I compared the genomic profiles of MSCs in a group of four patients and controls. DEGs were measured by counting tags from osteoporosis samples against normal samples and were normalized using the trimmed mean of M-values normalization method (TMM) method (edgeR).21 Significantly upregulated genes of MSCs in osteoporosis were those with q-values < 0.05 and log2 fold changes (FC) >
2, whereas the control groups were defined as q-values < 0.05 and log2 FC < -2. For pathway enrichment analysis of DEGs, the right-tailed Fisher’s exact test was used to calculate a p-value to determine the probability so that the biological function assigned to a specific osteoporosis-induced network was due to chance alone.
For the RT-PCR analyses and subjects’ baseline characteristics, statistical comparisons were performed using IBM SPSS Statistics 23 (SPSS Inc., Chicago, IL, USA), and the data are presented as the means ± standard error of means (SEMs).
III. RESULTS
I compared the transcriptome of MSCs from four patients (aged 62–74 years) with severe osteoporosis and MSCs of the age-matched control group with normal BMD (60–71 years old) (Table 2). There were no statistically significant differences in the baseline characteristics such as age, height, weight, and BMI. The control group showed higher lumbar spine BMD compared to osteoporosis group but did not show statistical significancebecause of the degenerative changes of the study subjects’
spines. However, the BMDs for femur neck and total hip were significantly lower in the osteoporosis group.
Table 3 lists the 53 most highly expressed DEGs between the osteoporosis and normal subjects, which included 28 and 25 genes that were expressed at higher and lower levels, respectively, in osteoporosis subjects compared to normal subjects.
C-type lectin domain family 2 member B (CLEC2B, FC=5), endogenous retrovirus group K member 5 Gag polyprotein (LOC105374013, FC=5), NR4A1 (FC=4.2), NR4A3 (FC=4.15), and heparin-binding EGF-like growth factor (HBEGF, FC=3.17) were upregulated whereas the remaining DEGs, namely, matrix metallopeptidase 13 (MMP13, FC=−8), septin 7 (SEPT, FC=−8), solute carrier family 26 member 7 (SLC26A7, FC=−8), and sparc/osteonectin, cwcv, and kazal-like domains proteoglycan 3 (SPOCK3, FC=-8), were downregulated.
Table 2. Clinical characteristics of the study subjects for RNA-seq.
Control (n=4) Osteoporosis (n=4) p-value
Age, yrs 67.3±2.5 68.5±3.0 0.686
Height, cm 153.2±2.0 152.1±0.9 0.486
Weight, kg 76.3±2.8 63.3±3.6 0.057
BMI, kg/m2 32.6±1.2 27.3±1.3 0.114
Serum parameters
Calcium, mg/dL 9.5±0.3 9.8±0.4 0.686
Phosphorus, mg/dL 3.8±0.3 4.2±0.2 0.200
25(OH)D, ng/mL 14.2±3.0 17.0±3.2 0.486
Bone mineral density
Lumbar spine, g/cm2 1.181±0.062 0.902±0.117 0.114
Femur Neck, g/cm2 0.893±0.012 0.641±0.009 0.029
Total Hip, g/cm2 0.980±0.021 0.697±0.025 0.029
Data are mean ± SEM
Table 3. Fifty-three up- or downregulated genes in mesenchymal stem cells of osteoporosis subjects compared to controls by RNA-seq data.
