Abstract
Background
Airway epithelial cells provide a protective barrier against
environmental particles including potential pathogens. Epithelial
repair in response to tissue damage is abnormal in asthmatic airway
epithelium in comparison to the repair of normal epithelium after
damage. The complex mechanisms coordinating the regulation of the
processes involved in wound repair requires the phased expression of
networks of genes. Small non-coding RNA molecules termed microRNAs
(miRNAs) play a critical role in such coordinated regulation of gene
expression. We aimed to establish if the phased expression of specific
miRNAs is correlated with the repair of mechanically induced damage to
the epithelium.
Methods
To investigate the possible involvement of miRNA in epithelial repair,
we analyzed miRNA expression profiles during epithelial repair in a
cell culture model using TaqMan-based quantitative real-time PCR in a
TaqMan Low Density Array format. The expression of 754 miRNA genes at
seven time points in a 48-hour period during the wound repair process
was profiled using the bronchial epithelial cell line 16HBE14o^-
growing in monolayer.
Results
The expression levels of numerous miRNAs were found to be altered
during the wound repair process. These miRNA genes were clustered into
3 different patterns of expression that correlate with the further
regulation of several biological pathways involved in wound repair.
Moreover, it was observed that expression of some miRNA genes were
significantly altered only at one time point, indicating their
involvement in a specific stage of the epithelial wound repair.
Conclusions
In summary, miRNA expression is modulated during the normal repair
processes in airway epithelium in vitro suggesting a potential role in
regulation of wound repair.
Keywords: Epithelial cells, Wound repair, miRNA, Profiling, Cluster
analysis, Pathway analysis
Background
The airway epithelium has been recognized to play a central role in the
integration of innate and adaptive immune responses [[28]1-[29]4]. The
airway epithelium is also crucial to the origin and progression of
respiratory disorders such as asthma, chronic obstructive pulmonary
disease, cystic fibrosis and pulmonary fibrosis. In asthma, chronic
airway inflammation underlies aberrant repair of the airway that
subsequently leads to structural and functional changes in the airway
wall. This remodeling is responsible for a number of the clinical
characteristics of asthmatic patients.
Normal epithelial repair occurs in a series of overlapping stages.
Damage to the epithelium or challenge associated with damage can result
in loss of structural integrity or barrier function and local mucosal
activation [[30]5]. Studies in animals have shown that the repair of
normal airway epithelium after minor damage involves the migration of
the remaining epithelial cells to cover the damaged area. This is a
rapid process, suggesting an autonomous response by cells in the
vicinity of the damage [[31]6]. It includes an acute inflammatory
response, with recruitment of immune cells as well as epithelial
spreading and migration stimulated by secreted provisional matrix. Once
the barrier is reformed, the differentiated characteristics are then
restored. The regulation of these processes require complex sequential
changes in the epithelial cell biology driven by the phased expression
of networks of genes [[32]7].
One biological mechanism that plays a critical role in the coordinate
regulation of gene expression such as that required during epithelial
wound repair is the expression of small non-coding RNA molecules termed
microRNAs (miRNAs) [[33]8]. To date, more than 1000 human miRNAs have
been identified [[34]http://microrna.sanger.ac.uk], with documented
tissue-specific expression of some of these miRNAs in lung and
involvement in the development of lung diseases including lung cancer,
asthma and fibrosis [[35]9-[36]15]. MiRNAs have been demonstrated to
play a crucial role in epithelial cell proliferation and
differentiation [[37]16-[38]18]. The expression in lung epithelium of
Dicer, the enzyme responsible for processing of miRNA precursors, is
essential for lung morphogenesis [[39]16] and there is differential
expression of miRNAs during lung development [[40]17]. Furthermore,
transgenic over-expression of miR-17-92 (shown to be over-expressed in
lung cancer) in the lung epithelium promotes proliferation and inhibits
differentiation of lung epithelial progenitor cells [[41]18]. Recently,
it has been reported that miRNA-146a modulates survival of bronchial
epithelial cells in response to cytokine-induced apoptosis [[42]19]. In
experimental studies, mice lacking miR-155 demonstrated autoimmune
phenotypes in the lungs with increased airway remodeling and leukocyte
invasion, phenotypes similar to those observed in asthma
[[43]20,[44]21].
