BackgroundIn order to get global molecular understanding of one of the most important crop diseases worldwide, we investigated compatible and incompatible interactions between Phytophthora infestans and potato (Solanum tuberosum). We used the two most field-resistant potato clones under Swedish growing conditions, which have the greatest known local diversity of P. infestans populations, and a reference compatible cultivar.ResultsQuantitative label-free proteomics of 51 apoplastic secretome samples (PXD000435) in combination with genome-wide transcript analysis by 42 microarrays (E-MTAB-1515) were used to capture changes in protein abundance and gene expression at 6, 24 and 72 hours after inoculation with P. infestans. To aid mass spectrometry analysis we generated cultivar-specific RNA-seq data (E-MTAB-1712), which increased peptide identifications by 17%. Components induced only during incompatible interactions, which are candidates for hypersensitive response initiation, include a Kunitz-like protease inhibitor, transcription factors and an RCR3-like protein. More secreted proteins had lower abundance in the compatible interaction compared to the incompatible interactions. Based on this observation and because the well-characterized effector-target C14 protease follows this pattern, we suggest 40 putative effector targets.ConclusionsIn summary, over 17000 transcripts and 1000 secreted proteins changed in abundance in at least one time point, illustrating the dynamics of plant responses to a hemibiotroph. Half of the differentially abundant proteins showed a corresponding change at the transcript level. Many putative hypersensitive and effector-target proteins were single representatives of large gene families.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-497) contains supplementary material, which is available to authorized users.

AimsWe tested whether characteristic changes of the plasma lipidome in individuals with comparable total lipids level associate with future cardiovascular disease (CVD) outcome and whether 23 validated gene variants associated with coronary artery disease (CAD) affect CVD associated lipid species.Methods and ResultsScreening of the fasted plasma lipidome was performed by top-down shotgun analysis and lipidome compositions compared between incident CVD cases (n = 211) and controls (n = 216) from the prospective population-based MDC study using logistic regression adjusting for Framingham risk factors. Associations with incident CVD were seen for eight lipid species (0.21≤q≤0.23). Each standard deviation unit higher baseline levels of two lysophosphatidylcholine species (LPC), LPC16∶0 and LPC20∶4, was associated with a decreased risk for CVD (P = 0.024–0.028). Sphingomyelin (SM) 38∶2 was associated with increased odds of CVD (P = 0.057). Five triglyceride (TAG) species were associated with protection (P = 0.031–0.049). LPC16∶0 was negatively correlated with the carotid intima-media thickness (P = 0.010) and with HbA1c (P = 0.012) whereas SM38∶2 was positively correlated with LDL-cholesterol (P = 0.0*10−6) and the q-values were good (q≤0.03). The risk allele of 8 CAD-associated gene variants showed significant association with the plasma level of several lipid species. However, the q-values were high for many of the associations (0.015≤q≤0.75). Risk allele carriers of 3 CAD-loci had reduced level of LPC16∶0 and/or LPC 20∶4 (P≤0.056).ConclusionOur study suggests that CVD development is preceded by reduced levels of LPC16∶0, LPC20∶4 and some specific TAG species and by increased levels of SM38∶2. It also indicates that certain lipid species are intermediate phenotypes between genetic susceptibility and overt CVD. But it is a preliminary study that awaits replication in a larger population because statistical significance was lost for the associations between lipid species and future cardiovascular events when correcting for multiple testing.

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on .

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High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments.

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label-free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future.

The oomycete Phytophthora infestans is the causal agent of late blight in potato and tomato. Since the underlying processes that govern pathogenicity and development in P. infestans are largely unknown, we have performed a large-scale phosphoproteomics study of six different P. infestans life stages. We have obtained quantitative data for 2922 phosphopeptides and compared their abundance. Life-stage-specific phosphopeptides include ATP-binding cassette transporters and a kinase that only occurs in appressoria. In an extended data set, we identified 2179 phosphorylation sites and deduced 22 phosphomotifs. Several of the phosphomotifs matched consensus sequences of kinases that occur in P. infestans but not Arabidopsis. In addition, we detected tyrosine phosphopeptides that are potential targets of kinases resembling mammalian tyrosine kinases. Among the phosphorylated proteins are members of the RXLR and Crinkler effector families. The latter are phosphorylated in several life stages and at multiple positions, in sites that are conserved between different members of the Crinkler family. This indicates that proteins in the Crinkler family have functions beyond their putative role as (necrosis-inducing) effectors. This phosphoproteomics data will be instrumental for studies on oomycetes and host-oomycete interactions. The data sets have been deposited to ProteomeXchange (identifier PXD000433).

As high-resolution instruments are becoming standard in proteomics laboratories, label-free quantification using precursor measurements is becoming a viable option, and is consequently rapidly gaining popularity. Several software solutions have been presented for label-free analysis, but to our knowledge no conclusive studies regarding the sensitivity and reliability of each step of the analysis procedure has been described. Here, we use real complex samples to assess the reliability of label-free quantification using four different software solutions. A generic approach to quality test quantitative label-free LC-MS is introduced. Measures for evaluation are defined for feature detection, alignment and quantification. All steps of the analysis could be considered adequately performed by the utilized software solutions, although differences and possibilities for improvement could be identified. The described method provides an effective testing procedure, which can help the user to quickly pinpoint where in the workflow changes are needed.