Discrete placental gene expression signatures accompany diabetic disease classifications during pregnancy.

BACKGROUND

Gestational diabetes mellitus affects up to 10% of pregnancies and is classified into subtypes gestational diabetes subtype A1 (GDMA1) (managed by lifestyle modifications) and gestational diabetes subtype A2 (GDMA2) (requiring medication). However, whether these subtypes are distinct clinical entities or more reflective of an extended spectrum of normal pregnancy endocrine physiology remains unclear.

OBJECTIVE

Integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and spatial transcriptomics harbors the potential to reveal disease gene signatures in subsets of cells and tissue microenvironments. We aimed to combine these high-resolution technologies with rigorous classification of diabetes subtypes in pregnancy. We hypothesized that differences between preexisting type 2 and gestational diabetes subtypes would be associated with altered gene expression profiles in specific placental cell populations.

STUDY DESIGN

In a large case-cohort design, we compared validated cases of GDMA1, GDMA2, and type 2 diabetes mellitus (T2DM) to healthy controls by bulk RNA-seq (n=54). Quantitative analyses with reverse transcription and quantitative PCR of presumptive genes of significant interest were undertaken in an independent and nonoverlapping validation cohort of similarly well-characterized cases and controls (n=122). Additional integrated analyses of term placental single-cell, single-nuclei, and spatial transcriptomics data enabled us to determine the cellular subpopulations and niches that aligned with the GDMA1, GDMA2, and T2DM gene expression signatures at higher resolution and with greater confidence.

RESULTS

Dimensional reduction of the bulk RNA-seq data revealed that the most common source of placental gene expression variation was the diabetic disease subtype. Relative to controls, we found 2052 unique and significantly differentially expressed genes (-2<Log[fold-change]>2 thresholds; q<0.05 Wald Test) among GDMA1 placental specimens, 267 among GDMA2, and 1520 among T2DM. Several candidate marker genes (chorionic somatomammotropin hormone 1 [CSH1], period circadian regulator 1 [PER1], phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta [PIK3CB], forkhead box O1 [FOXO1], epidermal growth factor receptor [EGFR], interleukin 2 receptor subunit beta [IL2RB], superoxide dismutase 3 [SOD3], dedicator of cytokinesis 5 [DOCK5], suppressor of glucose, and autophagy associated 1 [SOGA1]) were validated in an independent and nonoverlapping validation cohort (q<0.05 Tukey). Functional enrichment revealed the pathways and genes most impacted for each diabetes subtype, and the degree of proximal similarity to other subclassifications. Surprisingly, GDMA1 and T2DM placental signatures were more alike by virtue of increased expression of chromatin remodeling and epigenetic regulation genes, while albumin was the top marker for GDMA2 with increased expression of placental genes in the wound healing pathway. Assessment of these gene signatures in single-cell, single-nuclei, and spatial transcriptomics data revealed high specificity and variability by placental cell and microarchitecture types. For example, at the cellular and spatial (eg, microarchitectural) levels, distinguishing features were observed in extravillous trophoblasts (GDMA1) and macrophages (GDMA2). Lastly, we utilized these data to train and evaluate 4 machine learning models to estimate our confidence in predicting the control or diabetes status of placental transcriptome specimens with no available clinical metadata.

CONCLUSION

Consistent with the distinct association of perinatal outcome risk, placentae from GDMA1, GDMA2, and T2DM-affected pregnancies harbor unique gene signatures that can be further distinguished by altered placental cellular subtypes and microarchitectural niches.