
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12306-7
10.1016/j.heliyon.2024.e36275
e36275
Research Article
Increased DNA damage of adipose tissue-derived mesenchymal stem cells under inflammatory conditions
Páhi Zoltán G. ab
Szűcs Diána cde
Miklós Vanda af
Ördög Nóra bcd
Monostori Tamás ce
Varga János e
Kemény Lajos ceg
Veréb Zoltán vereb.zoltan@med.u-szeged.hu
ce⁎⁎1
Pankotai Tibor pankotai.tibor@szte.hu
ab⁎
a Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, University of Szeged, Szeged, Hungary
b Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
c Competence Centre of the Life Sciences Cluster of the Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Szeged, Hungary
d Doctoral School of Clinical Medicine, University of Szeged, Szeged, Hungary
e Regenerative Medicine and Cellular Pharmacology Laboratory, Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
f USZ Biobank, University of Szeged, Szeged, Hungary
g Hungarian Centre of Excellence for Molecular Medicine (HCEMM), HCEMM-USZ Skin Research Group, University of Szeged, Szeged, Hungary
⁎ Corresponding author. Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, University of Szeged, Szeged, Hungary. pankotai.tibor@szte.hu
⁎⁎ Corresponding author. Competence Centre of the Life Sciences Cluster of the Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Szeged, Hungary. vereb.zoltan@med.u-szeged.hu
1 Lead author: vereb.zoltan@med.u-szeged.hu

20 8 2024
15 9 2024
20 8 2024
10 17 e362758 3 2024
7 8 2024
13 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cells have evolved various DNA repair mechanisms to prevent DNA damage from building up. Malfunctions during DNA repair can influence cellular homeostasis because they can bring on genomic instability through the improper recognition of DNA damage or dysregulation of the repair process. Maintaining proper DNA repair is also essential for stem cells (SCs), as they provide a differentiated cell population to the living organism. SCs are regularly used in personalized stem cell therapy. Patients must be treated with specific activators to produce these SCs effectively. This report investigated the impact of treating mesenchymal stem cells (MSC) with lipopolysaccharide, tumor necrosis factor, interferon-gamma, polyinosinic acid, interleukin 1 beta, while monitoring their transcription-related response using next-generation sequencing. RNA sequencing revealed robust gene expression changes, including those of specific genes encoding proteins implicated in DNA damage response. Stem cells can effectively repair specific DNA damages; moreover, they fail to undergo senescence or cell death when genetic lesions accumulate. Here, we draw attention to an elevated DNA repair activation following MSC induction, which may be the main reason for the ineffective stem cell transplantation and may also contribute to the genetic drift that can initiate tumor formation.

Graphical abstract

Image 1

Keywords

DNA repair
ADMSC
RNAseq
Stem cells
NHEJ
==== Body
pmc1 Background

Due to their self-renewal capacity, stem cells can generate various differentiated cell populations, allowing tissue regeneration [1]. Stem cells present in adult tissues provide unlimited lineage-specific differentiation during embryonic developmental stages. Some of these cells can divide without differentiation, while the differentiated daughter cells can also aid in the reproduction of the tissue mass [2]. Mesenchymal stem cells (MSCs) are immature cells in nearly all adult tissues and solid organs, including the bone marrow [3]. This fact makes it possible to collect human MSCs from vascularized tissues. MSCs can initiate tissue regeneration by releasing certain factors that stimulate neighboring cells to proliferate and effectively repair damaged tissues [4]. Human MSCs have endless capacity for self-renewal, facilitating their intense therapeutic use. MSCs exhibit a unique possibility for trans-differentiation into ectodermal, neuroectodermal, or endodermal cells, called "stem cell plasticity." Moreover, MSCs possess immunosuppressive properties, suggesting their potential clinical applications in regenerative medicine and therapies for treatment-resistant immune disorders.

Since the stromal vascular fraction of human MSCs can be extracted from fat tissues (referred to as adipose-derived mesenchymal stem cells, ADMSC), and used to substitute MSCs, adipose tissue can also be considered a potential source of stem cells [5]. Therefore, adipose tissue is a valuable source of MSCs. Finally, it has been demonstrated that the site of tissue harvesting influences the MSCs' ability to regenerate mainly through their yield, proliferation, and differentiation [6]. In order to ensure the purity and quality of MSCs for clinical applications, it is crucial to monitor the processes to expand them in a robust and reproducible way.

Various DNA-damaging agents are constantly posing a threat to our genome. These assaults can derive from exogenous sources, such as UV light or ionizing radiation, and endogenous sources, such as metabolic by-products brought on by oxidative stress or replication errors. These effects may comprise one or both DNA strands. Persistent generation of errors frequently leads to translocations and genomic instability, which may cause tumorigenesis [7]. Throughout evolution, various repair mechanisms have arisen to preserve genomic integrity. DNA damage response (DDR) activates checkpoint kinases during DNA repair, which delays the cell cycle necessary for repair. DDR is initiated by the recruitment and extensive spreading of key players around the lesions, forming a repair focus [8]. The ATM (Ataxia Telangiectasia Mutated) kinase is essential to this early signaling cascade because it phosphorylates the histone variant H2AX at Ser-139 (referred to as γH2AX) which is among the first post-translational modification that appear around the lesion [9]. Regulatory mechanisms protect genomic integrity and tissue homeostasis in adult stem and progenitor cells in various tissues [10]. Dysregulation of DNA repair pathways in MSCs can restrict MSCs' ability to self-renew and differentiate, which can diminish their tissue regeneration capability [11].

