PTEN Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma.

High-grade serous ovarian carcinoma (HGSC) remains a disease with poor prognosis that is unresponsive to current immune checkpoint inhibitors. Although PI3K pathway alterations, such as PTEN loss, are common in HGSC, attempts to target this pathway have been unsuccessful. We hypothesized that aberrant PI3K pathway activation may alter the HGSC immune microenvironment and present a targeting opportunity.

Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.

Background: Precision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g.

Chemical tools for profiling the intracellular ADP-ribosylated proteome.

The post-translational modification (PTM) ADP-ribosylation plays an important role in cell signalling and regulating protein function and has been implicated in the development of multiple diseases, including breast and ovarian cancers. Studying the underlying mechanisms through which this PTM contributes towards disease development, however, has been hampered by the lack of appropriate tools for reliable identification of physiologically relevant ADP-ribosylated proteins in a live-cell environment. Herein, we explore the application of an alkyne-tagged proprobe, 6Yn-ProTide-Ad (6Yn-Pro) as a chemical tool for the identification of intracellular ADP-ribosylated proteins through metabolic labelling.

Machine learning-based health environmental-clinical risk scores in European children.

Background: Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.