Abstract
Glucocorticoids (GCs) are potent anti-inflammatory and
immunosuppressive agents. However, their clinical usage is limited by
severe multisystemic side effects. Glucocorticoid induced osteoporosis
results in significant morbidity and mortality but the cellular and
molecular mechanisms underlying GC-induced bone loss are not clear. GC
use results in decreased osteoblast differentiation with increased
marrow adiposity through effects on bone marrow stem cells. GC effects
are transduced through its receptor (GR). To identify novel GR
regulated genes, we performed RNA sequencing (RNA-Seq) analysis
comparing conditional GR knockout mouse made by crossing the floxed GR
animal with the Col I promoter-Cre, versus normal floxed GR without
Cre, and that testing was specific for Col I promoter active cells,
such as bone marrow mesenchymal stem/osteoprogenitor cells (MSCs) and
osteoblasts. Results showed 15 upregulated genes (3- to 10-fold) and 70
downregulated genes (-2.7- to -10-fold), with the long noncoding RNA
X-inactive specific transcript (Xist) downregulated the most. The
differential expression of genes measured by RNA-Seq was validated by
qRT-PCR analysis of selected genes and the GC/GR signaling-dependent
expression of Xist was further demonstrated by GC (dexamethasone)
treatment of GR-deficient MSCs in vitro and by GC injection of C57BL/6
mice (wild-type males and females) in vivo. Our data revealed that the
long noncoding RNA Xist is a GR regulated gene and its expression is
induced by GC both in vitro and in vivo. To our knowledge, this is the
first evidence showing that Xist is transcriptionally regulated by
GC/GR signaling.
Keywords: glucocorticoid, Xist, MSC, differential expression.,
glucocorticoid receptor, long noncoding (lnc) RNA
Introduction
Glucocorticoids (GCs) are highly effective anti-inflammatory and
immunosuppressive agents and are frequently used to treat diseases such
as rheumatoid arthritis ([37]1, [38]2), asthma ([39]3, [40]4), and
pulmonary disease ([41]5). GCs exert their actions via intracellular
glucocorticoid receptors (GRs) ([42]6, [43]7) which, upon activation,
can either directly bind to glucocorticoid response elements (GREs) on
the target gene promoters and regulate their transcription
([44]8–[45]10), or indirectly through interaction with other
transcription factors such as NF-kB and AP-1 and inhibit their
transcriptional activities ([46]11–[47]13). It has long been known that
pharmacologic GC therapy results in bone loss/osteoporosis and
increases the incidence of bone fractures ([48]14–[49]16). However, it
is also known that physiological levels of GC are required for normal
bone acquisition, as demonstrated in several animal models in which GR
is deleted ([50]17) or GC signaling is disrupted in bone
([51]18–[52]20). Despite intensive investigations, the cellular and
molecular mechanisms underlying GC actions in bone are not clear. The
bone marrow mesenchymal stem cells (MSCs) are multipotent and capable
of differentiating into several distinct cell lineages in vitro,
including osteoblasts, adipocytes, chondrocytes, muscle cells, and even
neuronal cells ([53]21–[54]24). In bone tissues, MSCs give rise to
osteoblasts or marrow adipocytes governed, in large part, by marrow
microenvironment and developmental stages as these two pathways are
reported to have a reciprocal relationship ([55]25–[56]27). In this
study, we aimed to identify novel endogenous genes that are regulated
by GR in MSCs by analyzing GR-deficient MSCs. We found that among other
significantly up- or downregulated genes in GR cKO cells, that a long
noncoding RNA X-inactive specific transcript (Xist) was the most
significantly affected RNA species in GR cKO cells (down by tenfold).
As studies of LncRNAs in bone are sparse, we decided to focus on Xist
for further validation of this RNA-Seq data and examine Xist as a
potential modulator of bone turnover.
