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Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors

Open AccessPublished:November 14, 2022DOI:https://doi.org/10.1016/j.jbc.2022.102706

      Abstract

      The Red Blood Cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a Genome-Wide Association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci (mQTL) in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past two years, and first ten principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5x10-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline, and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations including N(6)-Methyl-L-lysine and protein PYROXD2, and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.

      Key words

      • Metabolite heterogeneity in fresh (<14 day old) RBCs donated by volunteer donors is linked to genetic polymorphisms;
      • We report 2,831 high-confidence SNP-metabolite linkages (p < 5.0 x 10-8). Pyruvate levels in fresh RBCs are associated with glucose-6-phosphate dehydrogenase (G6PD) status

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      Results

      Metabolomics analyses were performed on packed RBC samples derived from stored RBC components from 250 donors who had been previously characterized at the genome level via the Precision Transfusion Medicine array (Figure 1.A).(
      • Guo Y.
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      ) Through the workflow summarized in Supplementary Figure 1, a total of 2,831 SNP-metabolite associations were observed below the genome-wide correction threshold (p < 5.0 x 10-8 ). Data are summarized in tabulated form in Supplementary Table 1 by identifying the SNP with the smallest p-value within a +/- 500 kilobase range as the lead SNP; individual SNP associations are reported extensively in Supplementary Table 1. In Figure 1.B, we listed the top 10 hits identified by closest annotated gene to the significant SNP in order of -log10(p). Manhattan plots overlapping all the significant hits (FDR < 5 x 10-8) are shown in Figure 1.C, which also includes metabolite – gene pairs. QQplots for the top nine metabolite-associated SNPs are shown in Figure 1.D. Sensitivity analyses for 46 metabolites examining the impact of 1) more stringent variant QC; 2) the choice of imputation strategy for missing metabolite data; 3) the effect of blood storage additive; and 4) ancestry, are reported in Supplementary Table 1. Genetic associations identified for six metabolites have been previously reported (Supplementary Table 1). Ancestry plots were thus generated to show normalized metabolite abundances as a function of alleles, as distributed across genetic ancestries of the donors enrolled in this study (Figure 2.A). We further characterized the mQTL loci by generating LocusZoom plots to examine the local LD structure and performed in silico functional annotation using OASIS.
      Figure thumbnail gr1
      Figure 1– Study design and top 10 hits from the mQTL analysis from the REDS III RBC omics pilot recalled donor study. Metabolomics analyses were performed on 250 packed RBC samples from donors who had been previously characterized at the genome level via the Precision Transfusion Medicine array (A).30 In B, an overview of the top 10 hits (closest annotated gene to the identified SNP) as a function of significance (-log10(p)). In C, overlapped Manhattan plots of all the significant hits (FDR < 5 x 10-8), including metabolites – gene pairs. Each data point corresponds to a –log10(P value) from a multivariant linear regression model’s P value for an SNP. The black horizontal line represents an accepted P-value level of genome-wide significance (P = 5 × 10–8). In D, qqplots for the top 10 hits from the mQTL analysis.
      Figure thumbnail gr2
      Figure 2– Ancestry plots and association between methyl-lysine levels and polymorphisms in PYROXD2. In A, for the top GWA hits we generated box and whisker plots based on metabolite abundances as a function of allele variance across all genetic ancestries in this study. Consistently with previous mQTL studies,40,41,65-67 polymorphisms in the exonic region coding for the enzyme PYROXD2 were associated with variance in the levels of methyl-lysine, an observation that represents a sort of internal quality control for the present analysis compared to the literature. Manhattan plots and LocusZoom are shown in panels B-C, respectively.
