Bioinformatics Prediction and Experimental Validation of the Role of Macrophage Polarization and Ferroptosis in Gestational Diabetes Mellitus

Abstract

Purpose

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy that is associated with placental inflammation and adverse pregnancy outcomes. However, the mechanisms of inflammation in GDM are still unclear.

Methods

Bulk transcriptome, single-cell transcriptome, clinical information, and samples were collected from GSE154414, GSE70493, GSE173193 and a retrospective cohort. Bioinformatics prediction was used to explore the mechanisms of placental inflammation, and multiplex immunofluorescence was used to validate the results.

Results

First, we found that GDM is characterized by low-grade inflammation and is linked to several adverse pregnancy outcomes, as supported by our collected clinical data. Additionally, we identified ten hub genes (FCGR3B, CXCR1, MMP9, ITGAX, CCL5, GZMB, S100A8, LCN2, TGFB1, and LTF) as potential therapy targets and confirmed the binding of corresponding predictive therapeutic agents by molecular docking. Transcriptome sequencing analysis has shown that macrophages are primarily responsible for the emergence of placental inflammation, and that M1 macrophage polarization increased while M2 macrophage polarization decreased in GDM when compared to the control sample. Multiplex immunofluorescence staining of CD68, CD80, and ACSL4 was performed and suggested that ferroptosis of macrophages may contribute to placental inflammation in GDM.

Conclusion

In conclusion, our findings provide a better understanding of the mechanisms of inflammation in GDM and suggest potential therapeutic targets for this condition.

Graphical Abstract

Keywords:

• gestational diabetes mellitus
• placental inflammation
• macrophage
• therapy targets
• ferroptosis

Data Sharing Statement

The datasets presented in this study can be found in online repositories. The names of the repository and accession number(s) can be found in Supplementary Table 4. Detailed clinical information of the patients can be found in Supplementary Table 5.

Acknowledgments

We thank the authors who provided the GEO public datasets. We thank Dr.Jianming Zeng (University of Macau) and all the members of his bioinformatics team, biotrainee, for generously sharing their experience and codes. We also thank for generous help from Jun Zhang of China Pharmaceutical University and his official account.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that there were no financial or commercial relationships that might be viewed as having a potential conflict of interest.

Additional information

Funding

This study was supported by National Key Clinical Specialty Construction Project (Clinical Pharmacy) and High-Level Clinical Key Specialty (Clinical Pharmacy) in Guangdong Province. In addition, the study was also supported by funding of the Guangdong Province Hospital Association (No. 2021YXQN08).