While a number of studies have examined the role of miRNA in lung
development and in disease [[45]9-[46]15], their influence on the
regulation of gene expression involved in epithelial wound repair
remains unresolved and comprehensive studies on miRNA involvement in
epithelial repair and the pathogenesis of airway remodeling are
lacking. However in the skin, miRNAs were found to play a crucial role
in wound closure by controlling migration and proliferation of
keratinocytes in an in vitro model of wound repair [[47]22].
Thus the hypothesis of the study was that the stages of wound repair in
respiratory epithelium are regulated by the phased expression of
specific miRNA species. The aim was to investigate the possible
involvement of miRNAs by examining their expression profile in
epithelial repair in a cell culture model. Understanding the effect of
altered miRNA activity on protein expression during repair processes
can be further used to identify pathways targeted by miRNAs that
regulate epithelial wound repair, potentially providing a novel
therapeutic strategy for asthma and other respiratory diseases with
underlying aberrant epithelial wound repair.
Methods
Cell culture and wounding assays
The 16HBE14o- bronchial epithelial cell line was cultured under
standard conditions [[48]23]. For the wounding assay, cells were seeded
on 6-well plates at the initial density of 3x10^5 cells and cultured
until confluent. Forty eight hours after reaching full confluence cells
were damaged by scraping off the monolayer with a hatch-cross wounding
pattern using a P200 Gilson pipette tip. After that, the medium and
cell debris were removed by pipetting off the medium and 2 ml of fresh
serum-containing medium was added to the remaining cells. For all
experiments, at least two points of reference per well of a 6-well
plate were used for post-injury analyses. Several time-lapse
experiments were performed to establish consistent experimental
conditions and the timing of the stages of wound repair.
Time lapse microscopy
Time lapse images were captured at 15 minute intervals on a Leica DM
IRB phase-contrast inverted microscope (Leica; Milton Keynes, UK) in a
chamber maintained at 36 ± 1°C and 5% CO[2] atmosphere. The images were
collected with a cooled Hamamatsu ORCA digital camera (Hamamatsu
Photonics, Welwyn Garden City, UK) connected to a computer running
Cell^P software (Olympus, London, UK) for 30 hours (ensuring complete
wound closure is included in the time course). For quantitative
analysis of the area of damage and hence ongoing repair in time lapse
serial images ImageJ software [[49]24] was used.
RNA isolation
RNA isolation was performed with the use of an Exiqon RNA isolation
kit. Samples were collected in triplicate for each of the following
time points: baseline, 2, 4, 8, 16, 24 and 48 hours after wounding. RNA
isolation was performed according to the manufacturer’s protocol from
6-well plates (9.5 cm^2 of growth area) and the amount of starting
material was 1×10^6 cells per well. Samples were frozen at -70°C for
subsequent use in microarray experiments.
RNA quality control
The concentration of total RNA in each sample was determined using a
NanoDrop 1000 spectrophotometer. The integrity of total RNA extracted
was assessed by a Lab901 Gene Tools System. The passing criteria for
use in microarrays was: sample volume of 10–30 μl, RNA
concentration > 50 ng/μl, SDV ≤ 3.1 (ScreenTape Degradation Value),
which corresponds to RIN ≥ 9.0, purity: OD 260/280 > 1.7, OD
260/230 > 1.4.