      Consistent with previous mQTL studies(citations 40,41,64-67), the top SNP associated with levels of methyl-lysine, rs4539242, is in high linkage disequilibrium (LD) with both the missense mutation M461T (R2=1.0 in Europeans) and synonymous mutation F484F (R2=0.94 in Europeans), observations that represent an internal quality control for the present analysis (Manhattan plots and LocusZoom in Figure 2.B-C, respectively). Both mutations are themselves associated with levels of methyl-lysine (p=4.22x10-13 and p=1.28x10-44, respectively; Supplementary Table 1).
      The region coding for the enzyme lysophosphatidylserine acetyl-transferase 5 (LPCAT3) was found to be genetically heterogenous across volunteer blood donors. Polymorphisms in the region coding for LPCAT3 were The lead SNP, rs73264680, associated with associated with RBC levels of lysophospholipids (LPS), including linoleyl- (18:2), palmitoyl (16:0), stearoyl (18:0) or oleyl-LPS (18:1), rs73264680, is in perfect LD in Europeans with the missense mutation rs1984564/I217T within LPCAT3 (Figure 3.A-C; Supplementary Table 1; residue mapped against the structure of LPCAT3 - 7F3X.pdb in Figure 3.D).
      Figure thumbnail gr3
      Figure 3– LPCAT3 is polymorphic in healthy blood donors and associates with red blood cell lysophospholipid (LPS) levels. Manhattan plots for LPS, specifically linoleyl- (18:2 – A and related LocusZoom, highlighting the association with the region coding for LPCAT3 in B), palmitoyl (16:0), stearoyl (18:0) or oleyl (18:1 - C). In D, highlight of the polymorphic residue I271, mapped against the structure of LPCAT3 (7F3X.pdb).
      The lead SNP associated with UDP N acetyl glucosamine, rs4316067 (Supplementary Table 1), is located in an intron of NT5C3A (Figure 4A-B). The lead SNP associated with choline, rs2047287 (Figure 4C-D), is in strong LD (D’=1.0; R2=0.768 in Europeans) with the missense mutation T544M in FLVCR1.
      Figure thumbnail gr4
      Figure 4– Polymorphisms in NT5C3A and FLVCR1 are associated with variability in the levels of UDP-N-acetyl-glucosamine and choline in RBCs from healthy blood donors. Manhattan plots and LocusZooms are showns in panels A-B and C-D, respectively.
      Polymorphisms in bifunctional epoxide hydrolase 2 (EPHX2 - missense mutation rs751141/R221Q - p = 4.55 10-10; λ = 1.01) and spermine oxidase, where the intronic rs11087622 in SMOX – is in LD (R2=0.16; D’=0.71 in Europeans) with synonymous mutation A392A (Supplementary Table 1)-, are associated with variability in the levels of oxylipins (12,13-EpOME) and spermine, respectively (Figure 5).
      Figure thumbnail gr5
      Figure 5– Polymorphisms in EPHX2 and SMOX are associated with variability in the levels of oxylipins (12,13-EpOME) and spermine, respectively. Manhattan plots and LocusZooms are showns in panels A-B and C-D, respectively.
      A missense variant (rs12210538/M409T) in the carnitine transporter SLC22A16 is associated with variability in the levels of RBC free and acetyl-carnitine (Supplementary Table 1). Manhattan plots and LocusZooms are shown in Supplementary Figure 2. Additional SNPs associated with carnitine levels include palmitoyl-carnitine (nearest gene HTR5A-AS1) and undecanoyl-carnitine (nearest gene EPHX2).
      A series of significant associations were identified between the levels of oxylipins like 9.10-EpOME (EPHX2 – Uniprot names provided for protein products of the nearest gene within parentheses in this paragraph) or 14-DHoHE (LOC401177 or LOC100505817) or 9-HETE, 15-HETE (NAP1L3), dopamine (G6PD), glycolytic metabolites (glucose and VAV2, hexose phosphate, including fructose 6-phosphate and FN3K; lactate and PNMA5), purines (hypoxanthine and TACR2, TSPAN15; urate and FOLR1 and APP), amino acids (glutamine and PLEKHB2; glutamate and BACH1-IT2; methionine and TOP1MT; taurine and LPHN3, threonine and mR8058), free fatty acids (palmitoleic acid and METTL2B; oleic acid; arachidonic acid and FADS1; docosapentaenoic acid and SGCZ), sphingolipids (sphingosine 1-phosphate and EDARADD or SORCS2 or KDM6A), uridine diphosphate (UDP and ZNF485 – Supplementary Figure 3-15).
      Finally, polymorphisms in SPTA1 and G6PD are associated with variability in the levels of S-adenosyl-methionine (intronic - p = 2.52 10-10; λ = 1.03; Supplementary Table 1) and pyruvate μM (missense mutation V98M - p = 2.87 10-12; λ = 1.12; Supplementary Table 1), respectively (Figure 6.A-B and C-D for Manhattan plots and LocusZoom).
      Figure thumbnail gr6
      Figure 6– Polymorphisms in SPTA1 and G6PD are associated with variability in the levels of S-adenosyl-methionine and pyruvate, respectively. Manhattan plots and LocusZooms are showns in panels A-B and C-D, respectively.

