Statistical Similarities between Transcriptomics and Quantitative Shotgun Proteomics Data

Pavelka, Norman; Fournier, Marjorie; Swanson, Selene K.; Pelizzola, Mattia; Ricciardi‐Castagnoli, Paola; Florens, Laurence; Washburn, Michael P.

doi:10.1074/mcp.m700240-mcp200

Cited by 152 publications

(223 citation statements)

References 57 publications

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“…The additive-multiplicative error structure has also been reported with quantitation by other MS-based methodologies, and the additive component may arise from the integration of count-based signal inherent with the majority of MS instrumentation (29,31,32) and/or the presence of a small basal unspecific background signal. As a consequence, heterogeneity of variance is, to varying degree, likely to be an inherent iTRAQ, like other MS-based quantitation techniques, faces the problem of how to combine readings from multiple peptides to estimate an abundance ratio for the parent protein.…”

Section: Discussionmentioning

confidence: 99%

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Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Karp

Huber

Sadowski

et al. 2010

Molecular & Cellular Proteomics

469

459

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iTRAQ (isobaric tags for relative or absolute quantitation) is a mass spectrometry technology that allows quantitative comparison of protein abundance by measuring peak intensities of reporter ions released from iTRAQ-tagged peptides by fragmentation during MS/MS. However, current data analysis techniques for iTRAQ struggle to report reliable relative protein abundance estimates and suffer with problems of precision and accuracy. The precision of the data is affected by variance heterogeneity: low signal data have higher relative variability; however, low abundance peptides dominate data sets. Accuracy is compromised as ratios are compressed toward 1, leading to underestimation of the ratio. This study investigated both issues and proposed a methodology that combines the peptide measurements to give a robust protein estimate even when the data for the protein are sparse or at low intensity. Our data indicated that ratio compression arises from contamination during precursor ion selection, which occurs at a consistent proportion within an experiment and thus results in a linear relationship between expected and observed ratios. We proposed that a correction factor can be calculated from spiked proteins at known ratios. Then we demonstrated that variance heterogeneity is present in iTRAQ data sets irrespective of the analytical packages, LC-MS/MS instrumentation, and iTRAQ labeling kit (4-plex or 8-plex) used. We proposed using an additive-multiplicative error model for peak intensities in MS/MS quantitation and demonstrated that a variancestabilizing normalization is able to address the error structure and stabilize the variance across the entire intensity range. The resulting uniform variance structure simplifies the downstream analysis. Heterogeneity of variance consistent with an additive-multiplicative model has been reported in other MS-based quantitation including fields outside of proteomics; consequently the variancestabilizing normalization methodology has the potential to increase the capabilities of MS in quantitation across diverse areas of biology and chemistry. Molecular & Cellular Proteomics 9:1885-1897, 2010.Different techniques are being used and developed in the field of proteomics to allow quantitative comparison of samples between one state and another. These can be divided into gel-(1-4) or mass spectrometry-based (5-8) techniques. Comparative studies have found that each technique has strengths and weaknesses and plays a complementary role in proteomics (9, 10). There is significant interest in stable isotope labeling strategies of proteins or peptides as with every measurement there is the potential to use an internal reference allowing relative quantitation comparison, which significantly increases sensitivity of detection of change in abundance. Isobaric labeling techniques such as tandem mass tags (11, 12) or isobaric tags for relative or absolute quantitation (iTRAQ) 1 (13, 14) allow multiplexing of four, six and eight separately labeled samples within one experiment. In contrast to mos...

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Section: Discussionmentioning

confidence: 99%

“…Pavelka et al (31) used a power law global error model in conjunction with quantitation data derived from spectral counts. Other authors have proposed that the higher CV at low signal arises from the majority of MS instrumentation measuring ion counts as whole numbers (32).…”

mentioning

confidence: 99%

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Karp

Huber

Sadowski

et al. 2010

Molecular & Cellular Proteomics

469

459

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“…Protein abundance was evaluated according to their normalized spectral abundance factors, calculated as the spectral count relative to the length of a given protein normalized by the sum of the relative spectral counts in the sample (34). We used the open source software package "plgem" (written in R and maintained by the BioConductor project) to establish some statistical significance on this data set (39). Briefly, the runs with the most replicates (negative controls) were used to fit a power law global error model on the mean versus S.D.…”

