Genetic regulation of carnitine metabolism controls lipid damage repair and aging RBC hemolysis in vivo and in vitro

Abstract

Recent large-scale multiomics studies suggest that genetic factors influence the chemical individuality of donated blood. To examine this concept, we performed metabolomics analyses of 643 blood units from volunteers who donated units of packed red blood cells (RBCs) on 2 separate occasions. These analyses identified carnitine metabolism as the most reproducible pathway across multiple donations from the same donor. We also measured l-carnitine and acyl-carnitines in 13 091 packed RBC units from donors in the Recipient Epidemiology and Donor Evaluation study. Genome-wide association studies against 879 000 polymorphisms identified critical genetic factors contributing to interdonor heterogeneity in end-of-storage carnitine levels, including common nonsynonymous polymorphisms in genes encoding carnitine transporters (SLC22A16, SLC22A5, and SLC16A9); carnitine synthesis (FLVCR1 and MTDH) and metabolism (CPT1A, CPT2, CRAT, and ACSS2), and carnitine-dependent repair of lipids oxidized by ALOX5. Significant associations between genetic polymorphisms on SLC22 transporters and carnitine pools in stored RBCs were validated in 525 Diversity Outbred mice. Donors carrying 2 alleles of the rs12210538 SLC22A16 single-nucleotide polymorphism exhibited the lowest l-carnitine levels, significant elevations of in vitro hemolysis, and the highest degree of vesiculation, accompanied by increases in lipid peroxidation markers. Separation of RBCs by age, via in vivo biotinylation in mice, and Percoll density gradients of human RBCs, showed age-dependent depletions of l-carnitine and acyl-carnitine pools, accompanied by progressive failure of the reacylation process after chemically induced membrane lipid damage. Supplementation of stored murine RBCs with l-carnitine boosted posttransfusion recovery, suggesting this could represent a viable strategy to improve RBC storage quality.

Graphical abstract

Figure 1.

Reproducibility of metabolite levels across 2 independent donations in 643 repeated blood donors from the REDS RBC Omics study. (A) Two distinct packed RBC units were donated by 643 donors and stored for 42 days, before metabolomics analyses. Metabolomics measurements on both index (first unit) and recalled (second) blood units from the same donors were correlated to test whether metabolite levels were reproducible at the end of storage across multiple donations from the same donor. (B) Results indicate a right-shifted correlation curve, because 80% of the measured metabolites showed significantly (P < .05) reproducible (ie, positively correlated) metabolite measurements across donations. (C) Over-represented among the top 20 most significantly reproducible metabolites, we identified multiple carnitine precursors and AC conjugates. (D) Pathway analysis of the top 50 most reproducible metabolites identified carnitine synthesis and metabolism as the most reproducible pathway across donations. (E) Representative metabolites in this pathway are shown, numbered according to their order of appearance in panel C. (F) Representative scatter plots showing matched index vs recalled unit measurements for representative small molecules in the carnitine synthesis and metabolism (Spearman rho and P value for these correlations are noted within each panel).

Figure 1.

Reproducibility of metabolite levels across 2 independent donations in 643 repeated blood donors from the REDS RBC Omics study. (A) Two distinct packed RBC units were donated by 643 donors and stored for 42 days, before metabolomics analyses. Metabolomics measurements on both index (first unit) and recalled (second) blood units from the same donors were correlated to test whether metabolite levels were reproducible at the end of storage across multiple donations from the same donor. (B) Results indicate a right-shifted correlation curve, because 80% of the measured metabolites showed significantly (P < .05) reproducible (ie, positively correlated) metabolite measurements across donations. (C) Over-represented among the top 20 most significantly reproducible metabolites, we identified multiple carnitine precursors and AC conjugates. (D) Pathway analysis of the top 50 most reproducible metabolites identified carnitine synthesis and metabolism as the most reproducible pathway across donations. (E) Representative metabolites in this pathway are shown, numbered according to their order of appearance in panel C. (F) Representative scatter plots showing matched index vs recalled unit measurements for representative small molecules in the carnitine synthesis and metabolism (Spearman rho and P value for these correlations are noted within each panel).

Figure 2.

Genetic factors contributing to l-carnitine levels in 13 091 human RBCs after storage for 42 days. (A) Unsupervised clustering of 13 091 index donors based on RBC metabolism at day 42 identified a subset of donors with significantly higher levels of l-carnitine compared to the rest of the population. (B) l-carnitine showed a skewed distribution across the population. (C) Line plots display the impact of donor age, sex, BMI, and ethnicity on end-of-storage l-carnitine levels in 13 091 blood units. (D-H) Breakdown of donor distributions based on additive solution, sex, age, BMI, and ethnicity for the donors with the highest and lowest end-of-storage RBC l-carnitine levels (n = 500 per group; y-axes show % of total donor in the high vs low subgroup).

Figure 3.

Genetic factors contributing to l-carnitine levels in 13 091 human RBCs after storage for 42 days. Genome-wide association studies (GWAS) were performed to determine the genetic underpinnings of end-of-storage l-carnitine levels in 13 091 packed RBC units from the REDS RBC Omics index donor cohort. (A) l-carnitine levels were used as an mQTL to perform a GWAS against 870 000 SNPs from a precision transfusion medicine array. (B) Manhattan plot generated via the l-carnitine mQTL analysis. y-axes indicate significance (−log(p)), with genome-wide adjusted significance thresholds at y = 5 × 10⁻⁸. (C) A representative QQ plot for the top SNP from this analysis (y-axis showing significance <10⁻²⁵⁰) for rs12210538. (D) This SNP coded for a missense mutation on the l-carnitine transporter SCL22A16. (E) Alphaphold predicted structure for SLC22A16 in. (F) The rs1220538 SNP was underrepresented in donors of Asian descent, followed by donors of African descent. (G) Locus zoom for rs272855 SNP (intron variant), mapping on a region on chromosome 5 that codes for SLC22A4/5 carnitine transporters. (H) Alphaphold predicted SLC22A5 structure. (I) The rs272855 SNP was found to be most common in donors of African descent and least common in donors of Asian descent.