      Sensitivity and Replication analyses

      We performed several sensitivity and analyses, including GWAS of 46 metabolites in 176 study participants with available metabolomics data generated from RBC samples stored for 42 days, as replication of findings in fresh blood from the same subjects. The top associations were replicated for N6-Methyl-L-Lysine, LPS16.0-18.2, UDP N-aceytl-glucosamine, Choline, Undecanoyl carnitine, spermine, spermine uM, and L-carnitine. These findings replicated in each of the five sensitivity analysis, with either the original lead SNP or a genome-wide significant SNP in high LD with the original reaching genome-wide significance (Supplementary Table 1). For docosahexaenoic acid (FA22.6), the association with rs28603189 replicated when stringent QC criteria were applied, when a different missing metabolite imputation strategy was employed, and when analysis was restricted to the participants whose RBC samples were stored in Additive 3 (R2=0.94), but not in the day 42 storage samples or the European-only analysis. The association between rs12033733 and S-adenosyl-L-methionine withstood the stringent QC but none of the other sensitivity analyses. Finally, although there were many genome-wide significant associations with pyruvate uM, the lambda was 1.115 and the QQ plot troubling (Figure 1), potentially indicating unaccounted for population structure for this metabolite. The association between pyruvate uM and rs142516556, a SNP near the G6PD gene, remained robust to the stringent QC, imputation method, and Additive 3 restricted GWAS (Supplementary Table1).

      Elevated pyruvate and pyruvate/lactate ratios are recapitulated in an independent human cohort of blood donors and mouse models of G6PD deficiency

      Pyruvate levels were found to be inversely proportional to G6PD activity in fresh RBCs from an independent cohort of G6PD deficient (n=10) and sufficient (n=27) blood donors (Figure 7.A-C). The differences in pyruvate levels between the two groups were exacerbated during blood bank storage up to 42 days (Figure 7.D). Similarly, RBCs from G6PD deficient mice (African A- and Mediterranean variant – Med-) and WT C57BL6/J or humanized canonical G6PD mice (Figure 7.E) were incubated with 1,2,3-13C3-glucose for 1h, to determine metabolic fluxes through glycolysis and the pentose phosphate pathway (PPP). Results (Figure 7.F) confirmed significant decreases in the labeled levels of oxidative phase metabolites of the PPP (13C3-phosphogluconate and 13C2-ribose-phosphate) in A- and Med- mice, which corresponded to increases in the ratios of labeled 13C3-pyruvate/lactate.
      Figure thumbnail gr7
      Figure 7– G6PD deficiency in fresh and stored RBCs from blood donors are associated with increases in pyruvate levels and pyruvate/lactate ratios. Pyruvate levels were found to be inversely proportional to G6PD activity in fresh RBCs from G6PD deficient (n=10) and sufficient (n=27) blood donors (A-C). These differences in pyruvate levels between the two groups were exacerbated during storage in the blood bank up to 42 days (D). Similarly, RBCs from G6PD deficient mice (African and Mediterranean variant) and WT C57BL6/J or humanized canonical G6PD mice (E) were incubated with 1,2,3-13C3-glucose for 1h, to determine metabolic fluxes through glycolysis and the pentose phosphate pathway (PPP). Results (F) confirmed significant decreases in the labeled levels of oxidative phase metabolites of the PPP (13C3-phosphogluconate and 13C2-ribose-phosphate) in A- and Med- mice, which corresponded to increases in the ratios of labeled 13C3-pyruvate/lactate.