Materials and methods
Experimental animals
Conditional GR knockout (GR cKO) mice were generated by breeding a
GR-floxed (GR^fl/fl) mouse ([57]28) with a 3.6 kb rat type I collagen
promoter-driven Cre transgenic mouse (Col3.6-Cre) ([58]29). Thus, the
cKO mice are deficient of GR in Col I-expressing cells. Mouse genotype
was confirmed by PCR analysis of tail genomic DNA using primers 5’-
AATCAGAATTGCTCACTCACAA-3’ (forward) and 5’-GAACTGGAAGTAGTAACACTG-3’
(reverse). PCR analysis of Cre was performed using primers
5’-GCATTTCTGGGGATTGCTTA-3’ (forward) and 5’-GTCATCCTTAGCGCCGTAAA-3’
(reverse). C57BL/6 mice (6-month-old males and females) were obtained
from the National Institute on Aging (NIA) aged rodent colonies. Mice
were group housed (4 - 5 mice/cage) in the Augusta University
Laboratory Animal Service facility under a 12-hr dark-light cycle and
fed with standard rodent chow and water ad libitum. After a week of
recovery, mice were intraperitoneally injected with synthetic
glucocorticoid dexamethasone (Dex) at a dosage of 3 mg/kg (n = 3 mice
per group) or equal volume of ethanol (vehicle control, n = 2). Twelve
hours after injection, mice were sacrificed and total cellular RNAs
were collected from bone tissues (femur and tibia). All animal
procedures were performed in accordance with a protocol (#2008-0302)
approved by the Augusta University Institutional Animal Care and Use
Committee (IACUC).
MSC isolation, cell culture and RNA isolation
Bone marrow mesenchymal stem cells (MSCs) were isolated from long bones
(femur and tibia) of 6-month-old male GR cKO and GR-floxed mice (n = 3
mice per genotype) in the Stem Cell Core facility at Augusta
University. The Core uses a procedure that includes a
negative-immuno-depletion (using magnetic beads conjugated with
anti-mouse CD11b and c, CD45R/B220, and PDCA-1) followed by a
positive-immuno-selection (using anti-Sca-1 beads). The MSCs isolated
using this procedure are negative for CD11b, CD11c, and CD45, and
positive for Sca-1, and are capable of undergoing osteogenic,
adipogenic, myogenic, and chondrogenic differentiations as demonstrated
by Alizarin Red-S (ARS) staining of mineralized bone matrix, Oil Red O
staining of intracellular lipid vacuoles, immunolabeling of
muscle-specific proteins myosin (cytoplasmic) and myogenin (nuclear),
and Alician blue staining of acidic polysaccharides ([59]30).
Importantly, they also differentiate into osteoblast-like lining cells
or even incorporate into trabecular bone after injection into mice
([60]31). Images of purified cells and data demonstrating successful
deletion of GR are provided as supplementary material ([61]
Supplementary Figure S1 ). The purified MSCs were cultured under
standard cell culture condition in DMEM supplemented with 10% FBS and
antibiotics. Total cell lysates were collected in TRIzol reagent and
sent to the Otogenetics Corporation (Atlanta, GA) for RNA isolation and
RNA-Seq analysis.
MSCs used in ChIP and luciferase reporter assays were isolated from
bone marrow of 6-month-old C57BL/6 mice using the same method described
above.
RNA-seq analysis
RNA QC, polyA cDNA preparation and QC, Illumina library preparation and
QC were all performed at the Otogenetics Corporation. RNA-Seq were
performed on a HiSeq2000 sequencing machine. A minimum of 20 million
reads (2 reads x 10 million fragments) were generated per sample. Basic
bioinformatics and differential expression analyses were performed on
DNAnexus platform. Raw data FASTq and bioinformatic reports were
delivered via secure Google Cloud Drive. The raw RNA-Seq data has been
deposited at NCBI SRA database (ID: PRJNA862943) and the processed
results provided in [62]Supplementary Table S1 .
Data analysis workflow
1. The sequencing data sets from illumina HiSeq2000 (fastq.gz) were
first mapped with Tophat (v2.0.5) against reference assembly UCSC
mm9 downloaded from illumina iGenome and then, the mapped files
(accepted_hits.bam) from each sample were input into cufflinks
(v2.0.2) to locate genomic regions with expression under the
guidance of ‘genes.gtf’ (annotation file from illumina iGnome).
Information of expression was recorded in ‘transcript.gtf’.
2. Cufflinks.cuffmerge was called to combine all ‘transcripts.gtf’
into a single ‘merged.gtf’, and mapping files generated by Tophat
were input into cufflinks.cuffdiff (v.2.0.2) to measure expression
level on regions (genes, transcripts, CDS, etc.) defined in
‘merged.gtf’.
3. Expression levels were measured with FPKM (Fragments Per Kilobase
per Million mapped), which is located in.fpkm_tracking files in
cuffdiff folder. Statistical test on difference between samples is
located in.diff files. The qvalue less than 0.05 is considered as
an indication for statistical significance.
