Chemoenzymatic Probes Reveal Peptidoglycan Recognition and Uptake Mechanisms in Candida albicans.

Candida albicans, the major fungal pathogen in humans, is under the strong influence of bacterial peptidoglycan fragments to undergo the yeast-to-hyphae transition, a key virulent step in C. albicans pathogenesis and infections. However, due to the synthetic difficulties of obtaining peptidoglycan fragments for biological studies, mechanistic details of how C. albicans recognizes and uptakes these peptidoglycan fragments have not been well elucidated. Notably, previous works have solely focused on the synthetic peptidoglycan ligand, muramyl dipeptide (MDP), despite its poor hyphal-inducing activity in C. albicans. In this work, we isolated and purified natural peptidoglycan fragments via enzymatic degradation of bacteria cell wall sacculi and chemoenzymatically installed a series of functional d-amino acids into the natural muropeptide, creating peptidoglycan probes that bear photoaffinity, bio-orthogonal, or fluorescent functionality. Using these chemoenzymatic peptidoglycan probes, we established that natural peptidoglycan fragments, which are potent hyphal-inducers, interact with the C. albicans Cyr1 sensor protein in the in-gel fluorescence assay as well as in in vitro pulldown studies. Moreover, we established that bacterial peptidoglycan probes enter C. albicans cells via an energy-dependent endocytic process.

Genome-wide association study of prostate cancer-specific survival.

BACKGROUND: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. METHODS: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR). RESULTS: We observed no significant association between genetic variants and prostate cancer survival. CONCLUSIONS: Common genetic variants with large impact on prostate cancer survival were not observed in this study. IMPACT: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes.

Prediction of individual genetic risk to prostate cancer using a polygenic score.

BACKGROUND: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.