Endocrine disrupting chemicals (EDCs) such as bisphenol S (BPS) are xenobiotic compounds that can disrupt endocrine signaling due to steric similarities to endogenous hormones. EDCs have been shown to induce disruptions in normal epigenetic programming (epimutations) and differentially expressed genes (DEGs) that predispose disease states. Most interestingly, the prevalence of epimutations following exposure to many EDCs persists over multiple generations. Many studies have described direct and prolonged effects of EDC exposure in animal models, but many questions remain about molecular mechanisms by which EDC-induced epimutations are introduced or subsequently propagated, whether there are cell type-specific susceptibilities to the same EDC, and whether this correlates with differential expression of relevant hormone receptors. We exposed cultured pluripotent (iPS), somatic (Sertoli and granulosa), and primordial germ cell-like (PGCLC) cells to BPS and found that differential incidences of BPS-induced epimutations and DEGs correlated with differential expression of relevant hormone receptors inducing epimutations near relevant hormone response elements in somatic and pluripotent, but not germ cell types. Most interestingly, we found that when iPS cells were exposed to BPS and then induced to differentiate into PGCLCs, the prevalence of epimutations and DEGs was largely retained, however, >90% of the specific epimutations and DEGs were replaced by novel epimutations and DEGs. These results suggest a unique mechanism by which an EDC-induced epimutated state may be propagated transgenerationally.

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.