Transcriptome analysis of hydrogen inhibits osteoclastogenesis of mouse bone marrow mononuclear cells
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- Published online on: July 31, 2023 https://doi.org/10.3892/etm.2023.12135
- Article Number: 436
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Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Magnesium (Mg), as well as its alloys, is a new type of biodegradable metallic material used in bone repair and regeneration (1). Compared with the currently approved metallic agents, such as titanium and stainless steel, Mg alloys possess improved osteo-inductive and osteo-conductive capacities, well adapted for bone healing in vivo and in vitro (2-4), hence Mg alloys are of great interest for orthopedic applications (5). One of the degradation products of Mg alloy implants in vivo, H2, is a physiologically inert gas. A previous study demonstrated that H2 has a variety of physiological functions and therapeutic effects, which have been studied in ~166 human disease models thus far (6). For musculoskeletal applications, the results of animal experiments have revealed that H2 can alleviate bone loss caused by microgravity (7,8) and that H2-rich water can inhibit the resorption of alveolar bone (9).
Osteoclasts originate from bone marrow mononuclear cells (BMMCs), which are derived from hematopoietic stem cells and they can differentiate into osteoclasts in vitro through Rankl-induced NF-кB, MAPK and AKT signaling pathways (10,11). These highly specialized cells can degrade and digest the bone matrix (12-14). Furthermore, the aberrant production and/or the abnormal lifespan of osteoclasts have been found to be the cause(s) of benign or malignant bone diseases (15,16). As such, osteoclasts have important roles in bone formation and remodeling (17).
Our previous study revealed that H2 can inhibit BMMC osteoclastogenesis in mice (18). It was observed that treatment with 50% H2 for 7 days could inhibit osteoclast formation, osteoclast function and osteoclast-related gene expression of osteoclast-induced BMMCs and that treatment with 50% H2 could reduce proliferation, promote apoptosis and inhibit the expression of osteoclast-related proteins in BMMCs cultured in osteoclast-induced medium.
Briefly, in pit formation assays, there was no difference among the three groups (25, 50 and 75% H2) when exposed to this inert gas for 5, 7 or 10 days. In reverse transcription-quantitative (RT-q) PCR analyses, when BMMCs were treated with H2 for 5 days, the expression of the three osteoclast-related genes could not be inhibited. When BMMCs were induced for 7 or 10 days, treatment with 2% H2 had no inhibitory effect on the expression of osteoclast-related genes, while treatment with 25, 50, or 75% H2 (especially 50 and 75% H2) significantly reduced the expression levels of the osteoclast-related genes Ctsk, Calcr and Mmp9. Therefore, based on these findings and the results of a previous study (19), treatment with 50% H2 for 7 days was selected as the optimal condition to investigate the role of H2 in BMMC proliferation and apoptosis. However, the mechanism by which this occurs, which is critical for the translational research of H2 and Mg alloys and the treatment of osteoporosis, remains to be elucidated. The present study used transcriptome sequencing to identify the possible mechanism behind the H2-mediated inhibition of osteoclastogenesis and revealed the key genes and signaling pathways involved, thereby providing a theoretical basis for the anti-osteoporosis function of H2.
Materials and methods
BMMC culture and osteoclast induction
BMMCs were isolated as previously reported (20,21). In brief, four-week-old C57BL/6 mice (~20 g) were purchased (Changzhou Cavens Laboratory Animal Co., Ltd.). After the animals (3 weeks old) were received, adaptive feeding was performed for 1 week. Food and water were freely available throughout the experiment. The animals were maintained at 20-25˚C, with a 12:12 h light and dark cycle and a relative humidity of 45-60%. When the animals were 4 weeks old, they were sacrificed by neck-dislocation and disinfected with 75% alcohol. Under sterile conditions, the bilateral femurs and tibias were removed. The metaphyses of the long bones were obtained and the bone marrow cavities were gently and repeatedly irrigated with serum-free α-minimum essential medium (α-MEM; HyClone; Cytiva). The collected washes were filtered through a 200-mesh sieve and centrifuged (Sigma 3K15; Sigma Laborzentrifugen GmbH) at 232 x g for 5 min at 20˚C. The supernatant was discarded and the pelleted cells were resuspended in sterile BL503A erythrocyte medium (Biosharp Life Sciences), gently mixed by inversion and lysed on ice for 5 min. Subsequently, the cell suspension was centrifuged at 161 x g for 5 min at 20˚C. and the supernatant containing the lysed red blood cells was discarded. The pelleted cells were resuspended, washed twice in serum-free α-MEM and resuspended in 3 ml of α-MEM containing 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.). The cells were statically inoculated in 60-mm culture dishes and incubated overnight in a humidified atmosphere of 5% CO2 at 37˚C. Thereafter, the supernatant was collected and centrifuged at 232 x g for 3 min at 20˚C to obtain the mouse BMMCs. The BMMCs were resuspended in α-MEM containing 10% FBS, appropriately diluted and inoculated in 3.5 cm diameter culture dishes at ~1x105 primary cells per well. The medium was replaced every two days.
