Drake, A. | Search bioRxiv

bioRxiv2018

Epigenetic derepression of FKBP5 by aging and stress contributes to NF-kB-driven inflammation and cardiovascular risk

Aging and psychosocial stress are associated with increased inflammation and disease risk, but the underlying molecular mechanisms are poorly understood. Because both aging and stress are also associated with lasting epigenetic changes, a plausible hypothesis is that stress exposure along the lifespan could confer disease risk by epigenetically deregulating molecules involved in inflammatory processes. Here, by combining large-scale analyses in human cohorts with mechanistic in vitro investigations, we found that FKBP5, a protein implicated in stress physiology, contributes to these relations. Across independent human cohorts (total n=3,131), aging and stress-related phenotypes were synergistically associated with epigenetic derepression of FKBP5. These age/stress-related epigenetic effects were recapitulated in an in vitro model of replicative senescence, whereby we exposed replicating human fibroblasts to stress (glucocorticoid) hormones. Unbiased genome-wide analyses in human blood linked higher FKBP5 mRNA with a proinflammatory profile and altered NF-{kappa}B-related gene networks. Accordingly, experiments in immune cells showed that FKBP5 overexpression promotes inflammation by strengthening the interactions of NF-{kappa}B regulatory kinases, whereas opposing FKBP5 either by genetic deletion (CRISPR/Cas9-mediated) or selective pharmacological inhibition prevented the effects on NF-{kappa}B. Further, the age/stress-related epigenetic signature enhanced FKBP5 responsivity to NF-{kappa}B through a positive feedback loop and was present in individuals with a history of acute myocardial infarction, a disease state linked to peripheral inflammation. These findings suggest that FKBP5-NF-{kappa}B signaling mediates inflammation associated with aging and stress, potentially contributing to cardiovascular risk, and may thus point to novel biomarker and treatment possibilities.\n\nSignificanceDiseases of the aging are the leading cause of morbidity and mortality. Elucidating the molecular mechanisms through which modifiable factors, such as psychosocial stress, confer risk for aging-related disease can have profound implications. Here, by combining studies in humans with experiments in cells, we find that aging and stress synergize to epigenetically derepress FKBP5, a protein implicated in stress physiology. Higher FKBP5 promotes inflammation by activating the master immune regulator NF-{kappa}B, whereas opposing FKBP5 - either genetically or pharmacologically- prevents the effects on NF-{kappa}B. Further, the age/stress-related epigenetic signature of FKBP5 is associated with history of myocardial infarction, a disease state linked to inflammation. These findings provide molecular insights into stress-related disease and may point to novel biomarker and treatment possibilities.

genomics↗

bioRxiv2018

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