Integrated analysis of hepatic mRNA and miRNA profiles identified molecular networks and potential biomarkers of NAFLD
SCIentIfIC RepoRts |
Integrated analysis of hepatic mRNA and miRNA profiles identified molecular networks and potential biomarkers of NAFLD
OPEN To enhance our understanding of molecular mechanisms and mine novel biomarkers of non-alcoholic fatty liver disease (NAFLD), RNA sequencing was performed to gain hepatic expression profiles of mRNAs and miRNAs in NAFLD and normal rats. Using DESeq with thresholds of a two-fold change and a false discovery rate (FDR) less than 0.05, 336 mRNAs and 21 miRNAs were identified as differentially expressed. Among those, 17 miRNAs (e.g., miR-144-3p, miR-99a-3p, miR-200b-3p, miR-200b-5p, miR200c-3p, etc.) might serve as novel biomarkers of NAFLD. MiRNA target genes (13565) were predicted by the miRWalk database. Using DAVID 6.8, the intersection (195 genes) of differentially expressed mRNAs and miRNA-predicted target genes were enriched in 47 gene ontology (GO) terms and 28 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Using Cytoscape, pathway interaction and protein-protein interaction (PPI) networks were constructed, and hub genes (e.g., Abcg8, Cyp1a1, Cyp51, Hmgcr, etc.) associated with NAFLD were obtained. Moreover, 673 miRNA-mRNA negative regulatory pairs were obtained, and networks were constructed. Finally, several representative miRNAs and mRNAs were validated by real-time qPCR. In conclusion, potential molecular mechanisms of NAFLD could be inferred from integrated analysis of mRNA and miRNA profiles, which may indicate novel biomarkers of NAFLD.
Nonalcoholic fatty liver disease (NAFLD) is the most common hepatic disease worldwide and is defined as
evidence of hepatic steatosis, either by imaging or histology, with no cause for secondary hepatic fat accumulation
such as significant alcohol consumption, use of steatogenic medication, or hereditary d1is.oTrhdeerprevalence
of NAFLD is 20?30% in Western countries, creating considerable concern with its progression from steatosis to
steatohepatitis, cirrhosis, and even hepatocellular carcin2.oRmeagarding the pathogenesis, the ?multiple hits?
hypothesis considers multiple insults in genetically predisposed subjects to induce NAFLD and provides a more
accurate explanation of NAFLD pathogenesis than do other hypotheses. Such hits include insulin resistance,
hormones secreted from adipose tissue, nutritional factors, gut microbiota and genetic and epigenetic factors,
among other3s. However, the molecular mechanisms in the pathogenesis of NAFLD are complex and still require
Over the past decades, mRNA expression profiling has been used as a powerful tool to uncover
molecular mechanisms and explore diagnostic and predictive biomarkers, particularly in complicated diseases such
as cance4r, diabetes5, cardiovascular diseas6eand NAFLD. One study reported that the expression of certain
metabolism-related genes was induced and specific metabolic and repair pathways were activated in 7N.AFLD
Others reported that uptake transporter genes were coordinately targeted for down-regulation at the global level
during the pathological development of NAFL8.DA recent study used high-throughput RNA sequencing to
explore mRNA expression profiling by Western-diet-induced NAFLD and found that numerous lipid metabolism
and cholesterol biosynthesis genes were alter9e.dAlthough numerous genes have been identified in NAFLD, the
establishment of gene regulatory networks is limited.
MiRNAs are endogenous, single-stranded RNAs and have been widely known to regulate gene expression
primarily through post-transcriptional mechani1s0m.MsiRNAs have multiple targets and regulate the
expression of hundreds of genes that have important cellular functions such as cell differentiation, development, and
proliferation and disease progressio1n1. Alterations in miRNA expression profiles have been described in the
pathophysiology of complex disease1s2?14, and specific miRNA expression profiles have been shown to be
associated with NAFLD. Several miRNAs, such as miR-122, miR-451, miR-200b and miR-27, have been found to
be deregulated in different diet-induced NAFLD rat mod1e5.lsResearchers have observed altered expression of
miR-33a/miR-33b* and miR-122 in liver samples, which have been designated possible contributors to hepatic
lipid metabolism in patients with NAFL16D. However, studies on the identification of NAFLD-relevant miRNAs
by high-throughput RNA sequencing are rare. By contrast, integrated analysis of mRNAs and miRNAs expression
profiles in exploring miRNA-mRNA regulatory networks provides a powerful tool and facilitates clarification of
the molecular mechanisms and identification of novel biomarkers of NAFLD.
