Supplementary information Materials and Methods
Construction and packaging of lentiviral vectors. A 500 bp human genomic fragment
encoding miR-335 (MIR335) was obtained by PCR from human genomic DNA
(Novagen) using the primers MIR335_2-F (5’- CTT GGA TCC GTG TAA CTG TGA
TTT AAG TC -3’) and MIR335_2-R (5’- CTT CTC GAG AGC CTA AGA GTT TTG
TTC T -3’).
To construct the lentiviral vector encoding miR-335 (pLV-EmGFP-MIR335) and
the control vector (pLV-EmGFP-miR-Mock), we cloned a synthetic polylinker
sequence containing restriction sites for BamHI, XhoI, and ApaI into the XbaI-SalI
restriction sites of the lentiviral vector pRRLsin18.PPT.CMV.GFP.Wpre (Manuscript
reference 23), to obtain the pRRL-LINK2 plasmid. We then cloned the SnaBI-XhoI
small fragment of plasmid pcDNA6.2-EmGFP-miR-mock (Invitrogen) into the
corresponding restriction sites of pRRL-LINK2, to obtain pLV-EmGFP-miR-Mock.
Finally, we cloned MIR335 into the BamHI-XhoI restriction sites of pLV-EmGFP-miRMock, to render pLV-EmGFP-MIR335.
Lentiviral supernatants were obtained by transient transfection of 293T cells as
previously described (1). The supernatants were filtered (0.22 m) to remove cellular
debris, and 1-ml aliquots were stored at -80ºC. Lentiviral stocks were titrated in HeLa
cells as described (1). For lentiviral transduction, bone marrow-derived hMSCs (5x105)
were seeded in 10 cm plates and, after one day in culture, incubated for 6 h with the
corresponding lentiviral supernatant at a multiplicity of infection (MOI) of 5, in the
presence of 8 g/ml Polybrene (Sigma-Aldrich). The medium was then replaced with
regular growth medium and cells were cultured for an additional 48 h, when gfp+
hMSC populations were isolated with a FACS Aria cell sorter (BD Biosciences). Only
cells with medium levels of gfp fluorescence intensity were selected (Fig. S3A). A cell
sorting purity of >97% was consistently achieved.
Microarray experiments. Agilent Human microRNA Microarray v2.0 (G4470B,
Agilent Technologies) was used to identify miRNAs expressed at relatively high level
in undifferentiated hMSCs. Total RNA (100 ng per sample) was hybridized to the
microarrays. We compared the miRNA expression profiles of undifferentiated hMSCs
with the same primary cell lines after 9 days of adipogenic or osteogenic induction, as
well as with skin fibroblasts. A total of four independent samples were used for each
condition, but in the case of adipogenic and osteogenic differentiation, the RNA
samples were combined into two pools (two samples/pool) before labeling. MicroRNA
labeling, hybridization and washing were carried out according to the manufacturer’s
instructions. Hybridized microarrays were scanned with a DNA microarray scanner
(Agilent G2565BA) and features were extracted using the Agilent Feature Extraction
(AFE) image analysis tool (version A.9.5.3) with default protocols and settings. Data
pre-processing and differential expression analysis of the microRNA and gene
expression data were done in R (2) using available Bioconductor packages
(http://www.bioconductor.com) (3).
The Agilent Whole Human Genome Microarray Kit (G4112F, Agilent
Technologies) was used to identify genes downregulated in hMSCs overexpressing
miR-335. Total RNA (1 g) from each sample was hybridized to the microarrays. We
compared the mRNA expression profiles of hMSCs transduced with the lentiviral
vector pLV-EmGFP-MIR335 with hMSCs transduced with the control vector pLVEmGFP-Mock. A total of three independent samples were used for each condition.
RNA labeling, hybridization and washing were carried out according to the
manufacturer’s instructions. Image acquisition, feature extraction, and signal
normalization were as described for miRNA microarrays.