      Discussion

      As part of the REDS-III RBC-Omics study, a cohort of 12,535 volunteer blood donors were enrolled to donate a unit of blood that was processed into RBC components that were characterized for storage hemolysis parameters. DNA samples derived from donation WBC were genotyped using a Precision Transfusion Medicine array mapping 879,000 SNPs.(
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      Overall, we report 2,831 SNP-metabolite associations meeting genome-wide significance. Of note, the smallest p-value found in the present study, p=1.90x10-63 for the association between rs4539242 within PYROXD2 and the RBC levels of N-methyl-lysine, was much smaller than the p-values describing any of the 27 loci associated with RBC hemolysis phenotypes in this population (citation 28), despite the much smaller cohort (250 vs 12,535) – suggesting the metabolic signatures are more directly determined by genetics than is hemolytic propensity, with the latter having a larger etiologic contribution from environmental factors.. The association between PYROXD2 and methyl-lysine had already been reported in previous mQTL studies,(
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      The RBC levels of multiple lysophosphatidylserines (LPS 16:0, 18:0, 18:1 and 18:2) were associated with variation in the lysophosphatidylcholine acetyl-transferase 5 gene (LPCAT3). LPCAT3 is a key enzyme of the Lands cycle,(
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      ) These results are suggestive of a role of LPCAT3 in phosphatidylserine metabolism, a class of lipids whose exposure in the outer membrane leaflet regulates erythrophagocytosis and clearance from the bloodstream, with implications for post-transfusion recovery of stored RBCs.(
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      ) In this view, it is worth noting that the levels of multiple acyl-carnitines, in equilibrium with the acyl-CoAs as part of the Lands cycle, were found to be associated with polymorphisms in the carnitine transporter SLC22A16. This observation may indicate a inter-subject variability in membrane lipid damage-repair capacity, with implications for exercise physiology or kidney disease, since this pathway is impacted by acute exercise(
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      ) spermine oxidase (SMOX) was found to be polymorphic in routine blood donors, which was here associated with varying levels of the product of its enzymatic activity, the polyamine spermine.
      One of the main findings of the genomic arm of the REDS-III RBC-Omics Study was the identification of polymorphisms associated with the expression of a less active isoform of G6PD (African variant) that are associated with an increased susceptibility to end of storage hemolysis of RBCs following oxidant insults.(
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      ) (G6PD is the rate-limiting enzyme of this pathway). These results were independently corroborated by the observation that failure to activate the PPP is a hallmark of the metabolic lesion to stored RBCs, in part attributable to the inability to inhibit glycolysis via the reversible oxidation of glyceraldehyde 3-phosphate dehydrogenase (GAPDH)(
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      ) Here we report that the levels of pyruvate in fresh RBCs and pyruvate/lactate ratios in stored RBCs are associated with the same G6PD polymorphisms. A causal role of this correlation is established in humanized murine models of G6PD deficiency, since the same metabolic change is seen in RBCs that differ only in their form of G6PD (African or Mediterranean(
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      ) Indeed, methemoglobin reductase would compete with lactate dehydrogenase for NADH, rendering the enzymatic step of lactic fermentation to regenerate NADH back to NAD+ no longer critical to preserve glycolytic fluxes (NAD+ is an essential cofactor for GAPDH activity upstream to pyruvate and ATP synthesis in glycolysis). The G6PD African variant in this donor population was also linked to variance in the levels of dopamine, confirming previous biomarker analyses from the metabolome of G6PD deficient vs sufficient blood donors(
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      The present study has several limitations. First, mQTL analyses were determined based upon genomic characterization of a cohort of volunteer routine blood donors. As such, disease-related polymorphisms that would be identified in cohorts of non-healthy patients (i.e., from persons who are ineligible to donate blood) would be intrinsically not amenable to identification as a result of our study design. On the other hand, while sufficiently healthy to donate blood and as a result probably has biases similar to other ‘healthy worker’ cohorts (
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      ), the donor population enrolled in this study also includes phenotypes of potential clinical relevance to disease phenotypes (e.g., to cardiovascular and other disease risk factors, such as obesity,(
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      )) but at a lower rate than the general population. As such, some of the genome-wide associations reported here (e.g., carnitine and SLC22A16) may be translationally relevant beyond transfusion medicine when interpreted in the context of markers relevant to specific diseases (e.g., carnitine metabolism and obesity(
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      )). Furthermore, fresh (∼10 day old – i.e. freshest samples available for this cohort) RBCs from volunteer donor volunteers were tested in this study. As such it is unclear whether the findings are relevant to transfusion medicine (e.g., genetic underpinning of metabolic heterogeneity in end of storage RBCs) or to physiological (e.g., hypoxia) or pathological conditions in which alterations to RBC metabolism are mechanistically relevant. Indeed, some metabolic markers of the RBC storage lesion only accumulate in end of storage units (e.g., hypoxanthine).(
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      ) However, replication studies were performed on end of storage (day 42) blood from the same units and donors, though only 176 biological replicates were available. As such, the present findings are suggestive of clinical relevance in the field of transfusion medicine, to the extent that the metabolic heterogeneity of fresh and end of stored units associates or is an etiological driver of post-transfusion performances, such as intra- or extra-vascular hemolysis and post-transfusion recoveries (
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      ). Future studies will need to address this issue in larger cohorts, by focusing on RBC samples stored for longer periods of time. Although the small (n=250) number of participants available still allowed for robust association discovery, a larger number of samples in more ancestrally diverse participants will increase the statistical power of future work and provide insights that are relevant to specific populations. Such studies could pave the way for the use of other orthogonal omics approaches to metabolomics (e.g., proteomics) to maximize the value of the genetic and metabolic data already available for this well-curated cohort. Similar studies could be possible on other cohorts from patients with hematological conditions, such as sickle cell disease, where metabolite levels could not only be associated with, but also mechanistically contribute to the etiology of thromboinflammatory comorbidities of clinical relevance (e.g., sphingosine 1-phosphate and systemic hypoxemia,(
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      ) vasocclusive crisis, cardiopulmonary function, kidney dysfunction, pain crisis, etc.).

