Abstract
Skin-derived precursors (SKPs) from dermis possess the capacities of
self-renewal and multipotency. In vitro and in vivo studies
demonstrated that they can differentiate into fibroblasts. However,
little is known about the molecular mechanism of the differentiation of
SKPs into fibroblasts. Here we compare the transcriptomes of mouse SKPs
and SKP-derived fibroblasts (SFBs) by RNA-Seq analysis, trying to find
differences in gene expression between the two kinds of cells and then
elucidate the candidate genes that may play important roles in the
differentiation of SKPs into fibroblasts. A total of 1971
differentially expressed genes (DEGs) were identified by RNA-Seq, which
provided abundant data for further analysis. Gene Ontology enrichment
analysis revealed that genes related to cell differentiation, cell
proliferation, protein binding, transporter activity and membrane were
significantly enriched. The most significantly up-regulated genes Wnt4,
Wisp2 and Tsp-1 and down-regulated genes Slitrk1, Klk6, Agtr2, Ivl,
Msx1, IL15, Atp6v0d2, Kcne1l and Thbs4 may play important roles in the
differentiation of SKPs into fibroblasts. KEGG analysis showed that
DEGs were significantly enriched in the TGF-β signaling pathway, Wnt
signaling pathway and Notch signaling pathway, which have been
previously proven to regulate the differentiation and self-renewal of
various stem cells. These identified DEGs and pathways could facilitate
further investigations of the detailed molecular mechanisms, making it
possible to take advantage of the potential therapeutic applications of
SKPs in skin regeneration in the future.
Introduction
Recent developments in stem cell biology have generated much excitement
about the potential for regenerative medicine and cell-based therapies
in a variety of clinical applications, such as treating leukemia,
Parkinson’s disease and wounds. As skin is easily accessible for
autologous transplantation, stem cells isolated from skin could be
promising candidates for prospective therapeutic applications.
Skin-derived precursors(SKPs) from dermis possess the capacities of
self-renewal and multipotency [[33]1–[34]2]. They can differentiate
into cells of both neural and mesodermal lineages, such as neurons,
glias, smooth muscle cells, osteogenic and adipogenic cells
[[35]1–[36]5]. It has also been reported that SKPs can differentiate
into fibroblasts. When attached onto culture dishes by serum, SKPs
initiated differentiation and developed into a fibroblast-like
morphology. These SKP-derived fibroblasts(SFBs) could express
fibroblast markers fibronectin and vimentin, but did not express SKP
marker nestin [[37]6]. In vivo experiments demonstrated that SKPs which
were transplanted into dermis became morphologically similar to the
endogenous fibroblasts at 2–3 weeks post-transplant and expressed
dermal fibroblast markers, but did not express markers of neurons or
peripheral glia [[38]7–[39]8]. As the predominant cells in dermis,
fibroblasts play a pivotal role in maintaining the form and function of
skin. The loss or function impairment of fibroblasts is mainly caused
by aging or by injury. Given that SKPs can differentiate into
fibroblasts, they might be useful for treating aged skin or
regenerating skin after damage since they can replenish lost or damaged
fibroblasts.
A complex interplay between the intrinsic genetic processes of stem
cells and their environment, including the effects of specific
cytokines, determines whether they self-renew, remain quiescent,
proliferate, differentiate, or undergo apoptosis. Hence, understanding
the nature of SKPs and the molecular process by which these cells
differentiate into fibroblasts is crucial for the success of cell based
therapies. As the phenotype of any given cell is ultimately the product
of its genes, it is necessary to identify gene expression of the cells
we are interested in, which for the present study are SKPs and SFBs.
RNA-seq is a high-throughput sequencing platform that can be used for
discovery and quantification of transcripts in a single experiment
[[40]9–[41]12] and reveal differential expression between different
samples [[42]13]. Recent studies have shown RNA-seq to be more accurate
over a larger dynamic range of gene expression than microarrays
[[43]14–[44]15].