Upregulated in the osteoporosis subjects
Gene symbol Entrez Gene name
log2 fold change
p-value q-value
CLEC2B C-type lectin domain family 2 member B 5 0.0002 0.04702 LOC105374013 endogenous retrovirus group K member 5 Gag
polyprotein 5 0.00005 0.01646
NR4A1 nuclear receptor subfamily 4 group A member 1 4.2 0.00005 0.01646 NR4A3 nuclear receptor subfamily 4 group A member 3 4.15 0.00005 0.01646 HBEGF heparin-binding EGF-like growth factor 3.17 0.00005 0.01646
EGR3 early growth response 3 3.13 0.00005 0.01646
NEFM neurofilament, medium polypeptide 3.13 0.00005 0.01646 NR4A2 nuclear receptor subfamily 4 group A member 2 3.09 0.00005 0.01646
IL11 interleukin 11 2.86 0.0002 0.04703
KRTAP2–2 keratin associated protein 2–2 2.53 0.00005 0.01646
CDC20 cell division cycle 20 2.39 0.00005 0.01646
DLGAP5 discs, large (Drosophila) homolog-associated
protein 5 2.37 0.00005 0.01646
ESM1 endothelial cell-specific molecule 1 2.36 0.00005 0.01646
DLX2 distal-less homeobox 2 2.33 0.0002 0.04702
GEM GTP binding protein overexpressed in skeletal
muscle 2.25 0.00015 0.0395
PTTG1 pituitary tumor-transforming 1 2.18 0.0001 0.02992
CEP55 centrosomal protein 55kDa 2.14 0.00005 0.01646
DUSP5 dual specificity phosphatase 5 2.09 0.00005 0.01646
HAS1 hyaluronan synthase 1 2.06 0.00005 0.01646
TK1 thymidine kinase 1, soluble 2.05 0.00005 0.01646
BUB1 BUB1 mitotic checkpoint serine/threonine
kinase 2.03 0.00005 0.01646
RRM2 ribonucleotide reductase regulatory subunit M2 2.02 0.0001 0.02992
CCNB1 cyclin B1 2 0.00005 0.01646
SERPINB10 serpin peptidase inhibitor, clade B (ovalbumin),
member 10 2 0.00005 0.01646
MLPH melanophilin 1.88 0.0002 0.04706
SHCBP1 SHC SH2-domain binding protein 1 1.83 0.00005 0.01646 TACC3 transforming, acidic coiled-coil containing
protein 3 1.76 0.0001 0.02992
CSRNP1 cysteine-serine-rich nuclear protein 1 1.64 0.00015 0.0395 Downregulated in the osteoporosis
subjects
Gene symbol Entrez Gene name
log2 fold change
p-value q-value
CFHR1 complement factor H-related 1 -8 0.00005 0.01646 FRMPD2 FERM and PDZ domain containing 2 -8 0.00005 0.01646
MMP13 matrix metallopeptidase 13 -8 0.00015 0.0395
PKD1 polycystic kidney disease 1 (autosomal
dominant) -8 0.00015 0.0395
RPL13 ribosomal protein L13 -8 0.00005 0.01646
SETP7 septin 7 -8 0.00005 0.01646
SLC26A7 solute carrier family 26 (anion exchanger),
member 7 -8 0.00005 0.01646
SPOCK3 sparc/osteonectin, cwcv and kazal-like domains
proteoglycan 3 -8 0.00005 0.01646
HAPLN1 hyaluronan and proteoglycan link protein 1 -7.26 0.00005 0.01646
MMP1 matrix metallopeptidase 1 -4.8 0.00005 0.01646
NPTX1 neuronal pentraxin I -4.39 0.00005 0.01646
COMP cartilage oligomeric matrix protein -3.45 0.00005 0.01646 NTRK2 neurotrophic tyrosine kinase, receptor, type 2 -2.77 0.00005 0.01646 COL11A1 collagen, type XI, alpha 1 -2.39 0.00005 0.01646
ITGA10 integrin subunit alpha 10 -2.31 0.0001 0.02992
ITGB2 integrin subunit beta 2 -2.28 0.00005 0.01646
FGFR2 fibroblast growth factor receptor 2 -2.22 0.00005 0.01646
PTGS1 prostaglandin-endoperoxide synthase 1 -2.14 0.00005 0.01646
ACAN aggrecan -1.91 0.00005 0.01646
MOXD1 monooxygenase, DBH-like 1 -1.72 0.0002 0.04702
PDK4 pyruvate dehydrogenase kinase 4 -1.72 0.00005 0.01646
ITGB8 integrin subunit beta 8 -1.6 0.00005 0.01646
FNDC1 fibronectin type III domain containing 1 -1.59 0.00015 0.0395
SYNPO2 synaptopodin 2 -1.45 0.00015 0.0395
EDIL3 EGF-like repeats and discoidin I-like domains 3 -1.42 0.0002 0.04702
The interactions among the identified DEGs were examined using IPA. RNA-seq data were mapped onto the networks to explore dynamic changes during the MSC process. The biological functions that were significantly associated with the genes in the core networks were identified by functional analysis based on the Ingenuity’s knowledge base. In all, 33 candidate network genes and 54 candidate functions/pathways genes were found. Table 4 shows the most significant gene networks of the DEGs of MSCs in osteoporosis patients. The top functions were related to cellular development, differentiation of cells, differentiation of connective tissue cells, cellular growth, and proliferation. Moreover, the differentiation of adipocytes was also related to the identified DEGs (NR4A1, NR4A2 and NR4A3).