4. Cuffdiff analysis results were implemented at different levels,
including gene, isoform and CDS. All result files are tabdelimited
plain text and can be opened with Excel. For definition of each
field in files, please refer to manual of cufflinks at
[63]http://cufflinks.cbcb.umd.edu/manual.html.
Construction of human gene association network
Gene association network links genes or encoded proteins by their
functional interplays, including direct physical binding and indirect
interaction such as their participation in a common cellular process.
In this study, we used the human functional linkage network (FLN)
constructed by Linhu et al. as background network ([64]32). FLN is a
densely connected and weighted network composed of 21,657 genes and
22,388,609 edges. In this network, the nodes represent genes, and edge
weights the likelihood that the linked nodes participate in a common
biological process. The edge weight is a probabilistic confidence score
of the linkage. We normalized the original edge weight to the interval
[0,1].
Scoring network effect of a group of differentially expressed genes
A group of differentially expressed genes could affect other genes
through network links. For each gene i in the human gene association
network FLN, we quantified the influence of differentially expressed
genes by a network effect score. In general, the higher score a gene
receives, the deeper more pronounced it is affected by the
differentially expressed genes. Specifically, a node’s S[i] score is
defined as follows:
[MATH: Si=∑j=1nwj(v)Wi<
/mi>j(e) :MATH]
(3)
where n is the number of nodes in the network,
[MATH: wj(v) :MATH]
is the weight of the node j defined as absolute value of log2 ratio of
the expression level if the corresponding gene is differentially
expressed, otherwise it is zero.
[MATH:
Wij(e) :MATH]
is the linkage weight connecting gene i and j, and it is defined as 1
when i = j.
Real-time qRT-PCR analysis
The RNA-Seq analysis results were confirmed by real-time qRT-PCR
analysis of selected genes that are differentially expressed in GR cKO
and GR-floxed MSCs or the samples from bone tissues of wild type
C57BL/6 mice. qRT-PCR was performed as described previously using
TaqMan Reverse Transcription Reagents and a StepOnePlus Real-Time qPCR
System (Thermo Fisher Scientific). The mRNA levels were normalized to
β-actin and 18S rRNA (internal controls). The primer sequences used in
qRT-PCR are listed below ([65] Table 1 ). All PCR reactions were
performed in triplicates and all experiments were repeated at least two
times with similar results.
Table 1.
GenBank accession numbers, primer sequences and amplicon sizes of genes
used for qRT-PCR analysis.
Gene Accession # Forward Primer (5’-3’) Reverse Primer (5’- 3’)
Amplicon size (bp)
Nr3c1 [66]NM_008173 GGACAACCTGACTTCCTTGG CTGGACGGAGGAGAACTCAC 108
Xist [67]NR_001463 CCTGCAAGGGATACCGTTTAT ATGAAAGGCGAAGGAGTATGG 113
Ldhb [68]NM_001316322 CTGACCAGCGTCATCAATCA CACAGGTCTTTGAGGTCTTTCT 104
Aldh1a1 [69]NM_013467 GCAGCAGGACTCTTCACTAAA CACTGGGCTGACAACATCATA 107
Nsg1 [70]NM_010942 CCACAGGCGTAAGAACAAGA CCAGGGAAGGAGCTAAATGAA 93
Plac8 [71]NM_139198 ACTCTCTACCGAACCCGATAC CATGGCTCTCCTCCTGTTAATG 123
Sfrp1 [72]NM_013834 TGCAGTTCTTCGGCTTCTAC CTTAGAGGCTTCCGTGGTATTG 107
Dkk3 [73]NM_001360257 TCCACCGACTGCTTCAATAC CATTCACAATCCTAGCCCTACA 108
Gilz [74]NM_010286 GGGAGTACTGACTGGTCTCTTA CCCTCCCTCATATCGAGTCTTA 111
18S [75]NR_003278 CTGAGAAACGGCTACCACATC GCCTCGAAAGAGTCCTGTATTG 107
b-actin [76]NM_007393 TTCTTTGCAGCTCCTTCGTT ATGGAGGGGAATACAGCCC 149
[77]Open in a new tab
Western blot, immunofluorescence labeling and imaging
Western blot analysis was performed as previously described ([78]33).