Micro RNA profiling
Micro-RNA expression profiling of bronchial epithelial cells was
performed in three replicates per time point following wounding. TaqMan
Array Human MicroRNA Card A and B (Applied Biosystems) (based on Sanger
miRBase 9.2) was utilised for specific amplification and detection of
754 mature human miRNAs by TaqMan-based quantitative real-time PCR in a
TaqMan Low Density Array format (TLDA) using TaqMan MicroRNA Reverse
Transcription Kit and Megaplex RT Primers (Human Megaplex™ RT Primers
Pool A and B). The resulting cDNA combined with TaqMan Universal PCR
Master Mix, No AmpErase UNG was loaded into the arrays and TaqMan
real-time PCR was performed using the 7900HT Fast Real-Time PCR System
(Applied Biosystems) according to the manufacturer’s protocol.
Raw data were obtained using SDS 2.3 software (Applied Biosystems). All
SDS files were analyzed utilizing the RQ Manager 1.2 software (Applied
Biosystems). The comparative analysis of miRNA expression datasets
between baseline and each time point following the wounding assay was
performed using DataAssist software v.3.01 (Applied Biosystems). The
comparative CT method [[50]25] was used for calculating relative
quantitation of gene expression after removing outliers with use of
Grubbs’ outlier test together with a heuristic rule to remove the
outlier among technical replicates and data normalization was based on
the endogenous control gene expression method (U6 snRNA-001973) where
the mean of the selected endogenous control was used to normalize the
Ct value of each sample.
The data from miRNA profiling have been deposited in ArrayExpress
database (accession no. E-MEXP-3986).
Cluster analysis
To identify the clusters of miRNAs following the same expression
profile over time, we performed cluster analysis using STEM (Short Time
series Expression Miner) software available at:
[51]http://www.cs.cmu.edu/~jernst/stem[[52]26].
Target genes and pathways prediction
To identify potential common biological pathways for miRNAs showing
similar expression profiles in cluster analysis, we performed pathway
enrichment analysis. The best predicted candidate mRNA genes for each
differentially expressed miRNA were identified using the miRNA BodyMap
tool available at: [53]http://www.mirnabodymap.org. The tool enables
the selection of target genes based on the use of several prediction
algorithms at a time: DIANA, PITA, TargetScan, RNA22 (3UTR), RNA22
(5UTR), TargetScan_cons, MicroCosm, miRDB, RNA22 (5UTR), TarBase and
miRecords. To minimize the target prediction noise, only target genes
predicted by five or more of the prediction algorithms mentioned above
were included.
The lists with predicted target genes were then analysed with use of
The Database for Annotation, Visualization and Integrated Discovery
(DAVID) v.6.7 [[54]27,[55]28] to identify BioCarta & KEGG pathways
[[56]29,[57]30] enriched functional-related gene groups and biological
themes, particularly gene ontology (GO) terms [[58]31] in which the
analysed sets of target genes were statistically the most
overrepresented (enriched).
Statistics
The statistics applied by Data Assist software for each sample included
calculation of the relative quantification (RQ) = 2 (-ΔCt)/2(-ΔCt
reference). The standard deviation (SD) was calculated for CT values of
each of the three technical replicates and was used to calculate the RQ
Min and RQ Max [RQ Min = 2(-ΔCt – SD)/2(-ΔCt reference), RQ Max = 2
(-ΔCt + SD)/2(-ΔCt reference)]. Pearson's product moment correlation
coefficient (r) was calculated for CT or ΔCT values of sample pairs as
below and plotted on the Signal Correlation Plot and Scatter Plot
respectively.
[MATH: r=NΣXY-ΣXΣYNΣX2-
ΣX2NΣY2-
ΣY2<
/mrow> :MATH]
Distances between samples and assays were calculated for hierarchical
clustering based on the ΔCT values using Pearson’s correlation or the
Eucidian distance calculated as follows
[[59]https://products.appliedbiosystems.com]. For a sample pair, the
Pearson's product moment correlation coefficient (r) was calculated
considering all ΔCT values from all assays, and the distance defined as
1 – r. For an assay pair, r was calculated considering all ΔCT values
from all samples and the distance defined as 1 – r. Euclidean Distance
calculated as
[MATH: ΣΔCTi-ΔCTj2
:MATH]
where, for a sample pair, the calculation is done across all assays for
sample i and sample j while for an assay pair, the calculation is done
across all samples for assay i and assay j.
