Vogler, A. | Search bioRxiv

bioRxiv2026

Metabolomic signatures support the diagnostics of peritoneal endometriosis using generalised linear models

Endometriosis, a common inflammatory gynecological disorder affecting up to 10% of women worldwide, is characterized by the presence of endometrium-like tissue outside the uterus. Current diagnostic methods, such as ultrasound and MRI, effectively detect ovarian and deep endometriosis but fail to detect more common peritoneal type. Diagnosing peritoneal endometriosis currently necessitates invasive laparoscopy and histological confirmation. Despite numerous efforts, no new reliable biomarkers have successfully transitioned into routine clinical use. This study aimed to investigate the use of targeted metabolomics to discover metabolite ratios capable of identifying endometriosis in plasma samples. We analyzed a discovery population of 235 patients and a validation population of 278 patients. All cases and controls in both populations were diagnosed by laparoscopy. Control subjects included individuals presenting with symptoms such as pain, dysmenorrhea, infertility, or other benign conditions, but who had no laparoscopic evidence of endometriosis. Using generalized linear models (GLMs) and machine learning, the study identified specific metabolite ratios as potential biomarkers that can distinguish different types of endometriosis and enable mass spectrometry-based diagnostics for peritoneal endometriosis. The best-validated GLM, derived from the concentration ratios of amino acids, acylcarnitines, sphingomyelins, and phosphatidylcholines, consisted of Thr/SM(OH) C22:2 + PC aa C40:5/SFA_PC + lysoPC a C16:0/SM(OH) C16:1. This model yielded an AUC of 0.82 (95% CI 0.619-0.891, with 76% sensitivity and 81% specificity) for peritoneal endometriosis. This innovative approach offers a robust diagnostic model, addressing an unmet medical need by facilitating earlier detection of peritoneal endometriosis and improving overall clinical management.

systems biology↗

bioRxiv2026

A robust phylogenetic framework for ophiostomatoid fungi: orders Ophiostomatales and Microascales (Sordariomycetes, Ascomycota)

Ophiostomatoids are an ecological group of microfungi that commonly associate with bark and ambrosia beetles. As well as being insect symbionts, they play significant ecological roles as plant pathogens, and include species responsible for major forest tree diseases. Despite their ecological similarities, ophiostomatoids are distributed across two quite distantly related orders, the Microascales and Ophiostomatales. Historically, these fungi were considered a single natural group; however, molecular studies have revealed their independent origins and convergent ecological strategies. Previous phylogenetic studies of these fungi have typically focused on resolving taxonomic issues or understanding individual lifestyles, such as beetle-cultivated ambrosia lineages or vascular wilt pathogens. As a result, we lack a comprehensive phylogenetic framework that integrates dense species-level sampling with ecological data across both orders. Such frameworks are essential for understanding the broader phylogenetic and ecological context in which key fungal lifestyles have evolved. Here, we assembled and analysed all available sequence data for the Microascales and Ophiostomatales from seven widely used fungal marker loci to reconstruct a densely sampled phylogeny for each order. We evaluated locus performance and showed that whilst individual loci fail to resolve many taxa, concatenated datasets produce robust, well-supported topologies consistent with published genomic studies. By mapping ecological traits onto these trees, we show that lifestyle diversity and beetle associations are much more variable in the Microascales than in the Ophiostomatales, despite comparable species richness. Presenting both orders together provides a unique comparative perspective on the ecology and evolution of ophiostomatoids. As metabarcoding datasets of ophiostomatoids become increasingly common, this integrative framework can offer a valuable resource for environmental sequence identification and investigating fungal lifestyle switches, which in turn can support future biodiversity and ecology studies.

evolutionary biology↗