Correction: The Proteome of Human Liver Peroxisomes: Identification of Five New Peroxisomal Constituents by a Label-Free Quantitative Proteomics Survey
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PROVIDER: S-EPMC3812376 | biostudies-literature | 2013 Jan
REPOSITORIES: biostudies-literature
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Project description:The peroxisome is a key organelle of low abundance that fulfils various functions essential for human cell metabolism. Severe genetic diseases in humans are caused by defects in peroxisome biogenesis or deficiencies in the function of single peroxisomal proteins. To improve our knowledge of this important cellular structure, we studied for the first time human liver peroxisomes by quantitative proteomics. Peroxisomes were isolated by differential and Nycodenz density gradient centrifugation. A label-free quantitative study of 314 proteins across the density gradient was accomplished using high resolution mass spectrometry. By pairing statistical data evaluation, cDNA cloning and in vivo colocalization studies, we report the association of five new proteins with human liver peroxisomes. Among these, isochorismatase domain containing 1 protein points to the existence of a new metabolic pathway and hydroxysteroid dehydrogenase like 2 protein is likely involved in the transport or β-oxidation of fatty acids in human peroxisomes. The detection of alcohol dehydrogenase 1A suggests the presence of an alternative alcohol-oxidizing system in hepatic peroxisomes. In addition, lactate dehydrogenase A and malate dehydrogenase 1 partially associate with human liver peroxisomes and enzyme activity profiles support the idea that NAD(+) becomes regenerated during fatty acid β-oxidation by alternative shuttling processes in human peroxisomes involving lactate dehydrogenase and/or malate dehydrogenase. Taken together, our data represent a valuable resource for future studies of peroxisome biochemistry that will advance research of human peroxisomes in health and disease.
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Project description:Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.
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Project description:The present research investigates the tuber proteome of the 'medicinal' plant Jerusalem artichoke (abbreviated as JA) (Helianthus tuberosus L.) using a high-throughput proteomics technique. Although JA has been historically known to the Native Americans, it was introduced to Europe in the late 19th century and later spread to Japan (referred to as 'kiku-imo') as a folk remedy for diabetes. Genboku Takahashi research group has been working on the cultivation and utilization of kiku-imo tuber as a traditional/alternative medicine in daily life and researched on the lowering of blood sugar level, HbA1c, etc., in human subjects (unpublished data). Understanding the protein components of the tuber may shed light on its healing properties, especially related to diabetes. Using three commercially processed JA tuber products (dried powder and dried chips) we performed total protein extraction on the powdered samples using a label-free quantitate proteomic approach (mass spectrometry) and catalogued for the first time a comprehensive protein list for the JA tuber. A total of 2967 protein groups were identified, statistically analyzed, and further categorized into different protein classes using bioinformatics techniques. We discussed the association of these proteins to health and disease regulatory metabolism. Data are available via ProteomeXchange with identifier PXD030744.
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Project description:Polycystic ovary syndrome (PCOS) is the most common endocrinological disorder of fertile-aged women. Several adverse pregnancy outcomes and abnormalities of the placenta have been associated with PCOS. By using quantitative label-free proteomics we investigated whether changes in the plasma proteome of pregnant women with PCOS could elucidate the mechanisms behind the pathologies observed in PCOS pregnancies. A total of 169 proteins with ≥2 unique peptides were detected to be differentially expressed between women with PCOS (n = 7) and matched controls (n = 20) at term of pregnancy, out of which 35 were significant (p-value < 0.05). A pathway analysis revealed that networks related to humoral immune responses, inflammatory responses, cardiovascular disease and cellular growth and proliferation were affected by PCOS. Classification of cases and controls was carried out using principal component analysis, orthogonal projections on latent structure-discriminant analysis (OPLS-DA), hierarchical clustering, self-organising maps and ROC-curve analysis. The most significantly enriched proteins in PCOS were properdin and insulin-like growth factor II. In the dataset, properdin had the best predictive accuracy for PCOS (AUC = 1). Additionally, properdin abundances correlated with AMH levels in pregnant women.
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Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.
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