Results
Characterisation of epithelial wound repair model
To analyse the changes in miRNA expression profile during epithelial
wound repair, we used a previously well established in vitro model
mimicking this process [[60]23,[61]32-[62]34], that allowed real-time
monitoring of the rate of epithelial repair and quantitative analysis
using time-lapse microscopy. The following time points were selected
for miRNA expression profile analysis: baseline immediately before
wounding. (A) 2 hours after wounding: cells adjacent to the wound
initiate a response but have not migrated substantially. (B) 4 hours
after wounding: 25% of the original wound area has been covered by
cells. (C) 8 hours after wounding: 50% of wounded area covered by
cells. (D) 16 hours after wounding: wounded area completely covered by
cells. Once the wound is covered cell proliferation and
re-differentiation may still be in progress so additional time points
were added. (E) 24 hours post-wounding (F) 48 hours after wounding
(Figure [63]1). With the exception of cells damaged during the original
mechanical wounding, cell death was not seen in the repairing areas by
time lapse microscopy.
Figure 1.
Figure 1
[64]Open in a new tab
Stages of wound repair at different time points (A – 2 hrs, B – 4 hrs,
C – 8 hrs, D – 16 hrs, E – 24 hrs, F – 48 hrs post wounding), n=3 wells
for each time point.
Global miRNA expression profile altered during epithelial wound repair
Expression profiling analysis revealed a large number of mature miRNAs
that were modulated at different time points during epithelial repair
with a fold change above 2.0 (Table [65]1). Numerous miRNAs showed
significantly increased or decreased expression (>10-fold) at different
time points as compared to baseline (presented in Additional file
[66]1). Based on the high fold change values at different time points,
ten miRNAs were found to undergo a significant modulation (both up- or
down-regulation) at five or more of the seven time points analysed
(Additional file [67]2). We also observed that the alterations in
expression of some miRNA genes were limited to a single time point of
wound repair, whereas at the other time points the expression levels
did not differ much from the baseline, suggesting their involvement at
a particular stage of repair (marked in red in Additional file [68]1).
Table 1.
Number of miRNAs with >2.0-fold change in expression at different time
points after wounding
Time point (post wounding)
Mode of miRNA alteration
__________________________________________________________________
2 hrs
__________________________________________________________________
4 hrs
__________________________________________________________________
8 hrs
__________________________________________________________________
16 hrs
__________________________________________________________________
24 hrs
__________________________________________________________________
48 hrs
__________________________________________________________________
Upregulated
__________________________________________________________________
70
__________________________________________________________________
128
__________________________________________________________________
85
__________________________________________________________________
37
__________________________________________________________________
35
__________________________________________________________________
252
__________________________________________________________________
Downregulated 57 54 80 165 136 23
[69]Open in a new tab
Cluster analysis
We then hypothesized that, given the number of miRNA genes undergoing
significant changes during the epithelial repair process, a common
expression profile might be shared by miRNAs whose expression is
regulated by particular transcriptional activation pathways. Therefore,
we analysed the expression of miRNA genes with use of the clustering
algorithm STEM [[70]26], assigning each gene passing the filtering
criteria to the model profile that most closely matches the gene's
expression profile as determined by the correlation coefficient. Since
the model profiles are selected by the software by random allocation,
independent of the data, the algorithm then determines which profiles
have a statistically significant higher number of genes assigned using
a permutation test. It then uses standard hypothesis testing to
determine which model profiles have significantly more genes assigned
as compared to the average number of genes assigned to the model
profile in the permutation runs. Our cluster analysis revealed three
significant miRNA expression profiles (16, 1 and 18) over 48 hours of
wound repair (Figures [71]2, [72]3 and [73]4).
Figure 2.
Figure 2
[74]Open in a new tab
Profile 16 of miRNA with similar expression pattern during wound
repair.