This study addresses the transcriptional reprogramming of MSCs obtained from human adipose tissue following in vitro administration of lipopolysaccharide (LPS), tumor necrosis factor (TNF-α), interferon-gamma (IFN-γ), polyinosinic acid (PolyI:C), and interleukin 1 beta (IL-1β). After following the activation treatment, mRNA isolation and next-generation sequencing (NGS) were carried out. DNA repair-related processes were downregulated in several of the most severely impacted pathways. According to statistical analysis, the most dramatically affected processes are in association with DNA Double-Strand Break Repair. Furthermore, we observed that these expressional changes were caused by elevated DNA damage in MSCs, as indicated by increased γH2AX and 53BP1 foci in the PolyI:C-, TNF-α-, and IL1b-treated cells compared to the control, which might be the consequence of the treatment effectiveness.

2 Experimental procedures

2.1 Isolation of ADMSC

The collection of adipose tissue was in accordance with the guidelines of the Declaration of Helsinki and was approved by the National Public Health and Medical Officer Service (NPHMOS) and the National Medical Research Council (16821-6/2017/EÜIG, STEM-01/2017), which follows the Directive 2004/23/CE of the EU Member States on the practice of presumed written consent for tissue collection. Abdominal adipose tissues were removed from the patients (Sex:1/2 F/M, Age: 50.3 ± 14.5 years), and the isolation was performed within 1 h after plastic surgery, as previously described [12]. For maintaining the cell cultures, DMEM-HG medium (Biosera, Nuaille, France), supplemented with 10 % FBS (Biosera, Nuaille, France) and 1 % antibiotic–antimycotic solution was applied [13].

2.2 Characterization of ADMSC

According to the International Society for Cell and Gene Therapy (ISCT) criteria, the phenotype and differentiation capacity of ADMSCs were tested before the experiments [13,14]. Briefly, cell surface molecules were measured by flow cytometry, and the canonical three-way (adipocytes, chondrocytes, and osteocytes) in vitro differentiation was performed with a differentiation medium (Gibco's StemPro® Adipogenesis, Osteogenesis, and Chondrogenesis Differentiation Kits, Gibco) for 21 days. Differentiation was proved by histochemical stainings as previously published [12,13,15,16]. The surface antigen expression pattern was characterized by three-color flow cytometry using fluorochrome-conjugated antibodies with isotype-matching controls. For the measurement of the fluorochrome signal, the BD FACSAriaTM Fusion II flow cytometer (BD Biosciences Immunocytometry Systems, Franklin Lakes, NJ, USA) was applied, and data were processed by Flowing Software (Cell Imaging Core, Turku Centre for Biotechnology, Finland). The differentiation potential of adipose-tissue-derived mesenchymal stem cells was verified by differentiating into adipocyte, chondrocyte, and osteocyte lines. They were cultured in a 24-well plate, in 5 × 104 cells/well; after 24 h of incubation, the differentiation medium was added. The commercially available Gibco's StemPro® Adipogenesis, Osteogenesis, and Chondrogenesis Differentiation Kits were applied according to the manufacturer's guidelines (Gibco, Thermo Fisher Scientific, Waltham, MA USA). After 21 days of maintenance, the cells were fixed with 4 % methanol-free formaldehyde (Molar Chemicals, Hungary) for 20 min at room temparture (RT). Differentiation stages of AD-MSCs were validated using different staining. For visualization of lipid-laden particles, Nile red staining (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) was utilized, and Alizarin red staining (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) was applied to show the mineral deposits during osteogenesis. Toluidine blue staining (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) was wielded to label the chondrogenic mass.

The data obtained from the ADMSCs characterization and the patient characteristics that have been used in this study are shown in Fig. S1.

2.3 Treatment of ADMSC by TLR ligands and pro-inflammatory factors

Cells were cultured in DMEM-HG medium (Biosera, Nuaille, France), supplemented with 10 % FBS (Biosera, Nuaille, France), 1 % L-glutamine (Biosera, Nuaille, France) and 1 % Antibiotic–Antimycotic Solution (Biosera, Nuaille, France). The cells were then subjected to various treatments: (A) LPS [100 ng/mL, tlrl-peklps, ultrapure, Invivogen, San Diego, CA, USA], (B) TNFα [100 ng/mL, 300-01A, Peprotech, London, UK], (C) IL-1β [10 ng/mL, 200-01B, Peprotech, London, UK], (D) IFNу [10 ng/mL, 300-02, Peprotech, London, UK], or (E) PolyI:C [25 μg/mL, tlrl-pic, Invivogen, San Diego, CA, USA]. After adding inflammatory agents, the cells were maintained for another 24 h under standard conditions (37 °C, in 5 % CO2), with untreated cells as controls [12,13,15,16]. After the 24-h treatment period, the cells were harvested and processed for RNA isolation.