MicroRNA microarray data analysis. Data pre-processing and differential expression
analysis were done using the Bioconductor AgiMicroRna package (4). The Total Gene
Signal provided by the Agilent Feature Extraction image analysis software was used for
data analysis. Data were normalized between arrays using the quantile method (5). The
image analysis software attached a flag to each feature that identifies different
quantification errors of the signal and can be used to filter out microRNAs data that do
not reach a minimum quality. This filtering was done after normalization of the Total
Gene Signal. The gIsGeneDetected filtering removes microRNAs not expressed in any
experimental condition, so that analysis was limited to those microRNA genes flagged
by Agilent Feature Extraction software as detected in at least one experimental
condition (gIsGeneDetected=1). For differential expression analysis, the AgiMicroRna
package incorporates the linear modeling features of the Bioconductor limma package
(6). Limma fits a linear model to the expression value for each gene, to assess the
significance of differential expression between different experimental conditions. In
addition, limma uses empirical Bayes methods (7) to construct moderated statistics and
incorporates statistical tools to adjust for the multiplicity of the tests. The Benjamini and
Hochberg’s method (8) was used to control for false discovery, and a false discovery
rate (fdr) of 0.15 was selected.
Gene expression microarray data analysis. Data were pre-processed with the
Bioconductor Agi4x44PreProcess package (9). The data were background corrected and
normalized between arrays in order to compensate for systematic technical differences
between chips. We selected the MeanSignal and the BGMedianSignal for the
foreground and background signals, respectively, from the collection of signals
provided by the Agilent Feature Extraction image analysis software. These signals were
used for background correction and normalization. First, we produced a Background
Subtracted Signal using the half option in Agi4x44Preprocess. According to this
method, background signal is subtracted from the foreground signal and any intensity
below 0.5 is reset to 0.5 to produce positive corrected intensities. After background
correction, data were normalized between arrays using the quantile method (5). An
offset equal to 50 was added to the intensities before log-transforming, so that the log
ratios are shrunk towards zero at the lower intensities. The AFE software attaches a flag
to each feature that identifies different quantification errors of the signal. These
quantification flags can be used to filter out signals that don’t reach a minimum
arbitrary criterion of quality. The data were filtered to 1) keep features within the
dynamic range of the scanner and 2) retain features of good quality. To keep features
within the dynamic range, we demanded that, for every replicated spot across the whole
set of samples, at least 75% of the replicated probes in at least one experimental
condition had a quantification flag denoting the signal as within the dynamic range. To
retain good quality features for the analysis, we filtered out, for each replicated spot
across the whole set of samples, those probes for which more than 25% of the replicates
in at least one experimental condition had a flag indicating the presence of outliers. The
Bioconductor annotation package hgug4112a.db (10) was used to assign the
corresponding gene accession number code to each Agilent probe ID. Differential
expression was analyzed using the limma package (7) in the same way as described for
microRNA microarrays. In this case, a fdr of 0.05 was selected.
Supplementary Information Table and Figure Legends
Table S1. miRNAs regulated in differentiated human mesenchymal cells and skin
fibroblasts in comparison with undifferentiated hMSCs. miRNA genes that are
differentially expressed (fdr<0.15) in the three conditions tested (adipogenically
differentiated hMSCs, osteogenically differentiated hMSCs, and skin fibroblasts) in
comparison with undifferentiated hMSCs were selected as described in Supplementary
Methods. Gene names correspond to features present in the Agilent V2 Human miRNA
Microarray Kit (Sanger database v.10.1).
Table S2. List of genes downregulated in hMSCs exogenously overexpressing miR-335
that are also predicted by MiRanda, TargetScan, or PicTar algorithms as targets of this
miRNA.
Figure S1. Signal normalization in miRNA microarrays. Smooth curves fitted to the
scatter plots of standard deviation (SD) replicates as a function of the average
expression for each gene (log2 scale), using natural cubic splines with 5 knots. nor75,
normalization by 75% percentile; norQ, quantile normalization. (a) Adipogenically and
osteogenically differentiated hMSCs vs. undifferentiated hMSCs. (b) Skin fibroblasts
vs. undifferentiated hMSCs.
