Abstract
Osteoporosis is a bone condition characterized by reduced bone mineral
density (BMD), poor bone microarchitecture/mineralization, and/or
diminished bone strength. This asymptomatic disorder typically goes
untreated until it presents as a low-trauma fracture of the hip, spine,
proximal humerus, pelvis, and/or wrist, requiring surgery. Utilizing
RNA interference (RNAi) may be accomplished in a number of ways, one of
which is by the use of very tiny RNA molecules called microRNAs
(miRNAs) and small interfering RNAs (siRNAs). Several kinds of
antagomirs and siRNAs are now being developed to prevent the
detrimental effects of miRNAs. The goal of this study is to find new
antagonists for miRNAs and siRNAs that target multiple genes in order
to reduce osteoporosis and promote bone repair. Also, choosing the
optimum nanocarriers to deliver these RNAis appropriately to the body
could lighten up the research road. In this context, we employed gene
ontology analysis to search across multiple datasets. Following data
analysis, a systems biology approach was used to process it. A
molecular dynamics (MD) simulation was used to explore the possibility
of incorporating the suggested siRNAs and miRNA antagonists into
polymeric bioresponsive nanocarriers for delivery purposes. Among the
three nanocarriers tested [polyethylene glycol (PEG), polyethylenimine
(PEI), and PEG-PEI copolymer], MD simulations show that the integration
of PEG-PEI with has-mIR-146a-5p is the most stable (total
energy = -372.84 kJ/mol, Gyration radius = 2.1084 nm), whereas PEI is
an appropriate delivery carrier for has-mIR-7155. The findings of the
systems biology and MD simulations indicate that the proposed RNAis
might be given through bioresponsive nanocarriers to accelerate bone
repair and osteoporosis treatment.
Subject terms: Gene ontology, Biomedical engineering, Bone, Tissue
engineering and regenerative medicine
Introduction
Osteoporosis, a metabolic bone disease characterized by poor bone
density and degeneration of bone architecture that increases the risk
of fractures, affects about 10 million men and women in the United
States^[37]1. Fractures caused by osteoporosis may increase pain,
disability, skilled nursing assignments, overall health care expenses,
and death^[38]2. The primary method for diagnosing osteoporosis is to
measure bone mineral density (BMD) employing non-invasive dual-energy
x-ray absorptiometry. Therapy for osteoporosis includes
bisphosphonates, inhibitors of the receptor activator of nuclear factor
kappa-B ligand, estrogen agonists/antagonists, parathyroid hormone
analogues, and calcitonin^[39]3,[40]4. A cathepsin K inhibitor and a
monoclonal antibody against sclerostin are two emerging drugs that use
unique techniques to treat osteoporosis^[41]5,[42]6.
As per a prior investigation of the cost-effectiveness literature
concerning the effectiveness of oral bisphosphonates, it has been
determined that alendronate and risedronate exhibit the highest degree
of cost-effectiveness in females with low bone mineral density who have
not undergone prior fractures^[43]7. There exist variations in the
guidelines pertaining to the administration of denosumab for
therapeutic purposes. In economic evaluations conducted on therapy for
postmenopausal women, Denosumab outperformed risedronate and
ibandronate. However, despite being equally effective as generic
alendronate, it incurred higher costs^[44]8. According to a study,
Denosumab was found to be a more cost-effective option than
bisphosphonates and teriparatide for the treatment of osteoporosis in
older males^[45]9. In addition to its status as the primary cause of
fractures among the elderly, osteoporosis exhibits a significant
correlation with prolonged periods of bedrest, thereby increasing the
likelihood of severe outcomes^[46]10. Major therapeutic discoveries in
osteoporosis therapy have been achieved in recent years as researchers
obtain better knowledge of bone shape and the underlying processes that
cause osteoporosis.
However, the long-term use of these therapies is limited due to side
effects such as gastrointestinal intolerance, osteonecrosis,
over-suppression of bone turnover, thromboembolic disease, and
increased cancer risk^[47]11. Therefore, there is an urgent need to
develop novel anti-osteoporotic drugs that are safer, more effective,
and have a wider therapeutic window, while minimizing side effects.
Bone regeneration therapies hold potential for treating complex bone
fractures by restoring the function of damaged cells or
tissues^[48]12. Cytokines and growth factors, like bone morphogenetic
proteins (BMPs), are widely employed to enhance the regenerative
properties of materials. However, the clinical use of recombinant
osteogenic proteins is hindered by their instability, high cost, and
short lifespan^[49]11. Consequently, alternative approaches are
necessary to enhance the effectiveness of bone regeneration materials.
In this regard, engineering ribonucleic acid interference (RNAi), which
is a fundamental biological process that regulates gene expression at
the post-transcriptional level, could be considered^[50]13,[51]14. It
involves the specific silencing of target mRNA molecules through a
sequence-specific mechanism. The RNAi-utilizing method is based on two
kinds of small RNA molecules: microRNA (miRNA) and small interfering
RNA (siRNA)^[52]15. miRNAs are single-stranded (18–23 nucleotide) small
non-coding endogenous products that bind to targeted miRNAs and degrade
or block gene translation^[53]16. As a result, miRNAs may have a
considerable influence on a variety of pathological and physiological
processes. Because of their therapeutic potential, anti-miRNAs, or
antagomirs, may be used to prevent the negative effects of
miRNA^[54]17.
Along with miRNAs, siRNAs play a crucial role in the RNAi process that
results in gene knockdown. The mechanism of gene silencing by siRNAs is
more selective than that of miRNAs, which often affect numerous target
genes^[55]18. Synthetic siRNAs have emerged as potential therapeutic
agents for a variety of diseases, including cancer, metabolic
disorders, inflammatory disorders, and infectious diseases^[56]19. Due
to the variations in techniques, the use of siRNAs and miRNAs in
pharmaceutical applications may be regarded as a combination or
parallel treatment. siRNA molecules are designed to degrade specific
mRNA sequences, while antagomirs are modified RNA molecules that
modulate the activity of miRNAs within the cell.
Several therapeutic techniques for siRNAs have been developed and
gained traction in recent years^[57]20. In the context of osteoporosis
and bone repair, siRNA targeting Runt-related transcription factor 2
(Runx2)/Core-binding factor alpha-1 (Cbfa1) has shown promise in
inhibiting heterotopic ossification induced by bone morphogenetic
protein 4 (BMP4), demineralized bone matrix, and trauma in animal
models^[58]21. In addition, inhibition of SOST expression has been
found to enhance osteoblast activity and promote bone formation^[59]22.
These findings highlight the potential of siRNA-based strategies for
preventing and treating abnormal bone formation and improving bone
health.
