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. 2025 Jul 10;77(4):144. doi: 10.1007/s10616-025-00810-x

Modulatory roles of bone morphogenetic protein 2 and asporin in osteo/cementoblast differentiation potential of human periodontal ligament stem cells: a multivariate model analysis

Karina Gonzales Silvério 1, Gabriela Bessa Marconato Antunes 1, Bruno Cazotti Pereira 1, Francisco Naldo Gomes Filho 1, Nathalia Reiche Moreira 1, Renato Corrêa Viana Casarin 1, Enilson Antonio Sallum 1, Catharina Marques Sacramento 1,✉
PMCID: PMC12246313  PMID: 40656124

Abstract

Understanding the molecular mechanisms regulating osteo/cementogenic differentiation is critical for optimizing cell-based strategies in periodontal regeneration. This study employed multivariate statistical modeling to investigate, in vitro, the interaction between bone morphogenetic protein 2 (BMP2) and asporin (ASPN) in human periodontal ligament stem cells (hPDLSCs) and its impact on mineralization potential. Four primary hPDLSC populations—two with high osteo/cementogenic potential (HOP) and two with low potential (LOP)—were cultured under standard (SDM) and osteo/cementogenic (OM) conditions. Mineralization was assessed using Alizarin Red Staining (AR-S) and alkaline phosphatase (ALP) activity, while gene expression of ASPN, BMP2, osteocalcin (OCN), and runt-related transcription factor 2 (RUNX2) was quantified by quantitative real-time polymerase chain reaction (qRT-PCR). BMP2 protein levels were measured using the Luminex system. Data analysis incorporated Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Pearson correlations, correlation networks, and multivariate regression models in RStudio. HOP cells exhibited higher mineralization and BMP2 expression compared to LOP cells (p < 0.05). ASPN negatively correlated with BMP2 expression and mineralization, particularly in LOP cells (p < 0.01), reinforcing its inhibitory role. Multivariate modeling identified BMP2 as a key positive regulator of osteo/cementogenesis, while ASPN emerged as a significant inhibitory factor (p < 0.001). This study highlights the potential of multivariate models as powerful tools for uncovering molecular interactions and identifying novel therapeutic targets, paving the way for advancements in periodontal regeneration and cell-based therapies.

Graphical abstract

Created in BioRender. Sacramento, C. (2025).

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Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-025-00810-x.

Keywords: Cell differentiation, Periodontal ligament, Bone morphogenetic protein 2, Mesenchymal stem cells, Bone regeneration, Multivariate Analysis

Introduction

Periodontal regeneration remains a significant challenge in clinical practice, particularly in cases involving large defects where the reconstruction of dental supporting tissues, such as bone and cementum, is essential for new anchorage of periodontal fibers and to maintain tooth stability (Stavropoulos and Sculean 2017; Citterio et al. 2020). Despite current therapies, the predictability of outcomes remains limited, largely due to the complexity of the biological processes involved and the difficulty in promoting effective regeneration in extensive lesions (Sculean and Stavropoulos, 2017; Lin and Sahingur 2020). Proper restoration of these supporting tissues is crucial for ensuring stable attachment and prolonging the longevity of the tooth.

In this context, mesenchymal stem cells (MSCs) derived from oral tissues—including periodontal ligament stem cells (hPDLSCs), dental pulp stem cells (DPSCs), and gingival stem cells—have emerged as promising candidates for regenerative applications due to their accessibility, immunomodulatory capacity, and multilineage differentiation potential (Caputi et al. 2019; Marconi et al. 2020). In particular, hPDLSCs exhibit not only osteogenic and cementogenic capabilities but also responsiveness to physical and molecular stimuli, such as biomaterial surfaces and peptide-based signaling, making them valuable tools for the development of novel therapeutic strategies in periodontal and craniofacial regeneration (Lin and Sahingur 2020).

By contrast, a key obstacle to successful periodontal regeneration lies in the limited availability of hPDLSCs capable of differentiating into osteo/cementogenic lineages (Silvério et al. 2010; Assis et al. 2022). HPDLSCs are a heterogeneous population that plays a crucial role in periodontal tissue regeneration; however, most of these cells exhibit low osteo/cementogenic potential (LOP), while only a minority display a high potential (HOP) for producing mineral matrix and restoring supporting tissues (Singhatanadgit et al. 2009; Sununliganon and Singhatanadgit et al., 2012; Saito et al. 2020; Sacramento et al. 2022). This discrepancy between HOP and LOP phenotypes underscores the importance of understanding the mechanisms regulating osteo/cementogenesis activation and inhibition in these cells (Sacramento et al. 2024). Identifying therapeutic targets capable of converting LOP cells to a phenotype similar to HOP could significantly enhance regenerative outcomes.

Accordingly, bone morphogenetic proteins (BMPs), particularly BMP2, have emerged as key regulators in promoting osteo/cementogenesis (Silvério et al. 2012; Ru et al. 2023). BMP2 is known for its ability to stimulate bone formation and is one of the most extensively studied osteo/cementogenic factors in hPDLSCs (Hayrapetyan et al. 2015). Upon binding to its receptors, BMPR-I and BMPR-II, BMP2 activates the Smad1/5/8 signaling pathway. These Smad proteins then translocate to the nucleus, where they promote the transcription of key osteogenic genes such as runt-related transcription factor 2 (RUNX2) and osteocalcin (OCN), thereby driving the differentiation of hPDLSCs into mineralizing lineages (Phimphilai et al. 2006; Hayrapetyan, et al. 2015).