Depending on the H2 concentration in the incubator, the primary cells (P1) were divided into two groups as follows: The experimental group (BMMCs + 50% H2 + 20% O2 + 5% CO2 + 25% N2) and the control group (BMMCs + 20% O2 + 5% CO2 + 75% N2). The H2 in the incubator entered the cell culture medium by dissolution-diffusion. To induce osteoclast differentiation, 20 ng/ml macrophage colony stimulating factor (Csf1; PeproTech China) and 50 ng/ml receptor activator of NF-κB ligand (Rankl; R&D Systems, Inc.) were used. The BMMCs were cultured in separate incubators at 37˚C with or without 50% H2 as aforementioned for 7 days and six repetitions were tested for each treatment group. Therefore, a total of 12 samples were collected; and six C57BL/6 mice, half male and half female, were used in this experiment.
Ethics statement
The present study was conducted in accordance with the ethical standards of the Ethics Committee of Peking University Shenzhen Hospital, who approved the experimental protocols.
Total RNA extraction
Total RNA was extracted from the cells using TRIzol® (Thermo Fisher Scientific, Inc.), according to the manufacturer's instructions. In brief, 1.5 ml of TRIzol® was added to the pelleted cells, followed homogenization for 2 min and a 5-min horizontal rest period to permit the complete dissociation of nucleoprotein complexes. The mix was centrifuged at 12,000 x g for 5 min at 4˚C. The upper phase was transferred into new tubes and 0.3 ml of chloroform/isoamyl alcohol (24:1) was added. The samples were vortexed and centrifuged at 12,000 x g for 10 min at 4˚C. Thereafter, the upper aqueous phase was transferred into a new tube and an equivalent volume of isopropyl alcohol (Xilong Science Co., Ltd.) was added, followed by vortexing, precipitation and centrifuged at 17,500 x g for 20 min at 4˚C. The pelleted RNA was washed twice with 75% ethanol, air-dried and resuspended in 25 µl of diethyl pyrocarbonate-treated water. The RNA was qualified and quantified using an Agilent 2100 bioanalyzer (Agilent Technologies, Inc.) and a Nanodrop spectrophotometer (Thermo Fisher Scientific, Inc.), respectively.
mRNA library construction
mRNA library construction and transcriptome sequencing were performed at the Beijing Genomics Institution (Fig. 1). Oligo(dT) magnetic beads were used to obtain the purified mRNA, which was divided into smaller fragments. First-strand cDNA was generated by random hexamer-primed reverse transcription, followed by second-strand cDNA synthesis. The synthesized cDNA was subjected to end-repair and 3' adenylation and the adapters were ligated to the ends of the 3' adenylated cDNA fragments. The cDNA was amplified by PCR and the products were purified using Ampure XP beads (Agencourt; Beckman Coulter, Inc.) and dissolved in ethidium bromide solution. The double-stranded PCR products were denatured and circularized according to the splint oligo sequence to obtain the final library. Single-stranded circular DNA was designated as the final library. For quality control (QC), the library was validated using the Agilent Technologies 2100 bioanalyzer (Agilent Technologies, Inc.). The Standard Sensitivity RNA Analysis Kit (15 nt, DNF-471; Agilent Technologies, Inc.) was used as a detection kit for this section.
Transcriptome sequencing
The final library was amplified with φ29 DNA polymerase to make DNA nanoballs (DNBs), which had more than 300 copies of each molecule. The DNBs were loaded onto the patterned nanoarray and 150 paired-end base reads were generated using combinatorial probe-anchor synthesis on the BGIseq500 platform (BGI Group).