For RNA-seq, after trypsinizing and counting ADMSCs using an EVE automatic cell counter from NanoEntek (NanoEntek, Seoul, Korea), 3 × 105 cells were seeded in a T25 cm2 flask in culture media described above, and the cells were grown for 24 h. After replacing the media, the following treatments were applied: (I) 100 ng/mL human recombinant TNF-α (Peprotech, London, UK), (II) 100 ng/mL LPS, (III, ultrapure, Invivogen, San Diego, CA, USA) 10 ng/mL human recombinant IL-1β (Peprotech, London, UK), (IV) 25 ng/mL PolyI:C (Invivogen, San Diego, CA, USA), (V) 10 ng/mL human recombinant IFN-γ (Peprotech, London, UK) and (VI) untreated control. Upon treatment, cells were incubated for 24 h under standard conditions (37 °C, in 5 % CO2), and then pelleted and used for RNA isolation. Following a 24-h treatment period, cells were seeded for immunostaining. After gently washing the cells with Ca2+- and Mg2+-free phosphate buffered saline (PBS), they were fixed with 4 % methanol-free formaldehyde (Molar Chemicals, Hungary) for 20 min at RT.

2.4 RNA isolation

For RNA isolation, the cells were trypsinized and collected, and then the cell pellet was suspended in 1 mL TRI Reagent® (Genbiotech Argentina, Bueno Aries, Argentina) and stored at −80 °C for 24 h. After the samples had been thawed, 200 μL chloroform was added, and they were thoroughly mixed before being incubated at RT for 10 min. The samples were centrifuged at 13,400 g for 20 min at 4 °C for phase separation. After transferring the aqueous phase into new tubes, 500 μL 2–propanol was added and thoroughly mixed. Then, the incubation and phase-separation steps were repeated. Next, the pellets were washed with 750 μL 75 % EtOH–DEPC after the supernatants were removed. The samples were centrifuged at 7500 g for 5 min at 4 °C after the supernatants were decanted, and the samples were dried for 20 min at 45 °C. The pellets were suspended in RNase-free water and incubated for 10 min at 55 °C. The concentration was measured using an IMPLEN N50 UV/Vis Nanophotometer (Implen GmbH, Munich, Germany), and samples were stored at −80 °C.

2.5 NGS analysis

After single-end sequencing of each sample, FastQC was performed to check the quality of RNA sequences. We utilized the Trimmomatic tool to remove adapter sequences and filter out low-quality reads from our dataset. The parameters were set to trim reads using a sliding window approach with a window size of 4 bases and a quality threshold of 20 (SLIDINGWINDOW:4:20). Additionally, we discarded any reads shorter than 20 bases after trimming by setting the MINLEN parameter to 20. In our study, we employed the HISAT2 aligner to map RNA-seq reads against the GRCh38 reference genome, ensuring accurate alignment. Post-alignment quality control was conducted using CollectRNAseqMetrics from the Picard tools suite, which facilitated the evaluation of exon-specific alignment rates and the detection of any potential 5′-3′ transcriptional bias. For gene expression quantification, the featureCounts utility was utilized to assign reads to genomic features. Differential gene expression analysis was performed using the DESeq2 package, designed for the statistical analysis of count data and capable of handling biological variability within replicates.

In our analysis, we used the general workflow recommended by the DESeq2 package. The analysis workflow was structured as follows:

Data Preparation: We defined experimental conditions using a factor with levels corresponding to each treatment group (CTRL, LPS, PolyI:C, TNF-α, IL-1β, IFN-γ), each replicated three times. This was encapsulated within a colData DataFrame, which was paired with the gene_counts matrix to create a DESeqDataSet object. This object serves as the foundational data structure for subsequent DESeq2 operations. Pre-filtering: To enhance the efficiency and accuracy of our analysis, we filtered out genes with low read counts across all samples, retaining only those genes with more than 10 reads in total. This step reduces computational burden and improves the robustness of statistical inference. Normalization and Differential Expression Analysis: We executed the DESeq2 function, which internally performs several critical steps:

Size Factor Estimation: Adjusts for differences in library sizes across samples.Dispersion Estimation: Estimates gene-wise dispersion to account for biological variability.

Negative Binomial GLM Fitting and Wald Statistics: Fits a model for each gene and tests for differential expression.

Extracting Results: We generated results tables for specific comparisons (e.g., TNFa vs. CTRL), applying a log2 fold change threshold of 0 and an alpha of 0.05 to control the type I error rate.

Log2 Fold Change Shrinkage: To improve the interpretability and stability of log2 fold changes, especially for genes with low counts or high dispersion, we applied the lfcShrink method using the 'ashr' approach. This method provides shrunken log2 fold changes, which are particularly useful for ranking and visualizing results.