However, attempts to use recombinant BMP2 in periodontal treatments have yielded inconsistent results, possibly due to challenges such as inadequate dosage and limitations within the cellular microenvironment (Wikesjö et al. 2003; Takahashi et al. 2005). As a promising alternative, enhancing BMP2 expression endogenously could optimize the regenerative capacity of these cells in a more controlled and sustainable manner (Hayrapetyan, et al. 2015; Sacramento et al. 2022).

More recently, Asporin (ASPN) has been identified as a potent inhibitor of BMP2, directly interfering with signaling pathways that promote osteo/cementogenesis (Yamada et al. 2006; Ueda et al. 2016). ASPN, also known as Periodontal Ligament-Associated Protein 1 (PLAP-1), is an extracellular matrix protein that negatively regulates bone mineralization by modulating BMP2 activity and limiting tissue formation (Ikegawa 2008). Experimental models have demonstrated that ASPN interferes with BMP-dependent Smad signaling, and studies in ASPN-deficient mice have shown enhanced bone formation (Yamada et al. 2007; Sakashita et al. 2021). Although these findings highlight ASPN as a relevant antagonist in bone metabolism, its specific role in limiting the osteogenic commitment of hPDLSCs—particularly in populations with low mineralization potential—remains poorly characterized. In preliminary transcriptomic analyses from our group, ASPN emerged as the main BMP antagonist consistently upregulated during osteogenic induction in LOP cells, while other classical inhibitors—such as Noggin, Chordin, and Gremlin (Dudarić et al. 2013; Schneider et al. 2014; Wang et al. 2017; Hu et al. 2017)—showed minimal modulation (Supplementary Table 1). These findings suggest that ASPN may represent a key molecular barrier to differentiation in these cells, warranting further investigation into its regulatory role and interaction with BMP2 in the context of periodontal regeneration.

To address these challenges, the application of precise data science analyses becomes highly advantageous. Predictive statistical models can help better differentiate cell populations and identify factors that directly influence regenerative capacity (Kowal et al. 2021; Zhu et al. 2021). Building a multivariate model is beneficial not only for understanding cellular interactions but also for identifying effective therapeutic targets (Tewary et al. 2018). Such an integrated approach allows us to evaluate both the individual impact of BMP2 and ASPN and their combined influence on cellular processes, helping to define strategies to improve the osteo/cementogenic capacity of hPDLSCs. (Table 1)

Table 1.

qRT-PCR primers’ sequences

Gene Primer (5–3 sequences) Anneling temperature Product Size
18S F: CGGACAGGATTGACAGATTGATAGC 51 °C 118 bp
R: TGCCAGAGTCTCGTTCGTTATCG
ACTB F: CCAACCGCGAGAAGATGA 61 °C 538 bp
R: CCAGAGGCGTACAGGGATAG
ASPN F: CCAACCGCGAGAAGATGA 60 °C 223 bp
R: CCAAGCAAGGTCTTCCAAAG
BMP-2 F: CTTCCACCCCTCTTTCTTCC 58 °C 221 bp
R: GTCTCCCGGAAACACTTGAA
GAPDH F: ACATCATCCCTGCCTCTAC 51 °C 171 bp
R: CCACCTTCTTGATGTCATCATATTTG
OCN F: AGCTCAATCCGGACTGT 55 °C 150 bp
R: GGAAGAGGAAAGAAGGGTGC
RUNX2 F: CCGTCCATCCACTCTACCAC 55 °C 139 bp
R: ATGAAATGCTTGGGAACTGC
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18S: 18S Ribosomal RNA, ACTB: Beta-actin, ASPN: Asporin, BMP2: Bone Morphogenetic Protein 2, GAPDH: Glyceraldehyde-3-Phosphate Dehydrogenase, OCN: Osteocalcin, RUNX2: Runt-Related Transcription Factor 2

Considering that BMP2 signaling is fundamental for the differentiation of hPDLSCs into osteo/cementoblasts, promoting the expression of genes that sustain bone formation, the interaction between ASPN and BMP2 may serve as a central regulatory mechanism in osteo/cementogenesis. Specifically, in LOP cells, ASPN appears to act as a constraint, restricting BMP2 activity and consequently limiting osteo/cementogenic potential (Sacramento et al. 2022; Pereira et al. 2024).

Thus, the aim of this study was to investigate the interaction between BMP2 and ASPN and their relationship with the mineralization potential of hPDLSCs. Through an integrated statistical analysis, we aimed to understand how the regulation and modulation of these factors contribute to osteo/cementogenic differentiation, with the ultimate goal of identifying potential therapeutic interventions to enhance the osteogenic potential of LOP cells and improve the effectiveness of regenerative therapies for large periodontal defects.