In brief, whole cell lysates of GR-floxed and GR cKO BMSCs were
collected in a lysis buffer. Equal amounts of total protein (40 ug)
were separated on 7% SDS-PAGE, transferred onto nitrocellulose
membrane, and blocked in 5% non-fat dry milk for 2 hr at RT. The
membrane was then incubated with an anti-GR polyclonal antibody (1:500
dilution, Santa Cruz #sc-1002) and anti-β-actin antibody (1:1000
dilution, Abcam #ab8227) for at least 1 hr at RT. After several washes
the membrane was incubated with IRDye 800 goat anti-rabbit IgG
secondary antibody (1:5000 dilution, LI-COR Biotechnology) and imaged
using Odyssey Infrared Imaging System.
For immunofluoresence labeling, GR-floxed and GR cKO cells were seeded
in chamber slides and treated with or without 10 nM dexamethasone (Dex)
for 30 min. Cells were then fixed with freshly prepared 4%
paraformaldehyde containing 0.2% Triton X-100 for 15 min and blocked in
2% BSA for 1 hr at RT before incubating with anti-GR primary antibody
(1:500 dilution) for at least 1 hr at RT. After several washes, the
slides were incubated with goat anti-rabbit IgG-FITC secondary antibody
(1:600 dilution) for 1 hr at RT in dark. The slides were washed three
times in PBS for 5 min each and stained with DAPI (300 nM) to visualize
the nucleus. Finally, the slides were washed, mounted with Vectorshield
mounting media (Vector Laboratories), and analyzed using a Nikon TE2000
fluorescence microscope equipped with COOLSNAP Monochrome Camera.
Images were acquired and processed with Metamorph Imaging System.
Chromatin immunoprecipitation assays
ChIP assays was performed using a SimpleChIP Plus Sonication Chromatin
IP Kit (#56383) and a monoclonal antibody against GR (#3660S) according
to the manufacturer’s instructions (Cell Signaling Technology, Inc.).
Briefly, MSCs isolated from 6-month-old male C57BL/6 mice were cultured
in 150mm plates under standard cell culture condition in DMEM
supplemented with 10% FBS and antibiotics. When cells reach ~90%
confluency they were treated with 100 nM Dex for 30 min. to induce GR
nuclear translocation. The cells were washed, cross-linked and
chromatin fragmented according to the manufacturer’s instructions. The
fragmented chromatin (from 2 plates) were precipitated overnight at 4°C
with anti-GR or normal rabbit IgG (control). After reversal cross-link
and DNA purification, the GR-bound Xist promoter fragments were PCR
amplified with following primer pairs (numbers in brackets indicate
amplicon size). GRE 1: (F) TGAAGAGCCCTTCCTTG, (R)
GTAAAGGTTACTTTGTCTAACT (132bp); GRE 2/3: (F) TGTCCTTTATTATTCATGGGA, (R)
GTGTCTGATCTCTTTCATGT (130bp); GRE 4: (F) GATAATTTAGGAACCAAGGA, (R)
CTTCTACTTGGACAAACC (134bp).
Transfection and luciferase assays
MSCs from 6-month-old C57BL/6 mice were transiently transfected with
Xist1.2-Luc reporter construct (custom-built and sequence confirmed,
VectorBuilder Inc., Chicago, IL) and the internal control pRL-null
vector (Promega Corporation) using jetPEI DNA transfection reagent
following manufacturer’s instructions (Genesee Scientific). 18 hr after
transfection cells were treated with 10 nM of Dex for 6 hr before
harvesting for luciferase activities measuement using a dual luciferase
assay kit (Promega Corporation) and a Cytation 5 multifunctional reader
(Agilent Technologies). Values of firefly luciferase were normalized to
the renilla luciferase activity. Luciferase values shown in the figures
are representative of transfection experiments performed in triplicate
from three independent experiments.
Statistical analysis
Data were analyzed by either unpaired t-test (Mann-Whitney) or ordinary
one-way ANOVA (Tukey’s multiple comparisons test) where appropriate
using Prism GraphPad software version 9.2.0. A p-value less than 0.05
was considered significant.
