADH in mRNA expression profiling results were
selected for qRT-PCR validation. The qRT-PCR results confirmed that the
expression of these genes decreased after induction of pancreatitis,
which was in agreement with the RNA-seq results. Compared with AP
group, the expression levels of PPARα, ACSL1, CPT1A, EHHADH, ACAA1A,
ACADM, ACADSB, ALDH1B1 and HADH in APFL group were significantly lower
(Fig. [81]8).
Figure 8.
Figure 8
[82]Open in a new tab
Quantitative RT-PCR validation of the selected dysregulated genes
associated with fatty acid degradation. The expression levels of PPARα
(a), ACSL1 (b), CPT1A (c), EHHADH (d), ACAA1A (e), ACADM (f), ACADSB
(g), ALDH1B1 (h) and HADH (i) in NC, AP, FL and APFL were validated
using qRT-PCR. The bar graph shows the expression of each gene in AP,
FL, APFL relative to the average expression levels in NC. All error
bars indicated S.D. *P < 0.05 vs NC, **P < 0.01 vs NC, ^# P < 0.05 vs
FL.
Coefficient analysis of fold change data between qRT-PCR and RNA-seq
Correlation analysis showed significantly positive correlation in fold
change data between qRT-PCR and RNA-seq (a correlation coefficient
R = 0.975), confirming our transcriptome analysis (Fig. [83]9).
Figure 9.
Figure 9
[84]Open in a new tab
Linear regression analysis of fold change data between qRT-PCR and
RNA-seq. Black dots represent log[2] transformed fold change values of
a single gene in APFL sample obtained from qRT-PCR (X-axis) and RNA-seq
analysis (Y-axis). R: correlation coefficient.
Discussion
Recent studies demonstrated that acute pancreatitis patients with fatty
liver or obesity are at higher risk for developing severe acute
pancreatitis (SAP) than non-fatty liver pancreatitis patients^[85]9.
Moreover, the occurrence of systemic inflammatory response syndrome
(SIRS)^[86]14, severe metabolic disorders (SMD) and acute respiratory
distress syndrome (ARDS)^[87]15 is also significantly increased.
However, if multiple lines of evidences proved that fatty liver disease
is a negative prognostic factor for pancreatitis^[88]16–[89]19, the
precise mechanisms remain largely unknown. Therefore, it is of major
importance to approach these mechanistic issues by comparing the
pathophysiology of APFL and AP. It is why the present study compared
the changes of gene expression between APFL and AP using RNA-seq
method.
In this study, we found a large number of DEGs between APFL and AP
groups. KEGG pathway analyses of these DEGs indicated that PPARα
signalling pathway and fatty acid degradation pathway may be involved
in the pathogenesis of APFL (Fig. [90]6). This provides clues that
fatty liver may aggravate pancreatitis through the above pathways.
The Peroxisome Proliferator Activated Receptor alpha (PPARα) is a
transcription factor belonging to the nuclear hormone receptors
superfamily^[91]20, [92]21. Upon interaction with their ligands, such
as unsaturated fatty acids (FA) and prostaglandins, PPARα translocates
into the nucleus and dimerizes with the retinoid X receptor (RXR).
Then, the complex triggers activation of target genes involved in fatty
acid oxidation and other biological functions (Fig. [93]6c). PPARα
plays an important role in metabolic regulation and affects the
different links of lipid metabolism, including fatty acid uptake, fatty
acid activation, intracellular fatty acid binding, mitochondrial and
peroxisomal fatty acid oxidation^[94]22. In the present study, we found
that the expression level of PPARα gene was significantly decreased in
the APFL group compared with the AP group. It was interesting to note
that the expression levels of many classical PPARα targets, including
carnitine palmitoyl transferase 1a (CPT1A), enoyl-CoA,
hydratase/3-hydroxyacyl CoA dehydrogenase (EHHADH), acyl-CoAsynthetase
long-chain family member 1 (ACSL1) and acetyl-Coenzyme A
acyltransferase 1A (ACAA1A), were also sharply decreased in the APFL
group compared with the AP group (Fig. [95]8). Cpt1a is a protein that
catalyzes the rate-limiting step of fatty acid β-oxidation^[96]23, and
PPARα can stimulate acyl-CoA import into the mitochondria by increasing
the expression of Cpt1a. Decreased expression of hepatic Cpt1a could
reduce fatty acid catabolism but promote anabolic pathways thus
resulting in lipid accumulation and hypertriglyceridemia^[97]24. Apart
from CPT1A, EHHADH, an enzyme that is involved in peroxisomal oxidation
of fatty acids^[98]25, was also significantly decreased in the APFL
group compared with the AP group. EHHADH is a bifunctional enzyme,
which carries both enoyl-CoA hydratase and 3-hydroxyacyl-CoA
dehydrogenase activity^[99]25. Therefore, the decline in the expression
of this enzyme will result in the disruption of peroxisomal β-oxidation
of acyl-CoAs. Acyl-CoA synthetase activity is essential to convert
fatty acids to their acyl-CoA derivatives. Some cytosolic acyl-CoA
synthetases are under transcriptional control of PPARα, such as ACSL1
and ACSL5^[100]26. Furthermore, we found that the expression of ACSL1
was sharply decreased in the APFL group, which would further result in
lipid metabolism disruption. Therefore, lipid metabolism pathway
regulated by PPARα was inhibited in APFL rats.
