Biology subjects

Kindschuh, W. F.

Publications and source records attributed to Kindschuh, W. F..

4 recordsLinked to original sources

bioRxiv2026

Metagenomics reveals a phylogenetically informed microbial signature associated with Morgellons disease

Morgellons disease is a poorly understood and controversial condition characterized by cutaneous, often fibrous lesions, as well as variable systemic symptoms, such as fatigue, muscle and joint pain, and cognitive dysfunction. While there have been links suggested between Morgellons and known pathogens, most frequently Borrelia burgdorferi, the current medical consensus is that Morgellons disease is a form of delusional parasitosis, where patients falsely believe they are infected with parasites. Despite this controversy, there has not been a deep metagenomic exploration of Morgellons disease. Here, we use deep metagenomic sequencing and metagenomic analysis to characterize Morgellons lesions and unaffected skin from five individuals in a family cohort. We find that Morgellons lesions contain sequences poorly represented in existing databases, and demonstrate that lesions may harbor sequences with novel function. We further demonstrate that MAGs assembled from lesion samples vary taxonomically from MAGs assembled from unaffected skin, and that there is a phylogenetically informed microbial signature associated with Morgellons lesions. These findings motivate further investigation into a possible microbial etiology in Morgellons disease.

microbiology↗

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↗

bioRxiv2021

Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

Spontaneous preterm birth (sPTB) is a leading cause of maternal and neonatal morbidity and mortality, yet both its prevention and early risk stratification are limited. The vaginal microbiome has been associated with PTB risk, possibly via metabolic or other interactions with its host. Here, we performed untargeted metabolomics on 232 vaginal samples, in which we have previously profiled the microbiota using 16S rRNA gene sequencing. Samples were collected at 20-24 weeks of gestation from women with singleton pregnancies, of which 80 delivered spontaneously before 37 weeks of gestation. We find that the vaginal metabolome correlates with the microbiome and separates into six clusters, three of which are associated with spontaneous preterm birth (sPTB) in Black women. Furthermore, while we identify five metabolites that associate with sPTB, another five associate with sPTB only when stratifying by race. We identify multiple microbial correlations with metabolites associated with sPTB, including intriguing correlations between vaginal bacteria that are considered sub-optimal and metabolites that were enriched in women who delivered at term. We propose that several sPTB-associated metabolites may be exogenous, and investigate another using metabolic models. Notably, we use machine learning models to predict sPTB risk using metabolite levels, weeks to months in advance, with high accuracy. We show that these predictions are more accurate than microbiome-based and maternal covariates-based models. Altogether, our results demonstrate the potential of vaginal metabolites as early biomarkers of sPTB and highlight exogenous exposures as potential risk factors for prematurity.

systems biology↗