Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

This article introduces ESDA, a new analytic tool for integrating multiple data types to identify the most distinguishing features following an exposure. Using the ESDA, we were able to identify signatures of infectious diseases. The results of the study indicate that integration of multiple types of large datasets can be used to identify distinguishing features for infectious diseases. Understanding the changes from different exposures will enable development of diagnostic tests for infectious diseases that target responses from the patient. Using the ESDA, we will be able to build a database of human response signatures to different infections and simplify diagnostic testing in the future.

Oncogenic transformation of mammary epithelial cells by transforming growth factor beta independent of mammary stem cell regulation.

BACKGROUND: Transforming growth factor beta (TGFß) is transiently increased in the mammary gland during involution and by radiation. While TGFß normally has a tumour suppressor role, prolonged exposure to TGFß can induce an oncogenic epithelial to mesenchymal transition (EMT) program in permissive cells and initiate the generation of cancer stem cells. Our objective is to mimic the transient exposure to TGFß during involution to determine the persistent effects on premalignant mammary epithelium. METHOD: CDßGeo cells, a transplantable mouse mammary epithelial cell line, were treated in vitro for 14 days with TGFß (5 ng/ml). The cells were passaged for an additional 14 days in media without TGFß and then assessed for markers of EMT and transformation. RESULTS: The 14-day exposure to TGFß induced EMT and transdifferentiation in vitro that persists after withdrawal of TGFß. TGFß-treated cells are highly tumorigenic in vivo, producing invasive solid de-differentiated tumours (100%; latency 6.7 weeks) compared to control (43%; latency 32.7 weeks). Although the TGFß-treated cells have initiated a persistent EMT program, the stem cell population was unchanged relative to the controls. The gene expression profiles of TGFß-treated cells demonstrate de-differentiation with decreases in the expression of genes that define luminal, basal and stem cells. Additionally, the gene expression profiles demonstrate increases in markers of EMT, growth factor signalling, TGFß2 and changes in extra cellular matrix. CONCLUSION: This model demonstrates full oncogenic EMT without an increase in stem cells, serving to separate EMT markers from stem cell markers.