Mass spectrometry is a fundamental tool for discovery and analysis in the life sciences. With the rapid advances in mass spectrometry technology and methods, it has become imperative to provide a standard output format for mass spectrometry data that will facilitate data sharing and analysis. Initially, the efforts to develop a standard format for mass spectrometry data resulted in multiple formats, each designed with a different underlying philosophy. To resolve the issues associated with having multiple formats, vendors, researchers, and software developers convened under the banner of the HUPO PSI to develop a single standard. The new data format incorporated many of the desirable technical attributes from the previous data formats, while adding a number of improvements, including features such as a controlled vocabulary with validation tools to ensure consistent usage of the format, improved support for selected reaction monitoring data, and immediately available implementations to facilitate rapid adoption by the community. The resulting standard data format, mzML, is a well tested open-source format for mass spectrometer output files that can be readily utilized by the community and easily adapted for incremental advances in mass spectrometry technology.

High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool “Normalyzer”. It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics.org/normalyzer.

It is possible that the low levels of production of exopolysaccharides (EPSs) by lactic acid bacteria could be improved by altering the levels of enzymes in the central metabolism that influence the production of precursor nucleotide sugars. To test this hypothesis, we identified and cloned the galU gene, which codes for UDP glucose pyrophosphorylase (GalU) in Streptococcus thermophilus LY03. Homologous overexpression of the gene led to a 10-fold increase in GalU activity but did not have any effect on the EPS yield when lactose was the carbon source. However, when galU was overexpressed in combination with pgmA , which encodes phosphoglucomutase (PGM), the EPS yield increased from 0.17 to 0.31 g/mol of carbon from lactose. A galactose-fermenting LY03 mutant (Gal ⁺ ) with increased activities of the Leloir enzymes was also found to have a higher EPS yield (0.24 g/mol of carbon) than the parent strain. The EPS yield was further improved to 0.27 g/mol of carbon by overexpressing galU in this strain. However, the highest EPS yield, 0.36 g/mol of carbon, was obtained when pgmA was knocked out in the Gal ⁺ strain. Measurements of the levels of intracellular metabolites in the cultures revealed that the Gal ⁺ strains had considerably higher glucose 1-phosphate levels than the other strains, and the strain lacking PGM activity had threefold-higher levels of glucose 1-phosphate than the other Gal ⁺ strains. These results show that it is possible to increase EPS production by altering the levels of enzymes in the central carbohydrate metabolism.

In 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. Quantitative 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. In 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.