I also investigated IPA canonical pathways in the DEGs of MSCs in osteoporosis patients (Table 5). The associated effects were categorized in the mitotic roles of polo-like kinase, integrin signaling, Signal transducer and activator of transcription 3 (STAT3) pathway, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling, cell cycle: G2/M DNA damage checkpoint regulation and cell cycle regulation. In addition, pathways related to osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis were altered. Interestingly, changes in the adipogenesis pathway were also found.
Table 4. Enriched function annotation of differentially expressed gene sets using ingenuity pathway analysis.
Functions
Annotation p-value Molecules
Cartilage
development 4.72E-08 COL11A1, DLX2, HAPLN1, HBEGF, ITGA10, ITGB8, MMP13
Development of
connective tissue 5.20E-07 COL11A1, DLX2, HAPLN1, HBEGF, IL11, ITGA10, ITGB8, MMP13
Width of hypertrophic chondrocyte layer
6.22E-06 FGFR2, ITGA10, MMP13, PKD1
Quantity of
leukocytes 2.11E-05
ACAN, BUB1, EGR3, FGFR2, HBEGF, IL11, ITGB2, ITGB8, MMP13, NR4A1, PTTG1, SHCBP1, TACC3
Quantity of T-
lymphocytes 1.26E-04 ACAN, EGR3, FGFR2, ITGB2, ITGB8, NR4A1, PTTG1, SHCBP1, TACC3
Generation of cells 7.23E-04 COL11A1, EGR3, IL11, ITGB2, ITGB8, NR4A1, NR4A2, PTGS1, TK1
Differentiation of
lymphocytes 1.17E-03 DUSP5, EGR3, ITGB2, ITGB8, NR4A1, NR4A2, TK1
Development of
blood cells 1.28E-03 EGR3, IL11, ITGB2, ITGB8, NR4A1, NR4A2, PTGS1, TK1
T cell development 1.60E-03 EGR3, ITGB2, ITGB8, NR4A1, NR4A2, PTGS1, TK1
Cellular
homeostasis 2.05E-03 EGR3, ITGB2, ITGB8, NR4A1, NR4A2, PDK4, PTGS1, TK1
Quantity of
macrophages 3.14E-03 HBEGF, IL11, ITGB2, SHCBP1
Differentiation of 3.28E-03 DUSP5, EGR3, FGFR2, IL11, ITGB2, ITGB8,
cells NR4A1, NR4A2, NR4A3, TK1 Differentiation of T-
lymphocytes 7.03E-03 EGR3, ITGB2, ITGB8, NR4A1, NR4A2 Quantity of
thymocytes 7.53E-03 EGR3, ITGB2, NR4A1, TACC3 Quantity of CD4+
T-lymphocytes 1.40E-02 ACAN, ITGB2, SHCBP1 Quantity of
phagocytes 1.44E-02 HBEGF, IL11, ITGB2, MMP13, SHCBP1 Development of
2.11E-02 FGFR2, IL11, NR4A1, NR4A2, NR4A3
Development of
lymphatic system 2.31E-02 EGR3, IL11, TK1 Differentiation of
adipocytes 2.58E-02 NR4A1, NR4A2, NR4A3
Apoptosis 2.76E-02 EGR3, FGFR2, IL11, ITGB2, NR4A1, PTTG1
Table 5. Canonical ingenuity pathway analysis of the 53 differentially expressed genes of the mesenchymal stem cells of osteoporosis subjects.