The development of effective and safe delivery vehicles for therapeutic
RNAi modulators is crucial for advancing RNAi technologies in the
treatment of bone diseases^[60]23. Polymer-based delivery systems,
such as those utilizing polyethylene glycol (PEG), polyethyleneimine
(PEI), and PEG-PEI, play a significant role in this field^[61]24. These
systems offer distinct advantages and show promise in enhancing the
efficacy and safety of RNAi modulator delivery. However, it is
important to recognize that the behavior of specific RNAi modulators
can vary when combined with different polymer-based delivery systems.
The choice of polymer-based system is influenced by the specific
characteristics and requirements of the RNAi modulators, including
their size, charge, and stability.
Biological activity, on the other hand, is based on molecular
interactions, which are the result of macromolecular structures^[62]25.
Molecular dynamics (MD) simulations have evolved into an advanced
method for identifying the relationships between macromolecular
structure and function^[63]26. In addition, physiologically appropriate
durations may be compared to simulation process timeframes. Knowledge
of dynamic macromolecule characteristics is essential for structural
bioinformatics to shift the paradigm from single-structure research to
conformational ensemble assessment^[64]27. MD simulation, which can
mimic biological macromolecules like proteins and genes as well as
their interactions with diverse materials like polymeric nanocarriers,
addresses prospective therapy methods for a variety of disorders.
The integration of systems biology and MD simulation is a powerful
approach in understanding the behavior and stability of RNAi modulators
in polymer-based delivery systems for the treatment of bone diseases.
Systems biology enables the exploration of complex interactions and
regulatory mechanisms associated with RNAi, while MD simulation
provides insights into the structural dynamics and binding affinity of
RNAi modulators and nanocarriers.
To date, several antagomirs and siRNAs have been proposed to mitigate
the negative effects of miRNAs. To the best of our knowledge, no
research has suggested antagomirs and siRNAs and discovered their
transport using bioresponsive nanocarriers for bone healing and
osteoporosis therapy. Almost little prior research has looked at the
use of tailored and specialized small molecules in bone repair. The
goal of this work is to offer novel antagonists for miRNAs and siRNAs,
which decrease osteoporosis by targeting various genes and enhancing
bone repair. Gene ontology techniques were utilized for this item by
accessing several databases. The collected data was then subjected to a
systems biology methodology. In addition, the integration of the
proposed siRNAs and miRNAs with three different kinds of polymeric
nanocarriers was studied using MD simulation.
Materials and methods
Functional enrichment analysis
To identify genes associated with bone regeneration, the regeneration
gene database^[65]28 was utilized, resulting in the identification of
21 relevant genes. For the identification of genes associated with
osteoporosis, the Disgenet database^[66]29 was employed with a score
threshold of > 0.3, leading to the inclusion of 40 genes. The score
threshold was set at > 0.3 to prioritize genes with a stronger
association with osteoporosis, ensuring their relevance to the disease.
This approach helped filter out genes with weaker or less significant
associations, focusing on more robust candidates for further analysis.
The gene ontology (GO) term and KEGG pathway enrichment^[67]30,[68]31
analyses were conducted using the cluster profiler function package in
the R language^[69]32. A significance threshold of p-value < 0.05 was
applied to identify key pathways. The obtained results were visualized
using the ggplot2 R package^[70]33.
Construction and analysis of PPI and TFs network
The protein–protein interaction (PPI) network of the identified genes
was constructed using the STRING database^[71]34. The resulting PPI
network was visualized using Cytoscape v.3.9.0 software^[72]35. The
network's topological characteristics were examined using the cytoHubba
plug-in in Cytoscape. Additionally, the key genes within the PPI
network were identified using the Molecular Complex Detection (MCODE)
app^[73]36. To predict the transcription factors (TFs) associated with
the candidate genes involved in osteoporosis and bone regeneration, the
ChEA3 web server was employed^[74]37. ChEA3 utilizes the Fisher's Exact
Test to determine the TFs that are closely associated with the input
gene set^[75]37.
Analysis by GeneMANIA
To gain further insights into the functions and potential interactions
of the identified genes, we employed the GeneMANIA tool^[76]38.
GeneMANIA is a powerful and user-friendly tool that allows for the
analysis of gene lists and the generation of hypotheses regarding gene
function. In our study, we inputted the names of the genes of interest
and specified Homo sapiens as the study species. GeneMANIA leveraged
comprehensive genomics and proteomics information to expand the gene
clusters, identifying genes with similar functions or shared
properties. The results of this analysis provided an interactive
functional association network, visually illustrating the relationships
among the genes and datasets. This network helped uncover potential
functional interactions and provided valuable insights for further
exploration of the genes' roles in osteoporosis and bone regeneration.
Anti-miRNAs determining and siRNAs designing
To explore the regulatory mechanisms of the identified genes, we
employed a combination of bioinformatics tools and databases. Firstly,
the candidate genes were submitted to the Enrichr server^[77]39 for
miRNA analysis. The Enrichr database provided valuable insights into
the potential miRNAs that may target these genes. To validate the
results obtained from Enrichr, we utilized the miRWalk database^[78]40,
which incorporates multiple datasets, including TargetScan, miRDB, and
miRTarBase, to generate predicted and validated miRNA-binding sites for
known genes.
Next, we focused on the TFs that regulate the expression of specific
miRNAs. The TransmiR database^[79]41 was employed to identify the TFs
associated with miR-1277, miR-7155, miR-146a, miR-503, and miR-542.
This database provided valuable information on the regulatory
relationships between TFs and miRNAs. The resulting TF-miRNA regulatory
network was visualized using the Cytoscape software, allowing us to
better understand the interactions between miRNAs and their potential
targets.
To inhibit the activity of the validated miRNAs, antisense RNAs, also
known as anti-miRNAs or miRNA sponges were designed using the miRNAsong
tool^[80]42. This web-based tool facilitated the generation of specific
constructs that could effectively bind and sequester the targeted
miRNAs. The sequences of HIF1A, IL1B, TNFSF11, and IL6 genes were
retrieved from the NCBI database and utilized in the design process.
Furthermore, to specifically target and silence the selected genes,
siRNAs were designed using Eurofins Genomic's siRNA design tool^[81]43.
The nucleotide sequences of each gene were aligned with nucleotide
BLAST to ensure the safety and specificity of the designed siRNAs,
minimizing the potential for off-target effects. By utilizing these
computational tools and databases, we aimed to uncover potential
miRNA-gene regulatory interactions and design effective anti-miRNAs and
siRNAs for further analysis.
Molecular dynamics simulation
To investigate the behavior of the polymeric nanocarriers and RNAi
complexes, MD simulations were performed. The polymeric nanocarriers,
consisting of 50 monomers, were designed using the CHARMM GUI^[82]44.