Figure 3.
Figure 3
[75]Open in a new tab
Profile 1 of miRNA with similar expression pattern during wound repair.
Figure 4.
Figure 4
[76]Open in a new tab
Profile 18 of miRNA with similar expression pattern during wound
repair.
Profile 16 included genes that gradually increase between 2 and
16 hours and then display a sudden drop in expression 16 hours
post-wounding, which corresponded to the completion of cell
proliferation and the restoration of the monolayer after wounding in
time lapse observations. Profile 1 was characterized by significant
decrease of miRNA expression 4 hours after wounding followed by a
significant increase with a maximum 16 hours post-wounding, suggesting
induction of transcription of genes involved in the early response to
stress due to mechanical cell damage which are subsequently switched
off. Profile 18 shared some similarities with profile 1, although it
showed a more gradual decrease in miRNA expression 4 hours
post-wounding, that then increased steadily to reach a maximum at
16 hours and afterwards gradually decreased. The different profiles of
miRNA expression are shown in Figures [77]2, [78]3 and [79]4. The miRNA
genes sharing the common expression pattern during epithelial wound
repair are listed in Additional file [80]3.
Identification of biological processes regulated by miRNAs
Pathways in clusters of miRNAs
The next question to be addressed was if miRNA clusters of
characteristic expression profile during epithelial wound repair
identified using STEM were regulating target genes from the same
biological pathways or processes. To analyse this, we used highly
predicted miRNA targets (mRNAs confirmed to be a target of specific
miRNA by at least five different algorithms) to create a list of
potential miRNA target genes, which were then analysed utilising the
DAVID online database for annotation and visualization [[81]27,[82]28].
The use of DAVID enabled the integration of the miRNA target genes into
common pathways or GO processes. Analysis of targets predicted for each
miRNA expression cluster generated by STEM enabled us to predict four
significantly enriched pathways for profile 16, including the
neurotrophin signalling pathway, ERBB signalling pathway, MAPK
signalling pathway and the RIG-I-like receptor signalling pathway. Six
pathways were predicted for the targets of miRNAs demonstrating
expression in profile 1: adherence junction, acute myeloid leukaemia,
small lung cancer, cell cycle, pathways in cancer and the chemokine
signalling pathway. No common pathways were predicted for profile 18.
The predicted pathways are shown in Additional files [83]4 and [84]5.
For all the profiles, DAVID also predicted numerous biological
processes where miRNAs targets play a significant role (see Additional
file [85]6). In general, predicted biological processes and pathways
were mainly associated with cell cycle regulation and induction of
mitotic divisions, switching on anti-apoptotic genes (ECM, PKB/Akt and
IKK) and genes stimulating proliferation (such as MEK, PPARγ) that are
of known importance in epithelial wound repair. Apart from well
documented biological processes, we also observed that, surprisingly,
the most significantly overrepresented were target genes involved in
the neurotrophin signalling pathway which suggests its importance in
epithelial wound repair process (Additional file [86]7).