Results
Differentially expressed genes and the biological processes they participate
in
We compared the expression levels of all genes in MSCs from GR cKO and
GR-floxed (WT) mice. Principal component analysis (PCA) data showed a
distinctive expression pattern between GR cKO and WT samples and a high
degree of similarity between the two GR cKO samples. Though the
difference between two WT samples is larger, the variation is within
the limit allowed ([79] Figure 1A ). Statistical significance (p value)
vs. magnitude of change (fold change) of genes is shown in a volcano
plot ([80] Figure 1B ). The red and blue colors represent genes
significantly up- and down-regulated, respectively, in GR cKO vs. WT
cells.
Figure 1.
[81]Figure 1
[82]Open in a new tab
(A) PCA plot showing a clear separation between GR cKO and WT samples.
(B) Volcano plot showing the number of upregulated (red dots) and
downregulated (blue dots) genes. Gray dots denote no significant
change.
Normalized data set showed that 739 genes were up- or downregulated
with p values less than 0.05. Among these 739 genes, 201 of them are
unknown genes. To increase the confidence level, we analyzed only the
genes whose expression in cKO vs WT has a difference larger than 3 and
q-value less than 0.05 and considered these genes as differentially
expressed. Results showed that 85 genes are differentially expressed,
of which 78 of them have known mouse gene symbols. Since the mouse and
human genes are highly conserved, we mapped the differentially
expressed genes to a human data base ([83]34) to establish potential
implications of GR in human disease and health. The mapping results
showed that these 85 differentially expressed mouse genes correspond to
75 distinct human genes. To explore the biological processes in which
these differentially expressed genes participate, we conducted gene
ontology (GO) and pathway enrichment analysis by DAVID
([84]https://david.ncifcrf.gov/). It was found that 8 GO terms in
biological process (GO BP terms) and 3 pathways, all associated with
inflammation and immune response, are enriched with the identified
differentially expressed genes ([85] Figure 2 ).
Figure 2.
[86]Figure 2
[87]Open in a new tab
Biological processes the differentially expressed genes involved in.
(A) Significantly enriched GO BP terms. (B) Significantly enriched
pathways.
Network and pathways significantly influenced by the differentially expressed
genes
A group of differentially expressed genes could exert their impact on
other genes through network. Using the 75 human orthologous genes as
seed genes, we scored the influence of these genes to all genes in the
human gene association network and took all genes whose scores were
greater than 150 as significantly influenced by the differentially
expressed genes. This led to 201 genes, including 11 differentially
expressed genes. We took these genes and their links whose weights are
larger than 0.2 from the background human gene association network. In
this way, we constructed a subnetwork that is significantly impacted by
the differentially expressed genes. This subnetwork includes 183 nodes
and 1017 links. As shown in [88]Figure 3 , this subnetwork has 2
connected components. We then used simulation annealing algorithm to
decompose the network into 7 dense clusters. As one can see that each
cluster includes many proteins from the same families, such as CCL
family in cluster 2, IL family in cluster 4, and MMP family in cluster
3, all of them are associated with inflammation.
Figure 3.
[89]Figure 3
[90]Open in a new tab
Sub-network significantly influenced by differentially expressed genes.
Red nodes denote differentially expressed genes.
We mapped the scores S[i]’s of genes to each human pathway in the KEGG
pathway database and then scored pathways by the average scores of
genes in that pathway. The pathways with higher scores are the ones
possibly influenced by the identified differentially expressed genes.
We also mapped the 201 top scored genes to the pathways to identify
significantly enriched genes, and then chose the pathways that are
significantly enriched with the top scored genes and also have the
highest pathway scores as significantly affected by the differentially
expressed genes. These pathways are summarized in [91]Table 2 . It can
be seen that, in addition to regulating inflammation and immune
response, GR cKO also affected other cellular functions including cell
lineage commitment, differentiation, communication, and adhesion.
Table 2.
Pathways significantly affected by the differentially expressed genes.