      Experimental procedures

      REDS-III RBC-Omics study participants and samples

      RBC-Omics was conducted under regulations applicable to all human subject research supported by federal agencies as well as requirements for blood product manipulation specified and approved by the FDA. The data coordinating center (RTI International) of REDS-III was responsible for the overall compliance of human subjects regulatory protocols including institutional review board approval from each participating blood center, from the REDS-III Central Laboratory (Vitalant Research Institute), and from the data coordinating center, as previously detailed.(
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      Donor sex, age and ethnicity impact stored red blood cell antioxidant metabolism through mechanisms in part explained by glucose 6-phosphate dehydrogenase levels and activity.
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      • Donor Evaluation III., S.
      Heterogeneity of blood processing and storage additives in different centers impacts stored red blood cell metabolism as much as storage time: lessons from REDS-III-Omics.
      ) Donors were enrolled at the four participating REDS-III US blood centers. Overall, 13,758 whole blood donors were enrolled and 13,403 (97%) age 18+ provided informed consent to participate in the study; of these, 12,353 were evaluated for hemolysis parameters (spontaneous, oxidative or osmotic) on RBCs stored for ∼39-42 days. Extreme hemolyzers (5th and 95th percentile) from the donors tested for end of storage oxidative hemolysis were asked to donate a second unit of blood. These units were sterilely sampled for metabolomics analyses (n = 250 for the freshest available time points, i.e., < 14 storage days). Blood collection, sample processing and other aspects of the screening and recall phases of the RBC-Omics Study have been extensively described.(
      • Kanias T.
      • Lanteri M.C.
      • Page G.P.
      • Guo Y.
      • Endres S.M.
      • Stone M.
      • Keating S.
      • Mast A.E.
      • Cable R.G.
      • Triulzi D.J.
      • Kiss J.E.
      • Murphy E.L.
      • Kleinman S.
      • Busch M.P.
      • Gladwin M.T.
      Ethnicity, sex, and age are determinants of red blood cell storage and stress hemolysis: results of the REDS-III RBC-Omics study.
      ,
      • Page G.P.
      • Kanias T.
      • Guo Y.J.
      • Lanteri M.C.
      • Zhang X.
      • Mast A.E.
      • Cable R.G.
      • Spencer B.R.
      • Kiss J.E.
      • Fang F.
      • Endres-Dighe S.M.
      • Brambilla D.
      • Nouraie M.
      • Gordeuk V.R.
      • Kleinman S.
      • Busch M.P.
      • Gladwin M.T.
      Multiple-ancestry genome-wide association study identifies 27 loci associated with measures of hemolysis following blood storage.
      )

      Sample processing and metabolite extraction

      An isotopically labeled internal standard mixture including a mix of 13C15N-labeled amino acid standards (2.5 μM) was prepared in methanol. A volume of 100μl of frozen RBC aliquots was mixed with water and the mixture of isotopically labeled internal standards (1:1:1, v/v/v). The samples were extracted with methanol (final concentration of 80% methanol). After incubation at −20°C for 1 hour, the supernatants were separated by centrifugation and stored at −80°C until analysis. Samples were vortexed and insoluble material pelleted as described.(
      • D'Alessandro A.
      • Fu X.
      • Kanias T.
      • Reisz J.A.
      • Culp-Hill R.
      • Guo Y.
      • Gladwin M.T.
      • Page G.
      • Kleinman S.
      • Lanteri M.
      • Stone M.
      • Busch M.P.
      • Zimring J.C.
      • Recipient E.
      • Donor Evaluation III., S.
      Donor sex, age and ethnicity impact stored red blood cell antioxidant metabolism through mechanisms in part explained by glucose 6-phosphate dehydrogenase levels and activity.
      ,
      • D'Alessandro A.
      • Culp-Hill R.
      • Reisz J.A.
      • Anderson M.
      • Fu X.
      • Nemkov T.
      • Gehrke S.
      • Zheng C.
      • Kanias T.
      • Guo Y.
      • Page G.
      • Gladwin M.T.
      • Kleinman S.
      • Lanteri M.
      • Stone M.
      • Busch M.
      • Zimring J.C.
      • Recipient E.
      • Donor Evaluation III., S.
      Heterogeneity of blood processing and storage additives in different centers impacts stored red blood cell metabolism as much as storage time: lessons from REDS-III-Omics.
      )