Methods

Evaluation of mineralization potential of hPDLSCS

This study, approved by the Ethics Committee of the Piracicaba Dental School, University of Campinas (protocol nº 63,957,722.0.0000.5418), utilized primary cells obtained from the Periocells biobank (B-041), which were previously isolated and characterized as human periodontal ligament stem cells (hPDLSCs) (Silvério et al. 2010). Briefly, hPDLSCs were isolated from the periodontal ligament (PDL) of extracted third molars from different donors using Magnetic Cell Sorting based on the expression of CD105⁺ (indicative of mesenchymal stem cells) and negative selection for CD34⁻ and CD45⁻ to exclude hematopoietic cells. The isolated cells were further characterized by flow cytometry and immunostaining, showing positivity for additional mesenchymal markers including CD73, CD90, CD166, CD146, and STRO-1, and negativity for hematopoietic markers. These cells fulfilled the minimal criteria proposed by the International Society for Cellular Therapy (ISCT), including plastic adherence, expression of MSC markers, and in vitro multipotent differentiation potential into osteogenic and adipogenic lineages, in addition to colony-forming unit–fibroblast (CFU-F) capacity, as previously described (Silvério et al. 2010).

Four populations of hPDLSCs (designated GB, GHP, MS, and NWB) were seeded at a density of 105 cells/well in 24-well plates and cultured under two distinct conditions: Standard Medium (SDM), consisting of DMEM (Gibco®) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin, and Osteo/cementogenic Medium (OM), comprising SDM further supplemented with 50 mg/mL ascorbic acid, 10 mM β-glycerophosphate, and 10⁻5 M dexamethasone. The cells were cultured for 21 days, with medium changes every other day.

To evaluate the osteo/cementogenic potential of these hPDLSCs and classify them as HOP or LOP, matrix mineralization was quantitatively assessed using Alizarin Red Staining (AR-S; Sigma-Aldrich). After fixation with absolute ethanol, cells were incubated with 40 mM AR-S for 10 min, washed four times with phosphate-buffered saline, and subsequently solubilized in citrate–phosphate-citric acid. Absorbance was measured at 562 nm using a VersaMax microplate reader (Molecular Devices).

RNA isolation and qRT-PCR

hPDLSCs were seeded at a density of 4 × 105 cells/well in 100cm2 culture plates with Standard Medium (SDM) and incubated for 24 h. Following this, experimental treatments were applied to the designated groups, with medium changes every other day. After 14 days of culture—chosen based on the differentiation timeline of hPDLSCs into osteo/cementoblasts or cementoblasts (Choi et al., 2011)—total RNA was extracted using TRIzol reagent (Invitrogen™), followed by phenol–chloroform extraction and isopropanol precipitation. Genomic DNA contamination was removed using Turbo DNA-free treatment (Ambion Inc.). RNA quality and concentration were assessed with the NanoDrop® 2000 spectrophotometer (Thermo Fisher Scientific). Complementary DNA (cDNA) was synthesized from 2 μg of total RNA using Roche Diagnostic Co. reagents.

Primers for genes 18S, ACTB, ASPN, BMP2, GAPDH, OCN, and RUNX2 (Table 1) were designed using Primer3web (http://primer3.ut.ee/) and verified using UCSC (https://genome.ucsc.edu/). Primer efficiency was validated, and real-time PCR was conducted on the LightCycler 480 system with the SYBR Green I Master kit (Roche Diagnostics GmbH). Each qPCR reaction used 20 ng of RNA, with water as a negative control. The optimal reference gene (18S, ACTB, or GAPDH) for normalization was identified using NormFinder software (https://moma.dk/normfinder-software). Data were expressed as log2∆CT values.

Total protein isolation and alkaline phosphatase enzymatic activity

hPDLSCs were seeded and cultured as described above. After 14 days, cells were trypsinized, centrifuged at 3,000 rpm for 5 min at 4 °C, and washed four times with phosphate-buffered saline (PBS), centrifuging after each wash. The final pellet was resuspended in RIPA Lysis Buffer (Thermo Fisher Scientific) according to the manufacturer’s guidelines. Lysates were centrifuged at 14,000 rpm for 15 min at 4 °C, and the supernatant was collected. Protein content was quantified using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific).

Alkaline phosphatase (ALP) activity was quantified using the 1-Step™ PNPP Substrate Solution (Thermo Fisher Scientific). A total of 100 µg of each protein sample was processed in 96-well plates, and optical density was measured at 405 nm using a VersaMax microplate reader (Molecular Devices). ALP activity values were normalized to total protein content.

Analysis of BMP2 protein levels via multiplex

BMP2 protein levels were quantified from total protein samples (100µL) using the R&D Systems. The assay was conducted in 96-well plates with sensitive panels according to the manufacturer’s instructions. Microspheres conjugated with monoclonal antibodies against BMP2 (L122889; BR67; R&D Systems) were added to the wells and incubated for 2 h at room temperature. Wells were then thoroughly washed, followed by the addition of secondary antibodies and a further 1 h incubation. Detection was carried out using Streptavidin–Phycoerythrin, and fluorescence intensity was quantified with the Luminex® multiplex reader. Values were normalized to total protein content.

Statistical and data analysis

Data analysis was conducted using RStudio (version 4.3.0–R 4.4.2) with specialized packages to comprehensively explore the dataset. The primary objective was to assess osteo/cementogenic potential, investigate the role of BMP2 and ASPN in mineralization, and characterize differences between HOP and LOP cells. Initially, normality was tested using the Shapiro–Wilk test.