Target pathways at different stages of wound repair
To identify the most important pathways involved at different stages of
epithelial wound repair in vitro we also performed pathway enrichment
of miRNAs significantly altered only at one time point of wound repair
(see Additional file [87]1, genes in red). For those genes, targets
were predicted as above and DAVID was used to identify potential
pathways and biological processes. The main observation for epithelial
cells in the early phase of repair (2 hours post-wounding) were miRNAs
being up-regulated, suggesting switching off target genes and processes
associated with response to cellular stress (MAP kinase pathway),
regulation of actin cytoskeleton, cell proliferation and migration. The
main pathways targeted by up-regulated miRNAs identified for the repair
4 hours after cell damage included genes involved in negative
regulation of transcription, RNA metabolism, regulation of cell motion
and the cytoskeleton. The most important processes at 8 hours after
wounding involved a number of up-regulated miRNAs at this time point
and indicating the switching off of genes involved in negative
regulation of gene expression and negative regulation of cell
communication. At 16 hours following epithelial cell wounding we
observed a number of miRNA genes that were down regulated and,
therefore, switching on genes involved in mitotic cell cycle, negative
regulation of cell death, cell proliferation, ERBB signalling pathway
(cell proliferation, survival, migration). This may suggest the
predominance of a proliferating phenotype of cells after the damaged
area was closed by spreading and migrating cells. After 24 hours
post-wounding we observed further down regulation of miRNA genes. Two
were of particular interest as they are responsible for switching on
genes involved in p53 signalling pathway (cell cycle arrest), IL-10
(anti-inflammatory response), regulation of apoptosis, cell death, RNA
transport and localization. This indicates that at this time point
cells have proliferated sufficiently and are beginning to
differentiate. At 48 hours after wounding, we observed mainly up
regulation of miRNA genes responsible for silencing genes involved in
protein catabolic processes, alternative splicing, spectrins, mRNA
splicing and processing as well as methylation indicating that cells
are undergoing physiological processes and restoring a normal
phenotype.
Discussion
The main finding of this study is the involvement of multiple miRNA
genes in the process of epithelial wound repair in vitro. We found
three distinct expression patterns of miRNA genes clusters that are
predicted to further regulate numerous pathways and biological
processes involved in wound repair. We have applied here for the first
time the cluster analysis of time-series miRNA expression data (using
STEM) to identify basic patterns and predict pathways (using DAVID)
involved in repair processes of airway epithelium.
Such an approach has enabled us to identify common miRNA expression
profiles during wound repair giving comprehensive information about
activated miRNA genes. The relationships amongst these genes, their
regulation and coordination during wound repair over time can also be
explored. Further validation of individual protein, gene or miRNA
changes will be required in subsequent studies, but it seems clear that
specific expression profiles of clusters of miRNAs correlates with
repair of mechanically induced damage to the epithelium. For expression
profile 16 we demonstrated that, among other plausible signalling
pathways, the neurotrophin signalling pathway may be involved in wound
repair in epithelial cells, in addition to the inflammatory response in
airway epithelium in allergy and asthma as reported previously
[[88]35-[89]38]. The involvement in wound repair may further suggest
that this pathway is important in the regulation of airway remodelling
in asthma. Indeed, in the study by Kilic et al. [[90]39] it was
observed that blocking one of the neurotrophins, nerve growth factor
(NGF), prevented subepithelial fibrosis in a mouse model of asthma and
that NGF overexpression exerted a direct effect on collagen expression
in murine lung fibroblasts. The involvement of neurotrophins in repair
processes has been also confirmed recently by Palazzo et al. [[91]40]
in wound healing in dermal fibroblasts. Moreover, miRNAs involved in
this pathway such as the miR-200 family were reported to control
epithelial-mesenchymal transition (EMT) [[92]41], the process that is
suggested to underlie airway remodelling in asthma. In the recent study
of Ogawa et al. [[93]42] utilising a mouse model of asthma, it was
observed that mice challenged with house dust mite allergen exhibited
an increase in NGF that was primarily expressed in bronchial epithelium
and was positively correlated with airway hyperresponsiveness and
substance P-positive nerve fibers. However in this model siRNA targeted
NGF inhibited hyperresponsiveness and modulation of innervation but not
subepithelial fibrosis and allergic inflammation.
For expression profile 1 we observed a significant down-regulation at
the beginning of wound repair followed by sharp increase in miRNA
expression with a maximum at 16 hours after cell damage. This may
indicate the induction of the six pathways predicted by enrichment
analysis in the early phase of wound repair, which are then being
switched off by the miRNAs with increased expression up to 16 hours
post-wounding.