      Ultra-High-Pressure Liquid Chromatography-Mass Spectrometry metabolomics

      Analyses were performed using a Vanquish UHPLC coupled online to a Q Exactive mass spectrometer (Thermo Fisher, Bremen, Germany). Samples were analyzed using a 3 minute isocratic condition or a 5, 9 and 17 min gradient as described.(
      • Nemkov T.
      • Reisz J.A.
      • Gehrke S.
      • Hansen K.C.
      • D'Alessandro A.
      High-Throughput Metabolomics: Isocratic and Gradient Mass Spectrometry-Based Methods.
      ,
      • Reisz J.A.
      • Zheng C.
      • D'Alessandro A.
      • Nemkov T.
      Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics.
      ,
      • Nemkov T.
      • Hansen K.C.
      • D'Alessandro A.
      A three-minute method for high-throughput quantitative metabolomics and quantitative tracing experiments of central carbon and nitrogen pathways.
      ) Solvents were supplemented with 0.1% formic acid for positive mode runs and 1 mM ammonium acetate for negative mode runs. MS acquisition, data analysis and elaboration was performed as described.(
      • Nemkov T.
      • Reisz J.A.
      • Gehrke S.
      • Hansen K.C.
      • D'Alessandro A.
      High-Throughput Metabolomics: Isocratic and Gradient Mass Spectrometry-Based Methods.
      ,
      • Reisz J.A.
      • Zheng C.
      • D'Alessandro A.
      • Nemkov T.
      Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics.
      ,
      • Nemkov T.
      • Hansen K.C.
      • D'Alessandro A.
      A three-minute method for high-throughput quantitative metabolomics and quantitative tracing experiments of central carbon and nitrogen pathways.
      ) Additional analyses, including untargeted analyses and Fish score calculation via MS/MS, were calculated against the ChemSpider database with Compound Discoverer 2.0 (Thermo Fisher, Bremen, Germany).

      Metabolite QC and Processing

      The quality control and processing of metabolites is detailed in Supplementary Figure 1. We first selected only those metabolites measured at Day 10 of storage, for which 250 participants had metabolite data. Metabolites with missing data and zeros were both treated identically as missing. We removed the following metabolites from further processing: a duplicate carnosine, a duplicate lorazepam, phosphate, triacanthine, and acetyl-L-carnitine. 507 metabolites remained for further processing. We also removed 22 drug metabolites with concentrations detected in greater than 50% of the participants, leaving 487 metabolites. We then separated the participant data by blood storage additive type and excluded metabolites with greater than 10% missingness from each additive set, respectively. After removing these metabolites, 382 remained. We separated these 382 metabolites into those quantified absolutely (against stable isotope-labeled internal standards, as described(
      • D'Alessandro A.
      • Culp-Hill R.
      • Reisz J.A.
      • Anderson M.
      • Fu X.
      • Nemkov T.
      • Gehrke S.
      • Zheng C.
      • Kanias T.
      • Guo Y.
      • Page G.
      • Gladwin M.T.
      • Kleinman S.
      • Lanteri M.
      • Stone M.
      • Busch M.
      • Zimring J.C.
      Heterogeneity of blood processing and storage additives in different centers impacts stored red blood cell metabolism as much as storage time: lessons from REDS-III-Omics.
      )) vs relatively. Relatively quantified metabolites were natural log-transformed. A suffix “(uM)” was added to the label of all the metabolites for which absolute concentrations were determined. These groups of metabolites then had missing metabolite levels imputed using QRILC,(
      • Wei R.
      • Wang J.
      • Su M.
      • Jia E.
      • Chen S.
      • Chen T.
      • Ni Y.
      Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.
      ) implemented in the R package QRILC.(
      • Wei R.
      • Wang J.
      • Su M.
      • Jia E.
      • Chen S.
      • Chen T.
      • Ni Y.
      Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.
      ) After imputation, all metabolites were inverse-normal transformed using the R package GenABEL rntransform command.(
      • Ongen H.
      • Buil A.
      • Brown A.A.
      • Dermitzakis E.T.
      • Delaneau O.
      Fast and efficient QTL mapper for thousands of molecular phenotypes.
      )

