Methods in molecular biology
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Mass spectrometry-based phosphoproteomics is an indispensible technique used in the discovery and quantification of phosphorylation events on proteins in biological samples. The application of this technique to tissue samples is especially useful for the discovery of biomarkers as well as biological studies. We herein describe the application of a large-scale phosphoproteome analysis and SRM/MRM-based quantitation to develop a strategy for the systematic discovery and validation of biomarkers using tissue samples.
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Blast-induced neurotrauma (BINT) has increased in incidence over the past decades and can result in cognitive issues that have debilitating consequences. The exact primary and secondary mechanisms of injury have not been elucidated and appearance of cellular injury can vary based on many factors, such as blast overpressure magnitude and duration. Many methodologies to study blast neurotrauma have been employed, ranging from open-field explosives to experimental shock tubes for producing free-field blast waves. ⋯ While cellular injury mechanisms have been identified following blast exposure, the various experimental models present both concurrent and conflicting results. Furthermore, the temporal response and progression of pathology after blast exposure have yet to be detailed and remain unclear due to limited resemblance of methodologies. This chapter summarizes the current state of blast neuropathology and emphasizes the need for a standardized preclinical model of blast neurotrauma.
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Kinases play a pivotal role in propagating the phosphorylation-mediated signaling networks in living cells. With the overwhelming quantities of phosphoproteomics data being generated, the number of identified phosphorylation sites (phosphosites) is ever increasing. Often, proteomics investigations aim to understand the global signaling modulation that takes place in different biological conditions investigated. ⋯ These algorithms employ different approaches to predict kinase consensus sequence motifs, mostly based on large scale in vivo and in vitro experiments. The context of the kinase and the phosphorylated proteins in a biological system is equally important for predicting association between the enzymes and substrates, an aspect that is also being tackled with available bioinformatics tools. This chapter summarizes the use of the larger phosphorylation databases, and approaches that can be applied to predict kinases that phosphorylate individual sites or that are globally modulated in phosphoproteomics datasets.
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The goals of this chapter are to provide an introduction into the variety of animal models available for studying traumatic brain injury (TBI) and to provide a concise systematic review of the general materials and methods involved in each model. Materials and methods were obtained from a literature search of relevant peer-reviewed articles. Strengths and weaknesses of each animal choice were presented to include relative cost, anatomical and physiological features, and mechanism of injury desired. ⋯ Therefore, this chapter reflects a representative sampling of the TBI animal models available and is not an exhaustive comparison of every possible model and associated parameters. Throughout this chapter, special considerations for animal choice and TBI animal model classification are discussed. Criteria central to choosing appropriate animal models of TBI include ethics, funding, complexity (ease of use, safety, and controlled access requirements), type of model, model characteristics, and range of control (scope).
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Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. ⋯ Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.