Ingenuity Canonical Pathways log(p-value) Ratio Molecules
Mitotic Roles of Polo-Like Kinase 3.11E+00 4.92E-02 CDC20, PTTG1, CCNB1
Leukocyte Extravasation Signaling 2.62E+00 2.09E-02 ITGB2, EDIL3, MMP13, MMP1
Oncostatin M Signaling 2.35E+00 5.88E-02 MMP13, MMP1
Role of IL-17F in Allergic Inflammatory
Airway Diseases 2.19E+00 4.88E-02 MMP13, IL11
Role of IL-17A in Arthritis 1.96E+00 3.70E-02 MMP13, MMP1 Granulocyte Adhesion and Diapedesis 1.90E+00 1.83E-02 ITGB2, MMP13,
MMP1
Agranulocyte Adhesion and Diapedesis 1.86E+00 1.76E-02 ITGB2, MMP13, MMP1
Integrin Signaling 1.72E+00 1.55E-02 ITGB2, ITGA10,
ITGB8
STAT3 Pathway 1.71E+00 2.74E-02 NTRK2, FGFR2
Role of Osteoblasts, Osteoclasts and
Chondrocytes in Rheumatoid Arthritis 1.63E+00 1.44E-02 MMP13, MMP1, IL11
DNA damage-induced 14-3-3σ Signaling 1.29E+00 5.56E-02 CCNB1
GADD45 Signaling 1.26E+00 5.26E-02 CCNB1
Complement System 1.07E+00 3.33E-02 ITGB2
NF-κB Signaling 1.06E+00 1.19E-02 NTRK2, FGFR2
ILK Signaling 1.04E+00 1.17E-02 ITGB2, ITGB8
IL-8 Signaling 1.02E+00 1.14E-02 ITGB2, HBEGF
IL-17A Signaling in Fibroblasts 1.01E+00 2.86E-02 MMP1 MSP-RON Signaling Pathway 8.95E-01 2.17E-02 ITGB2 Cell Cycle: G2/M DNA Damage Checkpoint
Regulation 8.78E-01 2.08E-02 CCNB1
Eicosanoid Signaling 8.02E-01 1.72E-02 PTGS1
Calcium-induced T Lymphocyte Apoptosis 7.88E-01 1.67E-02 NR4A1
CD40 Signaling 7.63E-01 1.56E-02 PTGS1
NF-κB Activation by Viruses 7.11E-01 1.37E-02 ITGB2 Role of Macrophages, Fibroblasts and
Endothelial Cells in Rheumatoid Arthritis 6.96E-01 7.12E-03 MMP13, MMP1 Cyclins and Cell Cycle Regulation 6.95E-01 1.32E-02 CCNB1
G Beta Gamma Signaling 6.57E-01 1.19E-02 HBEGF
Corticotropin Releasing Hormone Signaling 5.76E-01 9.62E-03 NR4A1
RhoA Signaling 5.55E-01 9.09E-03 SEPT7
Role of Pattern Recognition Receptors in
Recognition of Bacteria and Viruses 5.26E-01 8.40E-03 IL11
Adipogenesis pathway 4.97E-01 7.75E-03 FGFR2
Signaling by Rho Family GTPases 3.28E-01 4.69E-03 SEPT7 Protein Ubiquitination Pathway 2.84E-01 4.07E-03 CDC20 Glucocorticoid Receptor Signaling 2.63E-01 3.77E-03 MMP1
IPA software enables the systemic analysis of RNA-seq and other data in a biologic context. My up- or downregulated genes were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Then the networks of these focus genes were algorithmically generated based on their interrelationships. Fig.2 includes several genes encoding the regulators of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis and glucocorticoid receptor signaling. In the network, the NR4A family is associated with the roles of these cells in rheumatoid arthritis.