Subset Significant Results: Post-analysis, we filtered the results to include only genes with adjusted p-values less than 0.05, focusing on those statistically significant changes in gene expression.

Following the differential gene expression analysis, we conducted a Gene Set Enrichment Analysis (GSEA) to identify significantly enriched gene sets that could elucidate the biological pathways involved in the response to different treatments. For this purpose, we utilized the clusterProfiler package, a powerful tool for comparing biological themes among gene clusters. The GSEA was performed using the gseGO function from the clusterProfiler package, which is specifically designed for Gene Ontology analyses. The parameters were set as follows:

geneList: This was derived from the DESeq2 results, where genes were ranked based on their log2 fold changes.

OrgDb: We used org.Hs.eg.db for Homo sapiens gene annotation.

keyType: Gene identifiers were specified as "ENSEMBL".

ont: We analyzed all categories of Gene Ontology: Biological Process, Cellular Component, and Molecular Function.

minGSSize and maxGSSize: The size of the gene sets considered for analysis was restricted to between 10 and 200 to ensure statistical robustness.

pvalueCutoff: A threshold of 0.05 was used to determine the significance of the enriched pathways.

verbose: Set to TRUE

seed: Set to TRUE

nPerm: We performed 10,000 permutations to assess the enrichment score, providing a robust estimate of the p-values.

eps: This parameter was set to 0.

In case of KEGG analysis we employed the gseKEGG function from the clusterProfiler package, which is tailored for KEGG pathway analyses. The parameters for the KEGG analysis were set similarly to those used in the Gene Ontology analysis with the following modifications:

organism: Set to "hsa" (Homo sapiens).

keyType: Set to "kegg" to utilize KEGG gene identifiers.

minGSSize: The minimum size of gene sets considered was adjusted to 5 to include more specific and potentially relevant pathways.

Subsequent to these analyses, we specifically focused on pathways related to DNA repair and DNA damage. Both the gseGO and gseKEGG analyses were refined to filter for gene sets associated with these pathways.

To address the issue of multiple comparisons and control the false discovery rate, the Benjamini-Hochberg (BH) method was employed for p-value adjustment in both analyses. This method systematically reduces the risk of type I errors, ensuring that the reported findings are statistically robust and less likely to result from random chance.

2.6 Visualization of gene expression

For the visualization of the different gene expression, we used the EnhancedVolcano and ComplexHeatmap packages, the exact version numbers are listed in the Key Resources Table.

To visualize the network connections of repair pathways, we used GOxploreR, ggraph, scatterpie and igraph packages.

The general workflow is illustrated in Fig. 1, and the tools used for the data analysis are presented in the Supplementary Materials.Fig. 1 The schematic representation of the bioinformatic pipeline for next-generation sequencing data processing.

Fig. 1

2.7 Immunostaining of ADMSC cells

The samples were fixed for 20 min using 4 % paraformaldehyde and then washed three times with Phosphate Buffered Saline (PBS) solution. Subsequently, the tissues were permeabilized with 0.3 % Triton-X-100/PBS for 20 min at 25 °C. Then, sections were blocked with 5 % Bovine serum albumin/PBS (BSA/PBS) for 1 h. Samples were incubated with the following primary antibodies diluted in 1 % BSA/PBS with 0.1 % Tween 20 (1 % BSA/PBST): anti- γH2AX (Abcam ab26350) in 1:500, anti-53BP1 (Abcam ab36823) in 1:500. After washing steps, the following secondary antibodies were used: goat anti-rabbit IgG (H&L) Alexa 555 (Invitrogen, A21429) in 1:1000 and goat anti-mouse IgG (H&L) Alexa 488 (Molecular Probes, A11029) in 1:1000 dilution. Finally, cells were mounted with DAPI (4′,6-diamidino-2-phenylindole)-containing ProLong Gold antifade reagent (Life technologies). Samples were visualized with Olympus FluoView FV10i confocal microscopy. The same exposition time was used for every image captured. Images were quantified with ImageJ software.

2.8 Image analysis of the confocal pictures

For the ImageJ analysis, we carefully chose ten cells from five distinct areas within each tissue section. During the analysis, in MatLab FoCo, the foci number was recorded and added to the database. FoCo is a graphical user interface created in Matlab's graphical user interface design environment. The computed values were displayed using box plots in Sigma Plot 12.0, and the significance of each sample was determined using independent samples t-tests in IBM SPSS Statistics 27.0.

2.9 qPCR analysis

RT-qPCR reactions were conducted in a final volume of 10 μL using the GoTaq qPCR Master Mix from Promega (Madison, WI, USA) on a QIAGEN Rotor-GeneQ 5-plex HRM qPCR System (Qiagen, Hilden, Germany). All RT-qPCR amplifications followed the same thermal cycling conditions: an initial denaturation at 95 °C for 7 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 560 s, and concluded with a melting curve analysis. Primers were designed using Primer3 software (https://primer3.ut.ee/) and are listed in Table 1. The specificity of the primers was verified using NCBI BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). Each RT-qPCR measurement was performed in 2 technical duplicates.Table 1 Sequences of the gene-specific primers used in qRT-PCR reactions.