Bioinformatics analysis
The mouse (Mus_musculus; GCF_000001635.26_GRCm38.p6; NCBI) genome served as the reference genome. The sequencing reads were compared against the reference genome to annotate and quantify the genes.
The sequencing data were designated as raw reads or raw data. QC was performed on the raw reads and the qualified data was used for subsequent analyses. After QC, the filtered clean reads were compared to the reference sequences. Using the statistical comparison rate and the distribution of reads of the reference sequences, we determined whether the comparison results passed the second QC (QC of alignment). After the QC was qualified, the quantitative analysis of genes and the analysis of gene expression levels (principal components, correlation scores, differential gene screening) were performed. Additional analyses were carried out for selected differentially expressed genes (DEGs) such as Gene Ontology (GO; http://geneontology.org/) term enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/) pathway enrichment analysis, gene expression clustering analysis, SNP (single nucleotide polymorphism) and InDell analyses and splicing gene event analysis (Fig. 2).
The sequencing data were filtered using SOAPnuke software (v1.5.2, https://github.com/BGI-flexlab/SOAPnuke) as follows: i) Reads containing a junction (junction contamination) were removed; ii) reads containing >5% of unknown bases were eliminated; and iii) low quality reads (reads with a mass value <10, accounting for >20% of the total bases as low quality reads) were removed. The filtered ‘clean reads’ were saved in FASTQ format.
Sample correlation analysis
Pearson correlation coefficients for all gene expression levels between the two samples were calculated and presented as heat maps to reflect the correlations between the gene expression levels (the higher the correlation coefficient, the more similar the gene expression level).
Gene expression distribution analysis
The distribution of gene expression levels was presented as box plots and the dispersion of the distributed data was observed. The trends in the gene abundance and the expression level were presented as density maps and the intervals of the gene expression concentration areas were also analyzed. At the same time, the gene number of the expected number of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM; FPKM ≤1, FPKM=1-10, FPKM ≥10) in the three cases was counted using stacked bar graphs, so as to visually display the gene numbers in different FPKM intervals for each sample.
Differential expression analysis
Differential expression analysis of the paired groups was performed using the DESeq 2 method (22). The screening conditions were as follows: the difference multiple |log2FC| ≥1 and Q value ≤0.05. Volcano maps were generated and analyzed to identify the upregulated and downregulated DEGs.
Differential cluster analysis
According to the results of the identified DEGs, the R project pheatmap tool (v1.0.12, https://cran.r-project.org/web/packages/pheatmap/) was used for hierarchical clustering analysis. In terms of the gene expression, Bowtie 2 software (v2.2.5, http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) (23) was used to compare the clean reads to the reference gene sequences and RSEM software (v1.2.8, http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html) was used to calculate the gene expression level of each sample (24).
GO term enrichment analysis and GO annotation classification of DEGs
Enrichment analysis was used to determine whether each DEG was significantly enriched in terms of a specific molecular function, biological process, or metabolic pathway. According to the results of the GO annotations, the DEGs were classified according to their function. The phyper function within R software (v4.1.0, https://stat.ethz.ch/R-manual/R-devel/library/stats/html/Hypergeometric.html) was used for enrichment analysis and P-values were calculated. The false discovery rate (FDR) correction was performed using the P-value (v2.26.0, https://bioconductor.org/packages/release/bioc/html/qvalue.html) to obtain the Q-value. The GO terms that satisfied the Q-value ≤0.05 criterion were defined as significantly enriched and DEGs were assigned different biological functions. According to the results of the GO annotations, the DEGs were divided into three functional categories; molecular functions, cellular components and biological processes.
KEGG pathway enrichment analysis and KEGG annotation classification of DEGs
According to the results of the KEGG pathway annotations, the phyper function within R software was used for enrichment analysis. The FDR correction was performed using the P-value to obtain the Q-value. The unigenes that satisfied the Q-value ≤0.05 criterion were significantly expressed in GO term and KEGG pathway analyses. KEGG metabolic pathways related to DEGs were divided into seven categories: Cellular processes, environmental information processing, genetic information processing, human diseases (animal only), metabolic processes, organic systems and drug development. Pathway enrichment analysis determined the main biochemical metabolic pathways and signal transduction pathways related to the DEGs.
