Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly “under-performing” method (i.e., giving baseline average results), if applied at the “appropriate” time in the data-processing pipeline (before or after peptide aggregation) on a data set with the “appropriate” nature of missing values, can outperform a blindly applied, supposedly “better-performing” method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

The regulation of the circadian rhythm is relayed from the central nervous system to the periphery by melatonin, a hormone synthesized at night in the pineal gland. Besides two melatonin G-coupled receptors, mt(1) and MT(2), the existence of a novel putative melatonin receptor, MT(3), was hypothesized from the observation of a binding site in both central and peripheral hamster tissues with an original binding profile and a very rapid kinetics of ligand exchange compared with mt(1) and MT(2). In this report, we present the purification of MT(3) from Syrian hamster kidney and its identification as the hamster homologue of the human quinone reductase 2 (QR(2), EC ). Our purification strategy included the use of an affinity chromatography step which was crucial in purifying MT(3) to homogeneity. The protein was sequenced by tandem mass spectrometry and shown to align with 95% identity with human QR(2). After transfection of CHO-K1 cells with the human QR(2) gene, not only did the QR(2) enzymatic activity appear, but also the melatonin-binding sites with MT(3) characteristics, both being below the limit of detection in the native cells. We further confronted inhibition data from MT(3) binding and QR(2) enzymatic activity obtained from samples of Syrian hamster kidney or QR(2)-overexpressing Chinese hamster ovary cells, and observed an overall good correlation of the data. In summary, our results provide the identification of the melatonin-binding site MT(3) as the quinone reductase QR(2) and open perspectives as to the function of this enzyme, known so far mainly for its detoxifying properties.

Recent advances in the proteomics field have allowed a series of high throughput experiments to be conducted on chloroplast samples, and the data are available in several public databases. However, the accurate localization of many chloroplast proteins often remains hypothetical. This is especially true for envelope proteins. We went a step further into the knowledge of the chloroplast proteome by focusing, in the same set of experiments, on the localization of proteins in the stroma, the thylakoids, and envelope membranes. LC-MS/MS-based analyses first allowed building the AT_CHLORO database (http://www.grenoble.prabi.fr/protehome/grenoble-plant-proteomics/), a comprehensive repertoire of the 1323 proteins, identified by 10,654 unique peptide sequences, present in highly purified chloroplasts and their subfractions prepared from Arabidopsis thaliana leaves. This database also provides extensive proteomics information (peptide sequences and molecular weight, chromatographic retention times, MS/MS spectra, and spectral count) for a unique chloroplast protein accurate mass and time tag database gathering identified peptides with their respective and precise analytical coordinates, molecular weight, and retention time. We assessed the partitioning of each protein in the three chloroplast compartments by using a semiquantitative proteomics approach (spectral count). These data together with an in-depth investigation of the literature were compiled to provide accurate subplastidial localization of previously known and newly identified proteins. A unique knowledge base containing extensive information on the proteins identified in envelope fractions was thus obtained, allowing new insights into this membrane system to be revealed. Altogether, the data we obtained provide unexpected information about plastidial or subplastidial localization of some proteins that were not suspected to be associated to this membrane system. The spectral counting-based strategy was further validated as the compartmentation of well known pathways (for instance, photosynthesis and amino acid, fatty acid, or glycerolipid biosynthesis) within chloroplasts could be dissected. It also allowed revisiting the compartmentation of the chloroplast metabolism and functions.

Summary: DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. Availability and implementation: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages.

The development of chloroplasts and the integration of their function within a plant cell rely on the presence of a complex biochemical machinery located within their limiting envelope membranes. To provide the most exhaustive view of the protein repertoire of chloroplast envelope membranes, we analyzed this membrane system using proteomics. To this purpose, we first developed a procedure to prepare highly purified envelope membranes from Arabidopsis chloroplasts. We then extracted envelope proteins using different methods, i.e. chloroform/methanol extraction and alkaline or saline treatments, in order to retrieve as many proteins as possible, from the most to least hydrophobic ones. Liquid chromatography tandem mass spectrometry analyses were then performed on each envelope membrane subfraction, leading to the identification of more than 100 proteins. About 80% of the identified proteins are known to be, or are very likely, located in the chloroplast envelope. The validation of localization in the envelope of two phosphate transporters exemplifies the need for a combination of strategies to perform the most exhaustive identification of genuine chloroplast envelope proteins. Interestingly, some of the identified proteins are found to be N^α-acetylated, which indicates the accurate location of the N terminus of the corresponding mature protein. With regard to function, more than 50% of the identified proteins have functions known or very likely to be associated with the chloroplast envelope. These proteins are a) involved in ion and metabolite transport, b) components of the protein import machinery, and c) involved in chloroplast lipid metabolism. Some soluble proteins, like proteases, proteins involved in carbon metabolism, or proteins involved in responses to oxidative stress, were associated with envelope membranes. Almost one-third of the proteins we identified have no known function. The present work helps understanding chloroplast envelope metabolism at the molecular level and provides a new overview of the biochemical machinery of the chloroplast envelope membranes.

The unicellular green alga Chlamydomonas reinhardtii is a prime model for deciphering processes occurring in the intracellular compartments of the photosynthetic cell. Organelle-specific proteomic studies have started to delineate its various subproteomes, but sequence-based prediction software is necessary to assign proteins subcellular localizations at whole genome scale. Unfortunately, existing tools are oriented toward land plants and tend to mispredict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrion targeted. We thus developed a new tool called PredAlgo that predicts intracellular localization of those proteins to one of three intracellular compartments in green algae: the mitochondrion, the chloroplast, and the secretory pathway. At its core, a neural network, trained using carefully curated sets of C. reinhardtii proteins, divides the N-terminal sequence into overlapping 19-residue windows and scores the probability that they belong to a cleavable targeting sequence for one of the aforementioned organelles. A targeting prediction is then deduced for the protein, and a likely cleavage site is predicted based on the shape of the scoring function along the N-terminal sequence. When assessed on an independent benchmarking set of C. reinhardtii sequences, PredAlgo showed a highly improved discrimination capacity between chloroplast- and mitochondrion-localized proteins. Its predictions matched well the results of chloroplast proteomics studies. When tested on other green algae, it gave good results with Chlorophyceae and Trebouxiophyceae but tended to underpredict mitochondrial proteins in Prasinophyceae. Approximately 18% of the nuclear-encoded C. reinhardtii proteome was predicted to be targeted to the chloroplast and 15% to the mitochondrion.