Table 1Gene	Forward	Reverse	
BRCA1	CTGAGGACAAAGCAGCGGAT	TCTTGATCTCCCACACTGCAA	
CDC45	CAGCTCGGACAGGAAGAACTTT	ACAGGAGGGAAATAAGTGCGT	
CDK1	GGGTCAGCTCGTTACTCAAC	CCCAAAGCTCTGAAAATCCTG	
EXO1	ACACTAAGCTACGCTGGGC	TTCTTGAATGGGCAGGCATAG	
FOXM1	TGGAGCAGCGACAGGTTAAG	TTGTGGCGGATGGAGTTCTTC	
GTSE1	CTGCGGAGAAGCCCAAGAA	TTCCTTGCGAGATTGCTGGT	
H2A.X	GTGCTGGAGTACCTCACCG	TGGCGCTGGTCTTCTTGG	
HMGA2	GAAGACCCAAAGGCAGCAA	TTCAGTTTCCTCCTGAGCAG	
MCM8	AGTCTTCCCACAAAGTGTCCT	TCCGACCTGCTTCTCTCTGAT	
PLK1	AAGTACGGCCTTGGGTATCAG	GCAAGTGCTCGCTCATGTAA	
POLQ	GCACACTGCTACAGGACGAA	TGCAGCTCTCTCCTCCATCT	
UHRF1	ATGAGACGGAATTGGGGCTG	TTCTCCGGGTAGTCGTCGTA	
XRCC2	CCGCGTCAATGGAGGAGAAA	TCCACATCACACAGTCGTCG	

The following primers were designed to evaluate the expression of the specific genes.

3 Results

To reveal the gene expression responses in the cells prepared for MSC transdifferentiation and transplantation, the following treatments were applied: (I) IL-1β to suppress inflammation in adipose tissue, (II) TNF-α to promote cell proliferation and differentiation while suppressing apoptosis, (III) PolyI:C used for priming or boosting therapy to unleash lymphocytes and other factors of the targeted therapeutic pathway, (IV) LPS to boost the immune plasticity of the cells, and (V) IFN-γ to improve therapeutic effectiveness. Cells were harvested, and RNA sequencing was performed on each group following each treatment, involving independent biological triplicates. After analyzing the datasets, principal component analysis (PCA) was conducted, which confirmed that each treatment brought on equivalent changes in the triplicates and produced unique gene expression profiles (Fig. 1, Fig. 2A).Fig. 2 Gene expressional changes in the stromal vascular fraction of human MSC cells following tumor necrosis factor (TNF-α), lipopolysaccharide (LPS), interleukin 1 beta (IL-1β), polyinosinic acid (PolyI:C), or interferon-gamma (IFN-γ) treatment. A) Principal component analysis of the dataset, B) Heatmap and cluster analysis of the differentially expressed genes. Red represents the upregulated genes with higher z-score, while blue indicates the downregulated genes. D1, D2, and D3 refer to the biological replicates. C) Volcano plot representation of the differentially expressed DNA repair-related genes. For visualization of the most significant DNA repair genes, we employed a cut-off value of 1 for absolute log2 fold-change (upper horizontal dashed line) and 0.05 for statistical significance (lower horizontal dashed line). The dotted vertical line denotes to –log10 (Pcut-off).

Fig. 2

First, samples could be discriminated against depending on the treatment using PCA clusterization of the NGS data. The PCA plot also revealed that clusters involving the following specimens localize in close proximity: (I) LPS- and IL-1β-treated and (II) PolyI:C- and TNF-α-treated (Fig. 2A and B). Second, we examined the clusters based on differentially expressed genes compared to control. This analysis showed that control samples (referred to as CTRL in Fig. 2) can be sorted into the same cluster, similar to each treatment group. After successfully clustering the samples, we identified the differentially expressed genes affected by the applied treatments. In the case of TNF-α and PolyI:C, out of the 2755 and 2861 differentially expressed genes, 1533 and 1511 were upregulated, and 1222 and 1350 genes were downregulated, respectively. Upon IFN-γ, IL-1β, and LPS treatment, 2334 (1430 upregulated and 904 downregulated), 993 (380 downregulated and 613 upregulated), and 1614 (781 upregulated and 833 downregulated) differentially expressed genes were identified, respectively. The number of genes also exhibits a similar tendency to the results demonstrated by the PCA plot: those treatments, such as IL-1β and LPS, which are closer to the control cluster affect the gene expression of fewer genes. In contrast, PolyI:C, TNF-α, and IFN-γ treatments induce more robust gene expressional changes.

