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BACKGROUND - Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.
METHODS - We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).
RESULTS - In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.
CONCLUSIONS - We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.
Purpose - The purpose of this study was to characterize the palmitoyl-proteome in lens fiber cells. S-palmitoylation is the most common form of protein S-acylation and the reversible nature of this modification functions as a molecular switch to regulate many biological processes. This modification could play important roles in regulating protein functions and protein-protein interactions in the lens.
Methods - The palmitoyl-proteome of bovine lens fiber cells was investigated by combining acyl-biotin exchange (ABE) chemistry and mass-spectrometry analysis. Due to the possibility of false-positive results from ABE experiment, a method was also developed for direct detection of palmitoylated peptides by mass spectrometry for validating palmitoylation of lens proteins MP20 and AQP5. Palmitoylation levels on AQP5 in different regions of the lens were quantified after iodoacetamide (IAA)-palmitate exchange.
Results - The ABE experiment identified 174 potential palmitoylated proteins. These proteins include 39 well-characterized palmitoylated proteins, 92 previously reported palmitoylated proteins in other tissues, and 43 newly identified potential palmitoylated proteins including two important transmembrane proteins in the lens, AQP5 and MP20. Further analysis by direct detection of palmitoylated peptides confirmed palmitoylation of AQP5 on C6 and palmitoylation of MP20 on C159. Palmitoylation of AQP5 was found to only occur in a narrow region of the inner lens cortex and does not occur in the lens epithelium, in the lens outer cortex, or in the lens nucleus.
Conclusions - AQP5 and MP20 are among 174 palmitoylated proteins found in bovine lens fiber cells. This modification to AQP5 and MP20 may play a role in their translocation from the cytoplasm to cell membranes during fiber cell differentiation.
Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.
State-of-the-art strategies for proteomics are not able to rapidly interrogate complex peptide mixtures in an untargeted manner with sensitive peptide and protein identification rates. We describe a data-independent acquisition (DIA) approach, microDIA (μDIA), that applies a novel tandem mass spectrometry (MS/MS) mass spectral deconvolution method to increase the specificity of tandem mass spectra acquired during proteomics experiments. Using the μDIA approach with a 10 min liquid chromatography gradient allowed detection of 3.1-fold more HeLa proteins than the results obtained from data-dependent acquisition (DDA) of the same samples. Additionally, we found the μDIA MS/MS deconvolution procedure is critical for resolving modified peptides with relatively small precursor mass shifts that cause the same peptide sequence in modified and unmodified forms to theoretically cofragment in the same raw MS/MS spectra. The μDIA workflow is implemented in the PROTALIZER software tool which fully automates tandem mass spectral deconvolution, queries every peptide with a library-free search algorithm against a user-defined protein database, and confidently identifies multiple peptides in a single tandem mass spectrum. We also benchmarked μDIA against DDA using a 90 min gradient analysis of HeLa and Escherichia coli peptides that were mixed in predefined quantitative ratios, and our results showed μDIA provided 24% more true positives at the same false positive rate.
Failure to properly repair damaged due to myocardial infarction is a major cause of heart failure. In contrast with adult mammals, zebrafish hearts show remarkable regenerative capabilities after substantial damage. To characterize protein dynamics during heart regeneration, we employed an HPLC-ESI-MS/MS (mass spectrometry) approach. Myocardium tissues were taken from sham-operated fish and ventricle-resected sample at three different time points (2, 7, and 14 days); dynamics of protein expression were analyzed by an ion-current-based quantitative platform. More than 2000 protein groups were quantified in all 16 experiments. Two hundred and nine heart-regeneration-related protein groups were quantified and clustered into six time-course patterns. Functional analysis indicated that multiple molecular function and metabolic pathways were involved in heart regeneration. Interestingly, Ingenuity Pathway Analysis revealed that P53 signaling was inhibited during the heart regeneration, which was further verified by real-time quantitative polymerase chain reaction (Q-PCR). In summary, we applied systematic proteomics analysis on regenerating zebrafish heart, uncovered the dynamics of regenerative genes expression and regulatory pathways, and provided invaluable insight into design regenerative-based strategies in human hearts.