No studies performed with mice employing a transcriptome comparison
between SKPs and SFBs have been reported. Therefore, the aim of this
study is to compare the transcriptomes of mouse SKPs and SFBs by
RNA-Seq analysis. Such an analysis would help determine the candidate
genes that might play important roles in the differentiation of SKPs
into fibroblasts, explain the molecular mechanisms, and then accelerate
the therapeutic application of SKPs in skin.
Materials and Methods
Cell Isolation
BALB/c mice at postnatal day 3 for cell isolation were purchased from
the Center of Experimental Animal, West China Hospital, Sichuan
University. All animal procedures were approved by the Institutional
Animal Care and Use Committee of Sichuan University(2013006A). The
protocol for isolating SKPs has been described previously in detail
[[45]16]. Briefly, dorsal skin was dissected from neonatal BALB/c mice
and cut into 2–3 mm^2 pieces. These dissected pieces were washed three
times with Hank’s balanced salt solution(Invitrogen, USA) and digested
with 0.1% trypsin(Invitrogen, USA) under gentle agitation for 30–50 min
at 37(C. When tissue pieces became pale, they were washed three times,
once with wash medium(DMEM/F12(3:1)(Invitrogen, USA) containing 1%
penicillin/streptomycin(Cambrex, USA)) plus 10% fetal bovine serum and
twice with Hank’s balanced salt solution. The epidermis was then
removed from the dermis. Afterwards, dermis pieces were digested by
collagenase type XI(Sigma-Aldrich, USA) for one hour at 37(C,
mechanically dissociated with scissors and subsequently triturated
repeatedly in wash medium with a 1000 μl pipette tip. The supernatant
was collected and the trituration was repeated until tissue pieces
became thin. After the dissociated cell suspension was filtered through
a 40 μm cell strainer and centrifuged at 1200 rpm for 7 min, the pellet
was suspended in proliferation medium(DMEM/F12(3:1) containing 0.1%
penicillin/streptomycin, 40 ng/ml FGF2(Collaborative, USA), 20 ng/ml
EGF(Collaborative, USA) and 2% B27 supplement(Invitrogen, USA)) to an
optimum density of 10000–30000 cells/ml of medium. Finally, cells were
cultured in proliferation medium at 37(C and used for experiments at
the second generation. Cells were passaged every 3–4 days and used for
experiments at the second generation. Primary fibroblasts(PFBs) were
isolated from the dermis of neonatal BALB/c mice and cultured in DMEM
plus 10% fetal bovine serum.
Induction of Differentiation
For the induction of SKP differentiation into SFBs, SKP spheres were
collected and suspended in DMEM plus 10% fetal bovine serum, and then
plated onto a cell culture dish(Corning, USA) whose surface was coated
with poly-L-lysine(Sigma-Aldrich, USA).
Immunocytochemistry
Cells were fixed by 4% paraformaldehyde. The fixed cells were blocked
with 3% BSA for 30 min and subsequently incubated with primary antibody
overnight at 4℃. After washing with PBS 3 times, they were incubated
with secondary antibody for 1 h at room temperature. Finally they were
incubated with DAPI for 1 min. A parallel culture with secondary
antibody only was employed as a negative control, a culture without any
antibody was used as a blank control. Primary antibodies were
monoclonal anti-fibronectin(Abcam, UK, 1:250), monoclonal
anti-vimentin(Abcam, UK, 1:200), monoclonal anti-nestin(Abcam, UK,
1:500), monoclonal anti-SOX2(Abcam, UK, 1:200) and polyclonal
anti-collagen 1(Abcam, UK, 1:500). Secondary antibodies were Alexa
Fluor 488 goat-rabbit, Alexa Fluor 555 goat-rabbit and Alexa Fluor 488
goat-mouse(Invitrogen, USA, 1:500). The preceding protocol was
performed in triplicate for each cell type described.
RNA-Seq
Library Preparation. cDNA library preparation was performed at
BGI-Shenzhen. The total RNA from SKPs and SFBs were firstly treated
with DNase I to degrade contaminating DNA. mRNA was enriched by using
oligo(dT) magnetic beads. The mRNA was broken into short
fragments(about 200 bp) in fragmentation buffer. The RNA fragments were
then ligated to adaptors and converted into cDNA, which was purified
using magnetic beads. End reparation and 3’-end single nucleotide
A(adenine) addition was then performed. Finally, sequencing adaptors
were ligated to the fragments. The resulting fragments were enriched by
PCR amplification. The library products were used for sequencing using
IlluminaHiSeq^TM 2000. SKPs were considered as the control and SFBs
were the treatment.