Furthermore, we conducted volcano plots to examine how the treatments influenced vital cellular pathways (Fig. 2C). The volcano plots indicate that DNA repair genes are predominantly upregulated in the treated MSC cells. The following numbers of genes associated with DNA repair were identified upon the treatments as follows: (I) TNF-α treatment—of 1533 upregulated and 1222 downregulated genes, 137 and 18 DNA repair-related genes, respectively; (II) LPS treatment—of the 781 upregulated and 833 downregulated genes, 107 and 7 DNA repair-related genes, respectively; (III) IL-1β treatment—of 613 upregulated and 380 downregulated genes, 102 and 6 DNA repair-related genes, respectively; (IV) PolyI:C treatment—of 1511 upregulated and 1350 downregulated genes, 161 and 24 DNA repair-related genes, respectively; and (V) IFN-γ treatment—of 1430 upregulated and 904 downregulated genes, 131 and 16 DNA repair-related genes, respectively (supplementary files: 2, 3, 4, 5, 6). These results indicate that each treatment induces intensive regulation in DNA repair pathways, and DNA repair-related genes are predominantly upregulated (Fig. 2C).

Pathway enrichment analysis revealed that all treatments brought on the upregulation of genes that encode proteins involved in the DNA double-strand break repair pathway (DSBR). PolyI:C treatment notably exhibited a more pronounced influence on DNA repair pathways than other treatments (Fig. 3A and B). PolyI:C induces reactive oxygen species (ROS) and activates etinoic acid-inducible gene I (RIG-I)-like receptors, affecting DNA repair pathways through RIG-I and ROS-mediated mechanisms, which may have an impact on the non-homologous end-joining pathway (Fig. 3A and B). TNF-α treatment demonstrated a broader effect on upregulating common DNA repair pathways in MSC cells. Aside from DNA repair pathways, TNF-α treatment can trigger various signaling pathways involving caspase, nuclear factor kappa B, p53, and c-Jun N-terminal kinase [17]. Intriguingly, negative regulation of DDR was also detected, indicating the multifaceted effects of TNF-α treatment (Fig. 3A and B). LPS, IL-1β, and IFN-γ treatments have fewer but common effects on DSBR pathways; however, IFN-γ exhibits the most negligible impact on the repair pathways (Fig. 3A and B). Network analysis visualized in a pie chart represents the connection between the treatments, the distribution of the treatments and the regulation of Gene Ontology (GO) terms. These results underlie that genes induced by PolyI:C treatment are implied in more DNA repair pathways than genes induced by the other treatments (Fig. 3C). To validate the results of the NGS analysis, 13 genes were selected from the list of DNA repair-related genes. We then quantified RNA expression levels across samples using qPCR, calculated the mean expression values, and depicted these average expression levels (Fig. 3D). The results show an upregulation of these genes, consistent with previous findings. Furthermore, to validate our in silico findings, we performed immunostaining on control and TNF-α-, LPS-, IL-1β-, PolyI:C-, or IFN-γ-treated MSC cells. We utilized an antibody against γH2AX, one of the initial DDR factors, to monitor if DNA repair is activated in treated cells. We observed significantly elevated levels of γH2AX following each treatment, indicating that MSC cells indeed activate the DDR pathway. Samples subjected to LPS treatment showed noticeably higher γH2AX levels (Fig. 4).Fig. 3 The affected signaling pathways related to DNA repair in response to TNF-α, LPS, IL-1β, PolyI:C, or IFN-γ treatment. A) Schematic representation of the enriched DNA repair pathways in response to TNF-α, LPS, IL-1β, PolyI:C, or IFN-γ treatment. B) Dendrogram of the affected DNA repair pathways highlighted on panel A) where the network diagram indicates the proportions of treatments concerning various pathways. C) Enriched DNA repair-related pathways according to the KEGG database. D) The qPCR validation of the RNA-seq data the measured expression of each condition of the 13 genes are shown. The error bars represent the standard deviation.

Fig. 3

Fig. 4 Monitoring the changes of γH2AX and 53PB1 protein levels in ADMSC cells following TNF-α, LPS, IL-1β, PolyI:C, and IFN-γ. Confocal microscopy images of patient-derived cells treated with TNF-α, LPS, IL-1β, PolyI:C, or IFN-γ and stained with A) anti-53BP1 and B) γH2AX antibody. The quantification of the foci is depicted on the right side of each panel. The values represent the mean ± standard deviation from three independent experiments (N = 100 cells in each experiment). Statistical significance in both bar charts was calculated using independent samples t-tests (P < 0.001***).

Fig. 4

To further validate that the treatments affected DSBR, we employed an antibody specific to 53BP1 (Fig. 4). We also detected a significantly higher number of 53BP1 foci in TNF-α-, LPS-, IL-1β -, PolyI:C-, or IFN-γ- treated cells, respectively, which is consistent with the results of γH2AX. The highest value was observed in samples treated with LPS (Fig. 4). These data underscore that transcriptional reprogramming triggered by TNF-α, IL-1β, PolyI:C, IFN-γ, or LPS leads to DNA damage induction and initiates DSBR in adipose tissues.