The design process involved selecting the system type, specifying the
monomer unit and number of monomers, determining the system size,
performing equilibrium simulation, and generating input files for
equilibration and production steps. The designed nanocarriers,
including PEG, PEI, and PEG-PEI, served as crucial structures for
subsequent molecular dynamics simulations. The RNAi structures were
constructed using Maestro 12.8 from Schrodinger^[83]45. The force
fields for the polymers and RNA were generated using the PolyParGen
online tool^[84]46 and CHARMM GUI, respectively.
The all-atom MD simulations were conducted using GROMACS 2021^[85]47
with the OPLSA force field^[86]48. The nanocarriers in this study were
intentionally designed to have a rod-like shape. This specific shape
was selected due to its recognized advantages in nanocarrier design,
particularly in terms of efficient encapsulation and delivery of cargo
molecules. The elongated morphology of rod-shaped nanocarriers
facilitates their ability to accommodate and transport cargo within
their structures^[87]49. Each carrier-RNA complex was solvated in the
SPCE water model with a concentration of 0.15 M NaCl. Periodic boundary
conditions were applied, and the Particle Mesh Ewald (PME) method was
used to calculate the electrostatic interactions. The temperature was
maintained at 310 K, and the pressure was kept at 1 atm. Prior to the
production runs, the systems underwent energy minimization using the
steepest descent method for 5000 steps. Subsequently, the systems were
equilibrated in the NVT (constant number of particles, volume, and
temperature) and NPT (constant number of particles, pressure, and
temperature) ensembles. The production runs were performed for 100 ns
with a time step of 2 fs.
The root-mean-square deviation (RMSD), radius of gyration (Rg), and
solvent-accessible surface area (SASA) were analyzed using the GROMACS
2021 software. The analysis of van der Waals (vdW), electrostatic, and
total energy was performed using the mmpbsa command in GROMACS 2021.
The visualization of the simulation results was carried out using VMD
1.9.4. By conducting MD simulations, we aimed to gain insights into the
structural stability, conformational changes, and energetics of the
polymeric nanocarriers and their interactions with RNAi. These
simulations provided valuable information for evaluating the
feasibility and efficacy of the proposed delivery system. All the steps
and methods that are employed in this study are shown in Supplementary
Fig. [88]S1.
Results
GO and KEGG enrichment
GO and KEGG pathway enrichment analyses were performed to gain insights
into the biological processes, molecular functions, cellular
components, and pathways associated with bone regeneration (BRP) and
osteoporosis (OSP). For BRP, 21 genes were identified from the
regeneration gene database (Table [89]1), while 40 genes related to OSP
with a score > 0.3 were obtained from the DisGeNET database (Table
[90]2).
Table 1.
The list of 40 genes, participated in the osteoporosis gathered from
DisGeNET database.
No. Name Entrez ID The gene whole name
1 WDR1 9948 WD repeat domain 1
2 PTH 5741 Parathyroid hormone
3 LRP5 4041 LDL receptor related protein 5
4 TNFRSF11B 4982 TNF receptor superfamily member 11b
5 PARK7 11315 Parkinsonism associated deglycase
6 ENO1 2023 Enolase 1
7 CYP19A1 1588 Cytochrome P450 family 19 subfamily A Member 1
8 ACTG1 71 Actin gamma 1
9 PRDX3 10935 Peroxiredoxin 3
10 ATIC 471 5-aminoimidazole-4-carboxamide ribonucleotide
formyltransferase/IMP cyclohydrolase
11 PNP 4860 Purine nucleoside phosphorylase
12 BGLAP 632 Bone gamma-carboxyglutamate protein
13 TNFSF11 8600 TNF superfamily member 11
14 CAP1 10487 Cyclase associated actin cytoskeleton regulatory protein
1
15 KL 9365 Klotho
16 GPX1 2876 Glutathione peroxidase 1
17 GSN 2934 Gelsolin
18 TPI1 7167 Triosephosphate isomerase 1
19 TGF-β1 7040 Transforming growth factor beta 1
20 ANXA2 302 Annexin A2
21 TPM4 7171 Tropomyosin 4
22 VDR 7421 Vitamin D receptor
23 IDH2 3418 Isocitrate dehydrogenase (NADP( +))2
24 IGF1 3479 Insulin like growth factor 1
25 SOD2 6648 Superoxide dismutase 2
26 ESR1 2099 Estrogen receptor 1
27 SIRT1 23411 Sirtuin 1
28 ESR2 2100 Estrogen receptor 2
29 POMC 5443 Proopiomelanocortin
30 AR 367 Androgen receptor
31 IL6 3569 Interleukin 6
32 PKM 5315 Pyruvate kinase M1/2
33 COL1A2 1278 Collagen type I alpha 2 chain
34 GPD2 2820 Glycerol-3-phosphate dehydrogenase 2
35 LEP 3952 Leptin
36 REN 5972 Renin
37 P4HB 5034 Prolyl 4-hydroxylase subunit beta
38 TLN1 7094 Talin 1
39 GAPDH 2597 Glyceraldehyde-3-phosphate dehydrogenase
40 VCL 7414 Vinculin
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Table 2.
The list of 21 genes participated in the bone regeneration gathered
from regeneration gene database.
No Name Entrez ID Gene name
1 GSK3B 2932 Glycogen synthase kinase 3 beta
2 CSF3 1440 Colony stimulating factor 3
3 BDNF 627 Brain derived neurotrophic factor
4 MCAM 4162 Melanoma cell adhesion molecule
5 MMP2 4313 Matrix metallopeptidase 2
6 HGF 3082 Hepatocyte growth factor
7 PTGS2 5743 Prostaglandin-endoperoxide synthase 2
8 NGF 4803 Nerve growth factor
9 MMP9 4318 Matrix metallopeptidase 9
10 HIF1A 3091 Hypoxia inducible factor 1 subunit alpha
11 FGF2 2247 Fibroblast growth factor 2
12 BMP7 655 Bone morphogenetic protein 7
13 RUNX2 860 RUNX family transcription factor 2
14 BMP4 652 Bone morphogenetic protein 4
15 BMP2 650 Bone morphogenetic protein 2
16 SHH 6469 Sonic hedgehog signaling molecule
17 IL1B 3553 Interleukin 1 beta
18 FGF18 8817 Fibroblast growth factor 18
19 SPP1 6696 Secreted phosphoprotein 1
20 KDR 3791 Kinase insert domain receptor
21 TEK 7010 TEK receptor tyrosine kinase
[92]Open in a new tab
GO enrichment analysis revealed distinct functional characteristics for
the OSP and BRP genes (Fig. [93]1). OSP genes were primarily involved
in biological processes such as ossification and the generation of
precursor metabolites and energy. In terms of cellular localization,
OSP genes were predominantly found in the secretory granule lumen,
cytoplasmic vesicle lumen, and vesicle lumen. Moreover, OSP genes
exhibited molecular functions related to receptor-ligand activity and
signaling receptor activator activity.
