Bupivacaine crystal deposits after long-term epidural infusion

The case of a 45-year-old male patient (body weight 52kg, height 1.61m) with a locally invasive gastric carcinoma infiltrating into the retroperitoneal space is reported. Because of severe cancer pain a tunnelled thoracic epidural catheter (EC) was placed at thoracic spinal level 7/8 and a local anesthetic (LA) mixture of bupivacaine 0.25 % and morphine 0.005 % was infused continuously at 6mlh−1. To optimize pain therapy the concentration was doubled (bupivacaine 0.5 %, morphine 0.01 %) 3 months later but the infusion rate was reduced to 3mlh−1 thus the total daily dose did not change. The patient died 6 months after initiation of the epidural analgesia from the underlying disease. The total amount of bupivacaine infused was 69g and of morphine 1.37g. The patient never reported any neurological complications. The autopsy revealed large white crystalline deposits in the thoracic epidural space which were identified as bupivacaine base by infrared spectrometry. Morphine could not be detected. A histological examination showed unreactive fatty tissue necrosis within the crystalline deposits but nerve tissue could not be identified. It is concluded that the bupivacaine crystalline deposits arose due to precipitation but the clinical significance with regard to sensory level and neuraxial tissue toxicity is unknow

Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays

The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation

<p>Abstract</p> <p>Background</p> <p>Crucial foundations of any quantitative systems biology experiment are correct genome and proteome annotations. Protein databases compiled from high quality empirical protein identifications that are in turn based on correct gene models increase the correctness, sensitivity, and quantitative accuracy of systems biology genome-scale experiments.</p> <p>Results</p> <p>In this manuscript, we present the <it>Drosophila melanogaster </it>PeptideAtlas, a fly proteomics and genomics resource of unsurpassed depth. Based on peptide mass spectrometry data collected in our laboratory the portal <url>http://www.drosophila-peptideatlas.org</url> allows querying fly protein data observed with respect to gene model confirmation and splice site verification as well as for the identification of proteotypic peptides suited for targeted proteomics studies. Additionally, the database provides consensus mass spectra for observed peptides along with qualitative and quantitative information about the number of observations of a particular peptide and the sample(s) in which it was observed.</p> <p>Conclusion</p> <p>PeptideAtlas is an open access database for the <it>Drosophila </it>community that has several features and applications that support (1) reduction of the complexity inherently associated with performing targeted proteomic studies, (2) designing and accelerating shotgun proteomics experiments, (3) confirming or questioning gene models, and (4) adjusting gene models such that they are in line with observed <it>Drosophila </it>peptides. While the database consists of proteomic data it is not required that the user is a proteomics expert.</p

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

<p>Abstract</p> <p>Background</p> <p>Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function.</p> <p>Results</p> <p>We experimentally annotated the bacterial pathogen <it>Salmonella </it>Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in <it>Salmonella </it>and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in <it>Salmonella </it>pathogenesis. We also characterized post-translational features in the <it>Salmonella </it>genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our <it>in vivo </it>proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function.</p> <p>Conclusion</p> <p>This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of <it>Salmonella </it>as a resource for systems analysis.</p

Capillary zone electrophoresis-tandem mass spectrometry with activated ion electron transfer dissociation for large-scale top-down proteomics

Capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) has been recognized as an efficient approach for top-down proteomics recently for its high-capacity separation and highly sensitive detection of proteoforms. However, the commonly used collision-based dissociation methods often cannot provide extensive fragmentation of proteoforms for thorough characterization. Activated ion electron transfer dissociation (AI-ETD), that combines infrared photoactivation concurrent with ETD, has shown better performance for proteoform fragmentation than higher energy-collisional dissociation (HCD) and standard ETD. Here, we present the first application of CZE-AI-ETD on an Orbitrap Fusion Lumos mass spectrometer for large-scale top-down proteomics of Escherichia coli (E. coli) cells. CZE-AI-ETD outperformed CZE-ETD regarding proteoform and protein identifications (IDs). CZE-AI-ETD reached comparable proteoform and protein IDs with CZE-HCD. CZE-AI-ETD tended to generate better expectation values (E values) of proteoforms than CZE-HCD and CZE-ETD, indicating a higher quality of MS/MS spectra from AI-ETD respecting the number of sequence-informative fragment ions generated. CZE-AI-ETD showed great reproducibility regarding the proteoform and protein IDs with relative standard deviations less than 4% and 2% (n = 3). Coupling size exclusion chromatography (SEC) to CZE-AI-ETD identified 3028 proteoforms and 387 proteins from E. coli cells with 1% spectrum level and 5% proteoform-level false discovery rates. The data represents the largest top-down proteomics dataset using the AI-ETD method so far. Single-shot CZE-AI-ETD of one SEC fraction identified 957 proteoforms and 253 proteins. N-terminal truncations, signal peptide cleavage, N-terminal methionine removal, and various post-translational modifications including protein N-terminal acetylation, methylation, S-thiolation, disulfide bonds, and lysine succinylation were detected