Section: Methodsmentioning

confidence: 99%

Unraveling the Ubiquitome of the Human Malaria Parasite

Ponts

Saraf

Chung

et al. 2011

Journal of Biological Chemistry

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Malaria is one of the deadliest infectious diseases worldwide. The most severe form is caused by the eukaryotic protozoan parasite Plasmodium falciparum. Recent studies have highlighted the importance of post-translational regulations for the parasite's progression throughout its life cycle, protein ubiquitylation being certainly one of the most abundant. The specificity of its components and the wide range of biological processes in which it is involved make the ubiquitylation pathway a promising source of suitable targets for anti-malarial drug development. Here, we combined immunofluorescent microscopy, biochemical assays, in silico prediction, and mass spectrometry analysis using the multidimensional protein identification technology, or MudPIT, to describe the P. falciparum ubiquitome. We found that ubiquitin conjugates are detected at every morphological stage of the parasite erythrocytic cycle. Furthermore, we detected that more than half of the parasite's proteome represents possible targets for ubiquitylation, especially proteins found to be present at the most replicative stage of the asexual cycle, the trophozoite stage. A large proportion of ubiquitin conjugates were also detected at the schizont stage, consistent with a cell activity slowdown to prepare for merozoite differentiation and invasion. Finally, for the first time in the human malaria parasite, our results strongly indicate the presence of heterologous mixed conjugations, SUMO/UB. This discovery suggests that sumoylated proteins may be regulated by ubiquitylation in P. falciparum. Altogether, our results present the first stepping stone toward a better understanding of ubiquitylation and its role(s) in the biology of the human malaria parasite.

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“…It has been shown that the use of PLGEM-based standard deviations to calculate signal-to-noise ratios in a NSAF data set improves determination of protein expression changes, as it is more conservative with proteins of low abundance than proteins with high abundance. The goodness-of-fit of the model to the NSAF data and the relevant algorithmic details of the PLGEM method are explained in detail elsewhere (Pavelka et al, 2008). Principal component analysis of exported normalized FT-IR spectra was done using SIMCA-P 12.0.1.…”

Section: Statisticsmentioning

confidence: 99%

High-fat diet alters gut microbiota physiology in mice

Daniel¹,

Gholami²,

Berry³

et al. 2013

The ISME Journal

588

386

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The intestinal microbiota is known to regulate host energy homeostasis and can be influenced by highcalorie diets. However, changes affecting the ecosystem at the functional level are still not well characterized. We measured shifts in cecal bacterial communities in mice fed a carbohydrate or high-fat (HF) diet for 12 weeks at the level of the following: (i) diversity and taxa distribution by high-throughput 16S ribosomal RNA gene sequencing; (ii) bulk and single-cell chemical composition by Fourier-transform infrared-(FT-IR) and Raman micro-spectroscopy and (iii) metaproteome and metabolome via highresolution mass spectrometry. High-fat diet caused shifts in the diversity of dominant gut bacteria and altered the proportion of Ruminococcaceae (decrease) and Rikenellaceae (increase). FT-IR spectroscopy revealed that the impact of the diet on cecal chemical fingerprints is greater than the impact of microbiota composition. Diet-driven changes in biochemical fingerprints of members of the Bacteroidales and Lachnospiraceae were also observed at the level of single cells, indicating that there were distinct differences in cellular composition of dominant phylotypes under different diets. Metaproteome and metabolome analyses based on the occurrence of 1760 bacterial proteins and 86 annotated metabolites revealed distinct HF diet-specific profiles. Alteration of hormonal and anti-microbial networks, bile acid and bilirubin metabolism and shifts towards amino acid and simple sugars metabolism were observed. We conclude that a HF diet markedly affects the gut bacterial ecosystem at the functional level.

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Statistical Similarities between Transcriptomics and Quantitative Shotgun Proteomics Data

Cited by 152 publications

References 57 publications

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Addressing Accuracy and Precision Issues in iTRAQ Quantitation

Unraveling the Ubiquitome of the Human Malaria Parasite

High-fat diet alters gut microbiota physiology in mice

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