In addition to some classic PPARα regulatory targets, the expression of
other key enzymes in regulating fatty acid β-oxidation, such as
acyl-Coenzyme A dehydrogenase (ACADM), short/branched chain acyl-CoA
dehydrogenase (ACADSB) and hydroxyacyl-CoA dehydrogenase (HADH) have
also been shown to be down-regulated in APFL group. Taken together, our
results revealed that rats with fatty liver after induction of acute
pancreatitis can appear more serious lipid metabolic disorder than
non-fatty liver rats.
Over the past decade, several studies have confirmed that severe
disturbance of lipid metabolism will aggravate acute pancreatitis
processes^[101]27, [102]28. More specifically, excess free fatty acids
cause oxidative stress, microcirculatory disturbance, free radical
accumulation, and acinar necrosis in pancreatitis^[103]29–[104]32. In
addition, the interstitial release of triglyceride degradation products
may exacerbate cellular disruption and increase inflammatory mediators,
leading to systemic inflammatory response syndrome (SIRS) and organ
failure. Nawaz et al.^[105]33 have found that elevated serum
triglycerides (TG) are independently associated with persistent organ
failure in acute pancreatitis patients. Zeng et al.^[106]34 proposed
that disturbance of lipid metabolism might be a risk factor for
respiratory failure. Wu et al.^[107]35 reported that lipid metabolism
disorder is a risk factor for acute renal injury in acute pancreatitis
patients. TG-mediated lipotoxicity promotes the development of mild
pancreatitis to severe pancreatitis. Lipotoxicity therefore may be an
attractive target to design novel interventions for severe acute
pancreatitis^[108]33. In conclusion, the disturbance of lipid
metabolism can indeed aggravate acute pancreatitis in many ways.
Our research has some guiding significance in clinical therapy. To our
knowledge, this is the first study to further compare the gene
expression profiles between APFL and AP by using RNA-seq method. We
found that rats with fatty liver after induction of acute pancreatitis
can appear more serious lipid metabolic disorder than non-fatty liver
rats. Acute pancreatitis patients with severe degrees of hepatic
steatosis may have a higher burden of lipid metabolic disorder, which
would further aggravate the course of pancreatitis. So clinicians
should be aware of this high-risk group and take effective measures to
promptly correct lipid metabolism disorder in order to prevent the
further development of the disease.
Previous studies have shown that inflammatory responses and
pro-inflammatory cytokines are early up-regulated in acute pancreatitis
and may exacerbate its severity. We also found that the gene expression
of chemokines such as IL-1β, IL-6, IL1R1 and IL1R2 increased
significantly in APFL group when compared with AP group (Fig. [109]7).
These signaling molecules have been shown to play a pivotal role in the
progression of experimental acute pancreatitis^[110]1, [111]36,
[112]37. Moreover, the excessive activation of JAK/STAT signaling
pathway and toll-like receptor signaling pathway was also found in APFL
group as shown in heat map. JAK/STAT signaling pathway is involved in
the regulation of many inflammatory responses^[113]38. Toll-like
receptor signaling pathway belongs to innate immune responses, which
plays an important role in pancreatitis^[114]39, [115]40. The
over-activation of the above pathways in APFL group suggests that fatty
liver may aggravate pancreatitis through JAK/STAT and Toll-like
receptor signaling pathway.
In conclusion, fatty liver can aggravate pancreatitis through a variety
of mechanisms. In the present study, a significant number of
differentially dysregulated genes were obtained by comparing the gene
expression profiles of APFL and AP. Our study also provided the first
evidence that the disorders of PPARα signaling pathway and fatty acids
degradation pathway are involved in the course of APFL (Fig. [116]10),
which sheds some new insight on our understanding of the
pathophysiology of pancreatitis.
Figure 10.
Figure 10
[117]Open in a new tab
The schematic diagram of fatty liver aggravates pancreatitis. Fatty
liver may aggravate pancreatitis by affecting lipid metabolism. The
disorders of fatty acids degradation pathway and PPARα signaling
pathway are involved in the course of APFL. Fatty liver may inhibit
these two pathways to aggravate lipid metabolism disorder, which may
further aggravate pancreatitis.
Methods
Experimental fatty liver model
The rat model of fatty liver was established by feeding a high fat
diet. The high fat diet (HFD) group received the D12492 feed (Research
Diets Inc.) and had 60% of their energy from fat, 20% from
carbohydrates, and 20% from proteins. After two months on the HFD, the
fatty liver rat model was established. The Normal diet group had 5% of
their energy from fat, 76% from carbohydrates, and 19% from proteins.
All rats were given free access to water and food.