To evaluate osteo/cementogenic potential, absolute levels of mineralization, ALP activity, gene expression, and protein expression were analyzed using the non-parametric Kruskal–Wallis test. Fold changes (log2FC) were calculated to assess modulation between OM and SDM conditions, expressed as the ratio OM/SDM, and compared using the Mann–Whitney test. These results were used to classify cells as HOP or LOP based on markers such as mineralization, gene expressions (RUNX2, OCN), ALP activity, and BMP2/ASPN modulation.

Subsequent analyses focused on BMP2 and ASPN to understand their regulatory roles in HOP and LOP cells. Expression data (Log2ΔCt) were visualized using heatmaps (Pheatmap) to illustrate differences between HOP and LOP populations. Linear Discriminant Analysis (LDA; MASS) was used to determine if absolute gene expression (Log2ΔCt) values could differentiate HOP from LOP. Principal Component Analysis (PCA; FactoMineR) was performed to evaluate whether variations in gene and protein modulation patterns (Log2FC) could effectively differentiate HOP from LOP cells.

Pearson correlation analysis was conducted to examine linear associations between BMP2 and ASPN gene (Log2ΔCt) protein levels (Log2pg/µg), as well as mineralization. Correlations, ranging from -1 to 1, were used to identify relationships among gene/protein variables and mineralization. Positive correlations indicate direct relationships, negative correlations suggest inverse relationships.

To assess the combined influence of variables, a multivariate model (Mineralization ∼ BMP2gene + ASPNgene + BMP2protein + GroupHOP/LOP) was implemented using the stats package in R. Bonferroni correction was applied to expression and correlation p-values to reduce false positives.

A correlation matrix (corrplot) was generated to evaluate interactions between BMP2, ASPN, and mineralization outcomes, visualizing the strength and direction of these relationships. Network analysis (igraph) was also conducted to further illustrate these interactions, highlighting key nodes and connections to identify central regulators in the osteo/cementogenic process. Metrics such as Percentage Increase in Mean Squared Error (%IncMSE) and Increase in Node Purity (IncNodePurity) were calculated using the randomForest algorithm to assess the relative importance of BMP2 and ASPN in mineralization. %IncMSE measures the decrease in model accuracy when a variable is permuted, while IncNodePurity reflects how much a variable contributes to reducing variance within the model. Finally, the multivariate model was visualized with pairwise scatter plots in ggplot2, providing a comprehensive depiction of relationships among variables and illustrating interactions between BMP2, ASPN, and mineralization.

Statistical differences were considered significant at p < 0.05. Graphical representations include symbols denoting p-values: p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).

Results

HOP shows greater mineralization and RUNX2 modulation compared to LOP

The AR-S staining results for HOP (GHP and NWB) and LOP (GB and MS) cells demonstrate that, even under SDM conditions, HOP cells exhibit a significantly greater mineralization capacity compared to LOP cells (p < 0.05). Upon osteo/cementogenic induction, HOP cells displayed a significant increase in mineralization relative to both their own SDM condition (p < 0.01) and to LOP cells (p < 0.01), while LOP cells did not exhibit significant changes following osteo/cementogenic induction (Fig. 1A, B).

Fig. 1.

Fig. 1

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Mineralization capacity analysis of high and low osteo/cementoblast potential hPDLSCs. A Representative images of Alizarin Red S (AR-S) staining demonstrate that high osteo/cementogenic potential (HOP) cells cultured in osteogenic medium (OM) for 21 days exhibit markedly more intense staining than low osteo/cementogenic potential (LOP) cells, indicating superior mineralization capacity. B Quantitative analysis of AR-S staining confirms significantly higher mineralization levels in HOP cells under OM compared to LOP (**p < 0.01). C, D Gene expression analysis reveals significantly greater positive modulation (log2FC) of RUNX2 in HOP cells after 14 days of osteogenic induction (**p < 0.01), whereas E, F OCN expression levels did not show significant differences between HOP and LOP under any condition. G, H Enzymatic activity analysis of alkaline phosphatase (ALP) showed no significant modulation in either group following 14 days of induction

For osteo/cementogenic gene modulation, RUNX2 expression did not significantly increase in either HOP or LOP cells compared to their respective SDM conditions (p > 0.05). However, LOP cells exhibited higher RUNX2 transcript levels compared to HOP cells in both SDM and OM conditions (p < 0.05) (Fig. 1C). In the fold change analysis (OM/SDM), both HOP and LOP showed positive modulation in response to osteo/cementogenesis, with significantly greater modulation in HOP than in LOP (Fig. 1D; p < 0.01).

For OCN, no significant differences were observed between or within groups, nor were there distinct modulation patterns during osteo/cementogenesis (Fig. 1E, F; p > 0.05). In terms of ALP activity, LOP cells displayed higher enzymatic activity than HOP cells under both SDM and OM conditions (p < 0.05), and both cell types showed increased ALP activity upon osteo/cementogenic induction (Fig. 1G; p < 0.01). However, there were no significant differences in fold change patterns between HOP and LOP (Fig. 1H; p > 0.05).

These findings confirm the greater mineralization potential of HOP cells, as evidenced by their ability to produce more mineralized matrix and greater modulation of RUNX2 expression during osteo/cementogenesis. This underlines the osteo/cementogenic advantage of HOP, suggesting that LOP cells may face intrinsic regulatory challenges limiting their differentiation into osteo/cementogenic lineages.

