Problems inherent to a meta-analysis of proteomics data: a case study on the plants' response to Cd in different cultivation conditions

This meta-analysis focuses on plant-proteome responses to cadmium (Cd) stress. Initially, some general topics related to a proteomics meta-analysis are discussed: (1) obstacles encountered during data analysis, (2) a consensus in proteomic research, (3) validation and good reporting practices for protein identification and (4) guidelines for statistical analysis of differentially abundant proteins. In a second part, the Cd responses in leaves and roots obtained from a proteomics meta-analysis are discussed in (1) a time comparison (short versus long term exposure), and (2) a culture comparison (hydroponics versus soil cultivation). Data of the meta-analysis confirmed the existence of an initial alarm phase upon Cd exposure. Whereas no metabolic equilibrium is established in hydroponically exposed plants, an equilibrium seems to be manifested in roots of plants grown in Cd-contaminated soil after long term exposure. In leaves, the carbohydrate metabolism is primarily affected independent of the exposure time and the cultivation method. In addition, a metabolic shift from CO2-fixation towards respiration is manifested, independent of the cultivation system. Finally, some ideas for the improvement of proteomics setups and for comparisons between studies are discussed.

BIOLOGICAL SIGNIFICANCE

This meta-analysis focuses on the plant responses to Cd stress in leaves and roots at the proteome level. This meta-analysis points out the encountered obstacles when performing a proteomics meta-analysis related to inherent technologies, but also related to experimental setups. Furthermore, the question is addressed whether an extrapolation of results obtained in hydroponic cultivation towards soil-grown plants is possible.

MS2PIP: a tool for MS/MS peak intensity prediction

MOTIVATION

Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS(2) signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research.

RESULTS

We present a new tool called MS(2)PIP for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. MS(2)PIP pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of MS(2)PIP were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool.

AVAILABILITY

MS(2)PIP code is available for both training and predicting at http://compomics.com/.

Towards a human proteomics atlas

Proteomics research has taken up an increasingly important role in life sciences over the past few years. Due to a strong push from publishers and funders alike, the community has also started to freely share its data in earnest, making use of public repositories such as the highly popular PRIDE database at EMBL-EBI. Reuse of these publicly available data has so far been confined to rather specific, targeted reanalyses, but this limited reuse is set to expand dramatically as repositories continue to grow exponentially. Examples of large-scale reuse are readily found in other omics disciplines, where more comprehensive public data have already accumulated over longer periods. Here, a typical example of integrative data reuse is provided by the construction of so-called expression atlases. We here therefore investigate the issues involved in using the human data currently stored in the PRIDE database to construct a robust, tissue-specific protein expression atlas from tandem-MS based label-free quantification.