Prioritizing potential endocrine active high resolution mass spectrometry (HRMS) features in Minnesota lakewater

Highlights: • Suite of LC-HRMS features at higher abundance in endocrine active sites • Confidently identified features omitted or listed as inactive by EDSP21 Dashboard • Feature prioritization dependent on grab versus composite field sampling -- Abstract: Liquid chromatography high-resolution mass spectrometry (LC-HRMS) shows great potential for expanding our understanding of relevant unknown chemical components present within complex environmental mixtures. This study identified potentially endocrine active components within Minnesota lakewater by prioritizing LC-HRMS features uniquely present at sunfish spawning habitats where male fish showed signs of estrogen agonism. Porewater samples from four locations within the same lake were analyzed using liquid chromatography quadrupole time of flight mass spectrometry (LC-QToF/MS) with positive (ESI+) and negative (ESI−) electrospray ionization. Plasma vitellogenin concentrations of captured male sunfish was used to designate sites as either endocrine active (ACT; 2 sites) or reference (REF; 2 sites). Assuming unique chemical presence at active sites contributed to endocrine activity, features at significantly higher intensities (p-value < 0.05, t-value > t-critical, log-fold change > 0.1; equal variance t-test of log2 transformed data) in ACT sites were then compiled into a suspect search list for feature identification. Adducts and isotopes of prioritized features were deprioritized using pattern recognizing algorithms using mass, retention time, and intensity. Feature identities were reported according to established confidence metrics using spectral libraries and elemental composition algorithms. This LC-HRMS approach identified a number of features omitted by targeted analysis with higher relative abundances in ACT sites, including plant essential oils, fatty acids, and mycotoxins. Multivariate analysis determined whether features were either present at both sites (AB) or unique to individual ACT sites (A or B). Detection frequency across datasets indicated bias in feature prioritization influenced by the chosen sampling method and sample acquisition mode. The majority of features prioritized by this workflow remain tentatively identified or unidentified masses of interest, reflective of current limitations in shared spectral libraries for soft ionization analyses. Strategies similar to this workflow have the potential to reduce bias in database-driven toxicological prioritization frameworks.