In our previous study, we constructed NAFLD and normal rat models and observed different phenotypic
characteristi1c7s.To enhance our understanding of the mechanisms associated with the pathogenesis of NAFLD
and mine potential biomarkers, here, we detected expression profiles of mRNAs and miRNAs in liver tissues and
constructed pathway interaction, PPI and miRNA-mRNA regulatory networks through integrated analysis of
the crucial mRNAs and miRNAs obtained. Our present study may pave a new way for understanding molecular
mechanisms of NAFLD and uncover novel biomarkers.
Quality control of RNA sequence experiment. RNA samples with optimal RIN (RNA Integrity
Number) values (?8) were considered to construct libraries for sequencing. Library quality was also assessed
(supplementary Table?S1). The libraries with sufficient concentration and a peak length within the ranges
350?500 bp and 138?150bp were sequenced on an Illumina HiSeq platform to generate mRNA and miRNA
sequences, respectively. In the present study, the mRNA sequencing generated an average of 48348613 raw
reads for each sample. After quality filtering (removing adapter and low-quality reads), an average of 48309324
(99.9%) high-quality, clean reads were obtained for each sample. A large portion of clean reads (92.5%) were
mapped to the rat genome. Among those reads, we observed 21174764 spliced reads and 20772406 non-spliced
reads. Moreover, the miRNA sequencing yielded an average of 10088010 raw reads for each sample, and 8629406
(85.5%) clean reads were filtered out. A large number of clean reads (97.9%) were mapped to the genome. The
high percentage of mapped reads is consistent with what would be expected for high-quality
d1a8t.Tasheetqsuality characteristics of sequence reads for each sample are listed in supplementary Table?S2. Additionally, the RNA
expression levels were determined by reads per kilobase per million mapped reads and are provided in
supplementary Table?S3. Hierarchical clustering of representative mRNA and miRNA expressions revealed samples in
the same group clustered closely, which indicated high reproducibility in biological replicates1)(.Fig.?
Differentially expressed mRNAs and miRNAs. Using cutoffs of a two-fold change and an FDR
less than 0.05, we identified 336 differentially expressed mRNAs (DEGs); 105 were down-regulated, and 201
were up-regulated in NAFLD rats. Among these DEGs, Abcg8, Cyp1a1, Cyp51, Hmgcr,. ewtecre decreased,
whereas Apln, Cd36, Cyp7b1, Vcan, etc. were increased. We also identified 21differentially expressed miRNAs
(DEMs). Compared with the miRNAs in the normal group, 14 were down-regulated and 7 were up-regulated
in the NAFLD group. Among these DEMs, miR-33, miR-33-5p, miR-33-3p, miR-144-3p and miR-99a-3p were
down-regulated in NAFLD, whereas miR-200b, miR-200b-3p, miR-200b-5p, miR-200c-3p and miR-349 were
up-regulated. Volcano plots (Fig2).?present DEGs or DEMs as red blots and other detected mRNAs and miRNAs
as blue blots. Detailed information pertaining to the top 20 DEGs and total 21 DEMs is listed in1Tabnlde2s?,
respectively. Supplementary Table?S4 lists 336 DEGs.
