Robust, Sensitive, and Automated Phosphopeptide Enrichment Optimized for Low Sample Amounts Applied to Primary Hippocampal Neurons.

Because of the low stoichiometry of protein phosphorylation, targeted enrichment prior to LC-MS/MS analysis is still essential. The trend in phosphoproteome analysis is shifting toward an increasing number of biological replicates per experiment, ideally starting from very low sample amounts, placing new demands on enrichment protocols to make them less labor-intensive, more sensitive, and less prone to variability. Here we assessed an automated enrichment protocol using Fe(III)-IMAC cartridges on an AssayMAP Bravo platform to meet these demands. The automated Fe(III)-IMAC-based enrichment workflow proved to be more effective when compared to a TiO2-based enrichment using the same platform and a manual Ti(IV)-IMAC-based enrichment workflow. As initial samples, a dilution series of both human HeLa cell and primary rat hippocampal neuron lysates was used, going down to 0.1 µg of peptide starting material. The optimized workflow proved to be efficient, sensitive, and reproducible, identifying, localizing, and quantifying thousands of phosphosites from just micrograms of starting material. To further test the automated workflow in genuine biological applications, we monitored EGF-induced signaling in hippocampal neurons, starting with only 200 000 primary cells, resulting in ∼50 µg of protein material. This revealed a comprehensive phosphoproteome, showing regulation of multiple members of the MAPK pathway and reduced phosphorylation status of two glutamate receptors involved in synaptic plasticity.

Pathomx: an interactive workflow-based tool for the analysis of metabolomic data.

BACKGROUND: Metabolomics is a systems approach to the analysis of cellular processes through small-molecule metabolite profiling. Standardisation of sample handling and acquisition approaches has contributed to reproducibility. However, the development of robust methods for the analysis of metabolomic data is a work-in-progress. The tools that do exist are often not well integrated, requiring manual data handling and custom scripting on a case-by-case basis. Furthermore, existing tools often require experience with programming environments such as MATLAB® or R to use, limiting accessibility. Here we present Pathomx, a workflow-based tool for the processing, analysis and visualisation of metabolomic and associated data in an intuitive and extensible environment. RESULTS: The core application provides a workflow editor, IPython kernel and a HumanCyc™-derived database of metabolites, proteins and genes. Toolkits provide reusable tools that may be linked together to create complex workflows. Pathomx is released with a base set of plugins for the import, processing and visualisation of data. The IPython backend provides integration with existing platforms including MATLAB® and R, allowing data to be seamlessly transferred. Pathomx is supplied with a series of demonstration workflows and datasets. To demonstrate the use of the software we here present an analysis of 1D and 2D (1)H NMR metabolomic data from a model system of mammalian cell growth under hypoxic conditions. CONCLUSIONS: Pathomx is a useful addition to the analysis toolbox. The intuitive interface lowers the barrier to entry for non-experts, while scriptable tools and integration with existing tools supports complex analysis. We welcome contributions from the community.

Metabolic profiling predicts response to anti-tumor necrosis factor α therapy in patients with rheumatoid arthritis.

OBJECTIVE: Anti-tumor necrosis factor (anti-TNF) therapies are highly effective in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), but a significant number of patients exhibit only a partial or no therapeutic response. Inflammation alters local and systemic metabolism, and TNF plays a role in this. We undertook this study to determine if the patient's metabolic fingerprint prior to therapy could predict responses to anti-TNF agents. METHODS: Urine was collected from 16 RA patients and 20 PsA patients before and during therapy with infliximab or etanercept. Urine metabolic profiles were assessed using nuclear magnetic resonance spectroscopy. Discriminating metabolites were identified, and the relationship between metabolic profiles and clinical outcomes was assessed. RESULTS: Baseline urine metabolic profiles discriminated between RA patients who did or did not have a good response to anti-TNF therapy according to European League Against Rheumatism criteria, with a sensitivity of 88.9% and a specificity of 85.7%, with several metabolites contributing (in particular histamine, glutamine, xanthurenic acid, and ethanolamine). There was a correlation between baseline metabolic profiles and the magnitude of change in the Disease Activity Score in 28 joints from baseline to 12 months in RA patients (P = 0.04). In both RA and PsA, urinary metabolic profiles changed between baseline and 12 weeks of anti-TNF therapy. Within the responders, urinary metabolite changes distinguished between etanercept and infliximab treatment. CONCLUSION: The clear relationship between urine metabolic profiles of RA patients at baseline and their response to anti-TNF therapy may allow development of novel approaches to the optimization of therapy. Differences in metabolic profiles during treatment with infliximab and etanercept in RA and PsA may reflect distinct mechanisms of action.