Pathway Name Pathway Class 2 Pathway Class 1 Total Genes in Pathway
Mapped genes in pathway
ECM-receptor interaction Signaling molecules and interaction
Environmental Information Processing 82 24
Cytokine-cytokine receptor interaction Signaling molecules and
interaction Environmental Information Processing 273 72
IL-17 signaling pathway Immune system Organismal Systems 93 28
Toll-like receptor signaling pathway Immune system Organismal Systems
104 17
TNF signaling pathway Signal transduction Environmental Information
Processing 108 25
Focal adhesion Cellular community - eukaryotes Cellular Processes 199
35
Intestinal immune network for IgA production Immune system Organismal
Systems 49 9
Chemokine signaling pathway Immune system Organismal Systems 185 40
TGF-beta signaling pathway Signal transduction Environmental
Information Processing 84 11
Hematopoietic cell lineage Immune system Organismal Systems 97 13
NF-kappa B signaling pathway Signal transduction Environmental
Information Processing 95 14
Th17 cell differentiation Immune system Organismal Systems 107 12
Protein digestion and absorption Digestive system Organismal Systems 90
14
Osteoclast differentiation Development Organismal Systems 128 15
Th1 and Th2 cell differentiation Immune system Organismal Systems 92 10
T cell receptor signaling pathway Immune system Organismal Systems 103
11
PI3K-Akt signaling pathway Signal transduction Environmental
Information Processing 342 46
Jak-STAT signaling pathway Signal transduction Environmental
Information Processing 156 15
ErbB signaling pathway Signal transduction Environmental Information
Processing 86 11
HIF-1 signaling pathway Signal transduction Environmental Information
Processing 99 13
[92]Open in a new tab
Validation of the differentially expressed genes
To ensure that our interpretation was based on valid RNA-Seq data, we
confirmed, by real-time qRT-PCR, the differential expression of several
genes shown by RNA-Seq data to be significantly up- or downregulated in
GR cKO cells. These include X-inactive specific transcript (Xist),
dickkopf-related protein 3 (Dkk3), neuronal vesicle trafficking
associated 1 (Nsg1), secreted frizzled related protein 1 (Sfrp1),
lactate dehydrogenase b (Ldhb), placenta specific 8 (Plac8), and
aldehyde dehydrogenase 1 family member a1 (Aldh1a1). Consistent with
the RNA-Seq data, qRT-PCR results showed significantly deceased mRNA
levels of Xist, Dkk3, Nsg1, Sfrp1, and Ldhb, and significantly
increased mRNA levels of Plac8 and Aldh1a1 in GR cKO cells ([93] Figure
4 ). The Dkk and Sfrp family members are well known regulators of bone
and their expression is known to be induced by glucocorticoids
([94]35–[95]37).
Figure 4.
[96]Figure 4
[97]Open in a new tab
Validation of RNA-Seq data by qRT-PCR. RNA samples isolated from GR cKO
and GR-floxed MSCs were analysed for the expression of Xist and
indicated genes whose expression was shown to be up- or downregulated
in GR cKO cells. Numbers in bold are the results from RNA-Seq analysis.
qRT-PCR experiments were performed 3 times with similar results. PCR
reactions were performed in triplicates. t-test, p values are
indicated.
Xist expression is induced by glucocorticoids
Ranking the significantly up- or downregulated genes of RNA-Seq data
set, we found that the long noncoding RNA Xist was the most
differentially expressed RNA species in the GR cKO cells (down by
10.4-fold). We then asked whether Xist expression can be induced and
whether GR is required for its expression. To answer these questions,
we treated GR-floxed and GR cKO MSCs with a synthetic glucocorticoid
dexamethasone (Dex, 100 nM for 12 hr) and performed qRT-PCR analysis.
Results showed that Dex induced Xist RNA expression more than 3-fold
(ranging from 3 to 5 folds) in GR-floxed cells ([98] Figure 5A ). In
contrast, Xist expression was not induced in GR cKO cells ([99] Figure
5A ). To test whether Xist expression is also induced by glucocorticoid
in vivo, we intraperitoneally injected wild type C57BL/6 mice
(6-month-old males and females) with Dex (3 mg/kg, n = 3) or equal
volume of vehicle (ethanol, n = 2). Twelve hours after injection, mice
were sacrificed and total cellular RNAs were collected from bone
tissues (femur and tibia). Again, qRT-PCR results showed significant
induction of Xist RNA in both male and female mice (>30-fold in males
and >50-fold in females) ([100] Figure 5B ). To confirm that the
increased Xist expression was due to the administration of
glucocorticoid, we examined the level of glucocorticoid induced leucine
zipper (Gilz), a gene known to be induced by glucocorticoids ([101]33,
[102]38). As expected, Dex treatment significantly increased Gilz mRNA
expression in these samples ([103] Figure 5C ). It is noted that the
magnitude of inductions in bone tissue was much greater than that in
purified MSCs due to, most likely, the contribution from the
hematopoietic lineage cells in the bone marrow. Together, these results
demonstrate, for the first time, that 1) Xist is a glucocorticoid
inducible gene, and 2) Xist RNA can be induced in male mice, although
at a lower magnitude than in female mice.