Annotation, target gene identification and functional enrichment. A literature search revealed
that altered expression of miR-33-5p, miR-33, miR-182 and miR-200b has been reported in NAFLD, whereas
the remaining 17 DEMs (e.g., miR-144-3p, miR-99a-3p, miR-200b-3p, miR-200b-5p, miR-200c-3p, miR-33-3p,
miR-349, miR-184, etc.) have not been reported in NAFLD. Target genes of the 21 DEMs were predicted by the
online tool of the miRWalk database. We obtained 13760 target genes for the 21 DEMs. A Venn diagram3A(F)ig.?
showed that 195 overlapped genes were obtained between 13760 target genes and 336 DEGs, which were selected
for further analysis. Detailed information pertaining to the 195 overlapped genes is provided in supplementary
To further establish the main biological function and key pathways of the 195 overlapped genes, functional
enrichment was performed by DAVID. GO analysis revealed that the overlapped genes were enriched in 47 terms
such as cholesterol biosynthetic process, immune response and oxidation-reduction process (3FBi)g..?KEGG
pathway enrichment showed that 20 pathways were enriched such as steroid biosynthesis, NF-?B signaling
pathway and adipocytokine signaling pathway (F3iCg.)?.
Furthermore, pathway interaction networks were constructed and are presented i4n. FPiagt.?hways might
interact with each other through a specific gene or hub gene. For instance, steroid hormone biosynthesis and bile
secretion might interact with each other via gene Cyp7a1, while bile secretion might interact with fat digestion
and absorption through gene Abcg8. RT1-Da, RT1-Ba, RT1-Bb and RT1-Db1 were hub genes of several different
pathways that might interact with each other. Detailed information pertaining to genes enriched in different GO
terms and KEGG pathways is provided in supplementary Table?S6.
PPI (Protein-protein interaction) network analysis. To explore interactions among the 195 overlapped
genes, PPI was applied, and the most important modules were then screened. A PPI network with 51 nodes
was obtained, and the average number of neighbors was 6.9. The hub nodes with the greatest number of
neighbors (?8), such as Abcg8, Cyp1a1, and Cyp7a1, were identified (Fi5gA.?). By Molecular Complex Detection
(MCODE), one significant module with the highest score (11.5) was obtained, including 12 nodes such as
Cyp7b1, Cyp51, and Hmgcr (Fig.5?B). A sub-network with the first neighbors was constructed (F5Cig)..?
Regulatory networks of differentially expressed miRNAs and mRNAs. It is well known that mi-R
NAs play pivotal roles in regulating mRNA expression. To explore miRNA and mRNA regulatory networks in
NAFLD, the expression profiles of miRNA and mRNA were combined for further analysis. Integrated analysis
of 21 DEMs and 195 DEGs identified 673 negative interaction pairs. The regulatory networks were constructed
and visualized by Cytoscape; 114 pairs were up-regulated miRNAs. vdsown-regulated mRNAs (Fig6.?A), e.g.,
up-regulated miR-184 interacted with 9 down-regulated genes (Abcg8, Dhcr7, Hmgcr, Tmem255.)a. ,Neotctably,
a single miRNA might interact with different genes, while the same gene might be a target of different miRNAs.
We observed that the intersection genes Abcg8, Cyp1a1 and Tmem255a were the targets of miR-349, miR-378b,
miR-200b-3p, miR-200c-3p, miR-200b-5p and miR-184 (Fig.6? B). Additionally, we observed that miR-200b-3p,
miR-200b-5p and miR-200c-3p all interacted with Abcg8, Cyp1a1, Tmem255a, Gstm6 and Lat6(CF,iDg.)?. GO
and KEGG pathway analyses revealed that the target genes of the three miRNAs were enriched in cholesterol
biosynthetic process, metabolic pathways., (eFtcig.?6E,F).
In addition, 459 pairs were down-regulated miRNAs vs. up-regulated mRNAs (F7igA.)?, e.g., down-regulated
miR-33-5p interacted with 34 up-regulated genes (Abi2, Ciita, Sult1c2, Vca.n)., Ientcthis network, we
identified a batch of genes such as Apln, Cd36, and Cyp7b1 that were connected to more than 5 miRNAs7(BF)i.g.?
In addition, we observed that miR-33-3p interacted with 53 mRNAs, while miR-33-5p targeted 32 genes.
Furthermore, miR-33-3p and miR-33-5p targeted 23 common genes such as Abi2, Apln, and Vcan (F7iCg,sD? ).
