[show abstract][hide abstract] ABSTRACT: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
[show abstract][hide abstract] 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.
Journal of Nutrition 08/2013; · 4.20 Impact Factor
[show abstract][hide abstract] ABSTRACT: Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification, capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or "masterpool", in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406 sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.
Journal of Proteome Research 12/2012; · 5.06 Impact Factor
[show abstract][hide abstract] ABSTRACT: Huntington disease (HD) is a neurodegenerative disorder caused by an expansion of a polyglutamine repeat within the HD gene product, huntingtin. Huntingtin, a large (347 kDa) protein containing multiple HEAT repeats, acts as a scaffold for protein-protein interactions. Huntingtin-induced toxicity is believed to be mediated by a conformational change in expanded huntingtin, leading to protein misfolding and aggregation, aberrant protein interactions and neuronal cell death. While many non-systematic studies of huntingtin interactions have been reported, they were not designed to identify and quantify the changes in the huntingtin interactome induced by polyglutamine expansion. We used tandem affinity purification and quantitative proteomics to compare and quantify interactions of normal or expanded huntingtin isolated from a striatal cell line. We found that proteins preferentially interacting with expanded huntingtin are enriched for intrinsically disordered proteins, consistent with previously suggested roles of such proteins in neurodegenerative disorders. Our functional analysis indicates that proteins related to energy production, protein trafficking, RNA post-transcriptional modifications and cell death were significantly enriched among preferential interactors of expanded huntingtin. Expanded huntingtin interacted with many mitochondrial proteins, including AIFM1, consistent with a role for mitochondrial dysfunction in HD. Furthermore, expanded huntingtin interacted with the stress granule-associated proteins Caprin-1 and G3BP and redistributed to RNA stress granules under ER-stress conditions. These data demonstrate that a number of key cellular functions and networks may be disrupted by abnormal interactions of expanded huntingtin and highlight proteins and pathways that may be involved in HD cellular pathogenesis and that may serve as therapeutic targets.