To elucidate the common pathways related to osteoporosis and adipogenesis, the DEGs in the obesity-related network were identified from among the 53 DEGs using IPA (Fig.3). Numerous DEGs were related to the obesity pathway, such as the NR4A family, matrix metallopeptidase 1 (MMP1), and fibroblast growth factor receptor 2 (FGFR2). Among them, the NR4A family was highly upregulated in osteoporosis patients and was closely related to the adipogenesis pathway. In addition, TNF, which is an upstream regulator of the NR4A family, is related to the adipogenesis pathway, NF-κB signaling and glucocorticoid receptor signaling.
Fig. 2. Top networks showing interactions between selected differentially expressed genes in the mesenchymal stem cells (MSCs) of osteoporosis subjects.
Red indicates transcripts upregulated in the MSCs of osteoporosis subjects. Green indicates transcripts downregulated in the MSCs of osteoporosis subjects. White indicates transcripts not differentially expressed in my study but important in the network. The color gradient in the network indicates the strength of the expression denoted by fold changes. (→) indicates direct interaction between the transcript products; (--->) indicates indirect interaction between the transcript products; ⟲ indicates autoregulation.
Fig. 3. Differentially expressed genes (DEGs) in the obesity-related network among the 53 DEGs using ingenuity pathway analysis. Numerous DEGs were related to the obesity pathway, such as the NR4A family, matrix metallopeptidase 1 (MMP1), and fibroblast growth factor receptor 2 (FGFR2). Among them, the NR4A family was highly upregulated in osteoporosis patients and was closely related to the adipogenesis pathway. Red indicates transcripts upregulated in the mesenchymal stem cells (MSCs) of osteoporosis subjects. Green indicates transcripts downregulated in the MSCs of osteoporosis subjects. White indicates transcripts not differentially expressed in my study but important in the network.
The color gradient in the network indicates the strength of expression denoted by fold changes. (→) indicates direct interaction between the transcript products; (--->) indicates indirect interaction between the transcript products; ⟲ indicates autoregulation.
To validate this family as candidate common genes related to osteoporosis and adipogenesis, the expression levels of the mRNAs of NR4A1, NR4A2, and NR4A3 were compared using semi-quantitative RT-PCR with other independent subjects (two osteoporosis patients and controls, two obese patients and non-obese controls with similar BMDs (T-score < -1 and < -2.5]) (Fig.4). The baseline characteristics of these subjects are shown in Table 6. The expression level of NR4A1 mRNA was significantly higher in osteoporosis patients than in controls (p=0.018). However, there were also nonsignificant trends of increased expression of NR4A2 (p=0.056) and NR4A3 (p=0.664) in osteoporosis patients compared to controls. The expression levels of NR4A2 mRNA were significantly higher in obese patients than in controls (p=0.041). There were also trends of increased expression of NR4A1 (p=0.450) and NR4A3 (p=0.308) in obese patients compared to controls. I also compared the expression of the NR4A family between the MSCs of ovariectomized and sham-operated mice using RT-PCR. NR4A1 and NR4A3 were significantly upregulated in ovariectomized mice (p<0.001) (Fig.4).
Fig .4. Increased expression of the NR4A family in other osteoporosis subjects (A), obese subjects (B), and ovariectomized mice (C). Data are expressed as the expression level of NR4A1–3 relative to the expression level of actin. Sham: sham-operated DDY female mice; Ovx: ovariectomized DDY female mice.