Alternative splicing and differential alternative splicing analysis
Following filtering, the clean reads were compared with the reference genome for reference genome alignment using HISAT 2 software (v2.0.4, http://www.ccb.jhu.edu/software/hisat) (25). The alternative splicing events and the differential alternative splicing events between the two groups were identified by rMATS software (v3.2.5, http://rnaseq-mats.sourceforge.net) (26). The alternative splicing events were skipped exons (SE), alternative 5' splicing sites, alternative 3' splicing sites (A3SS), mutually exclusive exons and retained introns (RI).
Gene fusion analysis
Gene fusion is the process by which a portion or the whole sequence of one gene is fused with that of another gene to form a new gene. Ericscript software (v0.5.5, http://ericscript.sourceforge.net/) (27) was used for gene fusion analysis and gene fusion events were identified by comparing the sequences of paired-end relationships between the genome and the transcript. The mechanisms of gene fusion were chromosome translocations, intermediate deletions, chromosome inversions and trans splicing.
Target gene analysis
According to the expression and the enrichment of the DEGs, the target genes and related signaling pathways involved in the H2-mediated inhibition of BMMC osteoclastogenesis were predicted and analyzed.
Statistical analysis
Continuous variables were presented as the mean ± standard deviation (X ± S). The results were analyzed by one-way ANOVA, followed by LSD or Dunnett's multiple comparison test, or by Student's t-test using SPSS 19.0 software (IBM Corp.). P<0.05 was considered to indicate a statistically significant difference.
Results
In the present study, 12 samples were sequenced and analyzed using the BGISEQ-500 platform. On average, each sample yielded 6.71G of data and a total of 17,267 genes were detected. For the clean reads of the H2 group and the control group, the average comparison rates to the reference genome were 92.54 and 93.29%, respectively; and the average comparison rates to the reference gene set were 75.75 and 76.84%, respectively. All the rates were >70.00%, indicating that the reference genome selection was appropriate. According to the clean reads sequencing data of the H2 group and the control group, the 99.00% base correct recognition rates (Q20) were 96.05 and 96.49%, respectively and the 99.90% base correct recognition rates (Q30) were 86.78 and 87.79%, respectively. The sequencing quality met the requirements of subsequent analysis.
Samples show good biological repetition
As shown in Fig. 3, Pearson correlation coefficient analysis revealed that the correlation coefficient between any two samples was >0.95, indicating that the gene expression levels among the samples were very similar and the samples showed good biological repetition.
Gene expression distribution analysis shows that the sequencing data are well standardized
The sequencing data were standardized and a box plot distribution of the gene expression levels of each sample is shown in Fig. 4A. The median of the box plot of each sample was aligned. The stacked histogram results of the gene number accumulation in different FPKM intervals of the H2 group and the control group were shown in Fig. 4B and Table I. The differences in the gene number of the medium-high expression level (t=-0.564; P=0.585), low expression level (t=-1.779; P=0.106), or very low expression level (t=1.318; P=0.217) were not statistically significant. The gene expression box plot and the gene number stacked histogram showed that the sequencing data were well standardized and met the experimental requirements.
DEGs identified and their expression levels
In this experiment, the criteria for identifying the DEGs were as follows: |log2FC| ≥1 and Q-value ≤0.05. The results showed that there were 480 DEGs between the H2 group and the control group. Among them, 320 DEGs were upregulated and 160 DEGs were downregulated (Fig. 5).
As shown in Fig. 6, the results of RNA-seq cluster analysis showed that the H2 group and the control group could be completely separated and the corresponding gene expression patterns of the two groups were significantly different. The high expression values of the DEGs in the grouped samples are represented in red and the low expression values of the DEGs in the grouped samples are represented in blue.
GO term enrichment and KEGG pathway enrichment of DEGs
As shown in Table II, among the GO enriched terms, the top five enriched gene terms for cellular components were mainly related to extracellular region, extracellular space, extracellular matrix, collagen-containing extracellular matrix and collagen trimer. The top five enriched gene terms for molecular functions were mainly related to extracellular matrix structural constituent, growth factor activity, heparin binding, extracellular matrix structural constituent conferring tensile strength and aldehyde dehydrogenase activity. The top five enriched gene terms for biological processes were mainly related to regulation of signaling receptor activity, immune response, positive regulation of angiogenesis, cholesterol biosynthesis and sterol biosynthesis.