The miRNAs-mRNAs pairs are listed in supplementary Table?S7.
Validation by real-time PCR. To validate our findings based on RNA sequence data, we selected 10
mRNAs and 4 miRNAs (half were up-regulated, and half were down-regulated) to perform real-time PCR e-xper
iments. Log2 (fold change) values were transformed based on the ratios between NAFLD and normal group
average expression values. As shown in Fig8.?, the RNA expression patterns in real-time PCR data were consistent
with RNA sequence data, which might partially validate the reliability of our sequence data and our findings in
the present study.
In the present study, we combined the analysis of mRNA and miRNA profiles and constructed PPI, pathway
interaction and miRNA-mRNA regulatory networks to enhance our understanding of molecular mechanisms of
NAFLD and mine potential biomarkers.
By integrated analysis, we observed 14 decreased miRNAs (such as miR-33, miR-33-5p, miR-33-3p,
miR144-3p, and miR-99a-3p) and constructed 459 miRNA and mRNA regulatory pairs in NAFLD rats. MiR-33 is
a key regulator of lipid metabolism and transport that targets ATP-binding cassette A1 (ABCA1) and thus
suppresses HDL synthesis by attenuation of cholesterol efflux to apolipoprotein A1 and nascent19H.SDtLudies have
reported that genetic ablation and antagonism of miR-33 attenuates the progression of atheroscleros2i0s,2i1n. mice
However, there are conflicting reports regarding NAFLD. A study reported that persistent inhibition of miR-33
in high-fat-diet-fed mice might cause deleterious effects such as moderate hepatic steatosis and hyper-triglycer
idemia22. Researchers observed that high-fat-diet feeding reduced miR-33 expression along with an increase in
body weight, fasting blood glucose, triglyceride, cholesterol, AST and ALT in NAFLD m23.icAeltered expression
of miR-33-5p was also reported in Western-diet-induced NAFLD rats and high-fructose-diet-induced chronic
metabolic disorders mic2e4,25. However, little is known about miR-33-3p in NAFLD. Intriguingly, we observed that
the expression levels of miR-33, miR-33-5p and miR-33-3p were significantly decreased in high-fat-diet-induced
NAFLD. All data suggested that miR-33, its guide strand miR-33-5p and passenger strand miR-33-3p might play
pivotal roles in NAFLD. Moreover, the miRNA-mRNA regulatory networks revealed that 23 genes interacted
with both miR-33-5p and miR-33-3p. Among those genes, Apln and Vcan have been reported to be associated
with NAFLD.One study reported that the serum concentration of apelin-36 (Apln gene encoded protein) was
significantly higher in NAFLD patients than in cont26r.oAlsnother study revealed that Vcan encoded versican
might serve as a novel modulator of hepatic fibrosis. Hepatic expression of versican increased in cirrhotic livers
and a mouse model of fibrosis, while transient knockdown of versican inhibited expression of fibrogenic ge27n.es
In the present study, we observed that Apln and Vcan were up-regulated in NAFLD, consistent with the results
reported by other researchers. Collectively, there is strong evidence indicating the potential roles of miR-33-3p
and its target genes in NAFLD.
Researchers reported that the serum level of miR-99a was significantly decreased in NAFLD patients
compared with that in healthy cont2r8o.lDsecreased miR-144 could enhance TNF-?and IFN-? production, which
might contribute to the progression of NAFLD in r2a9.tIsn addition, miR-144 might contribute to the
pathogenesis of NASH in morbidly obese subjects30. In the present study, although we did not observe altered expression of
miR-99a or miR-144, we found that miR-99a-3p and miR-144-3p were significantly down-regulated in NAFLD
also been reported to be correlated with NAFL39D.Therefore, miR-349 may serve as a new biomarker of NAFLD,
which is worthy of in-depth investigation.