Mapping Reads to the Reference Genome. The original image data produced
by the sequencer was transferred into sequences using base calling.
These sequences were defined as “raw reads”. Prior to mapping these
reads to the reference database, all sequences were filtered to remove
adaptor sequences, N sequences(in which the percentage of unknown
bases(N) was greater than 10%) and low-quality sequences(the
percentages of low quality bases with a quality value ≤ 5 was greater
than 50% in a read). The remaining reads were mapped to the mouse
genome using SOAPaligner/SOAP2 [[46]17]. No more than 2 mismatches were
allowed in the alignment.
Normalized Expression Levels of Genes and Screening of Differentially
expressed genes(DEGs). The gene expression level was calculated by
using RPKM [[47]10] method(Reads Per Kb per Million reads). The used
formula was as follows:
[MATH: RPKM(A)=106CNL/10
3 :MATH]
RPKM(A) is the expression level of gene A, C is the number of reads
that uniquely aligned to gene A, N is the total number of reads that
uniquely aligned to all genes, and L is the number of bases of gene A.
The RPKM method is able to eliminate the influence of different gene
length and sequencing discrepancies on the calculation of gene
expression levels. Therefore, the RPKM values could be directly used
for comparing the difference of gene expression among samples. The
cutoff value for determining gene transcriptional activity was
determined based on a 95% confidence interval for all RPKM values for
each gene. A strict algorithm had been developed to identify DEGs
between two samples based on “The significance of digital gene
expression profiles” [[48]18]. We used a P-value corresponding to a
differential gene expression test at statistically significant levels
[[49]19]. ‘‘FDR(False Discovery Rate) ≤ 0.001 and the absolute value of
log2Ratio ≥ 1” were used to identify DEGs as the threshold.
Gene Ontology(GO) and KEGG Pathway Enrichment Analysis of DEGs. GO
enrichment analysis provides all GO terms that are significantly
enriched in DEGs compared to the genome background, and filters the
DEGs that correspond to biological functions. This method maps all DEGs
to GO terms in the database([50]http://www.geneontology.org/),
calculates gene numbers for every term, then uses hypergeometric test
to find significantly enriched GO terms in DEGs compared to the genome
background. The calculating formula is:
[MATH: P=1−∑i=0m−1(Mi)
(N−Mn−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 the
certain GO terms, m is the number of DEGs in M. The calculated p-value
went through Bonferroni Correction, using a corrected p-value ≤ 0.05 as
a threshold. GO annotation of DEGs was carried out using the
Blast2GOprogram. After getting GO annotation for DEGs, we used WEGO
software [[51]20] to do GO functional classification for DEGs and to
understand the distribution of gene functions of the species from the
macro level. For KEGG annotation, which is the major public
pathway-related database [[52]21], the calculating formula is the same
as that in GO analysis, where N is the number of all genes, n is the
number of DEGs in N, M is the number of all genes annotated to specific
pathways, and m was the number of DEGs in M.
Software and Databases. The software and databases used to analyze the
RNA-Seq data are shown in [53]Table 1.
Table 1. Software and Databases.