4 Discussion

DNA repair is crucial for the physiological function of the cells, particularly in stem cells that differentiate into various cell types. Because of its protracted duplicative character, the bone marrow MSC population, constituting a small percentage (0.001 %–0.01 %) of cells, is susceptible to DNA damage [18]. This vulnerability arises from the accumulation of DNA errors in each progeny cell during replication. DNA damage in MSCs can be caused by various factors, including external physical and chemical agents, reactive oxygen species generated during inflammation, altered metabolic pathways, deamination, and hydrolysis. Because these activities can break DNA double helix or function as mutagenic sources, they can interfere with DNA transcription or replication [11,19]. The cell type, the duration and timing of the exposure to external and internal stimuli, and the cellular microenvironment affect how the cell reacts to these impacts. Although several mechanisms exist to correct DNA errors in early-passaging (young) MSC cultures in vitro, long-term culture and aging likely alter this dynamic in vivo [11,18,[20], [21], [22], [23]].

Persisting DNA damage can induce downstream processes such as aging or cellular senescence. Genetic, epigenetic, transcriptional, and metabolic alterations, accompanied by decreased proliferation rate, are the hallmarks of aging. Several factors can cause DNA damage which accumulates in cells over time. The outcome depends on the balance between the extent of DNA damage and the capacity of the DNA repair system [22,24]. Excessive damage may overwhelm the repair mechanisms, leading to impaired biological functions such as loss of differentiation and regenerative capacity, cell death, mutagenesis, altered cell cycle and division, or senescence [11,18,25].

Senescence is a common phenomenon in aging or stressed MSCs [22,24]. Senescent MSCs undergo cell cycle arrest even though they are metabolically active. Senescence has a complicated molecular background that has dual effects. During tissue and organ formation, senescence inhibits cell division, yet it also protects against tumorigenesis by removing cells from the replicative cycle. Senescence elicits cell cycle arrest and is characterized by the emergence of a long-lasting cellular program known as the senescence-associated secretory phenotype (SASP). In this condition, senescent cells secrete numerous pro-inflammatory molecules into the tissue milieu, including IL-1, IL-6, IL-8, GROα/β, GM-CSF, MMP-1, MMP-3, MMP-10, ICAM-1, PAI-1, and IGFBPs [[26], [27], [28]]. Human mesenchymal stem cells that have been exposed to actinomycin D-induced senescence exhibit inhibition of DNA synthesis, reduction in the protein level of P21 and P16, elevation of β-galactosidase activity associated with senescence, and enlargement of γH2AX foci [29]. The SASP phenotype enhances the motility of lung tumor and osteosarcoma cell lines in vitro. While senescence benefits the preservation of the stem cell supply, the altered tumor microenvironment due to SASP may be detrimental to the organism [30]. Similar SASP appears in long-term cultures of senescent MSCs with different origins and certain hematological malignancies [24,26,31,32]. In addition to causing inflammation, the SASP phenotype attracts immune cells, which exacerbates microenvironmental inflammation [31]. Aging MSCs can induce neighboring cell aging in vitro, leading to a cascade effect [33]. The involvement of MSCs in tumor formation or metastasis remains controversial. Tumor suppressor mechanisms may be activated in response to the accumulation of DNA damage in MSCs, counteracting the resulting mutational burden. Various contexts highlight the complex interplay among senescence, MSC differentiation, and DNA repair. In certain cell types, DNA repair mechanisms are associated with epithelial–mesenchymal transition and metastasis, inhibiting differentiation [[34], [35], [36], [37], [38], [39]]. In tumor cells, DNA repair-induced senescence is linked to increased pro-inflammatory molecule release, such as IL-6 and IL-8, which promote regeneration and migration processes [28]. Repairing DNA defects in MSCs is crucial as these defects act as trigger points for the differentiation of different types of cells and tissues. The impact of DNA damage on MSC differentiation is controversial. While some cases indicate that DNA damage can be beneficial and protective, according to others, it hinders the differentiation pathways [40,41]. The immunological status of the environment and the balance between cell division and differentiation are critical for maintaining tissue integrity. The role of MSCs in inflammation is multifaceted. MSCs can exhibit either immunosuppressive or inflammation-stimulating behaviors in an inflammatory environment, depending on the molecular background of inflammatory signaling. In this study, we discovered that MSCs responded to the inflammatory environment by increasing proliferative activity rather than undergoing senescence, even though inflammation, especially when associated with ROS production, can lead to senescence. Human umbilical vein endothelial cells (HUVECs) underwent senescence due to damage signal TNF-α, which resulted in persistent DNA damage via the inflammatory associated JAK/STAT pathways [17,42]. Additionally, it is known that TNF-α treatment increased the level of 8-Oxoguanine DNA glycosylase 1, indicating extensive DNA repair. PolyI:C is known to elicit inflammatory stimuli via the toll-like receptor 3 (TLR3) pathway and initiate more likely apoptotic outcomes instead of senescence [43]. Since viral infection may pose a threat to stem cells, elevated DNA repair may be indicated by the activation of the TLR3 pathway. In a subset of stem cells, repeated activation of the toll-like receptor 4 (TLR4) pathway by LPS increased the level of γH2AX and brought on senescence [44]. MSCs activated by TLR3 and TLR4 play a crucial role in tryptophan breakdown and kynurenine production, essential for effectively inhibiting T-cell proliferation. IFN-γ participates in multiple immune responses mediated by MSCs. IFN-γ treatment generates an immunosuppressive type of MSC which suppresses T-cell proliferation to induce either local or systemic immunosuppression; nonetheless, in certain cell types, IFN-γ can cause DNA damage [45].

