Watters, E. | Search bioRxiv

bioRxiv2025

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles

In microbiome studies, sample processing errors are frequent and difficult to detect, especially in large studies involving multiple sites, personnel, and sample types. We present two complementary approaches to identify such errors using host DNA profiled via metagenomic sequencing of microbiome samples. The first approach compares host SNPs inferred from metagenomics to independently obtained genotypes (e.g., microarray genotypes) to match samples to their donors, while the second method compares metagenomics-inferred SNPs between samples to identify samples supplied by the same donor. Furthermore, we demonstrate that combining these methods with experimental metadata provides greater confidence in the identification of errors. Analyzing a longitudinal vaginal microbiome dataset, we demonstrate the ability of our approach to identify mislabeled samples. Using subsampling, we further show that our methods are robust to low sequencing coverage. Overall, our analysis highlights the frequency of processing errors in microbiome studies. We therefore recommend applying error-detection methods in all studies with suitable data.

bioinformatics↗

bioRxiv2024

Early prediction of preeclampsia using the first trimester vaginal microbiome

Preeclampsia is a severe obstetrical syndrome which contributes to 10-15% of all maternal deaths. Although the mechanisms underlying systemic damage in preeclampsia--such as impaired placentation, endothelial dysfunction, and immune dysregulation--are well studied, the initial triggers of the condition remain largely unknown. Furthermore, although the pathogenesis of preeclampsia begins early in pregnancy, there are no early diagnostics for this life-threatening syndrome, which is typically diagnosed much later, after systemic damage has already manifested. Here, we performed deep metagenomic sequencing and multiplex immunoassays of vaginal samples collected during the first trimester from 124 pregnant individuals, including 62 who developed preeclampsia with severe features. We identified multiple significant associations between vaginal immune factors, microbes, clinical factors, and the early pathogenesis of preeclampsia. These associations vary with BMI, and stratification revealed strong associations between preeclampsia and Bifidobacterium spp., Prevotella timonensis, and Sneathia vaginalis. Finally, we developed machine learning models that predict the development of preeclampsia using this first trimester data, collected ~5.7 months prior to clinical diagnosis, with an auROC of 0.78. We validated our models using data from an independent cohort (MOMS-PI), achieving an auROC of 0.80. Our findings highlight robust associations among the vaginal microbiome, local host immunity, and early pathogenic processes of preeclampsia, paving the way for early detection, prevention and intervention for this devastating condition.

microbiology↗