Analysis Software/Algorithm (Version) Database (Version)
Statistics of alignment Soap(2.21)
Functional annotation BLAST(2.2.23); Blast2GO(2.2.5) KEGG(updated
monthly if possible); NR(updated monthly if possible); GO(updated
monthly if possible)
Quantification of gene expression RPKM algorithm
Screening of DEGs Poisson distribution model
Expression pattern analysis Cluster(3.0); Java Tree View(1.1.6r2)
Gene Ontology enrichment analysis Hypergeometric distribution model
GO(updated monthly if possible)
Pathway enrichment analysis Hypergeometric distribution model
KEGG(updated monthly if possible)
[54]Open in a new tab
Real Time Quantitative Reverse Transcription PCR(qRT-PCR)
Main DEGs involved in cell differentiation and important signaling
pathways were selected: Bmp2, Kit, Id2, Bnc1, Wnt2b, Ptch2, Actg2,
Wnt4, Myh11, Acta2, Smad6, Smad9, Myc, Megf6, Dner, Slitrk1, Lvl, Klk6,
Agtr2, Tgfbr1, Smad3, Wnt11, Fzd10, Fzd4, Jun, Notch4, Notch1, Dlk1,
Dtx4. Total RNA was extracted using the RNeasy micro kit(Qiagen,
Germany) and then was converted to cDNA using the SuperScript II
Reverse Transcriptase kit(Invitrogen, USA) according to the
manufacturer’s protocol. qRT-PCR was performed using a StepOnePlus
Real-Time PCR System(Applied Biosystems) with Taqman primer/probe sets
from Applied Biosystems. Three independent biological and two technical
replicates were performed. mRNA expression levels were normalized by
the internal β-actin control and then represented as the log2 ratio of
the normalized values in fibroblasts to those in SKPs. Pearson
correlation coefficient between qRT-PCR data and RNA-Seq data was
calculated to validate RNA-Seq experiments. T-test was used to compare
the gene expression between SFBs and PFBs and P< 0.01 was considered to
be statistically significant.
Results
Differentiation of SKPs into Fibroblasts
SKPs were successfully isolated from mouse dermal tissue and showed
sphere-like structure in the suspension culture([55]Fig. 1A). SKPs
attached to the bottom of poly-L-lysine treated dishes and exhibited
fibroblast-like morphology([56]Fig. 1B and C) 3 days after serum
induction. Consistent with previous studies [[57]1, [58]8], SKP spheres
expressed SOX2([59]Fig. 2A), nestin([60]Fig. 2D), vimentin([61]Fig. 2G)
and fibronectin([62]Fig. 2J) when measured by immunocytochemistry. SFBs
expressed fibroblast markers vimentin([63]Fig. 2I),
fibronectin([64]Fig. 2L) and collagen 1([65]Fig. 2O), but did not
express SOX2([66]Fig. 2C) or nestin([67]Fig. 2F). The same result was
observed with PFBs([68]Fig. 2B, E, H, K and N). These results showed
that SKPs could differentiate into fibroblasts, which is consistent
with previous studies [[69]6–[70]8].
Fig 1. Morphology of SKPs and fibroblasts.
[71]Fig 1
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(A) SKPs exhibited sphere-like structure in suspension culture.(B) PFBs
were typically stellate or spindle shaped.(C) SFBs had the same
morphology as PFBs.
Fig 2. Characterization of SKPs and SFBs by immunocytochemistry.
[73]Fig 2
[74]Open in a new tab
SKP spheres expressed SOX2(A), nestin(D), vimentin(G) and
fibronectin(J), did not express collagen 1(M); SFBs expressed
vimentin(I), fibronectin(L) and collagen 1(O), did not express SOX2(C)
or nestin(F). The same results were observed with PFBs(B, E, H, K and
N). Scale bars, 25 μm(A-C, J-L); 50 μm(D-I, M-O).
Quality Assessment of Reads and Statistics of Alignment
To try to look into potential regulatory mechanisms that drive SKPs
differentiate to SFBs, we performed RNA-Seq to measure RNA profiles in
SKPs and SFBs. More than 20 million raw reads were generated from the
SKPs or SFBs library. After filtering the only adaptor sequences, those
containing N sequences and low quality sequences, the two RNA-Seq
libraries still generated over 19 million clean reads from each
library. The percentage of clean reads among raw tags in each library
ranged from 95.95% to 98.38%([75]Fig. 3). Of the total reads, more than
84% matched to the mouse genome. The remaining sequences were
unmatched([76]Table 2), because only reads aligning entirely inside
exonic regions could be matched(reads from exon-exon junction regions
could not be matched).
Fig 3. Classification of total raw reads.