As shown in Table III, among the KEGG pathway enriched terms, the top five enriched pathway terms were mainly related to ECM-receptor interactions, protein digestion and absorption, TNF signaling pathway, terpenoid backbone biosynthesis and sesquiterpenoid and triterpenoid biosynthesis.
Main types of gene variations
The results showed that the main alternative splicing events between the H2 group and the control group were RI and SE, which accounted for 39.04 and 32.62% of splicing events, respectively (Fig. 7 and Table IV).
As shown in Table V, the analysis of gene fusion events in the H2 group and the control group showed that different degrees of gene fusion occurred in both groups. The most common type of gene fusion event was inter-chromosomal, in addition to a small number of intra-chromosomal and read-through events.
Target genes and possible signaling pathways
The analysis of the expression of DEGs, as well as GO terms and KEGG pathways, identified a variety of targets involved in the H2-mediated inhibition of BMMC osteoclastogenesis as follows: Fos, Dusp1, Cxcl1, Reln, Itga2b, Plin2, Lif, Thbs1, Vegfa and Gadd45a (all upregulated) and Hspa1b, Gm4951, F830016B08Rik, Fads2, Hspa1a, Slc27a6, Cacna1b, Scd2, Lama3 and Col4a5 (all downregulated). The analysis also identified a variety of signaling pathways involved in immunity, inflammation, oxidative stress and apoptosis as follows: osteoclast differentiation cascades, as well as PI3K-AKT, FoxO, MAPK, PPAR, TNF, TGF-β, JAK-STAT, RAS, VEGF, HIF-1 and AMPK signaling pathways (Table VI).
Discussion
RNA-sequencing (quantification) is based on next-generation high-throughput sequencing technologies that provide accurate digital expression profiling information by measuring transcript sequence levels and performing comparative analyses to study the expression pattern of eukaryotic genes. Transcriptome research is the basis and the starting point of gene function and structure research, through which researchers can comprehensively and quickly obtain almost all the transcript sequence information for a specific tissue or organ of a certain species under specific conditions.
In the present study, RNA-seq was used to study the transcriptome of mouse BMMCs exposed to H2 in an osteoclast-induced environment. The main signaling pathways of DEGs were identified and they were involved in immunity, inflammation, apoptosis and cell differentiation. Our previous study reported that H2 could inhibit BMMC osteoclastogenesis in mice (18). H2 can penetrate membranes and enter cells, where it behaves as an antioxidant, inhibiting the differentiation of BMMCs to osteoclasts (28-30). It is well known that reactive oxygen species (ROS) (31), such as hydrogen peroxide (32) and superoxide dismutase (33), are osteoclast inducing factors, but they are reversely regulated by H2, possibly due to its strong reducing agent effect. Although several studies have reported that ROS regulate osteoclastogenesis (34-37), the existing studies have not reported the possible signaling pathways that are involved in this process.
Fan et al (38) demonstrated that H2-rich saline could decrease the expression levels of IL-6, JAK, STAT-3 and other signaling proteins by inhibiting the JAK-STAT signaling pathway, thereby inhibiting myocardial hypertrophy in rats. Chen et al (39) reported that H2 exerts anti-apoptotic effects by inhibiting Ras-ERK1/2-MEK1/2 and AKT signaling pathways in rat vascular smooth muscle cells. At the same time, other studies have shown that H2 could alleviate the damage induced by lipopolysaccharides in an acute lung injury model (40); H2 can also upregulate the expression of claudin-5 by activating the PI3K/AKT signaling pathway in alveolar epithelial cells, thereby reducing cell permeability and improving pulmonary edema (41). Other studies have demonstrated that H2 can exert antioxidant, anti-inflammatory and anti-apoptotic effects by inhibiting p38-MAPK (42), JNK-caspase 3(40) and NF-kB (11,43) signaling pathways, in addition to NRF2, WNT/β-catenin, thioredoxin interaction protein/NLR3 inflammasome and RHO/ROCK signaling pathways.
