A Comparison of Labeling and Label-Free Mass Spectrometry-Based Proteomics Approaches

Patel, Vibhuti J.; Thalassinos, Konstantinos; Slade, Susan E.; Connolly, Joanne B.; Crombie, Andrew T.; Murrell, J. Colin; Scrivens, James H.

doi:10.1021/pr900080y

Cited by 237 publications

(205 citation statements)

References 43 publications

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“…A total of 700 μg of soluble protein extract from MMA, TMA, and methanol-grown M. silvestris cells were used for proteomic analyses (47). The details of the method are shown in SI Materials and Methods.…”

Section: Methodsmentioning

confidence: 99%

Bacterial flavin-containing monooxygenase is trimethylamine monooxygenase

Chen

Patel

Crombie

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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137

184

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Flavin-containing monooxygenases (FMOs) are one of the most important monooxygenase systems in Eukaryotes and have many important physiological functions. FMOs have also been found in bacteria; however, their physiological function is not known. Here, we report the identification and characterization of trimethylamine (TMA) monooxygenase, termed Tmm, from Methylocella silvestris, using a combination of proteomic, biochemical, and genetic approaches. This bacterial FMO contains the FMO sequence motif (FXGXXXHXXXF/Y) and typical flavin adenine dinucleotide and nicotinamide adenine dinucleotide phosphate-binding domains. The enzyme was highly expressed in TMA-grown M. silvestris and absent during growth on methanol. The gene, tmm, was expressed in Escherichia coli, and the purified recombinant protein had high Tmm activity. Mutagenesis of this gene abolished the ability of M. silvestris to grow on TMA as a sole carbon and energy source. Close homologs of tmm occur in many Alphaproteobacteria, in particular Rhodobacteraceae (marine Roseobacter clade, MRC) and the marine SAR11 clade (Pelagibacter ubique). We show that the ability of MRC to use TMA as a sole carbon and/or nitrogen source is directly linked to the presence of tmm in the genomes, and purified Tmm of MRC and SAR11 from recombinant E. coli showed Tmm activities. The tmm gene is highly abundant in the metagenomes of the Global Ocean Sampling expedition, and we estimate that 20% of the bacteria in the surface ocean contain tmm. Taken together, our results suggest that Tmm, a bacterial FMO, plays an important yet overlooked role in the global carbon and nitrogen cycles.

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Section: Methodsmentioning

confidence: 99%

Bacterial flavin-containing monooxygenase is trimethylamine monooxygenase

Chen

Patel

Crombie

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

137

184

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“…Labelfree quantitative (LFQ) 1 workflows become increasingly popular as the often expensive and time-consuming labeling protocols are omitted. Moreover, LFQ proteomics allows for more flexibility in comparing samples and tends to cover a larger area of the proteome at a higher dynamic range (7,8). Nevertheless, the nature of the LFQ protocol makes shotgun proteomic data analysis a challenging task.…”

mentioning

confidence: 99%

Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics

Goeminne

Gevaert

Clement

2016

Molecular & Cellular Proteomics

121

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Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from stateof-the-art summarization-based methods and peptidebased regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. High-throughput LC-MS-based proteomic workflows are widely used to quantify differential protein abundance between samples. Relative protein quantification can be achieved by stable isotope labeling workflows such as metabolic (1, 2) and postmetabolic labeling (3-6). These types of experiments generally avoid run-to-run differences in the measured peptide (and thus protein) content by pooling and analyzing differentially labeled samples in a single run. Labelfree quantitative (LFQ) 1 workflows become increasingly popular as the often expensive and time-consuming labeling protocols are omitted. Moreover, LFQ proteomics allows for more flexibility in comparing samples and tends to cover a larger area of the proteome at a higher dynamic range (7,8). Nevertheless, the nature of the LFQ protocol makes shotgun proteomic data analysis a challenging task. Missing values are omnipresent in proteomic data generated by data-dependent acquisition workflows, for instance because of low-abundant peptides that are not always fragmented in complex peptide mixtures and a limited number of modifications and mutations that can be accounted for in the feature search. Moreover, the overall abundance of a peptide is determined by the surroundings of its corresponding cleavage sites as these influence protease cleavage efficiency (9). Similarly, some peptides are more easily ionized than others (10). These issues not only lead to missing peptides, but also increase variability in individual peptide intensities. The discrete nature of MS1 sampling following continuous ...

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“…A final concern involves the use of a wide variety of proteomic methods for the characterization of samples. Not only do the many documented approaches label enzymatically digested peptides in different ways (ex ICAT vs iTRAQ labeling, discussed above), there is evidence that the number of proteins detected and identified can vary by as much as 200% (as is the case when iTRAQ and labelfree methods were compared head to head using identical samples (Patel et al, 2009)). Clearly, although these studies have advanced our understanding of molecular alterations that occur in response to exposure to morphine, the field of proteomics would benefit from greater standardization of labeling techniques, methods, and morphine administration protocols for the induction of tolerance and physical dependence.…”

Section: Discussionmentioning

confidence: 99%

An Integrated Quantitative Proteomics and Systems Biology Approach to Explore Synaptic Protein Profile Changes During Morphine Exposure

Stockton

Devi

2013

Neuropsychopharmacol

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Morphine is a classic analgesic for the treatment of chronic pain. However, its repeated use is known to produce tolerance, physical dependence, and addiction; these properties limit its long-term therapeutic use and this has led to a quest for therapeutics without these unwanted side effects. Understanding the molecular changes in response to long-term use of morphine is likely to aid in the development of novel therapeutics for the treatment of pain. Studies examining the effects of chronic morphine administration have reported alterations in gene expression, synapse morphology, and synaptic transmission implying changes in synaptic protein profile. To fully understand the changes in protein profiles, proteomic techniques have been used. Studies using two-dimensional gel electrophoresis of various brain regions combined with mass spectrometry have found alterations in the levels of a number of proteins. However, neither the changes in brain regions relevant to morphine effects nor changes in the abundance of synaptic proteins have been clearly delineated. Recent studies employing subcellular fractionation to isolate the striatal synapse, combined with quantitative proteomics and graph theoryinspired network analyses, have begun to quantify morphine-regulated changes in synaptic proteins and facilitate the generation of networks that could serve as targets for the development of novel therapeutics for the treatment of chronic pain. Thus, an integrated quantitative proteomics and systems biology approach can be useful to identify novel targets for the treatment of pain and other disorders of the brain.

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A Comparison of Labeling and Label-Free Mass Spectrometry-Based Proteomics Approaches

Cited by 237 publications

References 43 publications

Bacterial flavin-containing monooxygenase is trimethylamine monooxygenase

Bacterial flavin-containing monooxygenase is trimethylamine monooxygenase

Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics

An Integrated Quantitative Proteomics and Systems Biology Approach to Explore Synaptic Protein Profile Changes During Morphine Exposure

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