Furthermore, the functional enrichment of the miRNA target genes revealed that numerous genes were
enriched in NAFLD-related processes and pathways such as cholesterol biosynthetic process, immune response,
steroid biosynthesis, NF-?B signaling pathway, fat digestion and absorption and bile secre40t?i4o3.nThe
constructed pathway interaction networks indicated that different pathways could interact with each other throu
specific genes or hub genes, such as Abcg8, Acat2, Cyp7a1, Cyp1a1, Cd36, Cd44, and RT1-Ba. PPI network and
MCODE module analysis also identified a batch of hub genes, including Abcg8, Cyp1a1, Cyp7b1, Cyp51, and
Hmgcr. In addition to the previously mentioned Abcg8, Cd36 and Cyp1a1, genes such as Cyp7b1, Cyp51 and
Hmgcr have also been reported to be correlated with NAFLD. It was reported that mRNA and protein expression
levels of CYP7B1 were increased in human NAFLD44. Down-regulation of Cyp51 was observed by transcriptome
analysis45. The hepatic expression of Hmgcr was reduced in morbidly obese individuals with liver steatosis and
fibrosis46. In-depth investigation of these specific genes or hub genes may provide insight into the molecular
mechanisms of NAFLD and identified novel biomarkers.
Several weak points in our present study should be noted. First, our results are based on rat models; few
findings have been replicated across human stud4i7e.sDiscrepancies in the findings are likely due to differences in
study design, phenotypic endpoint, genetic alterations.,Inet-cdepth investigation should be performed to
generalize our findings to humans. Second, we performed a bioinformatics analysis based on high-throughput RNA
sequencing data and conducted several RNA expression validations by real-time qPCR; thorough validation and
further functional experiments should be performed in the future. Third, we focused on exploring the negative
miRNA-mRNA regulatory pair, considering the down-regulation role for miRNA revealed by available studies.
However, recent evidence suggests that some miRNAs could activate gene expression in specific cell types and
conditions with distinct transcripts and proteins. Given this complexity, further exploration should be performed
to uncover the puzzling and interesting aspects of gene regulation by miRNAs.
In conclusion, our present integrated analysis may open a new avenue toward understanding the molecular
mechanisms associated with the pathogenesis of NAFLD and provide potential novel biomarkers or candidate
targets of NAFLD. Thorough and comprehensive investigation of the identified DEMs and their target genes
should be performed in the future, which may ascertain novel biomarkers of NAFLD.
Animals sample collection. As previously described17, five-week-old male Wistar rats (13g0? 10 g)
were obtained from Shanghai SLAC Laboratory Animal CO. LTD, China, and maintained in a
temperatureand humidity-controlled room. Fourteen rats were randomly divided into two groups: a normal grou=p7()n,
fed with a chow diet; and an NAFLD group (=n7), fed with a high-fat diet (88% chow diet, 10% lard and 2%
Animals were weighed and sacrificed after 4 weeks of feeding. Liver tissues were quickly removed and
stored at ?80 ?C after being snap frozen in liquid nitrogen. All animal procedures were approved by the Animal
Experiment Ethics Committee of Shanghai University of Traditional Chinese Medicine, and all methods were
performed in accordance with the relevant guidelines and regulations.
RNA extraction and sequencing. To obtain mRNA and miRNA expression profiles, total RNA of the
liver (100 mg) was extracted using TRIzol reagent (Invitrogen, USA). RNA quality was verified using an Agilent
2100 Bio-analyzer (Agilent Technologies, Santa Clara, CA). Libraries for mRNA and miRNA sequencing were
respectively generated using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB,USA) and the NEBNext
Multiplex Small RNA Library Prep Kit for Illumina (NEB,USA) according to the manufacturer?s instructions.
Then, the final purified libraries were evaluated using a BioAnalyzer 2100 and quantified. Using the Illumina
HiSeq platform, libraries for mRNA were sequenced, and 15b0p paired-end reads were generated; moreover,
libraries for miRNA were sequenced, and 50bp single-end reads were generated according to the manufa-ctur
er?s instructions. Seven biological replicate samples of each group were used, and a total of 28 libraries were
RNA sequencing data processing. We performed quality control on raw sequence data using the FastQC
tool. To obtain high-quality, clean data for analysis, adaptor sequence trimming and removal of low-quality reads
were performed48. Clean reads were aligned with the rat genome using TopHat v2.1.1. Based on the ultra-high
throughput short-read aligner Bowtie, TopHat aligned RNA-Seq reads to reference genomes and analyzed the
mapping reads to determine possible splice junctions between exons. RNA expression levels were determined
by reads per kilobase per million mapped reads. Hierarchical clustering of representative mRNAs and miRNAs
expressions was performed to reveal reproducibility in biological replicates.