Figure 4.

Polymorphic SLC22A16 and SLC22A5 associate with depletion of AC pools and elevated osmotic hemolysis. End-of-storage REDS RBC Omics index (n = 13 091) and recalled donor samples (n = 643; storage day 10, 23, and 42) underwent metabolomics only, or combined metabolomics, proteomics, lipidomics, and trace element analyses via ICP-MS (A). Correlation of these data sets in index (B-C) and recalled (D-E) sets highlighted a negative association between allele copies for SLC22A16 rs12210538 and SLC22A5 rs272855 and AC pools (x-axis indicate Spearman rho; y-axes indicate −log₁₀(p)). (F-G) Donors who are homozygous recessive for either polymorphism show significantly higher levels of osmotic hemolysis (violin plots show median ± interquartile ranges and distribution of values across the population; each individual dot is a separate sample; asterisks indicate significance: adjusted ∗P < .05 and ∗∗∗∗P < .0001).

Figure 5.

Genetic underpinnings of RBC carnitine levels in JAX Diversity Outbred mice. Eight genetically diverse founder strains underwent crossbreeding for 6 generations to obtain 350 genetically diverse mice. AC levels were measured at baseline and upon refrigerated storage of RBCs from these mice for 7 days (equivalent to day 42 in humans; A). (B) No significant false discovery rate–corrected association was observed between carnitine levels and genotypes, as gleaned by >120 000 SNPs monitored in this study. (C) However, mQTL analyses for end-of-storage carnitine showed an association with polymorphisms in a region on chromosome 11, (D) coding for the SLC22A5 transporter. (E) These associations held true for almost all the ACs tested here. (F) At the end of storage, RBCs from the 350 mice were transfused into ubi-GFP⁺ recipient mice to determine the correlations between end-of-storage AC pools and the percentage of transfused RBCs that still circulate upon 24h from transfusion. (G) Strong negative associations were observed for a series of hydroxy-ACs and other short/odd-chain ACs (eg, 3:0 and 9:0; representative scatter plots in panel G derived from the breakdown of oxidized fatty acids.

Figure 6.

Old RBCs deplete carnitine pools and have impaired Lands cycle. (A) Double biotinylation studies in mice afford labeling and sorting of in vivo RBCs based on their age in circulation. Numbers indicate the main steps for this experiment, from double biotinylation of the first mouse (old RBCs), to the biotinylation of the second mouse (young RBCs), to leukodepletion and streptavidin and thioflavin-T labeling, to separation of young (9-15 days) and old RBCs (41-47 days) via sorting. (B) Metabolomics analyses of young vs old RBCs shows age-dependent depletion in carnitine pools in the latter group. Asterisks next to each row of the heat map denote significance (unpaired t test: ∗∗P < .01 and ∗∗∗P < .0001). (C) Percoll-density gradient-based separation of human RBCs of different ages shows a decline in RBC carnitine content in older, smaller erythrocytes, with progressively smaller mean cell volumes (MCV) and higher band 4.1a:4.1b ratios. (D) Incubation of the old, young, and average age populations of RBCs with radioactive palmitate shows a carnitine/age-dependency of labeled palmitate incorporation in phosphatidylethanoloamines.

Figure 7.

Carnitine levels are associated with hemolysis and vesiculation in vivo and in vitro in humans and mice. (A) In REDS RBC Omics index donors, (B) depletion of carnitine pools in end-of-storage RBCs was associated with elevated hemolysis. These observations were validated in the recalled donor population, in which depletion of AC pools corresponded to (C) increased vesiculation rates and (D) hemolysis. (E) Interrogation of the REDS vein-to-vein database, in which lowest end-of-storage carnitine levels were associated with significantly lower Hb increments in donors receiving single-unit transfusion for units stored >4 to 5 weeks (F), especially for the last week of storage (F). Metabolomics (G) and lipidomics analyses (G’) show that AC pools are restored by l-carnitine supplementation, but not d-carnitine, in stored FVB mouse RBCs (poor storer strain characterized by high levels of storage-induced lipid peroxidation). l-carnitine supplementation boosts PTR of stored FVB RBCs (H).

Figure 7.

Carnitine levels are associated with hemolysis and vesiculation in vivo and in vitro in humans and mice. (A) In REDS RBC Omics index donors, (B) depletion of carnitine pools in end-of-storage RBCs was associated with elevated hemolysis. These observations were validated in the recalled donor population, in which depletion of AC pools corresponded to (C) increased vesiculation rates and (D) hemolysis. (E) Interrogation of the REDS vein-to-vein database, in which lowest end-of-storage carnitine levels were associated with significantly lower Hb increments in donors receiving single-unit transfusion for units stored >4 to 5 weeks (F), especially for the last week of storage (F). Metabolomics (G) and lipidomics analyses (G’) show that AC pools are restored by l-carnitine supplementation, but not d-carnitine, in stored FVB mouse RBCs (poor storer strain characterized by high levels of storage-induced lipid peroxidation). l-carnitine supplementation boosts PTR of stored FVB RBCs (H).