Figure 1.
[94]Figure 1
[95]Open in a new tab
GO enrichment analysis of (A) OSP dot plot, (B) OSP bar plot, (C) BRP
dot plot, and (D) BRP bar plot. OSP genes were primarily involved in
biological processes such as ossification and the generation of
precursor metabolites and energy, while BRP genes were mainly enriched
in ossification and cell migration in biological processes.
On the other hand, BRP genes were mainly enriched in ossification and
cell migration in biological processes. In terms of cellular
components, BRP genes were primarily associated with the
collagen-containing extracellular matrix and endoplasmic reticulum
lumen. Additionally, BRP genes displayed molecular functions related to
receptor-ligand activity and signaling receptor activator activity. The
KEGG pathway enrichment analysis further elucidated the specific
pathways associated with the OSP and BRP genes (Fig. [96]2). OSP genes
were significantly enriched in the parathyroid hormone synthesis and
platelet activation pathways. In contrast, BRP genes were predominantly
enriched in the PI3K-Akt signaling pathway, the calcium signaling
pathway, and the MAPK signaling pathway.
Figure 2.
[97]Figure 2
[98]Open in a new tab
KEGG enrichment analysis of (A) OSP dot plot, (B) OSP bar plot, (C) BRP
dot plot, and (D) BRP bar plot. OSP genes were significantly enriched
in the parathyroid hormone synthesis and platelet activation pathways.
In contrast, BRP genes were predominantly enriched in the PI3K–Akt
signaling pathway, the calcium signaling pathway, and the MAPK
signaling pathway^[99]30,[100]31.
These results suggest that the underlying mechanisms of OSP and BRP
involve distinct biological processes, cellular components, molecular
functions, and signaling pathways. OSP genes primarily contribute to
bone formation and energy metabolism, while BRP genes are involved in
processes such as cell migration and intracellular signaling.
PPI and cluster
To investigate the PPI and identify functional clusters within the OSP
and BRP gene sets, networks were constructed. The PPI network for BRP
genes consisted of 45 nodes and 241 edges, indicating a substantial
number of interactions among these genes. Similarly, the PPI network
for OSP genes comprised 70 nodes and 320 edges, suggesting a complex
network of interactions (Fig. [101]3).
Figure 3.
Figure 3
[102]Open in a new tab
Protein–protein interaction (PPI) network analysis for OSP and BRP. (A)
PPI network for BRP genes consists of 45 nodes (genes) and 241 edges
(interactions). (B) PPI network for OSP genes consists of 70 nodes
(genes) and 320 edges (interactions).
To further analyze the PPI networks, cluster analysis was performed
using MCODE. The cluster analysis revealed 2 distinct clusters within
the BRP gene network and 3 clusters within the OSP gene network. These
clusters represent groups of genes that exhibit higher connectivity and
potential functional relationships. Enrichment analysis was conducted
to assess the biological significance of the identified clusters. In
the BRP gene network, hub genes within clusters 1 and 2 were found to
be significantly enriched in terms associated with ossification and
osteoblast differentiation, indicating their potential roles in these
processes (Fig. [103]4).
Figure 4.
Figure 4
[104]Open in a new tab
Significant PPI network clusters obtained by MCODE, and their
corresponding enriched BP terms (p-value < 0.05) for (A) BRP and (B)
OSP genes. In the BRP gene network, hub genes within clusters 1 and 2
were found to be significantly enriched in terms associated with
ossification and osteoblast differentiation, indicating their potential
roles in these processes. Similarly, in the OSP gene network, hub genes
within clusters 1, 2, and 3 were significantly enriched in several
biological processes. These included ossification, tissue remodeling,
negative regulation of oxidative stress, and lipid transportation and
localization.
Similarly, in the OSP gene network, hub genes within clusters 1, 2, and
3 were significantly enriched in several biological processes. These
included ossification, tissue remodeling, negative regulation of
oxidative stress, and lipid transportation and localization. These
findings suggest the involvement of these hub genes in diverse
biological processes related to OSP pathology. Overall, the PPI network
and cluster analysis provided insights into the protein interactions
and functional clustering of genes involved in BRP and OSP. The
enrichment analysis highlighted specific biological processes
associated with hub genes within the identified clusters.
TFs-genes regulatory network
To explore the regulatory network between TFs and genes involved in BRP
and OSP, a TF-gene interaction analysis was performed. The top
predicted TFs with a P-value < 0.05 were identified and are presented
in Fig. [105]5. For BRP genes, the top TFs identified were CEBPB, CBX2,
SPP1, CTCF, NFIC, and TCF12. On the other hand, the top TFs for OSP
genes included GABPA, RELA, JUN, ZNF263, MAFK, JUND, CEBPB, TEAD4,
TFAP2A, CTCFL, CTCF, TCF12, MYOD1, TCF3, ESRRA, SRF, BHLHE40, IKZF1,
NR2F2, and FOSL1. Among these TFs, CEBPB, CTCF, and TCF12 were found to
be shared between the BRP and OSP genes, indicating their potential
regulatory roles in both conditions.
Figure 5.
[106]Figure 5
[107]Open in a new tab
Interaction of TFs and target genes between (A) BRP genes and (B) OSP
genes. The green color represents TFs and orange color represent genes.
Among these TFs, CEBPB, CTCF, and TCF12 were found to be shared between
the BRP and OSP genes, indicating their potential regulatory roles in
both conditions.
The CCAAT Enhancer Binding Protein Beta (CEBPB) is closely associated
with bone cells and has been shown to impact bone mass
regulation^[108]50. Dysregulated expression of CEBPB has been found to
have differential effects on bone mass, suggesting its crucial role in
bone metabolism and remodeling. Altered CEBPB activity may contribute
to the development or progression of osteoporosis.
The CCCTC-binding factor (CTCF) is a zinc finger protein involved in
DNA binding and the regulation of gene expression^[109]51. It plays a
role in organizing chromatin structure, which influences the expression
of target genes. In osteoblast primary cells, CTCF binding sites have
been found near the promoter region of RUNX2, a master regulator of
osteoblast differentiation^[110]52. This suggests that CTCF may
participate in the regulation of osteoblast function and bone
formation.
