Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses

Shaonan Hu; Simin Li; Wanchen Ning; Xiuhong Huang; Xiangqiong Liu; Yupei Deng; Debora Franceschi; Anthony Chukwunonso Ogbuehi; Bernd Lethaus; Vuk Savkovic; Hanluo Li; Sebastian Gaus; Rüdiger Zimmerer; Dirk Ziebolz; Gerhard Schmalz; Shaohong Huang

doi:10.3389/fnagi.2022.1032401

Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses

Front Aging Neurosci. 2022 Dec 5:14:1032401. doi: 10.3389/fnagi.2022.1032401. eCollection 2022.

Authors

Affiliations

¹ Stomatological Hospital, Southern Medical University, Guangzhou, China.
² Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, Beijing, China.
³ Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
⁴ Faculty of Physics, University of Münster, Münster, Germany.
⁵ Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany.
⁶ Department of Cariology, Endodontology and Periodontology, University of Leipzig, Leipzig, Germany.

Abstract

Objective: To identify the genetic linkage mechanisms underlying Parkinson's disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases.

Methods: The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated.

Results: Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD.

Conclusion: Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.

Keywords: Parkinson’s disease; bioinformatics; crosstalk genes; neurodegenerative disease; periodontitis.