Fetal High-Risk APOL1 Genotype Increases Risk for Small for Gestational Age in Term Infants Affected by Preeclampsia

Azhibekov, Timur; Durodoye, Razaq; Miller, Anna; Simpson, Claire L.; Davis, Robert L.; Williams, Scott M.; Bruggeman, Leslie A.

doi:10.1159/000529850

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Development of gestational hypertension and intrauterine growth restriction meets the diagnosis criteria of preeclampsia 29 . These findings are congruent with previous clinical reports showing that fetal APOL1 high-risk genotype is a risk factor for preeclampsia in the mother 12–14 and growth restriction in the infant 15 . Therefore, this BAC/APOL1-G1 IVF mouse model was a relevant mouse model to study the molecular mechanism of preeclampsia, especially preeclampsia associated with fetal APOL1-G1 induced one.…”

Section: Discussionsupporting

confidence: 92%

“…[12][13][14] Fetal APOL1 genotype is also reported to be associated with altered fetal growth in term infants and preeclampsia in preterm infants. 15 APOL1-G0 (low-risk variant) or APOL1-G2 (high-risk variant) transgenic mice under the control of nephrin gene (Nphs1) promotor develop preeclampsia. 16 However, it is still not known if fetal APOL1 high-risk variant causes preeclampsia.…”

Section: Introductionmentioning

confidence: 99%

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Preeclampsia in mice carrying fetuses with APOL1 risk variants

Yoshida,

Latt,

Shrivastav

et al. 2024

Preprint

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African-American women have a maternal mortality rate approximately three times higher than European-American women. This is partially due to hypertensive disorders of pregnancy, including preeclampsia. Fetal APOL1 high-risk genotype increases preeclampsia risk, although mechanisms remain elusive. We characterized two mouse models to investigate whether fetal-origin APOL1 induces preeclampsia and which cell types contribute. We in vitro fertilized mice with sperm from two transgenic mouse lines: APOL1 transgenic mice carrying human genomic locus constructs from bacterial artificial chromosomes (BAC) containing the APOL1 gene, mimicking expression and function of human APOL1 (BAC/APOL1 mice) and albumin promoter APOL1 transgenic mice expressing APOL1 in liver and plasma (Alb/APOL1 mice). Dams carrying either BAC/APOL1-G1 or Alb/APOL1-G1 fetuses had elevated systolic blood pressure, while dams carrying BAC/APOL1-G0 or Alb/APOL1-G0 fetuses did not. BAC/APOL1-G1 and Alb/APOL1-G1 fetuses weighed less than littermates, indicating intrauterine growth restriction. Single-nucleus RNA-seq of APOL1-G1 placentas showed increased expression of osteopontin/Spp1, most prominently in vascular endothelial cells with robust APOL1 expression. Cell-cell interaction analysis indicated pro-inflammatory signaling between placental cells and maternal monocytes. These models show that fetal origin APOL1-G1 causes preeclampsia, inducing pro-inflammatory response in placenta and maternal monocytes. The APOL1-G1 variant poses a multi-generational problem, causing effects in mothers and offspring.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Preeclampsia in mice carrying fetuses with APOL1 risk variants

Yoshida,

Latt,

Shrivastav

et al. 2024

Preprint

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APOL1 Nephropathy Risk Variants Through the Life Course: A Review

Itoku,

Isaac,

Wilson

et al. 2024

American Journal of Kidney Diseases

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Detection and assessment of immune and stromal related risk genes to predict preeclampsia: A bioinformatics analysis with dataset

Qin

2024

Medicine

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This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model’s precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine–cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.

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Fetal High-Risk APOL1 Genotype Increases Risk for Small for Gestational Age in Term Infants Affected by Preeclampsia

Cited by 3 publications

References 14 publications

Preeclampsia in mice carrying fetuses with APOL1 risk variants

Preeclampsia in mice carrying fetuses with APOL1 risk variants

APOL1 Nephropathy Risk Variants Through the Life Course: A Review

Detection and assessment of immune and stromal related risk genes to predict preeclampsia: A bioinformatics analysis with dataset

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