Biology subjects

Menkhorst, E.

Publications and source records attributed to Menkhorst, E..

2 recordsLinked to original sources

bioRxiv2025

Late-Onset Preeclampsia is characterised by Accelerated Placental Aging

Late-onset preeclampsia (LOPE) is a major pregnancy complication characterised by hypertension and placental dysfunction, resolving only upon delivery. Here, we show that LOPE placentae undergo accelerated molecular aging, marked by telomere attrition, DNA damage and trophoblast senescence. Using primary placental tissue and trophoblast organoids, we demonstrate oxidative stress as a driver of telomere shortening and angiogenic imbalance. Inflammation did not alter placental aging trajectories. Antioxidant treatment (superoxide dismutase) preserved telomere length, reduced DNA damage and restored angiogenic balance, highlighting oxidative stress as a modifiable determinant of placental aging. We identify reduced expression of telomeric repeat-containing RNAs (TERRAs) as a molecular hallmark of LOPE, and show that antisense oligonucleotide-mediated TERRA depletion exacerbates telomere erosion and senescence. Together, these findings delineate oxidative stress and TERRA loss as mechanisms driving placental decline, establish trophoblast organoids as a tractable model of placental aging, and reveal potential therapeutic avenues for mitigating preeclampsia-associated placental dysfunction.

developmental biology↗

bioRxiv2024

PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection

The knockoff framework, combined with variable selection procedure, controls false discovery rate (FDR) without the need for calculating p-values. Hence, it presents an attractive alternative to differential expression analysis of high-throughput biological data. However, current knockoff variable generators make strong assumptions or insufficient approximations that lead to FDR inflation when applied to biological data. We propose Partial Least Squares Knockoff (PLSKO), an efficient and assumption-free knockoff generator that is robust to varying types of biological omics data. We compare PLSKO with a wide range of existing methods. In simulation studies, we show that PLSKO is the only method that controls FDR with sufficient statistical power in complex non-linear cases. In semi-simulation studies based on real data, we show that PLSKO generates valid knockoff variables for different types of biological data, including RNA-seq, proteomics, metabolomics and microbiome. In preeclampsia multi-omics case studies, we combined PLSKO with Aggregation Knockoff to address the randomness of knockoffs and improve power, and show that our method is able to select variables that are biologically relevant.

bioinformatics↗