BMP2 and ASPN gene expression, along with BMP2 protein modulation, distinguish HOP from LOP

Under SDM conditions, BMP2 gene expression was lower in HOP compared to LOP (Fig. 2A; p < 0.05). Upon osteo/cementogenic induction, both groups increased BMP2 mRNA levels, with LOP reaching slightly higher levels (p < 0.05). However, FC analysis (OM/SDM) revealed greater positive modulation in HOP (Fig. 2B; p < 0.001). For ASPN, HOP cells exhibited higher transcript levels under SDM conditions (Fig. 2C; p < 0.05). Upon osteo/cementogenic induction, HOP downregulated ASPN while LOP upregulated it, resulting in similar ASPN levels between the groups in OM (p > 0.05). FC analysis confirmed this, with HOP showing negative modulation and LOP showing positive modulation of ASPN (Fig. 2D; p < 0.05).

Fig. 2.

Fig. 2

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Expression and modulation of BMP2 and ASPN in hPDLSCs. A, B BMP2 gene expression analysis reveals significantly higher positive modulation (OM/SDM) in HOP cells compared to LOP (***p < 0.001). C, D ASPN gene expression shows significant downregulation in HOP under OM, while LOP exhibits positive regulation following osteogenic induction (***p < 0.001). E, F BMP2 protein expression demonstrates significantly elevated levels in HOP under OM (*p < 0.05), with no corresponding increase observed in LOP. G Heatmap highlights the distinct expression profiles of BMP2 and ASPN across conditions, groups, and cell populations. H LDA separates HOP and LOP groups based on BMP2 and ASPN expression (Log2ΔCt). I PCA identifies BMP2 protein expression as a key contributor to the differential osteogenic potential of HOP and LOP cells

BMP2 protein levels were significantly higher in HOP compared to LOP under OM condition (Fig. 2E; p < 0.01). LOP cells did not exhibit significant changes, whereas HOP displayed a notable increase, indicating positive modulation (p < 0.001). LOP remained largely unchanged (fold change ≈ 0).

The heatmap (Fig. 2G) demonstrated that while overall BMP2 and ASPN levels were similar between HOP and LOP, the modulation patterns during osteo/cementogenesis differed. HOP exhibited increased BMP2 modulation and reduced ASPN expression, whereas LOP showed less pronounced BMP2 modulation and increased ASPN, indicating distinct regulatory mechanisms. LDA revealed a clear separation between HOP and LOP along the LD1 axis, which represents the variable that maximizes the difference between the two groups based on Log2ΔCT values for ASPN and BMP2. The density plot along LD1 shows distinct distributions for HOP (orange area) and LOP (blue area), reflecting consistent differences between the groups, though with some overlap. This overlap indicates intrinsic variability among the populations. (Fig. 2H). PCA, based on Log2FC, effectively distinguished HOP from LOP, with BMP2 (gene and protein) and ASPN contributing significantly to the separation (Fig. 2I). The color gradient highlights the relative importance of each variable, with BMP2 protein emerging as the most influential factor along Dim1 (38.6%), underscoring its critical role in osteo/cementogenic potential. In contrast, ASPN gene expression contributed predominantly to Dim2 (35.6%), reflecting its inhibitory effect. The size of the points represents the modulation of individual hPDLSC populations, illustrating variability within HOP (blue) and LOP (orange).

Overall, these results indicate that HOP’s superior osteo/cementogenic potential is driven by effective modulation of BMP2 and ASPN at both gene and protein levels, enhancing HOP’s ability to respond to osteo/cementogenic stimuli, whereas LOP is limited by elevated ASPN expression, reducing its differentiation potential.

ASPN negatively regulates osteo/cementogenesis while BMP2 protein promotes mineralization of hPDLSCs

Linear correlations were analyzed using absolute values (Fig. 3A-C) to directly assess expression or protein levels without normalization, providing a clearer view of intrinsic relationships. A negative correlation was observed between ASPN transcript levels and BMP2 protein levels (r = -0.5652, p = 0.004), indicating that high ASPN expression negatively affects BMP2 protein (Fig. 3C). For mineralization (Fig. 3D-F), ASPN mRNA showed a strong negative correlation (r = -0.7186, p < 0.001), while BMP2 protein levels positively correlated with mineralization (r = 0.4215, p = 0.041), linking higher BMP2 to improved mineralization (Fig. 3F).

Fig. 3.

Fig. 3

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Linear correlation analysis between ASPN, BMP2, and mineralization in hPDLSCs. A Linear correlation between BMP2 mRNA and BMP2 protein levels shows a positive, but non-significant association (p = 0.1204; r = 0.3257). B ASPN mRNA levels exhibit a weak negative linear correlation with BMP2 mRNA levels, though not statistically significant (p = 0.0925; r = -0.3511). C A significant negative linear correlation is observed between ASPN mRNA levels and BMP2 protein levels (**p = 0.004; r = -0.5652). D Linear correlation between BMP2 mRNA levels and mineralization indicates a positive trend, though not statistically significant (p = 0.0885; r = 0.3552). E A significant negative linear correlation is observed between ASPN mRNA levels and mineralization (***p < 0.001; r = -0.7186). F BMP2 protein levels show a significant positive linear correlation with mineralization (*p = 0.041; r = 0.4215)

These findings indicate that ASPN acts as a negative regulator of osteo/cementogenesis, reducing BMP2 protein expression and impairing mineralization, while BMP2 protein positively influences the mineralization process. The opposing roles of ASPN and BMP2 highlight their significance in regulating osteo/cementogenesis.

