Chemical cross-linking in combination with mass spectrometric analysis offers the potential to obtain low-resolution structural information from proteins and protein complexes. Identification of peptides connected by a cross-link provides direct evidence for the physical interaction of amino acid side chains, information that can be used for computational modeling purposes. Despite impressive advances that were made in recent years, the number of experimentally observed cross-links still falls below the number of possible contacts of cross-linkable side chains within the span of the cross-linker. Here, we propose two complementary experimental strategies to expand cross-linking data sets. First, enrichment of cross-linked peptides by size exclusion chromatography selects cross-linked peptides based on their higher molecular mass, thereby depleting the majority of unmodified peptides present in proteolytic digests of cross-linked samples. Second, we demonstrate that the use of proteases in addition to trypsin, such as Asp-N, can additionally boost the number of observable cross-linking sites. The benefits of both SEC enrichment and multiprotease digests are demonstrated on a set of model proteins and the improved workflow is applied to the characterization of the 20S proteasome from rabbit and Schizosaccharomyces pombe.

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BackgroundThe rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity.ResultsWe have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis.ConclusionsOur findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12989-016-0160-6) contains supplementary material, which is available to authorized users.

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