Effects of Iron Deficiency Anemia and Its Rapid Correction on the Serum Metabolomic Profile of Rhesus Infants

OBJECTIVES

To determine whether rapid correction of iron deficiency using intramuscular iron dextran normalizes serum metabolomic changes in a nonhuman primate model of iron deficiency anemia (IDA).

METHODS

Blood was collected from naturally iron-sufficient (IS; n = 10) and IDA (n = 12) male and female infant rhesus monkeys (Macaca mulatta) at 6 months of age. IDA infants were treated with intramuscular injections of iron dextran, 10 mg/weekly for 4-8 weeks. Iron status was reevaluated following treatment using hematological measurements and sera were metabolically profiled using HPLC/MS with isobaric standards for identification and quantification.

RESULTS

Early-life iron deficiency anemia negatively affects many cellular metabolic processes, including energy production, electron transport, and oxidative degradation of toxins. Slow iron repletion with dietary supplementation restores iron deficient monkeys from a hematological perspective, but the serum metabolomic profile remains differed from monkeys that had been iron sufficient their entire life. Whether rapid iron restoration through intramuscular injections of iron dextran normalizes serum metabolomic profile is not known. A total of 654 metabolites were measured with differences in 53 metabolites identified between IS and IDA monkeys at 6 months (P 0.05). Pathway analyses provided evidence of altered liver function, hypometabolic state, differential essential fatty acid production, irregular inosine and guanosine metabolism, and atypical bile acid production in IDA infants. After treatment, iron-related hematological parameters had recovered, but the formerly IDA infants remained metabolically distinct from the IS infants, with 225 metabolites differentially expressed between the groups.

CONCLUSIONS

As with slow iron repletion, rapid iron repletion does not normalize the altered serum metabolomic profile in rhesus infants with IDA, suggesting the need for iron supplementation in the pre-anemic stage.

FUNDING SOURCES

National Institutes of Health.

Normalization of mass spectrometry data (NOMAD)

iTRAQ and TMT reagent-based mass spectrometry (MS) are commonly used technologies for quantitative proteomics in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. Such MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that reference normalization does not effectively remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data sets. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality. NOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD effectively removes bias which improves valid across MS run comparisons.