Integrated model highlights the influence of ASPN and BMP2 on the osteo/cementogenic potential of hPDLSCs

To assess the relationship and impact of BMP2 and ASPN gene expression, as well as BMP2 protein levels, on mineralization, a comprehensive model was developed using FC data to reflect the relative modulation of these variables in response to osteo/cementogenic stimulation. The correlation matrix revealed significant negative correlations between ASPN gene expression and the other variables (BMP2 gene, BMP2 protein, and mineralization). The magnitude of these correlations was visually represented by dots, with larger sizes indicating stronger correlations, such as those between BMP2 protein and ASPN, and BMP2 protein and mineralization (Fig. 4A). The interaction network further confirmed these findings, showing a strong positive correlation between BMP2 protein and mineralization, while ASPN was negatively correlated with all other variables (Fig. 4B).

Fig. 4.

Fig. 4

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Correlation matrix, network, and model evaluation of ASPN, BMP2, and mineralization in hPDLSCs. A Correlation matrix illustrating relationships between ASPN gene expression, BMP2 gene expression, BMP2 protein levels, and mineralization. Negative correlations are observed between ASPN and BMP2 or mineralization, while BMP2 protein levels positively correlate with mineralization. B Network plot visualizing the strength and direction of correlations. Solid lines represent strong correlations, while dashed lines indicate weaker associations. %IncMSE C and IncNodePurity D feature importance plots highlight ASPN gene expression as the most significant predictor of group differentiation (HOP vs. LOP), followed by BMP2 protein expression. E Pairwise scatter plots and density distributions evaluating the relationships between variables across groups. Significant negative correlations are observed between ASPN gene expression and mineralization (*p < 0.05), while BMP2 protein expression shows a strong positive correlation with mineralization (***p < 0.001)

The %IncMSE metric identified ASPN expression as the most important variable distinguishing HOP from LOP, followed by BMP2 (both gene and protein), which reinforces ASPN’s role as a critical negative regulator of osteo/cementogenesis in LOP (Fig. 4C). Similarly, the IncNodePurity analysis highlighted ASPN as the key factor for group separation, followed by BMP2 protein, suggesting that ASPN is fundamental in defining the LOP phenotype, while BMP2 supports osteo/cementogenic differentiation (Fig. 4D).

The detailed analysis of the model included correlations among ASPN gene expression, BMP2 gene and protein expression, and mineralization (Fig. 4E). Overall, ASPN and BMP2 expression showed a negative correlation (r = -0.351). In HOP, this correlation was significant (r = -0.640, p < 0.05), indicating that high ASPN levels are linked to lower BMP2 expression. Conversely, in LOP, the correlation was positive but not significant (r = 0.364), highlighting different regulatory dynamics between these groups. ASPN gene expression also showed a significant negative correlation with BMP2 protein levels, particularly strong in HOP (r = -0.894, p < 0.001), whereas in LOP, it was not significant (r = -0.055). BMP2 gene expression was positively correlated with its protein levels, significant only in HOP (r = 0.724, p < 0.01), suggesting effective translation of BMP2 in HOP. Mineralization also had a significant negative correlation with ASPN expression in HOP (r = -0.671, p < 0.05). BMP2 protein expression showed a strong positive correlation with mineralization, especially in HOP (r = 0.717, p < 0.01).

These findings from the integrated model highlight the importance of jointly analyzing all variables to clearly demonstrate ASPN’s negative regulatory role in LOP and BMP2’s positive role in promoting osteo/cementogenesis, particularly in HOP.

Discussion

Periodontal regeneration remains a significant challenge in clinical practice, particularly in the reconstruction of dental supporting tissues such as bone and cementum, which are essential for providing stable anchorage to periodontal fibers and maintaining tooth stability (Stavropoulos and Sculean 2017; Citterio et al. 2020). Achieving predictable regenerative outcomes requires a comprehensive understanding of the underlying cellular mechanisms that govern these processes, ultimately aiming to identify therapeutic targets that enhance regenerative efficacy (Galli et al. 2021).

In this context, data science approaches have proven valuable in identifying such targets, especially through predictive statistical models capable of differentiating cell populations and unveiling key factors influencing regenerative capacity (Cahan et al. 2021; Del Sol and Jung 2021). This study employed an integrated strategy, combining univariate and multivariate analyses to investigate the osteo/cementogenic potential of HOP and LOP cells derived from the periodontal ligament, with a particular focus on the modulation of BMP2 and ASPN at both gene and protein levels.