The differentiation capacity and the unique immunosuppressive behaviour make MSC optimal candidates for therapeutic agents. Sounak and colleagues have shown in a plant model that stem cells migrating to the wound play a chimeric role in wound healing. Successful migration to the distal wound site requires active DNA repair pathways, and continuous DNA repair mechanisms are required to maintain migration and wound healing. Currently, there are only a limited number of human studies on how DNA repair mechanisms can affect stem cell function. Most of the data are based on experience with bone marrow MSC in vitro culture, with a focus on how long-term culture affects genotoxicity and differentiation [46,47]. This is important in cell therapy procedures where pre-culture of cells is required. However, in most MSC-based procedures, there is no culture period, and cells are administered to patients immediately after isolation. In MSC-based procedures used in regenerative medicine, cells are placed in an inflamed, possibly infected environment, and little is known about their behavior in vivo.

In the case of hematopoietic stem cells, we know that deficiencies in repairing genotoxic damage may be responsible for the development of acute high-grade graft-versus-host disease (GVHD), increasing its risk [48,49]. More recently, iPS cells have become a focus of research, where it is even more important to preserve DNA integrity because they can accumulate a number of mutations [50]. hESCs have shown that the XPC-HR23B complex may play a role in regulating the “stem cell state” in human hESCs, which means that it may also be important for therapeutic use [51].

Our study indicates that induced ADMSCs exhibit higher expression of several mRNAs. The common set of these transcripts includes mRNAs that encode proteins involved in DNA repair. The main cause of persistent DNA damage may be extensive transcriptional reprogramming, resulting in the transcriptional activation of this gene set associated with DNA repair [52,53]. Our findings imply that if these errors remain unrepaired, they may play a significant role in the failure of the MSC therapy and cause premature aging and senescence.

5 Conclusions

In conclusion, as revealed by the detection of increased DNA repair foci formation, our study highlighted that the transcriptional reprogramming induced by TNF-α, LPS, IL-1β, PolyI:C, and IFN-γ activates the downstream DNA repair cascade. This can have an impact on the regeneration of the tissue and its immunological status. Given that MSCs have a robust differentiation and migration capacity, DNA damage induced by various factors can lead to significant and prolonged changes, even in cells already in the process of differentiation. When these errors remain unrepaired or undergo improper repair, they may lead to cellular malformation and potentially initiate further tumor formation or abnormal tissue regeneration in the recipient's body. Based on these results, the therapeutic options must be carefully reconsidered.

Ethics approval and consent to participate

The collection of adipose tissue complied with the guidelines of the Declaration of Helsinki and was approved by the National Public Health and Medical Officer Service (NPHMOS) and the National Medical Research Council (16821-6/2017/EÜIG, STEM-01/2017, 6 September 2017) which follows the EU Member States’ Directive 2004/23/EC on the practice of presumed written consent for tissue collection for University of Szeged.

Consent for publication

Not applicable.

Funding

This research was funded by the 10.13039/501100018818 National Research, Development and Innovation Office grant GINOP_PLUSZ-2.1.1-21-2022-00043 (Z.V.) co-financed by the 10.13039/501100000780 European Union and the European Regional Development Fund. Tibor Pankotai is supported by the 10.13039/501100018818 National Research, Development and Innovation Office under NKFI-FK 132080 grant (T.P.), Zoltán Veréb under 10.13039/501100011019 NKFIH PD 132570 grant (Z.V.). Zoltán Veréb is a recipient of the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (BO/00190/20/5) (Z.V.). The project received funding from the EU's 10.13039/501100007601 Horizon 2020 Research and Innovation Program with grant agreement No. 739593 (T.P.). Project no. TKP-2021-EGA-05 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme (T.P.). Project no. 2022–2.1.1-NL-2022-00005 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the 2022–2.1.1-NL funding scheme (T.P.). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funders have no conflict of interest. The project also received fund from 10.13039/501100003825 Hungarian Academy of Sciences (POST-COVID2021-36 ).

Ethics statement

Not applicable.

Availability of data and materials

All data generated and analyzed during this study are included in this manuscript. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

CRediT authorship contribution statement

Zoltán G. Páhi: Writing – original draft, Formal analysis, Data curation. Diána Szűcs: Supervision, Formal analysis, Conceptualization. Vanda Miklós: Visualization, Investigation. Nóra Ördög: Investigation. Tamás Monostori: Investigation. János Varga: Investigation. Lajos Kemény: Validation, Formal analysis. Zoltán Veréb: Visualization, Validation, Supervision, Resources, Project administration. Tibor Pankotai: Writing – review & editing, Writing – original draft, Visualization, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

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Acknowledgements

We are grateful to Dr. Barbara N. Borsos for her critical review and suggestions. We also grateful to Manuéla Katona for her contribution in qPCR quantification experiment.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36275.
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