Highlights d The proteome of a photosynthetic bacterium was probed under light and CO 2 limitation d Protein abundance changed linearly with growth rate according to growth law d The response to light limitation exceeds the response to CO 2 d A resource allocation model suggests that proteins are not always utilized optimally

We used a brief trypsin treatment followed by peptide separation and identification using nano-LC followed by off-line MS/MS to identify the surface proteins on live Candida albicans organisms growing in biofilms and planktonic yeast cells and hyphae. One hundred thirty-one proteins were present in at least two of the three replicates of one condition and distributed in various combinations of the three growth conditions. Both previously reported and new surface proteins were identified and these were distributed between covalently attached proteins and noncovalently attached proteins of the cell wall.

There exist, at present, public web repositories for management and storage of proteomic data and also fungi-specific databases. None of them, however, is focused to the specific research area of fungal pathogens and their interactions with the host, and contains proteomics experimental data. In this context, we present Proteopathogen, a database intended to compile proteomics experimental data and to facilitate storage and access to a range of data which spans proteomics workflows from description of the experimental approaches leading to sample preparation to MS settings and peptides supporting protein identification. Proteopathogen is currently focused on Candida albicans and its interaction with macrophages; however, data from experiments concerning different pathogenic fungi species and other mammalian cells may also be found suitable for inclusion into the database. Proteopathogen is publicly available at http://proteopathogen.dacya.ucm.es.

Macrophages may induce fungal apoptosis to fight against C. albicans, as previously hypothesized by our group. To confirm this hypothesis, we analyzed proteins from C. albicans cells after 3 h of interaction with macrophages using two quantitative proteomic approaches. A total of 51 and 97 proteins were identified as differentially expressed by DIGE and iTRAQ, respectively. The proteins identified and quantified were different, with only seven in common, but classified in the same functional categories. The analyses of their functions indicated that an increase in the metabolism of amino acids and purine nucleotides were taking place, while the glycolysis and translation levels dropped after 3 h of interaction. Also, the response to oxidative stress and protein translation were reduced. In addition, seven substrates of metacaspase (Mca1) were identified (Cdc48, Fba1, Gpm1, Pmm1, Rct1, Ssb1, and Tal1) as decreased in abundance, plus 12 proteins previously described as related to apoptosis. Besides, the monitoring of apoptotic markers along 24 h of interaction (caspase-like activity, TUNEL assay, and the measurement of ROS and cell examination by transmission electron microscopy) revealed that apoptotic processes took place for 30% of the fungal cells, thus supporting the proteomic results and the hypothesis of macrophages killing C. albicans by apoptosis.

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.0 RNA-Seq and Encyclopedia of DNA Elements (ENCODE) data sets were used to obtain further information relative to cell/tissue specific chromosome 16 coding gene expression patterns and to infer the presence of missing proteins. Twenty-four shotgun 2D-LC-MS/MS and gel/LC-MS/MS MIAPE compliant experiments, representing 41% coverage of chromosome 16 proteins, were performed. Furthermore, mapping of large-scale multicenter mass spectrometry data sets from CCD18, MCF7, Jurkat, and Ramos cell lines into RNA-Seq data allowed further insights relative to correlation of chromosome 16 transcripts and proteins. Detection and quantification of chromosome 16 proteins in biological matrices by SRM procedures are also primary goals of the SpHPP. Two strategies were undertaken: one focused on known proteins, taking advantage of MS data already available, and the second, aimed at the detection of the missing proteins, is based on the expression of recombinant proteins to gather MS information and optimize SRM methods that will be used in real biological samples. SRM methods for 49 known proteins and for recombinant forms of 24 missing proteins are reported in this study.

Candida albicans public proteomic data sets, though growing steadily in the last few years, still have a very limited presence in online repositories. We report here the creation of a C. albicans PeptideAtlas comprising near 22000 distinct peptides at a 0.24 % False Discovery Rate (FDR) that account for over 2500 canonical proteins at a 1.2% FDR. Based on data from 16 experiments, we attained coverage of 41% of the C.albicans open reading frame sequences (ORFs) in the database used for the searches. This PeptideAtlas provides several useful features, including comprehensive protein and peptide-centered search capabilities and visualization tools that establish a solid basis for the study of basic biological mechanisms key to virulence and pathogenesis such as dimorphism, adherence, and apoptosis. Further, it is a valuable resource for the selection of candidate proteotypic peptides for targeted proteomic experiments via selected reaction monitoring (SRM) or SWATH-MS.

Pseudomonas aeruginosa is an important opportunistic pathogen with high prevalence in nosocomial infections. This microorganism is a good model for understanding biological processes such as the quorum-sensing response, the metabolic integration of virulence, the mechanisms of global regulation of bacterial physiology, and the evolution of antibiotic resistance. Till now, P. aeruginosa proteomic data, although available in several on-line repositories, were dispersed and difficult to access. In the present work, proteomes of the PAO1 strain grown under very different conditions and from diverse cellular compartments have been analyzed and joined to build the Pseudomonas PeptideAtlas. This resource is a comprehensive mass spectrometry-derived peptide and inferred protein database with 71.3% coverage of the total predicted proteome of P. aeruginosa PAO1. This is the highest published coverage among the eight bacterial PeptideAtlas datasets currently available. The proteins in the Pseudomonas PeptideAtlas cover 84% of metabolic proteins, 71% of proteins involved in genetic information processing, 72% of proteins responsible for environmental information processing, more than 80% of proteins related to quorum sensing and biofilm formation, and 81% of proteins responsible for antimicrobial resistance. It exemplifies a necessary tool for targeted proteomics studies, system-wide observations, and cross-species observational studies. Here we describe how this resource was built and some of the physiologically important proteins of this pathogen. SignificancePseudomonas aeruginosa is among the most versatile bacterial pathogens. Studies of its proteome are very important as they can reveal virulence factors and mechanisms of antibiotic resistance. The construction of a proteomic resource such as the PeptideAtlas enables targeted proteomics studies, system-wide observations, and cross-species observational studies.

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