Our findings reveal substantial differences in osteo/cementogenic behavior between HOP and LOP cells. HOP cells displayed higher intrinsic mineralization capacity than LOP cells even prior to induction, with a further significant increase observed after 21 days of stimulation. In contrast, LOP cells maintained low mineralization levels throughout, suggesting the presence of regulatory mechanisms that limit their differentiation potential. While RUNX2, a recognized marker of early osteogenic commitment (Liu and Lee 2013; Vimalraj et al. 2015), showed higher basal expression in LOP cells, the fold change after induction was significantly greater in HOP cells. This suggests that although LOP cells higher levels of RUNX2 at rest, they exhibit limited responsiveness upon osteo/cementogenic stimulation. HOP cells, by contrast, demonstrated a more dynamic transcriptional response, consistent with their superior mineralization capacity. These findings reflect the transcriptional plasticity of HOP cells and underscore the intrinsic heterogeneity of PDLSCs in their osteogenic potential (Silverio et al., 2020; Li et al. 2021; Citterio et al. 2020). Notably, no significant differences in OCN fold change or ALP activity were observed between groups, indicating some similarity in osteogenic response. Nevertheless, the distinct modulation pattern of RUNX2 appears to be a key determinant of the enhanced mineralization capacity observed in HOP cells, at least over 14 days of induction.

Among the regulators of osteogenesis, BMP2 stands out as a central factor in promoting osteo/cementogenic differentiation, in part by inducing transcription factors like RUNX2 that activate downstream osteogenic genes (Phimphilai et al. 2006; Hayrapetyan et al. 2015). In our analysis, BMP2 protein levels and their modulation clearly distinguished HOP from LOP populations. HOP cells exhibited more robust upregulation of BMP2 protein following stimulation, in parallel with increased RUNX2 induction and mineralization. These results suggest that HOP cells may possess more efficient post-transcriptional and translational mechanisms supporting BMP2 production and stability (Saito et al. 2020; Sacramento et al. 2024).

Conversely, while BMP2 mRNA levels were also upregulated in LOP cells after osteogenic induction, the absence of a corresponding increase in protein levels suggests impaired translation or enhanced degradation mechanisms. These deficits may stem from impaired translational machinery, misfolding, increased proteasomal degradation, or compromised post-translational modifications essential for BMP2 activation (Tintut et al. 1999; Jonason et al. 2009). Additional mechanisms such as intracellular retention or disrupted trafficking could further limit BMP2 availability (Martinez-Hackert et al. 2021), contributing to attenuated signaling and reduced osteogenic response in LOP cells.

On the other hand, ASPN emerged as a key modulator of the osteo/cementogenic potential of PDLSCs, primarily through its inhibitory effects on BMP2 signaling. In our study, ASPN expression was significantly downregulated in HOP cells following osteogenic induction, whereas LOP cells exhibited increased ASPN expression, particularly under osteo/cementogenic conditions. This contrasting regulation is consistent with previous evidence indicating ASPN as a negative regulator of BMP2 activity (Yamada et al. 2006; Sakashita et al. 2021).

Mechanistically, ASPN appears to impair BMP2 function at multiple levels. In vitro studies have shown that ASPN can bind to BMP2 via its leucine-rich repeat domain, competing with BMPR-IB and reducing Smad1/5/8 phosphorylation, thus inhibiting downstream activation of genes such as RUNX2 (Yamada et al. 2007; Tomoeda et al. 2008). This interference occurs both extracellularly and intracellularly, where ASPN may affect BMP2 folding or secretion, leading to intracellular accumulation (Yamaba et al. 2015). In vivo, Aspn-deficient mice exhibit enhanced BMP2 activity, increased osteoblast differentiation, and greater bone formation, reinforcing the inhibitory role of ASPN in this pathway (Sakashita et al. 2021). Conversely, ASPN overexpression in murine periodontal tissues is associated with reduced mineral deposition and impaired remodeling, closely mirroring the LOP phenotype (Yamada et al. 2007). In human PDLSCs, high ASPN expression correlates with decreased BMP2 protein levels and reduced differentiation capacity (Sacramento et al. 2022; Pereira et al. 2024). In our data, this inverse relationship was particularly evident in LOP cells, suggesting that ASPN may interfere with BMP2 stabilization or translation; however, further investigation is warranted. In this regard, although ASPN is recognized as a BMP2 antagonist, the precise molecular mechanisms—such as its potential effects on intracellular trafficking, receptor binding, and protein degradation—remain to be fully elucidated.

Despite this complexity, recent studies have proposed strategies to overcome ASPN-mediated inhibition. Treatment with active vitamin D3 [1,25(OH)₂D₃] has been shown to downregulate ASPN expression while increasing BMP2 gene and protein levels in LOP cells, thereby enhancing their osteo/cementogenic potential (Pereira et al. 2024). Zhang et al., (2020) further demonstrated that vitamin D3 can reverse LPS-induced inhibition of osteoblastic differentiation by downregulating ASPN, acting via a vitamin D response element in the ASPN/PLAP-1 promoter (Zhang et al. 2020). These findings highlight vitamin D3—and potentially other agents capable of suppressing ASPN—as promising candidates for enhancing the regenerative capacity of PDLSCs with low osteo/cementogenic potential. These results also encourage further research into targeted strategies to modulate the ASPN/BMP2 axis for improved periodontal regeneration.

In this context, we applied a multivariate statistical model that integrated gene expression, protein levels, and mineralization data to explore the interplay between these molecular factors. This comprehensive approach identified BMP2 as a positive predictor and ASPN as a negative predictor of mineralization, in line with their known biological functions (Yamada et al. 2007; Sacramento et al. 2022; Pereira et al. 2024). Specifically, higher BMP2 expression—especially at the protein level—was strongly associated with increased mineral deposition, whereas elevated ASPN levels were linked to reduced mineralization capacity. By considering multiple variables simultaneously, the model accounted for intergroup variability and offered a more detailed perspective on how BMP2 and ASPN may collectively influence the osteo/cementogenic potential of PDLSCs. Still, the directionality and causality of these associations require further validation through targeted functional assays.

