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Integrated Machine Learning and Bioinformatic Analyses Constructed a Network Between Mitochondrial Dysfunction and Immune Microenvironment of Periodontitis

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Abstract

Periodontitis is a prevalent and persistent inflammatory condition that impacts the supporting tissues of the teeth, including the gums and bone. Recent research indicates that mitochondrial dysfunction may be involved in the onset and advancement of periodontitis. The current work sought to reveal the interaction between mitochondrial dysfunction and the immune microenvironment in periodontitis. Public data were acquired from MitoCarta 3.0, Mitomap, and GEO databases. Hub markers were screened out by five integrated machine learning algorithms and verified by laboratory experiments. Single-cell sequencing data were utilized to unravel cell-type specific expression levels of hub genes. An artificial neural network model was constructed to discriminate periodontitis from healthy controls. An unsupervised consensus clustering algorithm revealed mitochondrial dysfunction-related periodontitis subtypes. The immune and mitochondrial characteristics were calculated using CIBERSORTx and ssGSEA algorithms. Two hub mitochondria-related markers (CYP24A1 and HINT3) were identified. Single-cell sequencing data revealed that HINT3 was primarily expressed in dendritic cells, while CYP24A1 was mainly expressed in monocytes. The hub genes based artificial neural network model showed robust diagnostic performance. The unsupervised consensus clustering algorithm revealed two distinct mitochondrial phenotypes. The hub genes exhibited a strong correlation with the immune cell infiltration and mitochondrial respiratory chain complexes. The study identified two hub markers that may serve as potential targets for immunotherapy and provided a novel reference for future investigations into the function of mitochondria in periodontitis.

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AVAILABILITY OF DATA AND MATERIAL

The date and materials of the current study are available from the corresponding author on reasonable request.

Abbreviations

ROS:

Reactive oxygen species

OXPHOS:

Oxidative phosphorylation

mtDAMPs:

Mitochondrial damage-associated molecular patterns

PRR:

Pattern recognition receptors

UMAP:

Uniform manifold approximation and projection

GEO:

Gene Expression Omnibus

DEGs:

Differentially expressed genes

FC:

Fold-change

PCA:

Principal component analysis

DMRGs:

Differentially expressed mitochondria-related genes

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LASSO:

Absolute shrinkage and selection operator

SVM-RFE:

Support vector machine recursive feature elimination

ANN:

Artificial neural network

ROC:

Receiver operating characteristic

DCA:

Decision curve analysis

CIBERSORTx:

Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts x

GSEA:

Gene set enrichment analysis

ssGSEA:

Single-sample gene set enrichment analysis

WGCNA:

Weighted correlation network analysis

MM:

Module membership

GS:

Gene significance

CDF:

Cumulative distribution function

AUC:

Area under the curve

MitoPathway:

Mitochondria-related pathway

CT:

Cycle threshold

CtTp:

Ct of target genes for periodontitis sample

CtGp:

Ct of house-keeping genes for periodontitis sample

CtGc:

Ct of housekeeping gene for controls

CtTc:

Ct of target genes for control

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ACKNOWLEDGEMENTS

We would like to exert a compelling appreciation for the GEO projects. We thank Dr. Jianming Zeng (University of Macau), Lao Junjun, Biomamba, and all members of their bioinformatics team, biotrainee, for generously sharing their experience and codes.

Funding

This work was financially supported by the National Natural Science Foundation of China [grant number 81700982]; the Chongqing Medical Reserve Talent Studio for Young People [grant number ZQNYXGDRCGZS2019004].

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Author notes

  1. Hang Chen and Limin Peng contributed equally to this work.

Authors and Affiliations

  1. College of Stomatology, Chongqing Medical University, Chongqing, China

    Hang Chen, Limin Peng, Zhenxiang Wang & Xiaonan Zhang

  2. Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing Medical University, Chongqing, China

    Hang Chen, Limin Peng, Zhenxiang Wang & Xiaonan Zhang

  3. Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Medical University, Chongqing, China

    Hang Chen, Limin Peng, Zhenxiang Wang & Xiaonan Zhang

  4. Department of Laboratory Medicine, Key Laboratory of Diagnostic Medicine (Ministry of Education), Chongqing Medical University, Chongqing, China

    Yujuan He

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Contributions

Xiaonan Zhang: supervision, project administration, funding acquisition. Hang Chen: conceptualization, methodology, software, investigation, formal analysis, visualization, validation, writing—original draft. Limin Peng: methodology, visualization, validation, resources, writing—review and editing. Zhenxiang Wang: data curation, validation, writing—review and editing. Yujuan He: writing—review and editing.

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Correspondence to Xiaonan Zhang.

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This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Stomatological Hospital of Chongqing Medical University (Approval No: 2020 LSNo.79).

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Supplementary Information

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10753_2023_1851_MOESM1_ESM.pdf

Supplementary Table 1: The detail of clinical information on 24 patients. Supplementary Table 2: Inclusion criteria and exclusion criteria for clinical participants. Supplementary Table 3: Characteristics of the bulk tissue transcriptomic datasets included in the study. Supplementary Table 4: Characteristics of the single-cell transcriptomic datasets included in the study. Supplementary Table 5: The primer sequences for qRT-PCR. Supplementary Table 6: The output results of the neural network model. Supplementary Fig. 1: The WGCNA results. (PDF 365 KB)

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Chen, H., Peng, L., Wang, Z. et al. Integrated Machine Learning and Bioinformatic Analyses Constructed a Network Between Mitochondrial Dysfunction and Immune Microenvironment of Periodontitis. Inflammation 46, 1932–1951 (2023). https://doi.org/10.1007/s10753-023-01851-0

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