Figure 5.
[104]Figure 5
[105]Open in a new tab
Xist RNA is induced by GCs. (A) qRT-PCR showing Dex induction of Xist
in GR-flox and GR cKO MSCs. (B, C) qRT-PCR showing Dex induction of
Xist (B) and GILZ (C) in mice (6-mo-old C57BL/6 mice). Results are from
2 (control) or 3 (treatment) individual mice. PCR reactions were
performed in triplicates. One-way ANOVA or t-test, p values are
indicated.
GR binds to Xist gene promoter region
To determine if Xist expression requires GR binding to the Xist
promoter region, we performed a computational analysis using a
transcription factor binding site identification software
([106]https://tfbind.hgc.jp) and found four putative glucocorticoid
response elements (GREs) within a 1.2 kb region upstream of Xist
transcription start site ([107] Figure 6A ). Chromatin
immunoprecipitation (ChIP) assays using wild-type MSCs and a monoclonal
antibody against GR showed that, upon activation of GR with Dex (100 nM
for 1 hr), GR antibodies precipitated GR-bound DNA fragments containing
GRE-1 (with a lower affinity), GRE-2/3, and GRE-4 ([108] Figures 6B–D
). The adjacent GRE-2 and -3 are separated by only 21 nucleotides, it
is not clear at this point which site GR binds to in this region.
Antibody against histone H3, a universal positive control, and normal
rabbit IgG served as positive and negative controls, respectively.
Input DNA samples (2% sonicated DNA) was also used as a positive
control for PCR reactions. Together, these results demonstrated that GR
can bind to at least two out of the four putative GREs present in this
1.2 kb Xist gene promoter fragment.
Figure 6.
[109]Figure 6
[110]Open in a new tab
ChIP assay showing GR binding to Xist promoter region. (A) Schematic
diagram of the approximate locations of GREs and flanking primers used
in PCR reactions. (B–D) agarose gel images showing PCR products
amplified from anti-GR antibody precipitated DNA fragments and primer
pairs flanking the indicated GREs. Input: 2% sonicated DNA; H3:
Anti-histone H3 mAb (positive control); IgG: normal rabbit IgG
(negative control); GR: Anti-GR mAb. Experiment was performed 3 times
with similar results. Shown is the results from one representative
experiment.
Xist promoter luciferase reporter activity
To determine the mechanism by which glucocorticoids activate Xist
transcription, we generated a 1.2kb mouse Xist promoter-driven
luciferase reporter construct (Xist1.2-Luc). This promoter fragment
contains 4 putative GREs shown above (ChIP assay). Xist1.2-Luc plasmid,
together with an internal control plasmid (pRL-null) encoding Renilla
luciferase were co-transfected into wild-type MSCs (from 6-month-old
male C57BL/6 mice) using jetPEI DNA transfection reagent (Genesee
Scientific). After overnight culture (~18 hr), the transfected cells
were challenged with or without 10 nM Dex for 6 hr before they were
lysed. Luciferase activity was measured using a dual-luciferase
reporter assay kit (Promega) and a Cytation 5 multifunctional reader
(Agilent Technologies). Results showed that the Xist1.2-Luc reporter
had reasonably high promoter activity (5 digits, firefly luciferase
driven by Xist promoter) before normalization (to internal control,
renilla luciferase expressed from promoterless vector). Unfortunately,
Dex treatment showed no stimulatory effect on this promoter-reporter
construct ([111] Figure 7A ). To confirm that the Dex reagent was
biologically active and the cells were stimulated, we isolated total
RNA from retrieved cell lysates used for luciferase assay and performed
qRT-PCR analysis. Result showed that the endogenous Xist RNA was
induced ([112] Figure 7B ), indicating that the Dex reagent was
effective and this naked artificial DNA construct does not respond to
GC stimulation in this setting. It is possible that long-range enhancer
elements and other genes surrounding Xist locus are required for Xist
expression as studies have shown that several distal enhancers are
associated with Xist-enhancing regulatory transcript (Xert), and Xert
is upregulated concomitantly with Xist and activates Xist in cis
([113]39).
Figure 7.