      Genotyping and Imputation

      Details of the genotyping and imputation of the RBC Omics study participants have been previously described by Page, et al.(
      • Page G.P.
      • Kanias T.
      • Guo Y.J.
      • Lanteri M.C.
      • Zhang X.
      • Mast A.E.
      • Cable R.G.
      • Spencer B.R.
      • Kiss J.E.
      • Fang F.
      • Endres-Dighe S.M.
      • Brambilla D.
      • Nouraie M.
      • Gordeuk V.R.
      • Kleinman S.
      • Busch M.P.
      • Gladwin M.T.
      Multiple-ancestry genome-wide association study identifies 27 loci associated with measures of hemolysis following blood storage.
      ) Briefly, genotyping was performed using a Transfusion Medicine microarray(
      • Guo Y.
      • Busch M.P.
      • Seielstad M.
      • Endres-Dighe S.
      • Westhoff C.M.
      • Keating B.
      • Hoppe C.
      • Bordbar A.
      • Custer B.
      • Butterworth A.S.
      • Kanias T.
      • Mast A.E.
      • Kleinman S.
      • Lu Y.
      • Page G.P.
      Development and evaluation of a transfusion medicine genome wide genotyping array.
      ) and the data are available in dbGAP accession number phs001955.v1.p1. Imputation was performed using 811,782 SNPs that passed quality control. After phasing using Shape-IT,(
      • Delaneau O.
      • Coulonges C.
      • Zagury J.-F.
      Shape-IT: new rapid and accurate algorithm for haplotype inference.
      ) imputation was performed using Impute2(
      • Howie B.
      • Marchini J.
      • Stephens M.
      Genotype Imputation with Thousands of Genomes.
      ) with the 1000 Genomes Project phase 3(
      • Howie B.
      • Marchini J.
      • Stephens M.
      Genotype Imputation with Thousands of Genomes.
      ) all-ancestry reference haplotypes. We used the R package SNPRelate(
      • Zheng X.
      • Levine D.
      • Shen J.
      • Gogarten S.M.
      • Laurie C.
      • Weir B.S.
      A high-performance computing toolset for relatedness and principal component analysis of SNP data.
      ) to calculate principal components (PCs) of ancestry.

      Genome-Wide Association Study

      We performed association analyses for each of the 382 metabolites using an additive SNP model in the R package ProbABEL(
      • Aulchenko Y.S.
      • Struchalin M.V.
      • van Duijn C.M.
      ProbABEL package for genome-wide association analysis of imputed data.
      ) and 243 study participants who had both metabolomics data and imputation data on serial samples from stored RBC components that passed respective quality control procedures. We adjusted for sex, age (continuous), frequency of blood donation in the last two years (continuous), blood donor center, and ten ancestry PCs. Statistical significance was determined using a p-value threshold of 5x10-8. We only considered variants with a minimum minor allele frequency of 1% and a minimum imputation quality score of 0.80.

      Replication and Sensitivity Analyses

      For replication, we followed the same procedures for post-processing of metabolites measured at Day 42 of storage. There were 176 participants with metabolite data generated from Day 42 samples. Association analyses and statistical significance were determined as described above. We selected 46 metabolites, oversampled for top hits from the GWAS analysis of early storage samples to analyze for potential replication and in the sensitivity analyses described below.
      We performed four sensitivity analyses using 46 metabolites and the original 243 recalled RBC-Omics participants. We performed a “stringent” GWAS, which required that evaluated variants have a minimum minor allele frequency of 5% and a minimum imputation quality score of 0.90. We performed an analysis using only those participants whose blood donations were collected at one of the three centers that used Additive 3 in their storage protocol. We also performed a sensitivity analysis including only those participants of European ancestry and using the variant data imputed using the European reference panel. Finally, we re-imputed the missing metabolites data as described above, swapping out the QRILC imputation procedure for a simple substitution of the missing value with the lowest detected value for the metabolite in question.

      OASIS Queries

      The OASIS: Omics Analysis, Search & Information a TOPMED funded resources(
      • Perry J.A.
      • Gaynor B.J.
      • Mitchell B.D.
      • O’Connell J.R.
      An Omics Analysis Search and Information System (OASIS) for Enabling Biological Discovery in the Old Order Amish.
      ), was used to annotate the top SNPS. OASIS annotation includes information on position, chromosome, alellele frequencies, closest gene, type of variant, position relative to closest gene model, if predicted to functionally consequential, tissues specific gene expression, and other information.