Table 6. Clinical characteristics of the study subjects for validation of NR4A family as candidate common genes related to osteoporosis and adipogenesis.
spine, g/cm2 1.180±0.164 0.719±0.023 0.108 1.200±0.235 0.979±0.058 0.515 Femur
Neck, g/cm2 0.875±0.030 0.610±0.034 0.028 0.724±0.028 0.730±0070 0.952 Total Hip,
g/cm2 1.083±0.050 0.696±0.030 0.022 0.761±0.011 0.892±0.101 0.417 Data are mean ± SEM
IV. DISCUSSION
I identified DEGs between postmenopausal osteoporosis patients and controls with normal BMD using RNA-seq data from human MSCs. Accurate NGS technology was utilized to perform RNA-seq. With the identified DEGs, the common pathways related to osteoporosis and adipogenesis were evaluated using IPA. The NR4A family may represent common genes between osteoporosis and adipogenesis.
MSCs may be the best resource for research on the relationship between bone and fat because they are the common origins of osteoblasts and adipocytes.6 With these MSCs, RNA-seq was performed to identify DEGs between osteoporosis patients and controls using the reliable NGS technology13-15,22. The DEGs between postmenopausal osteoporosis patients and controls were rigorously identified by applying a false discovery rate of q <0.05 and log2 FC >2 or <-2. In all, 53 DEGs were identified. Well-known genes related to osteoporosis such as Runt-related transcription factor 2 (Runx2), Low-density lipoprotein receptor-related protein 5 (LRP5), and SOST23 were not different between the osteoporosis and normal subjects, suggesting that it occurs at a later stage in the osteoblast lineage than that reflected by the MSCs.24 However, pathway analysis revealed that the DEGs were closely related to the differentiation of cells, particularly for connective tissue cells, suggesting that the genetic changes in MSCs related to the differentiation of these cells could affect the pathogenesis of osteoporosis. Interestingly, enriched function annotation from IPA analyses of DEG sets revealed that the differentiation of adipocytes was also related to the identified DEGs (NR4A1, NR4A2, and NR4A3).
The RNA-seq data showed that the NR4A family was upregulated in osteoporosis patients. According to the baseline characteristics of the subjects, there were no significant differences in the weight and BMI between osteoporosis and controls;
rather, the weight and BMI seemed to be lower in osteoporosis patients. Therefore, it was not possible that the obesity of the osteoporosis group affected the upregulation of the NR4A family. Hence, it could be inferred that the genetic preference of MSCs for adipogenesis might affect the pathogenesis of osteoporosis. In fact, bone and fat cells share a common origin (i.e., MSCs), and their fates are inter-related in both the physiologic and pathologic states.6
The NR4A subfamily of orphan nuclear receptors is made up of NR4A1/NUR77/NBFIB, NR4A2/NURR1, and NR4A3/NOR1.25The NR4As are early responders to stimuli such as ß-adrenergic signaling, insulin, growth factors, glucose, lipopolysaccharide, lipids, glucagon, inflammation, and various forms of cellular stress.25-27 These transcription factors are also involved in various functions such as cell cycle regulation, apoptosis, steroidogenesis, adipogenesis, and energy metabolism.25-26,28 Moreover, there is strong upregulation of NR4As in cases of extreme obesity, and these levels normalize after fat loss.25 This suggests the potential relationship between the NR4A family and obesity. The NR4A family is also induced by parathyroid hormone in bone.29-31 In an in-vitro study, there was cross-talk betweenthe ß-catenin signaling pathway and NR4A orphan nuclear receptorsin osteoblasts. NR4A receptors repressed ß-catenin-mediatedtransactivation, which is crucial in the developmentand function of bone tissue32. This also suggests
the possible relationship between the NR4A family and bone metabolism. My IPAs also revealed a possible relationship between NR4A1 and corticotropin-releasing hormone (CRH) signaling. NR4A2 is related not only to the adipogenesis pathway but also to glucocorticoid receptor signaling through TNF. Glucocorticoids markedly enhance marrow adipogenesis at the expense of osteoblast differentiation.10 Therefore, the NR4A family might represent commonly related genes for bone and fat metabolism via CRH signaling. NR4A1 and NR4A2 enhance transcriptional activity of the CRH and proopiomelanocortin (POMC) promoters, while also being upregulated by CRH treatment in pituitary cells, thereby mediating expression of hypothalamic and pituitary stress hormones33-34. In fact, RT-PCR indicated increased expression of the NR4A family in other osteoporosis and