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Cole RN, Ruczinski I, Schulze K, Christian P, Herbrich S, Wu L, DeVine LR, O'Meally RN, Shrestha S, Boronina TN, Yager JD, Groopman J, West KP. The plasma proteome identifies expected and novel proteins correlated with micronutrient status in undernourished Nepalese children. J Nutr 2013; 143:1540-8. [PMID: 23966331 PMCID: PMC6879017 DOI: 10.3945/jn.113.175018] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Micronutrient deficiencies are common in undernourished societies yet remain inadequately assessed due to the complexity and costs of existing assays. A plasma proteomics-based approach holds promise in quantifying multiple nutrient:protein associations that reflect biological function and nutritional status. To validate this concept, in plasma samples of a cohort of 500 6- to 8-y-old Nepalese children, we estimated cross-sectional correlations between vitamins A (retinol), D (25-hydroxyvitamin D), and E (α-tocopherol), copper, and selenium, measured by conventional assays, and relative abundance of their major plasma-bound proteins, measured by quantitative proteomics using 8-plex iTRAQ mass tags. The prevalence of low-to-deficient status was 8.8% (<0.70 μmol/L) for retinol, 19.2% (<50 nmol/L) for 25-hydroxyvitamin D, 17.6% (<9.3 μmol/L) for α-tocopherol, 0% (<10 μmol/L) for copper, and 13.6% (<0.6 μmol/L) for selenium. We identified 4705 proteins, 982 in >50 children. Employing a linear mixed effects model, we observed the following correlations: retinol:retinol-binding protein 4 (r = 0.88), 25-hydroxyvitamin D:vitamin D-binding protein (r = 0.58), α-tocopherol:apolipoprotein C-III (r = 0.64), copper:ceruloplasmin (r = 0.65), and selenium:selenoprotein P isoform 1 (r = 0.79) (all P < 0.0001), passing a false discovery rate threshold of 1% (based on P value-derived q values). Individual proteins explained 34-77% (R(2)) of variation in their respective nutrient concentration. Adding second proteins to models raised R(2) to 48-79%, demonstrating a potential to explain additional variation in nutrient concentration by this strategy. Plasma proteomics can identify and quantify protein biomarkers of micronutrient status in undernourished children. The maternal micronutrient supplementation trial, from which data were derived as a follow-up activity, was registered at clinicaltrials.gov as NCT00115271.
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Affiliation(s)
- Robert N. Cole
- Mass Spectrometry and Proteomics Core Facility, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of Biological Chemistry, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Ingo Ruczinski
- Department of Biostatistics, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Kerry Schulze
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of International Health, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Parul Christian
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of International Health, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Shelley Herbrich
- Department of Biostatistics, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Lee Wu
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of International Health, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Lauren R. DeVine
- Mass Spectrometry and Proteomics Core Facility, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of Biological Chemistry, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Robert N. O'Meally
- Mass Spectrometry and Proteomics Core Facility, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of Biological Chemistry, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Sudeep Shrestha
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of International Health, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Tatiana N. Boronina
- Mass Spectrometry and Proteomics Core Facility, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of Biological Chemistry, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - James D. Yager
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of Environmental Health Sciences, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - John Groopman
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of Environmental Health Sciences, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Keith P. West
- Center for Human Nutrition, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,Department of International Health, Bloomberg School of Public Health and School of Medicine, Johns Hopkins University, Baltimore, MD,To whom correspondence should be addressed. E-mail:
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Abstract
The cerebellum, like the cerebrum, includes a nuclear structure and an overlying cortical structure. Experiments in the past decade have expanded knowledge beyond the traditional function of the cerebellum to include critical roles in motor learning and memory and sensory discrimination. The initial steps in cerebellar development depend on inductive signaling involving FGF and Wnt proteins produced at the mesencephalic/metencephalic boundary. To address the issue of how individual cerebellar cell fates within the cerebellar territory are specified, we examined the expression of transcription factors, including mammalian homologues of LIM homeodomain-containing proteins, basic helix-loop-helix proteins, and three amino acid loop-containing proteins. The results of these studies show that combinatorial codes of transcription factors define precursors of the cerebellar nuclei, and both Purkinje cells and granule neurons of the cerebellar cortex. Examination of gene expression patterns in several hundred lines of Egfp-BAC (bacterial artificial chromosome) transgenic mice in the GENSAT Project revealed numerous genes with restricted expression in cerebellar progenitor populations, including genes specific for cerebellar nuclear precursors and Purkinje cell precursors. In addition, we identified patterns of gene expression that link granule and Purkinje cells to their precerebellar nuclei. These results identify molecular pathways that offer new insights on the development of the nuclear and cortical structures of the cerebellum, as well as components of the cerebellar circuitry.
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Affiliation(s)
- Daniver Morales
- Laboratory of Developmental Neurobiology, The Rockefeller University, New York, New York 10021-6399
| | - Mary E. Hatten
- Laboratory of Developmental Neurobiology, The Rockefeller University, New York, New York 10021-6399
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