The results of the above studies using different cell or animal models were basically consistent with the results of the present study. However, based on the results of gene enrichment analysis, the present study identified new signaling pathways that may have roles in BMMC osteoclastogenesis, including FoxO, PPAR, TNF, TGF-β, VEGF, HIF-1 and AMPK signaling pathways. These signaling pathways have roles in immunity, inflammation, proliferation, differentiation, apoptosis, cell cycle regulation and metabolism. At the same time, the findings of the present study were different from those of previous studies. For example, the studies of Takayanagi et al (44), Grigoriadis et al (45) and Jiao et al (46) showed that the Fos gene was an osteoclast-inducible gene, but the present study showed that H2 inhibited osteoclast differentiation and the Fos gene was highly expressed. The reason for this difference may be due to the different cells and culture methods used in the different experiments, or it may be due to other reasons that are currently unknown. Therefore, further studies are needed to determine whether these genes have biological effects on BMMCs through specific signaling pathways in an osteoclast-induced environment and the results should be compared with those of previous studies.
It is well known that osteoclasts serve an important role in the occurrence and development of osteoporosis and the present study showed that H2 could inhibit the differentiation of osteoclasts by acting on related genes through certain signaling pathways. This may be the mechanism by which H2 exerts its anti-osteoporosis effects. H2 is generated during the degradation of Mg alloys and the formation of osteoclasts can be inhibited by H2, so Mg alloys indirectly exert their anti-osteoporosis effects. Our previous study revealed that H2 can inhibit BMMC osteoclastogenesis in mice (18). However, the mechanism by which this occurs, which is critical for the translational research of H2 and Mg alloys and the treatment of osteoporosis, is still unclear. The present study used transcriptome sequencing to identify the possible mechanism behind the H2-mediated inhibition of osteoclastogenesis and revealed the key genes and signaling pathways involved, thereby providing a theoretical basis for the significance of H2 and an experimental basis for the application of Mg alloys in the treatment of fractures, bone defects and osteoporosis.
There were several limitations to the present study. First, although the primary mouse BMMCs used in this study were isolated as previously described (20,21), the cell populations were not well identified. Second, the possible molecular mechanism by which H2 inhibits BMMC osteoclastogenesis was not thoroughly studied. Thus, further studies are needed to determine whether H2 inhibits BMMC osteoclastogenesis through these pathways.
The present study is the first to investigate the transcriptome of mouse BMMCs exposed to H2 in an osteoclast-induced environment by RNA-seq, to the best of the authors' knowledge. The results showed that there were 480 DEGs between the H2 group and the control group. Among them, 320 DEGs were upregulated (Fos, Dusp1, Cxcl1, Reln, Itga2b, Plin2, Lif, Thbs1, Vegfa and Gadd45a) and 160 DEGs were downregulated (Hspa1b, Gm4951, F830016B08Rik, Fads2, Hspa1a, Slc27a6, Cacna1b, Scd2, Lama3 and Col4a5). Comprehensive analysis of DEGs, as well as GO terms and KEGG pathways, revealed that these DEGs were mainly involved in osteoclast differentiation cascades, as well as PI3K-AKT, FoxO, MAPK, PPAR, TNF, TGF-β, JAK-STAT, RAS, VEGF, HIF-1 and AMPK signaling pathways, which are involved in immunity, inflammation, apoptosis and cell differentiation. At the same time, the main alternative splicing events between the H2 group and the control group were RI and SE. Gene fusion occurred in both groups and inter-chromosomal fusion was the main way by which genes were fused. Therefore, the results of the present study revealed the possible target genes and signaling pathways of H2-mediated BMMC osteoclastogenesis and provided a theoretical basis for exploring the feasibility of H2 in the treatment of osteoporosis.
Acknowledgements
Not applicable.
Funding
Funding: The present study was supported by the State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University (grant no. mmc-kf20-02) and the San-Ming Project of Medicine in Shenzhen (grant no. SZSM201612092).
Availability of data and materials
The datasets generated and/or analyzed during the current study are available in the NCBI repository, https://www.ncbi.nlm.nih.gov/sra/PRJNA865067.
Authors' contributions
YL, WW, YZ and HZ developed the concepts in this study. YL performed the experiments, collected and analyzed the data and wrote the manuscript. YL and WW confirm the authenticity of all the raw data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The present study was conducted in accordance with the ethical standards of the Ethics Committee of Peking University Shenzhen Hospital, who approved the experimental protocols.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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