The DESeq package was used to detect DEGs and DEMs between the normal and NAFLD groups, with
thresholds of a two-fold change (the ratio between seven NAFLD rats? and seven normal rats? averaged signal
values) and an FDR less than 0.05. The DEGs and DEMs were visualized on a volcano plot, which was constructed
by plotting? log10 (FDR) on the y-axis and log2(fold change) on the x-axis.
Target genes prediction, functional enrichment. The target genes of DEMs were predicted by the
online tool of miRWalk (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/index.html). The intersection
of DEMs predicted target genes, DEGs were identified by a Venn diagram and the overlapped genes were used
for further analysis.
Using DAVID 6.8 (https://david.ncifcrf.go)v,/based on the default rat whole genome background, GO a-nal
ysis was performed to help elucidate the concrete biological functions of specific genes, and KEGG pathways
enrichment was employed to identify the critical signal pathways of the differential expressed genes. Any GO
terms and KEGG pathways with p values less than 0.05 were considered significantly enriched.
PPI, pathway interaction, miRNA-mRNA regulatory network construction. The overlapped
genes were mapped to the STRING database (https://string-db.org/) to screen PPI, and networks were visualized
by Cytoscape. Important hub proteins were screened by counting the degree of connectivity of each node in the
network. In the network, nodes represented proteins and lines represented the interactions. The most significant
modules were identified with the plug-in MCODE of Cytoscape with a cutoff MCODE sco>re5o.fThe enriched
pathway interaction networks were constructed using the Cytoscape plug-ins ClueGO and CluePedia. Negatively
Primers sequence (5?-3?)
correlated, differentially expressed miRNA-mRNA pairs were selected for screening, and the regulatory networks
were constructed and visualized by Cytoscape software. Sub-networks were also constructed with mRNAs
connected to a large number of different miRNAs (?5).
Real-time quantitative PCR assay. Total RNA was extracted from liver tissues using TRIzol (Invitrogen,
China) according to the manufacturer?s protocol. For miRNA detection, total RNA was polyadenylated and then
submitted to reverse transcription with primers and reverse transcriptase (Promega, USA) according to the
manufacturer?s instructions. For mRNA detection, RNA was reverse transcribed into cDNA and then subjected to a
real-time quantitative PCR (qPCR) assay. The qPCR assay was performed in triplicate with the SYBE Green PCR
Kit on an ABI StepOnePlus Real-time PCR system (Applied Biosystems, USA). Cycle threshold (CT) values were
calculated using the automated settings of the system. U6 and GAPDH were used as the internal controls for
miRNAs and mRNAs, respectively. The primer sequences are listed in Tabl3esa? nd 4.
Statistical analysis. Data were expressed as mean?s SD and were analyzed by one-way analysis of variance
(ANOVA) using SPSS v18.0 software. P values less than 0.05 were considered significantly different.
Data availability statement. All data generated during this study are included in this published article
(and its Supplementary Information files).
This work was supported by the National Natural Science Foundation of China (No. 81620108030) and the
Breeding Plan of Longhua Hospital (No. LYTD32). The funders played no role in study design, data collection or
analysis, decision to publish, or preparation of the manuscript.
G.J. and L.Z. conceived, designed and supervised the study. M.Z., Q.W., W.Z., L.Y., T.L. and P.Z. collected samples
and performed the experiments. M.Z., W.Z. and Q.W. analyzed the data. M.Z., L.Z. and G.J. wrote the paper. All
authors reviewed and approved the final manuscript.
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-25743-8.
Competing Interests: The authors declare no competing interests.
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