Ethanol tolerance, a polygenic trait of the yeast Saccharomyces cerevisiae, is the primary factor determining industrial bioethanol productivity. Until now, genomic elements affecting ethanol tolerance have been mapped only at low resolution, hindering their identification. Here, we explore the genetic architecture of ethanol tolerance, in the F6 generation of an Advanced Intercrossed Line (AIL) mapping population between two phylogenetically distinct, but phenotypically similar, S. cerevisiae strains (a common laboratory strain and a wild strain isolated from nature). Under ethanol stress, 51 quantitative trait loci (QTLs) affecting growth and 96 QTLs affecting survival, most of them novel, were identified, with high resolution, in some cases to single genes, using a High-Resolution Mapping Package of methodologies that provided high power and high resolution. We confirmed our results experimentally by showing the effects of the novel mapped genes: MOG1, MGS1, and YJR154W. The mapped QTLs explained 34% of phenotypic variation for growth and 72% for survival. High statistical power provided by our analysis allowed detection of many loci with small, but mappable effects, uncovering a novel “quasi-infinitesimal” genetic architecture. These results are striking demonstration of tremendous amounts of hidden genetic variation exposed in crosses between phylogenetically separated strains with similar phenotypes; as opposed to the more common design where strains with distinct phenotypes are crossed. Our findings suggest that ethanol tolerance is under natural evolutionary fitness-selection for an optimum phenotype that would tend to eliminate alleles of large effect. The study provides a platform for development of superior ethanol-tolerant strains using genome editing or selection.

Summary DNA sequencing is becoming more affordable and faster through advances in high-throughput technologies. This rise in data availability has contributed to the development of novel algorithms to elucidate previously obscure features and led to an increased reliance on complex workflows to integrate such tools into analyses pipelines. To facilitate the analysis of DNA sequencing data, we created metapipeline-DNA, a highly configurable and extensible pipeline. It encompasses a broad range of processing including raw sequencing read alignment and recalibration, variant calling, quality control and subclonal reconstruction. Metapipeline-DNA also contains configuration options to select and tune analyses while being robust to failures. This standardizes and simplifies the ability to analyze large DNA sequencing in both clinical and research settings. Availability Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is freely available at https://github.com/uclahs-cds/metapipeline-DNA.

Prostate cancer is frequently treated with radiotherapy. Unfortunately, aggressive radioresistant relapses can arise, and the molecular underpinnings of radioresistance are unknown. Modern clinical radiotherapy is evolving to deliver higher doses of radiation in fewer fractions (hypofractionation). We therefore analyzed genomic, transcriptomic, and proteomic data to characterize prostate cancer radioresistance in cells treated with both conventionally fractionated and hypofractionated radiotherapy. Independent of fractionation schedule, resistance to radiotherapy involved massive genomic instability and abrogation of DNA mismatch repair. Specific prostate cancer driver genes were modulated at the RNA and protein levels, with distinct protein subcellular responses to radiotherapy. Conventional fractionation led to a far more aggressive biomolecular response than hypofractionation. Testing preclinical candidates identified in cell lines, we revealed POLQ (DNA Polymerase Theta) as a radiosensitizer. POLQ-modulated radioresistance in model systems and was predictive of it in large patient cohorts. The molecular response to radiation is highly multimodal and sheds light on prostate cancer lethality. Significance: Radiation is standard of care in prostate cancer. Yet, we have little understanding of its failure. We demonstrate a new paradigm that radioresistance is fractionation specific and identified POLQ as a radioresistance modulator.

Linking genetic data with electronic health records in hospital biobanks promises to advance precision medicine, but limited ancestral diversity constrains discovery and generalizability. We analyzed 93,936 participants from the UCLA ATLAS Community Health Initiative to inform disease prevalence and genetic risk across five continental and 36 fine-scale ancestry groups. We discovered numerous unreported gene-phenotype associations, including FN3K with intestinal disaccharidase deficiency in Europeans and admixed Americans. Polygenic scores (PGS) robustly predicted common diseases, with effects markedly diminished in non-Europeans. Furthermore, we reduced the pronounced European bias in curated clinical variants using computational predictors, uncovering unreported disease-gene associations, including ANKZF1 and peripheral vascular disease in African Americans. Longitudinal data revealed that semaglutide efficacy varies across ancestries, is associated with PGS for type 2 diabetes, and is modulated by genetic variation in PTPRU. These findings illustrate how ancestrally diverse biobanks from a single health system yield robust disease associations and pharmacogenomic insights.

Summary: Linking genetic data with electronic health records in hospital biobanks promises to advance precision medicine, but limited ancestral diversity constrains discovery and generalizability. We analyzed 93,936 participants from the UCLA ATLAS Community Health Initiative to inform disease prevalence and genetic risk across five continental and 36 fine-scale ancestry groups. We discovered numerous unreported gene-phenotype associations, including FN3K with intestinal disaccharidase deficiency in Europeans and admixed Americans. Polygenic scores (PGS) robustly predicted common diseases, with effects markedly diminished in non-Europeans. Furthermore, we reduced the pronounced European bias in curated clinical variants using computational predictors, uncovering unreported disease-gene associations, including ANKZF1 and peripheral vascular disease in AFR. Longitudinal data revealed that semaglutide efficacy varies across ancestries, is associated with PGS for type 2 diabetes, and is modulated by genetic variation in PTPRU . These findings illustrate how ancestrally diverse biobanks from a single health system yield robust disease associations and pharmacogenomic insights.

Prostate cancer cell lines are particularly clinically homogenous, mostly representing metastatic states rather than localized disease. While there has been significant work in the development of additional models, few have been created without oncogenic transformation. We derived a primary prostate cancer cell line from a patient with localized Gleason 7 prostate cancer—designated CaB34—which spontaneously immortalized. We leveraged CaB34 to generate a paired radioresistant subline, CaB34-CF, using a clinically relevant fractionated radiotherapy schedule. These two paired cell lines were investigated extensively to determine their molecular characteristics and therapy responses. Both CaB34 and CaB34-CF express prostate-specific markers, including KRT18, NKX3.1, and AMACR. Multi-omic analyses using RNAseq and shotgun proteomics identified NNMT as the most significantly dysregulated component in CaB34-CF. A bioinformatic analysis determined that NNMT was more abundant within prostate tumors compared to benign prostate, suggesting a role in tumor progression. Knockdown studies of NNMT demonstrated significant radiosensitization of CaB34-CF cells, which was largely a result of increased radiation-induced cellular senescence. Growth as 3D organoids was significantly higher in the CaB34-CF line, and demonstrated a less structured pattern of expression of cytokeratin markers. Radiosensitization with NNMT siRNA was recapitulated in a 3D organoid clonogenic assay in CaB34-CF cells. Our research provides a paired primary model of treatment-naïve and radioresistant disease to address mechanisms of therapy resistance, while expanding the repertoire of localized prostate cancer cell lines for the research community. In addition, our findings present NNMT as a potential therapeutic target for sensitization of radioresistant disease.