Also, transcription factor 12 (TCF12) is a transcription factor that
regulates the differentiation of mesenchymal stromal cells (MSCs),
which are progenitor cells involved in bone formation. Studies have
demonstrated that TCF12 plays a role in osteogenesis and bone
regeneration^[111]53. Its downregulation has been associated with
enhanced bone regeneration, while its overexpression inhibits new bone
formation by affecting BMP signaling. Thus, TCF12 may contribute to the
balance between bone formation and resorption, which is crucial in
maintaining bone health^[112]54.
The shared presence of CEBPB, CTCF, and TCF12 in the regulatory
networks of both BRP and OSP genes suggests their potential involvement
in the underlying mechanisms of both bone regeneration and
osteoporosis. Understanding their regulatory roles and interactions
with target genes can shed light on the molecular processes driving
bone development, remodeling, and the pathological changes observed in
osteoporosis.
Choosing miRNAs and designing siRNAs
Based on the obtained results, GeneMANIA database analysis identified
five genes (BMP4, BMP2, BMP7, HIF1A, and IL1B) associated with
ossification and angiogenesis and five genes (BGLAP, PTH, TGF-β1,
TNFSF11, and IL6) involved in bone remodeling and ossification
(Fig. [113]6). GeneMANIA analysis revealed important genes associated
with bone and osteoporosis. Among them, BMP4, BMP2, and BMP7 were
identified as key regulators of bone development and remodeling,
highlighting their role in promoting osteoblast differentiation and
bone formation. Dysregulation of BMP signaling pathways has been linked
to various bone disorders, including osteoporosis.
Figure 6.
[114]Figure 6
[115]Open in a new tab
Gene ontology analyzing through GeneMANIA database. (A) OSP-involved
genes, (B) BRP-involved genes. GeneMANIA database analysis identified
five genes (BMP4, BMP2, BMP7, HIF1A, and IL1B) associated with
ossification and angiogenesis and five genes (BGLAP, PTH, TGF-β1,
TNFSF11, and IL6) involved in bone remodeling and ossification.
HIF1A, a hypoxia-inducible factor, was found to be associated with bone
and angiogenesis. Its binding to the RANKL promoter enhances
osteoclastogenesis, which can contribute to the bone loss observed in
osteoporosis^[116]55. This highlights the importance of HIF1A in the
bone remodeling process. Also, as IL1B, an inflammatory cytokine, is
implicated in ossification, it promotes the production of RANKL, a key
regulator of osteoclast differentiation and activity. Elevated IL1B
levels have been associated with increased bone resorption and
osteoporosis^[117]56.
Osteocalcin (BGLAP) serves as a marker of osteoblast activity and is
critical for bone mineralization^[118]57. Parathyroid hormone (PTH)
plays a crucial role in maintaining calcium homeostasis and bone
remodeling^[119]58. Transforming growth factor beta 1 (TGF-β1)
influences both osteoblast and osteoclast function, contributing to
bone remodeling^[120]59. Moreover, TNFSF11 (RANKL) is a central
mediator of osteoclastogenesis, and dysregulation of this gene can lead
to bone loss^[121]60. Also, IL6, an inflammatory cytokine, has been
linked to increased bone resorption and decreased bone formation in
osteoporosis^[122]61.
The identified genes are interconnected in their roles and pathways
related to bone and osteoporosis. BMPs, HIF1A, and IL1B can influence
osteoclastogenesis and bone resorption through RANKL
regulation^[123]62. BGLAP, PTH, TGF-β1, and TNFSF11 contribute to bone
remodeling and the maintenance of bone mass. IL6, along with IL1B,
promotes bone resorption processes. These findings emphasize the
intricate relationships between these genes in bone biology and their
relevance to osteoporosis. Further analysis using GeneMANIA and
previous studies suggested that to promote bone regeneration, HIF1A,
IL1B, TNFSF11, and IL6 should be downregulated, while BMP2, BMP4, BMP7,
BGLAP, PTH, and TGF-β1 should be overexpressed.
To achieve gene regulation, miRNAs were predicted, and anti-miRNAs were
designed to suppress the miRNAs' effect on target genes. MiRNA
prediction was performed using the EnrichR database and Fischer's exact
test, followed by validation using the Mirwalk database. Five
anti-miRNAs were selected: has-miR-1277 targeting BMP4, has-miR-7155-5p
targeting BMP2 and BMP7, has-miR-146a-5p targeting BGLAP and TGF-β1,
has-miR-503 targeting PTH, and has-miR-542-3p targeting BMP7. Certain
miRNAs, including miR-1277, miR-146a-5p, miR-503, and miR-542-3p, have
been associated with bone metabolism and osteoporosis. miR-1277 is
downregulated in osteoporotic bone tissue, potentially promoting
osteoblast activity and bone formation. Conversely, miR-146a-5p is
upregulated in osteoporosis, potentially enhancing osteoclast activity
and bone resorption. Additionally, reduced expression of miR-503 and
miR-542-3p in osteoporotic bone tissue suggests their involvement in
osteoblast differentiation and bone formation. However, the specific
role of miR-7155-5p in bone metabolism or osteoporosis remains unclear
and requires further investigation.
In addition, TFs regulating these miRNAs (miR-1277, miR-7155-5p,
miR-146a-5p, miR-503, and miR-542-3p) were explored using
TransmiR^[124]41. The regulatory network analysis identified TFs
associated with these miRNAs. CTCF, CEBPA, TCF12, and HIF1A were found
to regulate miR-7155-5p, while TNFSF11, IL1B, HIF1A, CEBPB, TCF12, and
TGF-β1 were implicated in the regulation of miR-146a-5p (Fig. [125]7).
These TFs have well-established roles in bone metabolism and
osteoporosis, further supporting the significance of these miRNAs in
bone formation^[126]13.
Figure 7.
[127]Figure 7
[128]Open in a new tab
TFs involved in the regulation of (A) has-miR-7155, (B) has-miR-1277,
(C) has-miR-503, (D) has-miR-542, and (E) has-miR-146a-5p. Green color
represents miRNAs and orang color represents TFs. The regulatory
network analysis identified TFs associated with these miRNAs. CTCF,
CEBPA, TCF12, and HIF1A were found to regulate miR-7155-5p, while
TNFSF11, IL1B, HIF1A, CEBPB, TCF12, and TGF-β1 were implicated in the
regulation of miR-146a-5p.
To suppress the expression of the selected genes (HIF1A, IL1B, TNFSF11,
and IL6), siRNAs were designed using Eurofins Genomic's siRNA design
tool. The nucleotide sequences were retrieved from NCBI, and siRNAs
with the best scores were chosen. The off-target effects of each siRNA
were evaluated using the BLAST tool to select the most suitable siRNAs.