[77]Fig 3
[78]Open in a new tab
After filtering the only adaptor sequences, containing N sequences and
low quality sequences, the RNA-Seq libraries of SKPs and SFBs generated
over 19 million clean reads each, and the percentage of clean reads
among raw tags in each library ranged from 95.95% to 98.38%.
Table 2. Summary of mapping results(mapping to reference genes).
Sample ID Total Reads Total Base Pairs Total Mapped Reads Perfect Match
< = 2bp Mismatch Unique Match Multi-position Match Total Unmapped Reads
FB_RNA 22184723(100.00%) 1087051427(100.00%) 18688411(84.24%)
15753372(71.01%) 2935039(13.23%) 14105047(63.58%) 4583364(20.66%)
3496312(15.76%)
SKP_RNA 19991830(100.00%) 979599327(100.00%) 17234956(86.21%%)
14226186(71.16%) 3008770(15.05%) 12832756(64.19%) 4402201(22.02%)
2756873(13.79%)
[79]Open in a new tab
Analysis of DEGs of SKPs and SFBs
A total of 1971 genes were differentially expressed between SKPs and
SFBs, with 747 genes up-regulated and 1224 down-regulated([80]Fig. 4).
Fig 4. DEGs between SKPs and SFBs.
Fig 4
[81]Open in a new tab
(A) The numbers of DEGs.(B) Scattered plot of DEGs.
GO and KEGG pathway Enrichment Analysis of DEGs
GO is an international standardized gene functional classification
system which offers a dynamic-updated controlled vocabulary and a
strictly defined concept to comprehensively describe properties of
genes and their products in any organism. GO covers three domains:
cellular component, molecular function and biological process. The
basic unit of GO is GO-term. Every GO-term belongs to a type of
ontology. GO enrichment analysis provides all GO terms that are
significantly enriched in DEGs compared to the genome background, and
filters the DEGs that correspond to biological functions. In this
study, 1971 DEGs could be categorized into 51 functional
groups([82]Fig. 5). In the three main domains(biological process,
cellular component and molecular function) of the GO classification,
28, 10 and 13 functional groups were identified, respectively. Among
these groups, the terms cellular process, metabolic process and
regulation of biological process in the biological process, the cell,
cell part and organelle in the cellular component, the binding,
catalytic activity and molecular transducer activity in the molecular
function were dominant. Genes related to cell differentiation, cell
proliferation, protein binding, transporter activity and membrane were
also significantly enriched. The top five most up-regulated and
down-regulated genes involved in these terms are listed in [83]Table 3.
Genes usually interact with each other to play roles in certain
biological functions. Pathway-based analysis helps to further
understand DEGs biological functions. KEGG pathway enrichment analysis
identifies significantly enriched metabolic pathways or signal
transduction pathways in DEGs compared with the whole genome
background. 51 pathways were identified to be significantly enriched in
DEGs between SKPs and SFBs([84]S1 Table). The most widely reported
pathways related to stem cell pluripotency and differentiation, such as
the TGF-β signaling pathway([85]S1 Fig.), Wnt signaling pathway([86]S2
Fig.) and Notch signaling pathway([87]S3 Fig.) were all included. The
main DEGs involved in these signaling pathways are listed in [88]Table
4.
Fig 5. GO functional classification(WEGO) of DEGs.
[89]Fig 5
[90]Open in a new tab
The results were summarized in three main domains: biological process,
cellular component and molecular function. In the three main domains,
28, 10 and 13 functional groups were identified respectively.
Table 3. Most up-regulated and down-regulated genes involved in important
terms about stem cells.
Term Up-regulated genes Down-regulated genes
cell differentiation Actg2, Wnt4, Myh11, Acta2, Tspan2 Slitrk1, Steap4,
Ivl, Klk6, Agtr2
cell proliferation Wisp2, Adra1d, Myocd, Thbs1, Tnfrsf11a Camp, Thbs4,
Msx1, Ace, IL15
protein binding Wisp2, Myh11, Npas4, Wnt4, Cnn1 Lcn2, Thbs4, Stfa3,
Agtr2, Sprr1b
transporter activity Gabra4, Trpc6, Accn3, Slc13a5, Lrp2 Slc14a1,
Atp6v0d2, Kcne1l, Clic5, Dmrt2
membrane Slc13a5, Gal3st1, Wnt4, Lrp2, Adra1d S100a8, Slitrk1,
Atp6v0d2, Cd79b, Cidec
[91]Open in a new tab
Table 4. List of possible signaling pathways and major DEGs involved in these
pathways.