Microtubules in animal cells assemble (nucleate) from both the centrosome and the cis-Golgi cisternae. A-kinase anchor protein 350 kDa (AKAP350A, also called AKAP450/CG-NAP/AKAP9) is a large scaffolding protein located at both the centrosome and Golgi apparatus. Previous findings have suggested that AKAP350 is important for microtubule dynamics at both locations, but how this scaffolding protein assembles microtubule nucleation machinery is unclear. Here, we found that overexpression of the C-terminal third of AKAP350A, enhanced GFP-AKAP350A(2691-3907), induces the formation of multiple microtubule-nucleation centers (MTNCs). Nevertheless, these induced MTNCs lacked "true" centriole proteins, such as Cep135. Mapping analysis with AKAP350A truncations demonstrated that AKAP350A contains discrete regions responsible for promoting or inhibiting the formation of multiple MTNCs. Moreover, GFP-AKAP350A(2691-3907) recruited several pericentriolar proteins to MTNCs, including γ-tubulin, pericentrin, Cep68, Cep170, and Cdk5RAP2. Proteomic analysis indicated that Cdk5RAP2 and Cep170 both interact with the microtubule nucleation-promoting region of AKAP350A, whereas Cep68 interacts with the distal C-terminal AKAP350A region. Yeast two-hybrid assays established a direct interaction of Cep170 with AKAP350A. Super-resolution and deconvolution microscopy analyses were performed to define the association of AKAP350A with centrosomes, and these studies disclosed that AKAP350A spans the bridge between centrioles, co-localizing with rootletin and Cep68 in the linker region. siRNA-mediated depletion of AKAP350A caused displacement of both Cep68 and Cep170 from the centrosome. These results suggest that AKAP350A acts as a scaffold for factors involved in microtubule nucleation at the centrosome and coordinates the assembly of protein complexes associating with the intercentriolar bridge.
PDX1/NKX6-1 pancreatic progenitors (PPs) give rise to endocrine cells both in vitro and in vivo. This cell population can be successfully differentiated from human pluripotent stem cells (hPSCs) and hold the potential to generate an unlimited supply of β cells for diabetes treatment. However, the efficiency of PP generation in vitro is highly variable, negatively impacting reproducibility and validation of in vitro and in vivo studies, and consequently, translation to the clinic. Here, we report the use of a proteomics approach to phenotypically characterize hPSC-derived PPs and distinguish these cells from non-PP populations during differentiation. Our analysis identifies the pancreatic secretory granule membrane major glycoprotein 2 (GP2) as a PP-specific cell surface marker. Remarkably, GP2 is co-expressed with NKX6-1 and PTF1A in human developing pancreata, indicating that it marks the multipotent pancreatic progenitors in vivo. Finally, we show that isolated hPSC-derived GP2 cells generate β-like cells (C-PEPTIDE/NKX6-1) more efficiently compared to GP2 and unsorted populations, underlining the potential therapeutic applications of GP2.Pancreatic progenitors (PPs) can be derived from human pluripotent stem cells in vitro but efficiency of differentiation varies, making it hard to sort for insulin-producing cells. Here, the authors use a proteomic approach to identify the secretory granule membrane glycoprotein 2 as a marker for PDX1+/NKX6-1+ PPs.
