Is label-free LC-MS/MS ready for biomarker discovery?

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. This article is protected by copyright. All rights reserved

DIANA—algorithmic improvements for analysis of data-independent acquisition MS data

Motivation: Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification. Results: We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples. Availability and implementation: DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet). Supplementary information: Supplementary data are available at Bioinformatics onlin

Intact salicylic acid signalling is required for potato defence against the necrotrophic fungus Alternaria solani.

Background 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. Results 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. Conclusions 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

Improved Label-Free LC-MS Analysis by Wavelet-Based Noise Rejection

Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification

In Vitro Evolution of Antibodies Inspired by In Vivo Evolution

In vitro generation of antibodies often requires variable domain sequence evolution to adapt the protein in terms of affinity, specificity, or developability. Such antibodies, including those that are of interest for clinical development, may have their origins in a diversity of immunoglobulin germline genes. Others and we have previously shown that antibodies of different origins tend to evolve along different, preferred trajectories. Apart from substitutions within the complementary determining regions, evolution may also, in a germline gene-origin-defined manner, be focused to residues in the framework regions, and even to residues within the protein core, in many instances at a substantial distance from the antibody’s antigen-binding site. Examples of such germline origin-defined patterns of evolution are described. We propose that germline gene-preferred substitution patterns offer attractive alternatives that should be considered in efforts to evolve antibodies intended for therapeutic use with respect to appropriate affinity, specificity, and product developability. We also hypothesize that such germline gene-origin-defined in vitro evolution hold potential to result in products with limited immunogenicity, as similarly evolved antibodies will be parts of conventional, in vivo-generated antibody responses and thus are likely to have been seen by the immune system in the past

An mTRAN-mRNA interaction mediates mitochondrial translation initiation in plants

Plant mitochondria represent the largest group of respiring organelles on the planet. Plant mitochondrial messenger RNAs (mRNAs) lack Shine-Dalgarno-like ribosome-binding sites, so it is unknown how plant mitoribosomes recognize mRNA. We show that “mitochondrial translation factors” mTRAN1 and mTRAN2 are land plant–specific proteins, required for normal mitochondrial respiration chain biogenesis. Our studies suggest that mTRANs are noncanonical pentatricopeptide repeat (PPR)–like RNA binding proteins of the mitoribosomal “small” subunit. We identified conserved Adenosine (A)/Uridine (U)-rich motifs in the 5′ regions of plant mitochondrial mRNAs. mTRAN1 binds this motif, suggesting that it is a mitoribosome homing factor to identify mRNAs. We demonstrate that mTRANs are likely required for translation of all plant mitochondrial mRNAs. Plant mitochondrial translation initiation thus appears to use a protein-mRNA interaction that is divergent from bacteria or mammalian mitochondria

A community proposal to integrate proteomics activities in ELIXIR

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on ‘The Future of Proteomics in ELIXIR’ that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes.   These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR’s existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper

Metabolic Engineering of Disaccharide Catabolism for Polysaccharide Formation in Lactococcus lactis and Streptococcus thermophilus

Exopolysaccharides (EPSs) produced by the lactic acid bacteria (LAB) Lactococcus lactis and Streptococcus thermophilus are important for the rheological behaviour and texture of a variety of fermented food products. Furthermore, EPSs from food-grade LAB have potential as food additives and functional food ingredients with both health and economic benefits. However, the production levels of EPSs from LAB are low, and this must be overcome before EPSs can be fully exploited. The objective of the work presented in this thesis was to improve the production of EPSs from disaccharides by L. lactis and S. thermophilus. The disaccharide metabolism and EPS biosynthesis of these organisms have been investigated by the construction of strains with different levels of central enzymes and the study of those strains under controlled growth conditions. The role of the enzyme beta-phosphoglucomutase (beta-PGM) was assessed in L. lactis by comparing a mutant lacking the enzyme with the parent strain. It was found that the enzyme was important for growth on maltose and essential for growth on trehalose. Furthermore, maltose-grown cells of the beta-PGM mutant accumulated polysaccharides. The predominant role of beta-PGM in trehalose metabolism was found to depend on another enzyme, trehalose 6-phosphate phosphorylase (TrePP). This novel enzyme, which phosphorylates trehalose 6-phosphate into beta-glucose 1-phosphate and glucose 6-phosphate, was characterised on both biochemical and genetical levels. Different EPS-producing strains of S. thermophilus were genetically typed, and the strains were found to group according to their EPS structure. The role of enzymes in the carbohydrate metabolism for EPS production in S. thermophilus was further investigated. First the pgmA gene, encoding phosphoglucomutase (PGM), was identified and cloned. Strains lacking PGM activity and those strains with elevated levels of PGM activity were found to produce similar levels of EPS. However, pgmA was essential for growth on glucose. The results implied that the Leloir pathway and the enzyme UDP-glucose pyrophosphorylase (GalU) were important for EPS production. A new semi-defined growth medium was developed and used for studies of strains with different levels of these enzymes. The Leloir pathway could be overexpressed in spontaneous mutants, while the gene encoding GalU had to be identified and cloned. The results indicate that simultaneous overexpression of PGM and GalU has a positive effect on EPS production. The highest EPS production could be achieved by inactivating pgmA and overexpressing the Leloir pathway. The results suggest ways of enhancing the EPS production with genetic engineering or through the natural selection of mutants