Signaling pathway Up-regulated DEGs Down-regulated DEGs
TGF-β signaling pathway Gdf6, Tgfb3, Inhb, Bmpr2, Acvr2a, Smad6/9, Myc,
Id3/4 Bmp2/4/6/7, Bmpr1, Tgfb1, Tgfbr1, Smad3, Cdkn2, Id2
Wnt signaling pathway Wnt4, Wnt2b, Wnt9a, C-myc Wnt7b, Wnt2, Wnt6,
Wnt11, Fzd10, Fzd4, C-jun, Fra-1, Cyc-d
Notch signaling pathway Megf6, Dner Dlk1, Notch4, Sned1, Megf11,
Notch3, Notch1, Notch2, Dtx1, Dtx4, Rbpj
[92]Open in a new tab
qRT-PCR for Data Validation
Main DEGs involved in cell differentiation and important signaling
pathways were selected to verify the RNA-seq data by qRT-PCR. Pearson
correlation coefficient between qRT-PCR data and RNA-Seq data was
0.960, which indicates that the RNA-Seq data is highly correlated with
the qRT-PCR data([93]Fig. 6). PFBs were also tested by qRT-PCR. There
was no significant difference in the expression of candidate genes
between SFBs and PFBs except Dlk1([94]Fig. 7). Although the expression
level of Dlk1 between SFBs and PFBs was significantly different by
qRT-PCR, it was down-regulated when compared with SKPs, which was
consistent with the results from RNA-Seq. These results confirmed that
RNA-Seq could provide reliable data for mRNA differential expression
analysis.
Fig 6. Correlation between RNA-Seq and qRT-PCR data of selected genes.
Fig 6
[95]Open in a new tab
Pearson correlation coefficient(r = 0.960) was used to determine the
similarity in gene expression pattern between RNA-Seq and qRT-PCR.
Fig 7. Validation of RNA-Seq results and comparison of gene expression
between SFBs and PFBs by qRT-PCR.
[96]Fig 7
[97]Open in a new tab
(A) Down-regulated genes.(B) Up-regulated genes. Fold changes shown
are((SFBs or PFBs gene expression level)/(SKPs gene expression level)).
Error bars represent SE; * represents statistically significant.
Disscussion
In this study we compared the transcriptional profiles of mouse SKPs
and SFBs by RNA-Seq. Up to 1971 genes were found to be significantly
differentially expressed, which was a much larger number than the
finding by microarray [[98]6]. The results suggest that RNA-Seq is a
very sensitive tool to compare the gene expression between cells. GO
analysis of the DEGs showed that these DEGs were significantly enriched
in cell membrane and in the process of cell differentiation, cell
proliferation, protein binding and transporter activity. Analysis of
DEGs that are significantly enriched and involved in the above terms
could help us identify the important candidate genes which might play
important roles in the transition from SKPs to fibroblasts. Analysis of
genes coding for cell membrane may also help us find the surface makers
of SKPs and fibroblasts.
Among the listed up-regulated genes in [99]table 3, the genes which
encode WNT4 and WISP2 are both evidence of Wnt signaling activation
[[100]22–[101]23]. The up-regulation of Wnt4 and Wisp2 indicates that
the activation of the Wnt signaling pathway plays an important role in
the transition from SKPs to fibroblasts. Thrombospondin-1(TSP-1), from
the cell proliferation term, is an endogenous activator of TGF-β. TSP-1
has been reported to increase and decrease in parallel with that of
TGF-β1 and collagen III [[102]24]. TSP-1 also activates protein kinase
B and decreases apoptotic signaling in suspended fibroblasts [[103]25].
The up-regulation of the gene Tsp1 indicates that it might help in
inducing SKPs to differentiate into fibroblasts through the TGF-β
signaling pathway by increasing the proliferation of SKPs.