Patients with chronic kidney disease (CKD) exhibit a myriad of metabolic derangements, including dyslipidemia characterized by low plasma concentrations of high-density lipoprotein (HDL)-associated cholesterol. However, the effects of kidney disease on HDL composition have not been comprehensively determined. Here we used a targeted mass spectrometric approach to quantify 38 proteins contained in the HDL particles within a CKD cohort of 509 participants with a broad range of estimated glomerular filtration rates (eGFRs) (CKD stages I-V, and a mean eGFR of 45.5 mL/min/1.73m). After adjusting for multiple testing, demographics, comorbidities, medications, and other characteristics, eGFR was significantly associated with differences in four HDL proteins. Compared to participants with an eGFR of 60 mL/min/1.73m or more, those with an eGFR under 15 mL/min/1.73m exhibited 1.89-fold higher retinol-binding protein 4 (95% confidence interval 1.34-2.67), 1.52-fold higher apolipoprotein C-III (1.25-1.84), 0.70-fold lower apolipoprotein L1 (0.55-0.92), and 0.64-fold lower vitronectin (0.48-0.85). Although the HDL apolipoprotein L1 was slightly lower among African Americans than among Caucasian individuals, the relationship to eGFR did not differ by race. After adjustment, no HDL-associated proteins associated with albuminuria. Thus, modest changes in the HDL proteome provide preliminary evidence for an association between HDL proteins and declining kidney function, but this needs to be replicated. Future analyses will determine if HDL proteomics is indeed a clinical predictor of declining kidney function or cardiovascular outcomes.
Copyright © 2017 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
We hypothesized that distinct protein expression features of benign and malignant pulmonary nodules may reveal novel candidate biomarkers for the early detection of lung cancer. We performed proteome profiling by liquid chromatography-tandem mass spectrometry to characterize 34 resected benign lung nodules, 24 untreated lung adenocarcinomas (ADCs), and biopsies of bronchial epithelium. Group comparisons identified 65 proteins that differentiate nodules from ADCs and normal bronchial epithelium and 66 proteins that differentiate ADCs from nodules and normal bronchial epithelium. We developed a multiplexed parallel reaction monitoring (PRM) assay to quantify a subset of 43 of these candidate biomarkers in an independent cohort of 20 benign nodules, 21 ADCs, and 20 normal bronchial biopsies. PRM analyses confirmed significant nodule-specific abundance of 10 proteins including ALOX5, ALOX5AP, CCL19, CILP1, COL5A2, ITGB2, ITGAX, PTPRE, S100A12, and SLC2A3 and significant ADC-specific abundance of CEACAM6, CRABP2, LAD1, PLOD2, and TMEM110-MUSTN1. Immunohistochemistry analyses for seven selected proteins performed on an independent set of tissue microarrays confirmed nodule-specific expression of ALOX5, ALOX5AP, ITGAX, and SLC2A3 and cancer-specific expression of CEACAM6. These studies illustrate the value of global and targeted proteomics in a systematic process to identify and qualify candidate biomarkers for noninvasive molecular diagnosis of lung cancer.
Recently, zebrafish and human cytochrome P450 (P450) 27C1 enzymes have been shown to be retinoid 3,4-desaturases. The enzyme is unusual among mammalian P450s in that the predominant oxidation is a desaturation and in that hydroxylation represents only a minor pathway. We show by proteomic analysis that P450 27C1 is localized to human skin, with two proteins of different sizes present, one being a cleavage product of the full-length form. P450 27C1 oxidized all--retinol to 3,4-dehydroretinol, 4-hydroxy (OH) retinol, and 3-OH retinol in a 100:3:2 ratio. Neither 3-OH nor 4-OH retinol was an intermediate in desaturation. No kinetic burst was observed in the steady state; neither the rate of substrate binding nor product release was rate-limiting. Ferric P450 27C1 reduction by adrenodoxin was 3-fold faster in the presence of the substrate and was ∼5-fold faster than the overall turnover. Kinetic isotope effects of 1.5-2.3 (on / ) were observed with 3,3-, 4,4-, and 3,3,4,4-deuterated retinol. Deuteration at C-4 produced a 4-fold increase in 3-hydroxylation due to metabolic switching, with no observable effect on 4-hydroxylation. Deuteration at C-3 produced a strong kinetic isotope effect for 3-hydroxylation but not 4-hydroxylation. Analysis of the products of deuterated retinol showed a lack of scrambling of a putative allylic radical at C-3 and C-4. We conclude that the most likely catalytic mechanism begins with abstraction of a hydrogen atom from C-4 (or possibly C-3) initiating the desaturation pathway, followed by a sequential abstraction of a hydrogen atom or proton-coupled electron transfer. Adrenodoxin reduction and hydrogen abstraction both contribute to rate limitation.
© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.