Figure S2. Global analysis of miRNA microarray results. (a) Heatmap of
differentially expressed miRNAs (fdr<15%) in differentiated human mesenchymal
cells. For each miRNA gene the mean intensity (log2 scale) of each experimental group
was obtained. Then, the intensity measures for each gene were standardized by
substracting the mean and dividing by standard deviation, which allows a comparison of
the intensities across genes. The experimental groups are A, adipogenically
differentiated hMSCs; O, osteogenically differentiated hMSCs; F, primary skin
fibroblasts. (b) A gene ontology analysis using the PANTHER software was performed
for the predicted targets (using TargetScan release 5.1) of all the miRNAs up- or
downregulated in at least two of the conditions tested. Enriched (p<0.001) categories in
the Pathways Ontology are shown.
Figure S3. Exogenous regulation of miR-335 expression in hMSCs. (a) Bone
marrow-derived hMSCs were transduced with the pLV-EmGFP-MIR335 or pLVEmGFP-mock lentiviral vectors. The figure shows a representative FACS analysis of
transduced (blue) and non-transduced (gray) cells. Transduced cells (gfp+), only those
demonstrating intermediate signal of gfp (indicated by shading), were purified by cell
sorting. (b) Relative expression of miR-335 was quantified in the purified transduced
cells by means of real-time RT-PCR. (c) Primary bone marrow-derived hMSCs were
transfected (Lipofectamine) with an inhibitor of miR-335 (anti-miRTM-335, Ambion) or
with a negative control oligo (anti-miRTM negative control #2, Ambion). Relative
expression of miR-335 was quantified in the transfected cells by real-time RT-PCR.
RNU-48 was used as endogenous control in all real-time RT-PCR experiments. All
PCR determinations were performed in triplicate. Error bars represent standard error *
p<0.01 (two-tailed t test).
Figure
S4.
MiR-335
is
downregulated
in
osteoblasts
upon
osteogenic
differentiation. Relative expression of miR-335 was quantified in Saos-2 cells before
and after osteogenic differentiation. PCR determinations were performed in triplicate.
Error bars represent standard error * p<0.05 (two-tailed t test).
Figure S5. The expression levels of miR-335 in hMSCs correlate with those of its
host coding-gene MEST. Relative expression levels of miR-335 (endogenous control
RNU-48) and MEST (endogenous control GAPDH) were measured by real-time RTPCR in bone marrow-derived hMSCs after adipogenic differentiation, osteogenic
differentiation, 48 h incubation with 20% Wnt-3a-containing conditioned medium, and
48 h incubation with 3 ng/ml IFN. Data are means  standard error. N = 3.
Figure S6. Bioinformatic analysis of the 5’ upstream region of the human MEST
locus. (a) Human, mouse, and dog MEST loci were aligned, and the extent of DNA
sequence homology was computed with the web-based program MULAN
(http://mulan.dcode.org). Using MULAN and multiTF (http://www.multitf.dcode.org)
with the TRANSFAC professional V10.2 library database (http://www.biobase.de)
database, a LEF1 and a TCF4-binding site were predicted with 0.95 matrix similarity in
the 5 kb upstream region of the MEST locus. Three STAT1 binding sites were also
predicted with 0.9 matrix similarity in the same region. (b) Sequence of the mapped
conserved LEF1 (blue), TCF4 (cyan), and STAT1 (pink)-binding sites.
Figure S7. Gene enrichment analysis of the predicted miR-335 targets. A gene
ontology analysis using the PANTHER software was performed for the non-redundant
gene list of predicted miR-335 targets obtained by combining miRanda, TargetScan and
PicTar computer algorythms. Upper panel, enriched (p<1E-06) Biological Processes.
Lower panel, enriched (p<1E-03) Molecular Functions.
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