Two anti-miRNAs (has-miR-7155-5p and has-miR-146a-5p) along with two
designed siRNAs for HIF1A and TNFSF11 were selected as potential bone
healing modulators (Table [129]3). These RNAi molecules can be further
analyzed for their effectiveness when coupled with various carriers for
delivery purposes. The selection of miRNAs and the design of siRNAs
provide potential strategies for modulating gene expression to promote
bone regeneration. Further experimental validation and delivery system
optimization are necessary to assess their therapeutic potential in
bone healing applications.
Table 3.
anti-miRNA structures for has-miR-146a-5p and has-miR-7155-5p.
No Target miRNA anti-miRNA Length
1 BMP2/BMP7 hsa-miR-7155-5p
5'-GAUGGCCCAAGACCCCAGAGACAGAUGGCCCAAGACCCCAGA-3' 42
2 BGLAP/TGF-β1 hsa-miR-146a-5p
5'-AACCCAUGGAAUUCAGUUCUCAACAGAACCCAUGGAAUUCAGUUCUCA-3' 48
[130]Open in a new tab
MD simulations
In our study, we conducted comprehensive analyses to investigate the
impact of different polymer types on RNA molecules under standardized
conditions. Supplementary Fig. [131]S2 represent the constructed
nanocarriers in this study. Also, the outcomes were visually
represented in Fig. [132]8, illustrating the interaction modes between
various carrier types and RNAi molecules. To assess the behavior of
each RNA, we calculated the
[MATH: Rg :MATH]
over time, as depicted in Fig. [133]9.
[MATH: Rg :MATH]
is a measure of the average distance between any point on the particle
and its center of mass^[134]63. The equation used to compute the
[MATH: Rg :MATH]
is as Eq. ([135]1):
[MATH:
Rg=1N∑i=1N|ri-rcom|2
:MATH]
1
Figure 8.
[136]Figure 8
[137]Open in a new tab
The snapshots of MD simulations among ant-miRNAs and siRNAs with PEG,
PEI, and PEG-PEI. The output interaction of has-miR-146a-5p with (A)
PEG, (B) PEI, and (C) PEG-PEI. The output interaction of has-miR-7155
with (D) PEG, (E) PEI, and (F) PEG-PEI. The output interaction of siRNA
HIF1A with (G) PEG, (H) PEI, and (I) PEG-PEI. The output interaction of
siRNA TNFSF11 with (J) PEG, (K) PEI, and (L) PEG-PEI. Yellow molecules
represent the polymer.
Figure 9.
[138]Figure 9
[139]Open in a new tab
Radius of gyration of anti-miRNAs and siRNAs during MD simulation. (A)
has-miR-146a-5p, (B) has-miR-7155, (C) HIF1A siRNA, (D) TNFSF11 siRNA.
Employing PEG-PEI as a carrier for has-miR-146a-5p and has-miR-7155-5p,
PEG for HIF1A, and PEI for TNFSF11 resulted in the formation of more
compact polymer-RNA complexes.
Here, N represents the total number of atoms in the system,
[MATH: ri :MATH]
denotes the position vector of atom i, and
[MATH: rcom :MATH]
represents the position vector of the center of mass of the particle.
We summarized the average
[MATH: Rg :MATH]
values for each RNA in Table [140]4. Notably, employing PEG-PEI as a
carrier for has-miR-146a-5p and has-miR-7155-5p, PEG for HIF1A, and PEI
for TNFSF11 resulted in the formation of more compact polymer-RNA
complexes.
Table 4.
siRNAs designed to target specified genes.
No Target siRNA structure Length
1 HIF1A AUGGAACAUGAUGGUUCAC 19
2 TNFSF11 AAGGAAUUACAACAUAUCG 19
[141]Open in a new tab
To investigate the conformational changes occurring during MD
simulations, we employed the RMSD as a quantitative measure. The RMSD
analysis offers valuable insights into the average spatial separation
between two distinct groups of atoms within the system. By calculating
the RMSD, we can gauge the extent of atomic fluctuations and ascertain
whether the system has reached equilibrium conditions. This analysis
yields a consistent value that reflects the overall displacement of
atoms over time, thereby providing valuable information about the
stability and dynamics of the molecular system^[142]64. The RMSD is
computed using Eq. ([143]2):
[MATH:
RMSD=1N∑i
=1N(|rit-ri(0)|)2
:MATH]
2
Here, N represents the total number of atoms in the system,
[MATH: rit :MATH]
denotes the current position vector of atom i at time t, and
[MATH: ri(0) :MATH]
represents its initial position vector. The RMSD analysis
(Fig. [144]10) supported the trends observed in the
[MATH: Rg :MATH]
analysis, indicating that PEG-PEI for has-miR-146a-5p and
has-miR-7155-5p, as well as PEG for HIF1A and PEI for TNFSF11,
exhibited lower fluctuations and higher stability. We have summarized
these findings in Table [145]4.
Figure 10.
[146]Figure 10
[147]Open in a new tab
RMSD fluctuation of anti-miRNAs and siRNAs during MD simulation. (A)
has-miR-146a-5p, (B) has-miR-7155, (C) HIF1A siRNA, (D) TNFSF11 siRNA.
Results showed that PEG-PEI for has-miR-146a-5p and has-miR-7155-5p, as
well as PEG for HIF1A and PEI for TNFSF11, exhibited lower fluctuations
and higher stability.
The SASA of the RNAi molecules is influenced by factors such as RNA
length, polymer ratio, and the hydrophilicity or hydrophobicity of the
molecules. Higher SASA scores suggest greater exposure of RNAs to
water, while lower scores indicate a higher degree of burial within the
polymer^[148]65. Our results confirmed that PEG-PEI for
has-miR-7155-5p, PEG for HIF1A, and PEI for TNFSF11 exhibited higher
hydrophobicity. Moreover, integrating has-miR-146a-5p with PEI resulted
in a lower SASA score compared to its interaction with PEG/PEI and PEG
polymers (Fig. [149]11).
Figure 11.
[150]Figure 11
[151]Open in a new tab
SASA of anti-miRNAs and siRNAs during MD simulation. (A)
has-miR-146a-5p, (B) has-miR-7155, (C) HIF1A siRNA, (D) TNFSF11 siRNA.
Integrating has-miR-146a-5p with PEI resulted in a lower SASA score
compared to its interaction with PEG/PEI and PEG polymers.
To deepen our understanding of RNAi molecules, we employed the
Molecular Mechanics/Poisson-Boltzmann Surface Area (MMPBSA) approach
within the GROMACS software to analyze their energy profiles. This
approach allowed us to explore the contributions of vdW, electrostatic,
and total resultant energies. It is important to note that the MMPBSA
method, as implemented in GROMACS, primarily focuses on capturing the
molecular mechanics and solvation energies associated with the binding
process. However, it does not explicitly incorporate entropy
contributions in the binding energy calculations^[152]66.