Experimental acute pancreatitis model
Ten-to-twelve week old Sprague-Dawley (SD) rats were obtained from the
Animal Experimental Center of The Fourth Military Medical University
(Xi’an, China). Acute pancreatitis (AP) was surgically induced as
described previously^[118]12. Briefly, the rats were anesthetized via a
peritoneal injection of 1% pentobarbital sodium (5 ml/kg). After
exposure of the common bile duct and the pancreas, microaneurysm clips
were placed on the bile duct. 5% sodium taurocholate (0.4 ml/kg,
Sigma-Aldrich) was slowly infused into the common biliopancreatic duct.
On completion of the infusion, the two microclips were removed. After
ensuring that there was no bile leakage at the puncture level, the
abdomen was closed in two layers. The entire procedure was performed
using sterile techniques. All the procedures involving animals were
reviewed and protocols were approved by Xijing Hospital Animal Care and
Use Committee. All methods were performed in accordance with NIH
guidelines.
Histological assessment
The rats were sacrificed 8 h after the induction of AP. Liver and
pancreas were collected for HE staining, which was performed as
described previously^[119]12. Briefly, tissues were fixed in 4% neutral
formalin for 24 h and embedded in paraffin to be cut into slices, which
were stained by hematoxylin and eosin.
Transcriptome analysis
The RNAseq technique was used in this study to analyze the gene
expression profiling in acute experimental pancreatitis rats with and
without fatty liver. The total RNA of liver samples was isolated using
the Trizol Kit (Promega, USA). RNA quality was verified using Agilent
2100 Bio-analyzer (Agilent Technologies, Santa Clara, CA). The cDNA
fragments were purified using a QIAquick PCR extraction kit following
the manufacturer’s instructions. Then the cDNA fragments were enriched
by PCR to construct the final cDNA library, which was sequenced on the
Illumina sequencing platform (IlluminaHiSeq™ 2500).
Transcript assembly and expression value estimation
All the clean reads were mapped to reference genome using
TopHat^[120]41. Cufflinks package was used to estimate expression
profile^[121]42, [122]43. Cufflinks were used to reconstruct transcript
based on genome annotation, and then the transcripts were merged by
cuffmerge. Finally, cuffquant and cuffnorm were used to estimate
transcript expression.
Differentially expressed genes (DEGs) and function enrichment analyses
DEGs were conducted using edger^[123]44. The false discovery rate (FDR)
was used to determine the threshold of the p-value in multiple tests. A
threshold of the FDR ≤ 0.05 was used to judge the significance of gene
expression differences. In this study, we adopted WebGestalt (an online
tool) to perform GO and KEGG analysis as described
previously^[124]45–[125]49. GO enrichment analysis of DEGs was
calculated according to the following equation:
[MATH: P=1−∑i=0m
−1(Mi
)(N−M
n−i)(Nn
) :MATH]
N is the number of all genes with GO annotation; n is the number of
DEGs in N; M is the number of all genes that are annotated to certain
GO terms; m is the number of DEGs in M. The formula used in pathway
analysis is the same as that used in GO analysis. N is the number of
all genes with KEGG annotation, n is the number of DEGs in N, M is the
number of all genes annotated to specific pathways, and m is the number
of DEGs in M. The calculated p-value was adjusted using the Bonferroni
correction, and the corrected p-value (Q-value) ≤ 0.05 was selected as
the threshold. Fragments per kilobase of exon per million fragments
mapped (FPKM) were calculated according to the following equation:
[MATH: FPKM=10<
mn>9(C)/NL
:MATH]
Given FPKM (X) is the expression of gene X, C is the number of reads
uniquely aligned to gene X, L is the number of bases in gene X and N is
the total number of reads uniquely aligned to all genes.
Quantitative RT-PCR for mRNAs
Total RNA was extracted using RNA Extraction Kit (TaKaRa Biotechnology,
Dalian, China) according to manufacturer’s instructions, and the
concentration of the total RNA was quantified by measuring the
absorbance at 260 nm. Five hundred ng RNA of each sample were subjected
to cDNA synthesis using TaKaRa PrimeScript RT reagent kit (TaKaRa
Biotechnology, Dalian, China). Quantitative real-time PCR was performed
using SYBR Premix Ex Taq II (TaKaRa) and measured on a LightCycler 480
system (Roche, Basel, Switzerland). GAPDH was used as an internal
control. The 2^−ΔΔCT method was used to calculate the relative
expression levels of each gene. The sequences of the PCR primers are
shown in Supplementary Table [126]S1.
Statistical analysbis
Heat map, senior bubble map, scatter plot map and venn map were
performed using the OmicShare tools, a free online platform for data
analysis ([127]www.omicshare.com/tools). The Pearson correlation
analysis was used to evaluate the fold change data between qRT-PCR and
RNA-seq. Statistical analyses were performed using SPSS 17.0 software
(IBM, Armonk, NY, USA). The Student t test was performed to examine the
significance of differences between two groups. P-values less than 0.05
were considered statistically significant.
Electronic supplementary material
[128]Supplementary Information^ (1.5MB, pdf)
Acknowledgements