Analogues of bioactive peptides like glucagon-like peptide 1 (GLP-1) are emerging as prominent drugs for treatment of metabolic disorders such as diabetes and obesity. As a consequence of this, the analysis of endogenous peptides from tissues holds a great promise for drug discovery including the identification of new, bioactive peptides.
Neuropeptides are peptide hormones in the brain, which elicit key signalling responses that affect diverse behavioral end endocrine functions including weight homeostasis, pain and psychiatric disorders. Neuropeptide research is challenged by difficulties in identifying new bioactive neuropeptides, but the emergence of a new generation of high-performance mass spectrometers (MS) makes large-scale identification of endogenous peptides extracted from tissue samples possible, a strategy referred to as peptidomics.
This enables unbiased and explorative studies and in principle allows for the identification of post-translational modifications (PTMs) as well as previously undescribed neuropeptides. Analysis of peptidomes has so far been challenged by technical issues due to unspecific protease digestion during sample preparation and computational challenges in data analysis as well as difficulties in the biological interpretation.
This calls for development of new sample preparation methods and bioinformatic approaches to reliably identify new potential neuropeptides. Previous studies show that heat inactivation either performed by focused microwave irradiation, by heating the excised tissue in a conventional microwave oven or by specialized controlled-heating instruments largely prevents the production of proteolytic peptide fragments when compared with traditional protocols based on snap freezing. Furthermore, several strategies have been reported for identification of neuropeptides from complex and large data sets based on cleavage analysis and de novo sequencing.
Researchers describe a compilation of methods into a simple and robust analytic framework for extracting, analyzing and identifying endogenous peptides in rat brain. Different heat inactivation procedures were compared and combined with protease inhibitor perfusion of animals, to further retain intact peptides in the sample.
The peptidomes extracts were analysed by single-shot nanoflow liquid chromatography in line with high-resolution tandem mass spectrometry. A sequential, computational framework was developed to efficiently analyze the resulting large data set in a stringent approach minimizing errors of false peptide identifications.
As proof-of-concept, the methodology was applied to large-scale neuropeptide identification from rat hypothalamus resulting in thousands of identified neuropeptides. In addition, an abundance of PTMs on these peptides are identified, and these data are combined in a resource format for visualization, qualitative and quantitative analyses.
Peptidomics for large-scale neuropeptide identification
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