The vdW energy term provided valuable insights into the interplay
between attractive and repulsive forces among non-bonded atoms. By
assessing steric interactions and atomic overlap, we gained a deeper
understanding of the spatial arrangements and potential clashes within
the RNAi molecules. In parallel, the electrostatic energy term
accounted for the interactions among charged atoms or groups. This
allowed us to capture long-range electrostatic effects, discern regions
of positive and negative charge distribution, and uncover electrostatic
attractions or repulsions between different molecular components.
By combining the vdW and electrostatic energy terms, we obtained the
total resultant energy, serving as a comprehensive measure of the
stability and strength of the RNAi molecules. Through our energy
analysis, we further characterized the RNAi molecules, considering the
vdW, electrostatic, and total resultant energies. These energy
components proved pivotal in assessing stability. By calculating the
average interaction energies between the polymers and RNAi over the
simulation time, as shown in Fig. [153]12, we observed that PEG-PEI
proved to be a suitable carrier for delivering has-miR-146a-5p, while
PEI was effective for has-miR-7155-5p. Similarly, PEG was well-suited
for delivering HIF1A, and PEI was advantageous for TNFSF11, as
evidenced by lower energy levels and enhanced stability (Table [154]5).
These findings collectively support the suitability of PEG-PEI, PEG,
and PEI as carriers for delivering specific RNA molecules involved in
bone formation and remodeling. The MD simulations provided valuable
insights into the stability, conformational changes, hydrophobicity,
and interaction energies of the polymer-RNA complexes, guiding the
selection of optimal carriers for further investigation and potential
therapeutic applications.
Figure 12.
[155]Figure 12
[156]Open in a new tab
Energy analysis between RNAis and polymers. (A) Average van der Waals
interaction energy of PEG, PEI, PEG-PEI with anti-miRNAs and siRNAs,
(B) average electrostatic interaction energy of PEG, PEI, PEG-PEI with
anti-miRNAs and siRNAs, and (C) average total interaction energy of
PEG, PEI, PEG-PEI with anti-miRNAs and siRNAs.
Table 5.
The average of results among RNAis and different type of carriers.
Carrier has-miR-146a-5p has-miR-7155-5p HIF1A TNFSF11
PEG PEI PEG-PEI PEG PEI PEG-PEI PEG PEI PEG-PEI PEG PEI PEG-PEI
vdW −163.27 −37.82 −130.99 −41.62 −255.16 −123.55 −152.37 −60.23 −25.3
−10.79 −102.79 −92.22
Electrostatic energy −44.97 17.07 −241.85 −37.18 −154.97 0.14 −27.79
−47.02 −21.07 −5.1 −180.03 −62.29
Total energy −208.25 −20.75 −372.84 −78.8 −410.13 −123.41 −180.16
−107.24 −46.37 −15.9 −282.82 −154.5
SASA 97.1941 92.29 91.0163 92.41 82.3212 85.5460 41.9838 45.6116
45.0956 43.2122 40.7477 43.9826
Gyration radius 2.4297 2.1736 2.1084 2.7556 2.4739 2.9489 1.6069 2.1073
2.1339 2.2696 1.7092 2.0530
RMSD 2.6534 2.2128 1.9522 3.1690 2.6966 2.9361 1.2511 1.7515 1.7996
2.2100 1.2948 1.6635
[157]Open in a new tab
Discussion
This research focused on the development of antagomirs and siRNAs as
potential therapeutic agents for modulating biological processes in
osteoporosis and bone healing. Gene ontology analysis and systems
biology techniques were employed to gather relevant information from
various databases. Furthermore, the feasibility of utilizing polymeric
bioresponsive nanocarriers for delivering the proposed siRNAs and miRNA
antagonists was evaluated through molecular dynamics simulations. The
findings from this study lay the foundation for further exploration and
optimization of these novel therapeutic approaches in the field of
bone-related disorders.
The integration of GO and KEGG pathway enrichment analyses revealed
distinct functional characteristics and signaling pathways associated
with BRP and OSP. The enrichment analysis identified key biological
processes, cellular components, and molecular functions specific to
each condition. OSP genes primarily contribute to bone formation,
energy metabolism, and receptor-ligand signaling, while BRP genes are
involved in processes such as cell migration and intracellular
signaling. Furthermore, the PPI network analysis and cluster analysis
provided a comprehensive view of the PPI and functional clustering
within the BRP and OSP gene sets. The identified clusters represented
groups of genes with higher connectivity and potential functional
relationships. Enrichment analysis of these clusters revealed their
significance in ossification, osteoblast differentiation, tissue
remodeling, regulation of oxidative stress, and lipid transportation.
The shared TFs CEBPB, CTCF, and TCF12 in the OSP and BRP genes indicate
their potential dual role in bone formation and remodeling, suggesting
their involvement in osteoporosis. Dysregulation of these TFs can
disrupt the delicate balance of bone remodeling, leading to impaired
bone formation and increased susceptibility to osteoporosis. Targeting
these TFs or their downstream signaling pathways holds promise as novel
therapeutic strategies for preventing and treating osteoporosis. Our
analysis sheds light on the complex transcriptional regulatory
mechanisms underlying bone and osteoporosis, offering valuable targets
for further investigation and potential therapeutic interventions.
Our analysis identified two microRNAs, miR-7155-5p and miR-146a-5p,
with potential roles in bone formation. While miR-7155-5p's functions
in bone cells require further investigation, miR-146a-5p has been
extensively studied and found to be a suppressor of osteoblastogenesis
and bone formation. Its expression increases with age and is associated
with age-induced bone loss^[158]67. Deletion of miR-146a-5p has been
shown to protect against bone loss in mice^[159]68. These findings
suggest that targeting miR-146a-5p could be a promising strategy for
mitigating osteoporosis.
The identification of TFs involved in regulating miRNAs associated with
bone formation, particularly miR-7155-5p and miR-146a-5p, sheds light
on the molecular mechanisms underlying osteoporosis. Dysregulation of
bone remodeling processes is a key factor in osteoporosis, leading to
decreased bone density and increased fracture risk. Among the TFs
identified in the TF-miRNA regulatory network, HIF1A and TNFSF11 have
known involvement in bone metabolism and regulation.
HIF1A, a critical factor in bone homeostasis, has been implicated in
both the maintenance of bone balance and the development of
osteoporosis. Through its interaction with specific regions in gene
promoters, such as TNFSF11, HIF1A enhances the production of RANKL, a
pivotal regulator of bone remodeling. This mechanism plays a
significant role in postmenopausal osteoporosis, where excessive bone
resorption occurs^[160]55. The dysregulation of TNFSF11/RANKL disturbs
the delicate equilibrium between bone resorption and formation,
contributing to the pathogenesis of osteoporosis.
