Biological and genetic determinants of glycolysis: Phosphofructokinase isoforms boost energy status of stored red blood cells and transfusion outcomes.

Mature red blood cells (RBCs) lack mitochondria and thus exclusively rely on glycolysis to generate adenosine triphosphate (ATP) during aging in vivo or storage in blood banks. Here, we leveraged 13,029 volunteers from the Recipient Epidemiology and Donor Evaluation Study to identify associations between end-of-storage levels of glycolytic metabolites and donor age, sex, and ancestry-specific genetic polymorphisms in regions encoding phosphofructokinase 1, platelet (detected in mature RBCs); hexokinase 1 (HK1); and ADP-ribosyl cyclase 1 and 2 (CD38/BST1). Gene-metabolite associations were validated in fresh and stored RBCs from 525 Diversity Outbred mice and via multi-omics characterization of 1,929 samples from 643 human RBC units during storage. ATP and hypoxanthine (HYPX) levels-and the genetic traits linked to them-were associated with hemolysis in vitro and in vivo, both in healthy autologous transfusion recipients and in 5,816 critically ill patients receiving heterologous transfusions, suggesting their potential as markers to improve transfusion outcomes.

Ferroptosis regulates hemolysis in stored murine and human red blood cells.

Red blood cell (RBC) metabolism regulates hemolysis during aging in vivo and in the blood bank. Here, we leveraged a diversity outbred mouse population to map the genetic drivers of fresh/stored RBC metabolism and extravascular hemolysis upon storage and transfusion in 350 mice. We identify the ferrireductase Steap3 as a critical regulator of a ferroptosis-like process of lipid peroxidation. Steap3 polymorphisms were associated with RBC iron content, in vitro hemolysis, and in vivo extravascular hemolysis both in mice and 13,091 blood donors from the Recipient Epidemiology and Donor evaluation Study. Using metabolite Quantitative Trait Loci analyses, we identified a network of gene products (FADS1/2, EPHX2 and LPCAT3) - enriched in donors of African descent - associated with oxylipin metabolism in stored human RBCs and related to Steap3 or its transcriptional regulator, the tumor protein TP53. Genetic variants were associated with lower in vivo hemolysis in thousands of single-unit transfusion recipients. Highlights: Steap3 regulates lipid peroxidation and extravascular hemolysis in 350 diversity outbred miceSteap3 SNPs are linked to RBC iron, hemolysis, vesiculation in 13,091 blood donorsmQTL analyses of oxylipins identified ferroptosis-related gene products FADS1/2, EPHX2, LPCAT3Ferroptosis markers are linked to hemoglobin increments in transfusion recipients.

Biological and Genetic Determinants of Glycolysis: Phosphofructokinase Isoforms Boost Energy Status of Stored Red Blood Cells and Transfusion Outcomes.

Mature red blood cells (RBCs) lack mitochondria, and thus exclusively rely on glycolysis to generate adenosine triphosphate (ATP) during aging in vivo or storage in the blood bank. Here we leveraged 13,029 volunteers from the Recipient Epidemiology and Donor Evaluation Study to identify an association between end-of-storage levels of glycolytic metabolites and donor age, sex, and ancestry-specific genetic polymorphisms in regions encoding phosphofructokinase 1, platelet (detected in mature RBCs), hexokinase 1, ADP-ribosyl cyclase 1 and 2 (CD38/BST1). Gene-metabolite associations were validated in fresh and stored RBCs from 525 Diversity Outbred mice, and via multi-omics characterization of 1,929 samples from 643 human RBC units during storage. ATP and hypoxanthine levels - and the genetic traits linked to them - were associated with hemolysis in vitro and in vivo, both in healthy autologous transfusion recipients and in 5,816 critically ill patients receiving heterologous transfusions, suggesting their potential as markers to improve transfusion outcomes. Highlights: Blood donor age and sex affect glycolysis in stored RBCs from 13,029 volunteers;Ancestry, genetic polymorphisms in PFKP, HK1, CD38/BST1 influence RBC glycolysis;Modeled PFKP effects relate to preventing loss of the total AXP pool in stored RBCs;ATP and hypoxanthine are biomarkers of hemolysis in vitro and in vivo.