      Locuszoom Plots

      We generated LocusZoom plots locally using v1.4 and plotted a margin of +/- 200 kilobases around each lead SNP against the November 2014 1000 Genomes European ancestry build

      Comparison with GWAS Catalog

      Lead SNPs for all metabolites were queried using the LDLink tool LDtrait (query date 5/6/2022)(
      • Machiela M.J.
      • Chanock S.J.
      LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants.
      ) by selecting an R2 threshold of 0.8 in a +/- 500,000 base pair window in the combined five European ancestries using Genome Build GRCh37. We noted SNPs that have been previously associated with other traits and considered replicated associations as those SNPs with previously reported associations to the same metabolite as found in our study population.

      Animal models

      All animal procedures were approved by the University of Virginia IACUC (protocol no. 4269). Humanized G6PD deficient (A-, Med-) and non-deficient (HuCan) mice were generated by replacing the murine G6PD locus in Bruce4 ES cells (C57BL/6 background) with either the A- (V68M/N126D), Med- (S188F) or huCan (B+) variant (manuscript in preparation). In short, nucleofected ES cells were drug-selected (Neo), G418 resistant clones were isolated, and the presence of homologous recombination (and absence of random integration) was confirmed (data not shown). Clones were then developed into full animals and correct homologous recombination was reconfirmed. The Neo cassette was flanked with FRT sites and removed by breeding with a germline FLP transgenic mouse – the FLP was subsequently removed. Generation of Cre-inducible G6PD Med- deficient mice was described previously. (
      • D'Alessandro A.
      • Howie H.L.
      • Hay A.M.
      • Dziewulska K.H.
      • Brown B.C.
      • Wither M.J.
      • Karafin M.
      • Stone E.F.
      • Spitalnik S.L.
      • Hod E.A.
      • Francis R.O.
      • Fu X.
      • Thomas T.
      • Zimring J.C.
      Hematologic and systemic metabolic alterations due to Mediterranean class II G6PD deficiency in mice.
      )

      G6PD deficient subjects

      An independent cohort was enrolled in this study at the Columbia University and New York Blood Center in New York, under IRB protocols no. AAAJ6862 and 401165, respectively. Male volunteers were recruited using flyers, person-to-person communication, and email, between November 2012 and August 2017. Screening was limited to males because G6PD deficiency is X-linked; Following screening and confirming G6PD activity, 10 G6PD-deficient and 30 G6PD-normal males donated 1 unit of whole blood at the New York Blood Center (New York, New York), (
      • Francis R.O.
      • D'Alessandro A.
      • Eisenberger A.
      • Soffing M.
      • Yeh R.
      • Coronel E.
      • Sheikh A.
      • Rapido F.
      • La Carpia F.
      • Reisz J.A.
      • Gehrke S.
      • Nemkov T.
      • Thomas T.
      • Schwartz J.
      • Divgi C.
      • Kessler D.
      • Shaz B.H.
      • Ginzburg Y.
      • Zimring J.C.
      • Spitalnik S.L.
      • Hod E.A.
      Donor glucose-6-phosphate dehydrogenase deficiency decreases blood quality for transfusion.
      ) each of which was processed into packed RBCs, leukoreduced, prior to metabolomics analysis.

      Data availability statement

      All the mQTL results and related elaborations described in the present study are provided in Supplementary Table 1. The raw genomics data were made available as per Page et al. J Clin Investigation 2021 (reference 28). The raw metabolomics data were made available as per D’Alessandro et al. Transfusion 2019 (reference 29).

      Acknowledgments

      Research reported in this publication was funded by the National Institute of General and Medical Sciences (RM1GM131968 to ADA), and R01HL146442 (ADA), R01HL149714 (ADA), R01HL148151 (ADA), R01HL161004 (ADA), and R21HL150032 (ADA) from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
      The authors wish to acknowledge NHLBI Recipient Epidemiology and Donor Evaluation Study-III (REDS-III), which was supported by NHLBI contracts NHLBI HHSN2682011-00001I, -00002I, -00003I, -00004I, -00005I, -00006I, -00007I, -00008I, and -00009I. The authors would like to express their deep gratitude Dr. Simone Glynn of NHLBI for her outstanding support throughout this study, the RBC-Omics research staff at all participating blood centers and testing labs for their exceptional performance and contribution to this project, and to all blood donors who agreed to participate in this study.

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