obese subjects compared to controls, although the number of subjects for the validation was small and the results were not completely consistent. However, the osteoporosis patients used for RNA-seq were not obese according to their BMIs, and the adipogenesis pathway was upregulated in these subjects. Increased marrow adipogenesis could be suggested as a reason. Although the amount of marrow fat for the subjects was not measured in this study, an inverse association between marrow adipose tissue and measures of bone density and strength in adults has been reported.35Aging is associated with a significant increase in marrow adiposity.36Excess marrow fat has been implicated in the pathogenesis of bone fragility in premenopausal women with idiopathic osteoporosis12 and anorexianervosa.36In this study, I also compared the expression of the NR4A family between the MSCs of ovariectomized and sham-operated mice using RT-PCR to investigate if these candidate genes could be commonly related to
the postmenopausal model. NR4A1 and NR4A3 were significantly upregulated in the MSCs of ovariectomized mice. Both in vitro and in vivo studies have shown that various conditions that promote bone marrow adipogenesis, particularly estrogen withdrawal, limit osteoblastogenesis.37The absence of estrogen receptor- activation accelerates the development of marrow adipocytes in ovariectomized animals.36The upregulated NR4A family might affect the pathogenesis of osteoporosis by increased marrow adipogenesis rather than systemic obesity.
This study had some limitations. First, the sample size of four subjects for each group may appear to be small. However, Itried to recruit the subjects of each group with prominent differences in medical characteristics to identify definite DEGs.
I recruited controls with normal BMD without any medical history that would have affected bone metabolism. Moreover, the osteoporosis subjects were also carefully selected. Subjects with a definite medical history of osteoporotic fracture were recruited in this study, not only those with low BMD (T-score ≤ -2.5). Despite the small sample number, I identified distinct transcriptional changes in osteoporosis between two different groups with a reliable medical history. In addition, due to the polygenic architecture of osteoporosis, the osteoporosis trait arises in each individual from the combined effects of a large number of genetic variants.38-39 In most cases, there is sufficient statistical power to identify some of these genetic variants even with a small sample size.39Second, I only analyzed the transcriptional differences related to the molecular genetic mechanism in osteoporosis. While individual omics studies (genome/transcriptome/epigenome/proteome) are useful for identifying molecular genetic mechanisms, defects at any level (DNA, mRNA, epigenes, and
proteins) may or may not translate into the next level(s), ultimately impacting disease risk. Moreover, epigenetic factors (epigenes) and their interactions (such as those between DNA methylation and miRNA) modulated by the environment affect gene expression into mRNAs/proteins and mRNA stability.39 Further multi-omics studies are needed to verify the identified DEGs in the molecular genetic mechanism in osteoporosis.
However, this study identified DEGs related to osteoporosis with the MSCs of reliable study subjects using NGS. Using MSCs as the common origin of osteoblasts and adipocytes, I searched for a common pathway related to adipogenesis and osteoporosis via IPA. Interestingly, the NR4A family was found as a possible candidate gene for the common pathway. I also validated these genes with other MSC samples from independent subjects. To the best of my knowledge, these procedures have not been attempted to investigate a common mechanism related to osteoporosis and adipogenesis. In this regard, this study has strengths. Moreover, these procedures using MSCs might be applicable to research for common
However, this study identified DEGs related to osteoporosis with the MSCs of reliable study subjects using NGS. Using MSCs as the common origin of osteoblasts and adipocytes, I searched for a common pathway related to adipogenesis and osteoporosis via IPA. Interestingly, the NR4A family was found as a possible candidate gene for the common pathway. I also validated these genes with other MSC samples from independent subjects. To the best of my knowledge, these procedures have not been attempted to investigate a common mechanism related to osteoporosis and adipogenesis. In this regard, this study has strengths. Moreover, these procedures using MSCs might be applicable to research for common