Multi-omics analysis identifies drivers of protein phosphorylation.

BACKGROUND: Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated. RESULTS: We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes. CONCLUSIONS: Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression.

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.

Assessing the Cumulative Contribution of New and Established Common Genetic Risk Factors to Early-Onset Prostate Cancer.

BACKGROUND: We assessed the evidence for association between 23 recently reported prostate cancer variants and early-onset prostate cancer and the aggregate value of 63 prostate cancer variants for predicting early-onset disease using 931 unrelated men diagnosed with prostate cancer prior to age 56 years and 1,126 male controls. METHODS: Logistic regression models were used to test the evidence for association between the 23 new variants and early-onset prostate cancer. Weighted and unweighted sums of total risk alleles across these 23 variants and 40 established variants were constructed. Weights were based on previously reported effect size estimates. Receiver operating characteristic curves and forest plots, using defined cut-points, were constructed to assess the predictive value of the burden of risk alleles on early-onset disease. RESULTS: Ten of the 23 new variants demonstrated evidence (P < 0.05) for association with early-onset prostate cancer, including four that were significant after multiple test correction. The aggregate burden of risk alleles across the 63 variants was predictive of early-onset prostate cancer (AUC = 0.71 using weighted sums), especially in men with a high burden of total risk alleles. CONCLUSIONS: A high burden of risk alleles is strongly associated with early-onset prostate cancer. IMPACT: Our results provide the first formal replication for several of these 23 new variants and demonstrate that a high burden of common-variant risk alleles is a major risk factor for early-onset prostate cancer. Cancer Epidemiol Biomarkers Prev; 25(5); 766-72. ©2015 AACR.

Genome-wide association scan for variants associated with early-onset prostate cancer.

Prostate cancer is the most common non-skin cancer and the second leading cause of cancer related mortality for men in the United States. There is strong empirical and epidemiological evidence supporting a stronger role of genetics in early-onset prostate cancer. We performed a genome-wide association scan for early-onset prostate cancer. Novel aspects of this study include the focus on early-onset disease (defined as men with prostate cancer diagnosed before age 56 years) and use of publically available control genotype data from previous genome-wide association studies. We found genome-wide significant (p<5×10(-8)) evidence for variants at 8q24 and 11p15 and strong supportive evidence for a number of previously reported loci. We found little evidence for individual or systematic inflated association findings resulting from using public controls, demonstrating the utility of using public control data in large-scale genetic association studies of common variants. Taken together, these results demonstrate the importance of established common genetic variants for early-onset prostate cancer and the power of including early-onset prostate cancer cases in genetic association studies.

Body mass index and renal cell cancer: the influence of race and sex.

BACKGROUND: Obesity is a risk factor for renal cell (or renal) cancer. The increasing prevalence of obesity may be contributing to the rising incidence of this cancer over the past several decades. The effects of early-age obesity and change in body mass index (BMI) on renal cancer have been studied less thoroughly, and the influence of race has never been formally investigated. METHODS: Using data gathered as part of a large case-control study of renal cancer (1214 cases and 1234 controls), we investigated associations with BMI at several time points, as well as with height. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were computed using logistic regression modeling. Race- and sex-stratified analyses were conducted to evaluate subgroup differences. RESULTS: Obesity (BMI ≥ 30 kg/m) early in adulthood (OR = 1.6 [95% CI = 1.1 to 2.4]) and 5 years before diagnosis (1.6 [1.1 to 2.2]) was associated with renal cancer. The association with early-adult obesity was stronger among whites than blacks (test for interaction, P = 0.006), whereas the association with obesity near diagnosis was marginally stronger in women than men (test for interaction, P = 0.08). The strongest association with renal cancer was observed for obese whites both in early adulthood and before interview (2.6 [1.5 to 4.4]); this association was not present among blacks. Estimates of the annual excess rate of renal cancer (per 100,000 persons) attributed to both overweight and obesity (BMI > 25 kg/m) ranged from 9.9 among black men to 5.6 among white women. CONCLUSION: Obesity, both early and later in life, is associated with an increased risk of renal cancer. The association with early obesity seems to be stronger among whites than blacks.