The Complete Spectrum of Yeast Chromosome Instability Genes Identifies Candidate CIN Cancer Genes and Functional Roles for ASTRA Complex Components

Chromosome instability (CIN) is observed in most solid tumors and is linked to somatic mutations in genome integrity maintenance genes. The spectrum of mutations that cause CIN is only partly known and it is not possible to predict a priori all pathways whose disruption might lead to CIN. To address this issue, we generated a catalogue of CIN genes and pathways by screening ∼2,000 reduction-of-function alleles for 90% of essential genes in Saccharomyces cerevisiae. Integrating this with published CIN phenotypes for other yeast genes generated a systematic CIN gene dataset comprised of 692 genes. Enriched gene ontology terms defined cellular CIN pathways that, together with sequence orthologs, created a list of human CIN candidate genes, which we cross-referenced to published somatic mutation databases revealing hundreds of mutated CIN candidate genes. Characterization of some poorly characterized CIN genes revealed short telomeres in mutants of the ASTRA/TTT components TTI1 and ASA1. High-throughput phenotypic profiling links ASA1 to TTT (Tel2-Tti1-Tti2) complex function and to TORC1 signaling via Tor1p stability, consistent with the role of TTT in PI3-kinase related kinase biogenesis. The comprehensive CIN gene list presented here in principle comprises all conserved eukaryotic genome integrity pathways. Deriving human CIN candidate genes from the list allows direct cross-referencing with tumor mutational data and thus candidate mutations potentially driving CIN in tumors. Overall, the CIN gene spectrum reveals new chromosome biology and will help us to understand CIN phenotypes in human disease

Sphingolipid accumulation causes mitochondrial dysregulation and cell death

Sphingolipids are structural components of cell membranes that have signaling roles to regulate many activities, including mitochondrial function and cell death. Sphingolipid metabolism is integrated with numerous metabolic networks, and dysregulated sphingolipid metabolism is associated with disease. Here, we describe a monogenic yeast model for sphingolipid accumulation. A csg2Δ mutant cannot readily metabolize and accumulates the complex sphingolipid inositol phosphorylceramide (IPC). In these cells, aberrant activation of Ras GTPase is IPC-dependent, and accompanied by increased mitochondrial reactive oxygen species (ROS) and reduced mitochondrial mass. Survival or death of csg2Δ cells depends on nutritional status. Abnormal Ras activation in csg2Δ cells is associated with impaired Snf1/AMPK protein kinase, a key regulator of energy homeostasis. csg2Δ cells are rescued from ROS production and death by overexpression of mitochondrial catalase Cta1, abrogation of Ras hyperactivity or genetic activation of Snf1/AMPK. These results suggest that sphingolipid dysregulation compromises metabolic integrity via Ras and Snf1/AMPK pathways

Population-based absolute risk estimation with survey data

Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level