Among the listed down-regulated genes, Ivl is related to epidermal cell
differentiation and the protein encoded by it is synthesized in
abundance during terminal differentiation of keratinocytes. ATP6V0D2
has been shown to be a regulator of osteoclast fusion and bone
formation. The knock out of Atp6v0d2 resulted in impaired osteoclast
fusion and increased bone formation [[104]26]. The down-regulation of
these genes is related to keratinocytes and osteocytes, which indicates
that the transition from SKPs to fibroblasts needs the inhibition of
the genes that could lead to the differentiation to other cells. Many
down-regulated genes have been reported to encode for functions in the
neural system. The Msx1 homeobox gene is expressed at diverse sites of
epithelial-mesenchymal interaction during vertebrate embryogenesis, and
has been implicated in signaling processes between tissue layers. It
has a critical role in mediating epithelial-mesenchymal interactions
during craniofacial bone and tooth development [[105]27]. The
down-regulation of Msx1 may increase mesenchymal differentiation.
Slitrk1 encodes a transmembrane protein containing leucine-rich repeats
that is produced predominantly in the nervous system. Previous work has
shown that Slitrk1-deficient mice display elevated anxiety-like
behavior and noradrenergic abnormalities [[106]28]. KLK6 plays a
functional role in oligodendrocyte development and the expression of
myelin proteins [[107]29]. AGTR2 regulates central nervous system
functions, including behavior [[108]30]. It also plays a role in the
central nervous system and cardiovascular functions that are mediated
by the renin-angiotensin system [[109]31]. IL-15 is reported to be a
key regulator of neurogenesis in the adult and is essential to
understanding diseases with an inflammatory component [[110]32]. The
expression pattern of mouse Kcne1l in the developing embryo revealed a
strong signal in ganglia, in the migrating neural crest cells of
cranial nerves, in the somites, and in the myoepicardial layer of the
heart. It was reported that KCNE1L could be involved in the development
of some neurological signs observed in patients with AMME contiguous
gene syndrome [[111]33]. Inhibiting the expression of the
down-regulated genes which encode the above proteins, which are related
to the neural system, may induce SKPs to differentiate into mesodermal
lineages rather than neural cells. Notch modulator THBS4 helps in
rodent subventricular zone astrogenesis following injury [[112]34]. The
down-regulation of Thbs4 suggests that the inhibition of the Notch
signaling pathway may be involved in the process of transition from
SKPs to fibroblasts.
Integration of extrinsic signals, epigenetic regulators, and intrinsic
transcription factors underlies the capacity of stem cells to undergo
differentiation. Differentiation is controlled by complex signaling
networks. KEGG analysis of the DEGs showed that the major developmental
signaling pathways including TGF-β signaling pathway, Wnt signaling
pathway and Notch signaling pathway were all significantly enriched.
These pathways were reported to be involved in the pluripotency and
self-renewal of various stem cells.