[114]Figure 7
[115]Open in a new tab
Transient transfection and luciferase-reporter assays. (A) MSCs were
transfected with Xist1.2-Luc promoter reporter construct for 18 hr and
then treated without or with 10 nM Dex for 6 hr before harvesting for
luciferase activity assay. (B) qRT-PCR analysis of RNA samples
retrieved from cell lysates (A) showing induction of endogenous Xist
RNA by Dex in transfected cells. Experiment was performed 3 times with
similar results. Shown is the result from one representative experiment
performed in hextuple. RNA was retrieved from a pool of lysates (6
wells in each group) and PCR reactions were performed in quadruple.
t-test, p values are indicated.
Discussion
In this study, we analyzed genes that are differentially expressed in
glucocorticoid receptor (GR) deficient mouse bone marrow mesenchymal
stem cells (MSCs). The analysis was performed in a blind fashion using
data generated from deep sequencing analysis of RNA samples prepared
from purified mouse MSCs of conditional GR knockout (GR cKO) and
GR-floxed control mice. The purpose of this RNA-Seq study was to
identify novel genes whose expression is regulated by GR for further
studies on the role of these genes in MSC differentiation and bone
formation. In addition to a list of protein coding genes, some of which
are known to be regulated by glucocorticoids and play important roles
in bone development ([116]35–[117]37), our data unexpectedly revealed,
that the expression of a long noncoding RNA (LncRNA), X-inactive
specific transcript (Xist), was the most downregulated gene in GR cKO
cells (tenfold lower than in GR-floxed cells). This data was confirmed
by qRT-PCR analysis ([118] Figure 4 ) and further, by glucocorticoid
(Dex) treatment of GR cKO and GR-floxed cells in vitro as well as by
Dex treatment of mice ([119] Figure 5 ). To our knowledge, this is the
first evidence showing that Xist is transcriptionally regulated by
glucocorticoid/GR signaling. Xist, located on the X chromosome, was
identified as a female-specific gene and functions in cis to silence
the transcription of one of the two X chromosomes in females to
regulate sex chromosome dosage compensation ([120]40–[121]43). The
current study showed that Xist expression can be induced by
glucocorticoids in both male and female mice ([122] Figure 5 ) though
any connection between Xist expression and glucocorticoid-induced bone
loss is yet to be determined. Several recent in vitro studies reported
the role of Xist in osteoblast differentiation but the results of these
publications are contradictory; with some studies showed Xist inhibits
MSC osteogenic differentiation ([123]44–[124]47) and others showed Xist
promotes MSC osteogenic differentiation ([125]48, [126]49). In
addition, recent evidence also showed that Xist is overexpressed in
osteosarcoma and promotes cancer cell proliferation and migration via
mechanisms such as regulation of microRNAs (miRNAs) and mTOR and other
signaling pathways ([127]50–[128]53). Xist loss- or gain-of-function
studies in animal models will be required to clarify the role Xist
plays in normal bone turnover and in glucocorticoid-induced bone loss.
Data availability statement
The data presented in the study are deposited in the NCBI SRA
repository, accession number PRJNA862943.
Ethics statement
The animal study was reviewed and approved by Institutional Animal Care
and Use Committee (IACUC) Augusta University.
Author contributions
YS, HZ and SS contributed to cell isolation, culture and qRT-PCR
analysis. XC, JC, CI, JZ, and XS contributed to data analysis,
manuscript writing and editing.
Funding
This work was supported by grants from The National Institute on Aging
(R01AG046248), National Institutes of Health (NIH). Research reported
in this publication was supported in part by the National Institute on
Aging of the National Institutes of Health under Award Numbers
R01AG046248.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[129]https://www.frontiersin.org/articles/10.3389/fendo.2022.1005944/fu
ll#supplementary-material
Supplementary Figure 1
Characterization of GR KO MSCs. (A) Bright field images showing
morphology of purified MSCs. (B) qRT-PCR showing the absence of GR mRNA
in GR KO cells. Inset: Agarose gel image showing qPCR products. (C)
FACS analysis showing percentages of CD45, CD11b, and Sca-1 positive
cell populations in purified GR-flox (top panel) and GR KO (bottom
panel) MSCs. (D) Western blot showing the absence of GR protein in GR
KO cells. (E) Immunostaing showing nuclear translocation of GR protein
in response to dexamethason (Dex) stimulation (100nM for 30min).
[130]Click here for additional data file.^ (694.1KB, jpg)
[131]Click here for additional data file.^ (2.5MB, xlsx)
References