Building on these findings, our results support a model in which BMP2 and ASPN act in antagonistic balance to regulate PDLSC differentiation. This regulatory interplay likely occurs through multiple mechanisms rather than a single pathway, potentially involving receptor-level interactions and translational control. Although PCA and LDA analyses enabled partial discrimination between HOP and LOP groups, some overlap remained—particularly in BMP2 and ASPN expression—likely reflecting their shared origin from the dental follicle, a structure known to give rise to heterogeneous PDLSC populations (Silvério et al. 2010; Li et al. 2021). Together, these observations offer a conceptual basis for future research focused on refining regenerative strategies through selective modulation of the BMP2–ASPN axis.

Finally, some limitations of this study should be acknowledged. The use of only four PDLSC populations derived from different donors—reflecting biological variation to some extent—may not fully capture the intrinsic heterogeneity of periodontal stem cell populations. Expanding the number of donor-derived lines in future studies will be important to increase statistical power and improve the generalizability of the findings. Moreover, while BMP2 was evaluated at both gene and protein levels, ASPN analysis was limited to gene expression. Further validation—particularly involving direct molecular interactions—is needed to clarify the mechanisms by which ASPN suppresses BMP2 activity. In this context, techniques such as co-immunoprecipitation, Smad phosphorylation assays, and gain- or loss-of-function studies will be essential (Tomoeda et al. 2008; Yamaba et al. 2015; Sakashita et al. 2021). Nevertheless, studies exploring other BMP antagonists—such as Noggin, Gremlin, and Chordin (Dudarić et al. 2013; Schneider et al. 2014; Wang et al. 2017; Hu et al. 2017)—as well as transcriptional repressors like Smad6 and Smad7 (Sakou et al. 1999), and regulatory miRNAs (van Wijnen et al. 2013), may further elucidate the molecular mechanisms underlying the divergent phenotypes observed in HOP and LOP cells. Additionally, our group is actively working to refine this preliminary model by incorporating experiments focused on protein–protein interactions and receptor-level dynamics within the BMP signaling pathway, with the goal of better defining the regulatory balance between ASPN and BMP2 in PDLSCs.

Despite these limitations, the integrative model presented here offers a valuable foundation for the identification of novel therapeutic targets and reinforces the relevance of modulating the ASPN/BMP2 axis as a promising approach to enhance periodontal regeneration and guide future research.

Conclusion

Our findings suggest that the enhanced osteo/cementogenic potential observed in HOP cells may be associated with more efficient BMP2 regulation and lower ASPN expression, whereas LOP cells appear to exhibit regulatory constraints potentially linked to elevated ASPN levels, which may interfere with BMP2 protein production and mineralization. The multivariate model highlighted ASPN as a potential modulator of BMP2-mediated differentiation, supporting its relevance as a molecular candidate for future investigation. While these results contribute to a better understanding of the molecular dynamics underlying PDLSC heterogeneity, further functional studies are needed to confirm the mechanisms proposed and evaluate their therapeutic applicability. Overall, this study underscores the utility of combining in vitro and computational approaches to explore regulatory networks and generate hypotheses for optimizing cell-based regenerative strategies.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 21 kb) (20.2KB, docx)

Acknowledgements

This study was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)—Finance 132559/2020-0-832416/1999-1. AI was used for writing assistance. Created in BioRender. Sacramento, C. (2025).

Author contributions

Conceptualization: Catharina Marques Sacramento, Karina Gonzales Silvério, Renato Correa Viana Casarin, Enilson Antonio Sallum. Data curation: Catharina Marques Sacramento, Karina Gonzales Silvério. Formal analysis: Catharina Marques Sacramento. Funding acquisition: Karina Gonzales Silvério. Investigation: Gabriela Bessa Marconato Antunes, Bruno Cazotti Pereira, Francisco Naldo Gomes Filho, Nathalia Reiche Moreira. Methodology: Catharina Marques Sacramento, Karina Gonzales Silvério. Project administration: Catharina Marques Sacramento, Karina Gonzales Silvério. Supervision: Catharina Marques Sacramento. Validation: Gabriela Bessa Marconato Antunes, Bruno Cazotti Pereira, Francisco Naldo Gomes Filho, Nathalia Reiche Moreira. Visualization: Catharina Marques Sacramento, Karina Gonzales Silvério, Gabriela Bessa Marconato Antunes, Bruno Cazotti Pereira, Francisco Naldo Gomes Filho, Nathalia Reiche Moreira. Writing – original draft: Catharina Marques Sacramento, Karina Gonzales Silvério, Gabriela Bessa Marconato Antunes. Writing – review & editing: Renato Correa Viana Casarin, Enilson Antonio Sallum.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq),132559/2020-0-832416/1999-1

Data availability

No datasets were generated or analysed during the current study.

Declarations

Conflict of interest

The authors declare they have no financial interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Supplementary Materials

Supplementary file1 (DOCX 21 kb) (20.2KB, docx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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