The process of wound repair in vivo in the airways involves cell
spreading and migration as the primary mechanisms in the first
12–24 hours after injury, while proliferation begins by 15–24 h and
continues for days to weeks. Similarly in our study we have confirmed
that epithelial wound repair in vitro mimics the in vivo situation but
in a shorter time frame, and that in its early stage this involves
spreading and migration of neighbouring epithelial cells to cover the
damaged area (2 and 4 hours after wounding). This is followed by
migration and proliferation of progenitor cells to restore cell numbers
(8 and 16 hours after cell damage) and differentiation to restore
function (24 and 48 hours post-wounding) (Figure [94]1)
[[95]43-[96]48].
Analysis of miRNAs involved at only specific time points of wound
repair revealed that during the early stages numerous miRNAs are being
significantly up-regulated, switching off pathways regulating cell
proliferation and differentiation and activating cellular stress
responses (chemokine signalling pathway) as well as cell migration and
cell death (corresponding to time points at 2, 4 and 8 hours after
injury). Furthermore, at later time points cells are undergoing
intensive proliferation and secreting extracellular matrix which is
supported by the involvement of ERBB signalling pathway and NFAT
pathway stimulating cell proliferation and the regulation of
transcription of immune genes (that corresponds to 16 hours after
injury). Once confluent, cells restore their phenotype so that the cell
cycle is arrested (inhibition of cell division) and differentiation
processes are switched on. In parallel to this, the IL-10
anti-inflammatory signalling pathway is induced to deactivate immune
cells stimulated during the early stages of wound repair.
Conclusions
In summary, we report here for the first time that expression of
multiple miRNAs is significantly altered during airway epithelium wound
repair processes. Different patterns of expression have been observed
and the target genes of those miRNA clusters coordinate several
biological pathways involved in the repair of injury. Our work provides
a starting point for a systematic analysis of mRNA targets specific for
wound repair. This will help to identify regulatory networks
controlling these processes in airway epithelium to better understand
their involvement in respiratory diseases.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AS performed in vitro cell experiments and wounding assays, miRNA
profiling, data analysis, cluster and pathway analysis, drafted the
paper and approved its final version. PL contributed to the study
design and methodology regarding cell experiments drafted the paper and
approved its final version. JWH contributed to the study design and
methodology regarding miRNA analysis, drafted the paper and approved
its final version.
Pre-publication history
The pre-publication history for this paper can be accessed here:
[97]http://www.biomedcentral.com/1471-2466/13/63/prepub
Supplementary Material
Additional file 1
List of significantly modulated mature miRNAs (>10.0-fold) and their
respective fold induction at each time point. * miRNAs with significant
change in expression at one time point only (marked in red).
[98]Click here for file^ (23.7KB, docx)
Additional file 2
Fold change of the top ten miRNAs undergoing significant modulation
(>10-fold) during wound repair process at, at least, five time points.
[99]Click here for file^ (14.6KB, docx)
Additional file 3
MiRNA genes assigned to each expression profile during wound repair
(values given for each time point represent expression change after
normalization in STEM software).
[100]Click here for file^ (22.3KB, docx)
Additional file 4
The significantly overrepresented pathways (enriched) in the analysed
sets of target genes of miRNAs included in the profile 16.
[101]Click here for file^ (194.2KB, jpeg)
Additional file 5
The significantly overrepresented pathways (enriched) in the analysed
sets of target genes of miRNAs included in the profile 1.
[102]Click here for file^ (164.4KB, jpeg)
Additional file 6
The most significant biological processes predicted using DAVID tool
undergoing regulation of miRNA target genes from the same expression
profile (processes were ranked based on their Fisher Exact Probability
value from the gene enrichment analysis to identify those showing
significant overrepresentation).
[103]Click here for file^ (21.7KB, docx)
Additional file 7
Neurotrophin signaling pathway with miRNA genes and their predicted
targets.
[104]Click here for file^ (152.6KB, jpeg)
Contributor Information
Aleksandra Szczepankiewicz, Email: alszczep@ump.edu.pl.
Peter M Lackie, Email: p.m.lackie@soton.ac.uk.
John W Holloway, Email: j.w.holloway@soton.ac.uk.
Acknowledgements