The TGF-β signaling pathway is a series of molecular signals initiated
by the binding of an extracellular ligand to a TGF-β receptor on the
surface of a target cell, and ending with regulation of a downstream
cellular process. The TGF-β superfamily comprises nearly 30 growth and
differentiation factors that include TGF-βs, activins, inhibins, and
bone morphogenetic proteins(BMPs). TGF-β inhibits proliferation of
multipotent hematopoietic progenitors and promotes lineage commitment
of neural precursors. BMPs block neural differentiation of mouse and
human embryonic stem cells(ESCs) [[113]35]. The TGF-β signaling pathway
takes part in the differentiation process of various stem cells. Master
differentiation genes in ESCs are secluded by repressive chromatin
marks. TRIM33-Smad2/3 and Smad4-Smad2/3 complexes mediated TGF-β
signals enable the transcriptional activation of ESCs [[114]36]. Small
interfering RNA experiments proved that TGF-β1 signaling through Smad2
and Smad3 plays an important role in the development of smooth muscle
cells from totipotential ESCs [[115]37]. TGF-β1 also suppresses ESC
chondrogenic induction [[116]38]. Canonical TGF-β signaling via Smad4
regulates the balance between proliferation and differentiation of
neural stem cells in the midbrain [[117]39]. The activation of
TGF-β/BMP signaling pathway drives mesenchymal stem cells to
differentiate into osteoblasts [[118]40]. It also plays a crucial role
in osteoblast differentiation of the adipose-derived stem cells. The
members which are involved in the process include Smad 1, Smad5, Smad8,
P38, ASK1, MKK3, MKK6, Runx2, collagen type 1, and osteopontin
[[119]41]. The TGF-β signaling pathway regulates the differentiation
and proliferation of stem cells in different stages. In this study, The
down-regulation of the gene Smad3 led to up-regulation of transcription
factors MYC and down-regulation of CDKN2 and ID2, which may be
important in the differentiation from SKPs to fibroblasts. The Wnt
signaling pathway was also reported to regulate the proliferation and
differentiation of embryonic stem cells [[120]42] and various kinds of
adult stem cells [[121]43–[122]46]. In the Wnt signaling pathway, the
up-regulated ligands WNT7B, WNT2, WNT6, WNT11 and the down-regulated
WNT4 and WNT2B bound to their receptors, and led to the up-regulation
of C-myc and down-regulation of C-jun, Fra-1 and Cyc-d, which might
also contribute to the differentiation of SKPs into fibroblasts. Notch
signaling exhibits dynamic expression characteristics in bone
marrow-derived mesenchymal stem cells during the process of
differentiation into hepatocytes. It was found to be necessary to
initiate differentiation into hepatocytes, but must be down-regulated
for the differentiation to proceed continuously [[123]47]. It has been
shown that low adipogenic clones had significantly higher mRNA
expression levels of Notch2, Notch3 and Notch4, Jagged1, as well as
Delta1, compared with those of high adipogenic clones. This indicated
that the activation of Notch signaling inhibited the adipogenic
differentiation of adipose-derived mesenchymal stem cell clones
[[124]48]. Most of the DEGs involved in the Notch signaling pathway,
including the genes encoding ligands, receptors and transcription
factors, were down-regulated. These data suggest that the Notch
signaling pathway plays an important role in keeping SKPs quiescent and
the inhibition of this signaling pathway may activate SKPs to
differentiate.
In conclusion, we compared the transcriptional profiles between SKPs
and SFBs by RNA-Seq. GO analysis of the DEGs showed that the
significantly up-regulated genes Wnt4, Wisp2 and Tsp-1 and the
significantly down-regulated genes Slitrk1, Klk6, Agtr2, Ivl, Msx1,
IL15, Atp6v0d2, Kcne1l and Thbs4 might play important roles in the
transition of SKPs to fibroblasts. KEGG analysis showed that DEGs were
significantly enriched in the TGF-β signaling pathway, Wnt signaling
pathway and Notch signaling pathway, which have been previously shown
to regulate the differentiation and self-renewal of various stem cells.
These identified DEGs and pathways could facilitate further
investigations of the detailed molecular mechanisms, making it possible
to take advantage of the potential therapeutic applications of SKPs in
skin regeneration.
Supporting Information
S1 Fig. The detailed information of TGF-β signaling pathway.
Up-regulated genes are marked with red borders and down-regulated genes
with green borders.
(TIF)
[125]Click here for additional data file.^ (1MB, tif)
S2 Fig. The detailed information of Wnt signaling pathway.
Up-regulated genes are marked with red borders and down-regulated genes
with green borders.
(TIF)
[126]Click here for additional data file.^ (1.1MB, tif)
S3 Fig. The detailed information of Notch signaling pathway.
Up-regulated genes are marked with red borders and down-regulated genes
with green borders.
(TIF)
[127]Click here for additional data file.^ (494.7KB, tif)
S1 Table. KEGG pathway enrichment analysis of DEGs.
(DOCX)
[128]Click here for additional data file.^ (32.3KB, docx)
Funding Statement
This work was supported by the National Natural Science Foundation of
China (81071312), ([129]http://www.nsfc.gov.cn/). The funders had no
role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
References