In this study, we investigated the functional role of specific
molecules, namely has-miR-7155-5p and has-miR-146a-5p anti-miRNAs, as
well as siRNAs targeting HIF1A and TNFSF11, in the context of bone
formation and remodeling. To effectively deliver these molecules, we
assessed the performance of different carriers, namely PEG, PEI, and
PEG-PEI. The goal was to identify the most suitable carrier for
efficient delivery of these therapeutic agents in the context of
bone-related processes.
The study found that PEG-PEI, PEG, and PEI exhibited stability for
delivering has-miR-146a-5p, HIF1A, and TNFSF11, respectively, as
indicated by low RMSD and
[MATH: Rg :MATH]
fluctuations. The integration of has-miR-146a-5p with PEI resulted in a
lower SASA score, indicating improved complex stability. Moreover,
PEG-PEI, PEG, and PEI showed higher hydrophobicity for delivering
has-miR-7155-5P, HIF1A, and TNFSF11, respectively. The analysis of
energy levels also supported the enhanced stability of the carrier
systems, with PEG-PEI, PEG, and PEI demonstrating lower energy levels.
These findings highlight the potential of these carrier systems for
efficient delivery and sustained release of therapeutic molecules in
bone-related applications.
It is worth noting that previous studies have successfully developed a
PEG-PEI Alginate hydrogel complex, which combined miR-146a/PEG-PEI
nanoparticles with basic fibroblast growth factor (bFGF). This
innovative approach utilized Alginate gel as a carrier and demonstrated
the effectiveness of miR-146a/bFGF/PEG-PEI alginate hydrogel in
promoting the proliferation and odontogenic differentiation of dental
pulp cells (DPCs) in the presence of inflammation^[161]69. These
findings support the notion that a well-designed PEG-PEI alginate
hydrogel nanocomplex can create a favorable microenvironment for
enhancing the tissue regeneration capability of DPCs in response to
inflammation.
Furthermore, we discussed a previous study that introduced a novel
cationic mixed micellar nanoparticle (MNP) system comprised of
poly(ε-caprolactone)-block-poly(2-aminoethylethylene phosphate)
(PCL-b-PPEEA) and poly(ε-caprolactone)-block-poly(ethylene glycol)
(PCL-b-PEG) as a carrier for HIF-1α siRNA in the treatment of hypoxic
tumors^[162]70. The MNP system demonstrated efficient delivery of
siRNA, leading to the inhibition of HIF-1α expression and subsequent
suppression of critical processes such as cell proliferation,
migration, and angiogenesis under hypoxic conditions. These findings
highlight the potential of the MNP system as a targeted therapeutic
approach for addressing the challenges posed by the hypoxic tumor
microenvironment.
In conclusion, our study underscores the importance of TFs and miRNAs
in bone formation and the pathogenesis of osteoporosis. The interplay
between TFs and miRNAs presents potential therapeutic targets for the
treatment of osteoporosis and the improvement of bone health. These
findings enhance our understanding of the molecular mechanisms involved
in bone regeneration and osteoporosis, laying the groundwork for future
research and the development of targeted interventions.
Additionally, our investigation highlights the efficacy of PEG-PEI,
PEG, and PEI as carriers for delivering specific molecules implicated
in bone formation and remodeling. These carrier systems exhibit promise
for therapeutic applications in bone-related disorders. Nevertheless,
further studies are necessary to validate their effectiveness in vivo
and explore their broader impact on bone regeneration and remodeling
processes. Overall, this study provides valuable insights into the
intricate molecular landscape of bone biology and osteoporosis,
offering potential avenues for therapeutic interventions and advancing
our comprehension of bone healing and disease management.
Supplementary Information
[163]Supplementary Figures.^ (1,005.3KB, docx)
Abbreviations
bFGF
Basic fibroblast growth factor
BMD
Bone mineral density
BMPs
Bone morphogenetic proteins
BMP4
Bone morphogenetic protein 4
BRP
Bone regeneration
CEBPB
CCAAT Enhancer Binding Protein Beta
CTCF
CCCTC-binding factor
Cbfa1
Core-binding factor alpha-1
DPCs
Differentiation of dental pulp cells
GO
Gene ontology
MSCs
Mesenchymal stromal cells
miRNA
MicroRNA
MNP
Mixed micellar nanoparticle
MCODE
Molecular Complex Detection
MMPBSA
Molecular Mechanics/Poisson–Boltzmann Surface Area
MD
Molecular dynamics
OSP
Osteoporosis
PTH
Parathyroid hormone
PME
Particle Mesh Ewald
PEG
Polyethylene glycol
PEI
Polyethyleneimine
PPI
Protein–protein interaction
Rg
Radius of gyration
Runx2
Runt-related transcription factor 2
RNAi
Ribonucleic acid interference
RMSD
Root-mean-square deviation
siRNA
Small interfering RNA
SASA
Solvent-accessible surface area
TCF12
Transcription factor 12
TFs
Transcription factors
TGF-β1
Transforming growth factor beta 1
vdW
Van der Waals
Author contributions
All authors contributed to investigation, conceptualization, analysis,
and were involved in the writing process.
Data availability
All the data that was used in this study are presented along with the
findings. The 21 bone regeneration-involved genes were identified
utilizing the regeneration gene database
([164]http://regene.bioinfo-minzhao.org/), while the genes associated
with osteoporosis disease were identified using the Disgenet database
([165]https://www.disgenet.org/) with a score > 0.3, which includes 40
genes.
Code availability
The protein–protein interaction (PPI) network of genes was constructed
using the STRING database ([166]https://string-db.org/), while the PPI
network was visualized using Cytoscape v.3.9.0 software
([167]https://cytoscape.org/). The GO term and KEGG pathway enrichment
analyses were performed based on the Cluster Profiler function package
of the R language ([168]https://github.com/YuLab-SMU/clusterProfiler)
to identify the key pathways (p-value < 0.05 was considered
significant). Then, the results were visualized using the ggplot2 R
package ([169]https://ggplot2.tidyverse.org/).
Competing interests
The authors declare no competing interests.
Footnotes
The original online version of this Article was revised: The original
version of this Article contained an error in the spelling of the
author Nima Beheshtizadeh, which was incorrectly given as Nima
Behestizadeh.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Change history
2/5/2024
A Correction to this paper has been published:
10.1038/s41598-024-53032-0
Supplementary Information
The online version contains supplementary material available at
10.1038/s41598-023-45183-3.
References