ABSTRACT
Red blood cells (RBCs) are the most numerous cell type in the body and serve a vital purpose of delivering oxygen to essentially all tissues. In addition to the central role of RBCs in health and disease, RBC storage is a requirement for the >90 million units of RBC transfusions given to millions of recipients each year, worldwide. It is well known that there is genetic donor-to-donor variability in how human RBCs store, rendering blood a nonstandardized therapeutic with a wide range of biological properties from unit to unit, by the time it is transfused. As with humans, genetic variation exists in how murine RBCs, from different strains of mice, store and perform after transfusion. The genetic mechanisms for variation, in humans and mice, both remain obscure. Combining advanced metabolomics, genetics, and molecular and cellular biology approaches, we identify genetic variation in six-transmembrane epithelial antigen of prostate 3 (Steap3) expression as a critical and previously unrecognized mechanism of oxidative damage of RBCs during storage. Increased levels of Steap3 result in degradation of cellular membrane through lipid peroxidation, leading to failure of RBC homeostasis and hemolysis/clearance of RBCs. This article is the first report of a role of Steap3 in mature RBCs; it defines a new mechanism of redox biology in RBCs with a substantial effect upon RBC function and provides a novel mechanistic determinant of genetic variation of RBC storage.
Subject(s)
Cell Cycle Proteins/genetics , Erythrocytes/metabolism , Genetic Variation , Oxidation-Reduction , Oxidative Stress , Oxidoreductases/genetics , Animals , Biomarkers , Blood Preservation , Chromosome Mapping , Erythrocytes/pathology , Gene Expression Regulation , Genotype , Metabolomics/methods , Mice , Mice, Transgenic , Mutation , Phenotype , Polymorphism, Single Nucleotide , Quantitative Trait LociABSTRACT
State-of-the-art, genome-wide assessment of mouse genetic background uses single nucleotide polymorphism (SNP) PCR. As SNP analysis can use multiplex testing, it is amenable to high-throughput analysis and is the preferred method for shared resource facilities that offer genetic background assessment of mouse genomes. However, a typical individual SNP query yields only two alleles (A vs. B), limiting the application of this methodology to distinguishing contributions from no more than two inbred mouse strains. By contrast, simple sequence length polymorphism (SSLP) analysis yields multiple alleles but is not amenable to high-throughput testing. We sought to devise a SNP-based technique to identify donor strain origins when three distinct mouse strains potentially contribute to the genetic makeup of an individual mouse. A computational approach was used to devise a three-strain analysis (3SA) algorithm that would permit identification of three genetic backgrounds while still using a binary-output SNP platform. A panel of 15 mosaic mice with contributions from BALB/c, C57Bl/6, and DBA/2 genetic backgrounds was bred and analyzed using a genome-wide SNP panel using 1449 markers. The 3SA algorithm was applied and then validated using SSLP. The 3SA algorithm assigned 85% of 1449 SNPs as informative for the C57Bl/6, BALB/c, or DBA/2 backgrounds, respectively. Testing the panel of 15 F2 mice, the 3SA algorithm predicted donor strain origins genome-wide. Donor strain origins predicted by the 3SA algorithm correlated perfectly with results from individual SSLP markers located on five different chromosomes (n=70 tests). We have established and validated an analysis algorithm based on binary SNP data that can successfully identify the donor strain origins of chromosomal regions in mice that are bred from three distinct inbred mouse strains.
