Analysis of placental transcriptome data as a tool for identifying of molecular pathways related to great obstetrical syndromes

Мұқаба

Дәйексөз келтіру

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Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

Numerous histological studies have demonstrated that the key pathogenetic mechanisms of great obstetrical syndromes (GOS) are associated with impaired placentation processes. However, the molecular basis of this discovery is still unclear. Therefore, the purpose of this work was to characterize the molecular mechanisms and search for GOS new genetic markers based on an integrative analysis of data obtained by whole genome expression profiling of placental tissue with preeclampsia, fetal growth restriction, premature birth and physiological pregnancy. The significant role of oxidative stress, ferroptosis and intercellular interactions disorders in placenta has been shown as common mechanisms of GOS pathogenetics. We have identified 64 genes significantly dysregulated at least in two pregnancy complications. The most significant cell populations enriched with these genes were maternal endothelial cells and syncytiotrophoblast cells. Computational analysis and topology assessment of the protein-protein interaction network made it possible to identify SOD1, ACTG1, TXNRD1, TKT, GCLM, GOT1, ACO1 and UBB as hub genes. We also emphasized key regulators (MAPK3, MID1, LCMT1, DUSP10, TOPS, SOX10, EGFR, TFAP2A, GLIS1, NR2F1, NR2F2, PAX5, HSF1 and BCL6) triggering a cascade of reactions with the involvement of detected differentially expressed genes. These genes are overrepresented in the MAP kinase and interferon-gamma response signaling pathways. The above mentioned genes and their products are the most promising biomarkers for the development of new approaches to the risk factors assessment and GOS targeted therapy. Further studies should be aimed at clarifying their functional and diagnostic significance in the development of pregnancy pathological conditions.

Толық мәтін

Рұқсат жабық

Авторлар туралы

Е. Trifonova

Research Institute of Medical Genetics, Tomsk National Research Medical Center; Siberian State Medical University

Хат алмасуға жауапты Автор.
Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk; Tomsk

A. Марков

Research Institute of Medical Genetics, Tomsk National Research Medical Center

Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk

A. Zarubin

Research Institute of Medical Genetics, Tomsk National Research Medical Center

Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk

A. Babovskaya

Research Institute of Medical Genetics, Tomsk National Research Medical Center

Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk

М. Gavrilenko

Research Institute of Medical Genetics, Tomsk National Research Medical Center

Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk

T. Gabidulina

Siberian State Medical University

Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk

V. Stepanov

Research Institute of Medical Genetics, Tomsk National Research Medical Center

Email: ekaterina.trifonova@medgenetics.ru
Ресей, Tomsk

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2. Fig. 1. Study design. FB – physiological pregnancy; PE – preeclampsia; PR – premature birth; IGR – fetal growth restriction; MOS – major obstetric syndromes; DEG – differentially expressed genes.

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3. Fig. 2. Clustering of samples included in the integrative analysis. Dataset identification numbers according to the GEO database are given. Datasets obtained as a result of our own studies in Russian and Yakut population samples are designated “OUR_Y” and “OUR_R”, respectively.

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4. Fig. 3. Venn diagram demonstrating the commonality and specificity of DEGs identified during the integrative analysis. PE – preeclampsia, IGR – fetal growth restriction, PR – premature birth.

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5. Fig. 4. Protein-protein interactions of ALS-associated gene products based on transcriptome analysis.

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6. Fig. 5. Analysis of gene network topology using the cytoHubba plugin. The central genes of the network with the maximum connection coefficient (rank) are highlighted in red.

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7. Fig. 6. Major signaling pathways associated with master regulators.

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8. Fig. 7. Heat map reflecting the variability of the expression level of a number of genes associated with ALS in cell populations identified in the placenta during the analysis of data obtained earlier by Vento-Tormo R. et al. [75].

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9. Fig. 8. The main cell populations enriched in genes associated with ALS. The specificity of genes for a particular cell type was identified based on previously conducted single-cell sequencing. a – Vento-Tormo R. et al. [75]; b – Suryawanshi et al. [76]. The Fold change indicator reflects the change in the fold enrichment of cells with specific transcripts relative to the background set of genes (for more details, see [74]).

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