Evaluation of novel biomarkers of nephrotoxicity in Cynomolgus monkeys treated with gentamicin

Most studies to evaluate kidney safety biomarkers have been performed in rats. This study was conducted in Cynomolgus monkeys in order to evaluate the potential usefulness of novel biomarkers of nephrotoxicity in this species. Groups of 3 males were given daily intramuscular injections of gentamicin, a nephrotoxic agent known to produce lesions in proximal tubules, at dose-levels of 10, 25, or 50mg/kg/day for 10days. Blood and 16-h urine samples were collected on Days -7, -3, 2, 4, 7, and at the end of the dosing period. Several novel kidney safety biomarkers were evaluated, with single- and multiplex immunoassays and in immunoprecipitation-LC/MS assays, in parallel to histopathology and conventional clinical pathology parameters. Treatment with gentamicin induced a dose-dependent increase in kidney tubular cell degeneration/necrosis, ranging from minimal to mild severity at 10mg/kg/day, moderate at 25mg/kg/day, and to severe at 50mg/kg/day. The results showed that the novel urinary biomarkers, microalbumin, α1-microglobulin, clusterin, and osteopontin, together with the more traditional clinical pathology parameters, urinary total protein and N-acetyl-β-D-glucosaminidase (NAG), were more sensitive than blood urea nitrogen (BUN) and serum creatinine (sCr) to detect kidney injury in the monkeys given 10mg/kg/day gentamicin for 10days, a dose leading to an exposure which is slightly higher than the desired therapeutic exposure in clinics. Therefore, these urinary biomarkers represent non-invasive biomarkers of proximal tubule injury in Cynomolgus monkeys which may be potentially useful in humans.

Quantitative LC-MS determination of liposomal encapsulated prednisolone phosphate and non-encapsulated prednisolone concentrations in murine whole blood and liver tissue

The underlying pharmacokinetic profile of liposomal drug delivery systems is not yet fully known. This is primarily due to a lack of suitable quantitative bioanalytical methodology to simultaneously determine separate liposomal-encapsulated and non-encapsulated drug tissue concentrations in complex biological samples. Here, an LC-MS method was developed which enables the simultaneous quantification of separate liposomal-encapsulated prednisolone phosphate and non-encapsulated prednisolone concentrations in whole blood and liver tissue. Liquid chromatography, negative electrospray ionization and Orbitrap-MS analysis allowed highly accurate and sensitive detection of prednisolone phosphate (PP) and prednisolone (P) in complex matrix. Using dexamethasone phosphate and dexamethasone as internal standards, the quantitative LC-MS method was optimized and validated for high selectivity, sensitivity and quantitative accuracy of PP and P from liposomes. The lower limits of quantitation were 0.99μmol/L blood and 0.53nmol/g liver for PP, and 229nmol/L blood and 0.514nmol/g liver for P. Quantitative accuracies of 84-118% were observed. The intra-run precision was ≤11%. Application of this new LC-MS method will yield the first liposomal pharmacokinetic profile showing accurate encapsulated and non-encapsulated drug tissue concentrations separately. This is also the first quantitative LC-MS method for the simultaneous quantification of the prodrug PP and its parent drug P in whole blood and liver tissue samples.

Indirect protein quantification of drug-transforming enzymes using peptide group-specific immunoaffinity enrichment and mass spectrometry

Immunoaffinity enrichment of proteotypic peptides, coupled with selected reaction monitoring, enables indirect protein quantification. However the lack of suitable antibodies limits its widespread application. We developed a method in which multi-specific antibodies are used to enrich groups of peptides, thus facilitating multiplexed quantitative protein assays. We tested this strategy in a pharmacokinetic experiment by targeting a group of homologous drug transforming proteins in human hepatocytes. Our results indicate the generic applicability of this method to any biological system.

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson's disease

Background

The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson’s disease, the relevance of this cellular model in the context of Parkinson’s disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated.

Results

We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD.

Conclusions

The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson’s disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer’s disease but lower than for Huntington’s disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1154) contains supplementary material, which is available to authorized users.

Paleoproteomic study of the Iceman's brain tissue

The Tyrolean Iceman, a Copper-age ice mummy, is one of the best-studied human individuals. While the genome of the Iceman has largely been decoded, tissue-specific proteomes have not yet been investigated. We studied the proteome of two distinct brain samples using gel-based and liquid chromatography-mass spectrometry-based proteomics technologies together with a multiple-databases and -search algorithms-driven data-analysis approach. Thereby, we identified a total of 502 different proteins. Of these, 41 proteins are known to be highly abundant in brain tissue and 9 are even specifically expressed in the brain. Furthermore, we found 10 proteins related to blood and coagulation. An enrichment analysis revealed a significant accumulation of proteins related to stress response and wound healing. Together with atomic force microscope scans, indicating clustered blood cells, our data reopens former discussions about a possible injury of the Iceman's head near the site where the tissue samples have been extracted.

Improved reporter ion assignment of raw isobaric stable isotope labeled liquid chromatography/matrix-assisted laser desorption/ionization tandem time-of-flight mass spectral data for quantitative proteomics

RATIONALE

Isobaric labeling strategies (e.g. iTRAQ or TMT) are commonly applied in tandem mass spectrometric (MS/MS) level quantitative proteomics. However, we frequently observed missing isotope reporter ion signals in a large-scale liquid chromatography/matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometric (LC/MALDI-TOF/TOF) quantitative proteomics experiment. To understand this issue, we systematically investigated the processing of MS/MS spectra into peak lists prior to peptide identification and quantification.

METHODS

A 15-protein standard, with six proteins in different concentrations, was labeled with iTRAQ 4-plex, iTRAQ 8-plex or TMT 6-plex, tryptic digested and measured using LC/MALDI-TOF/TOF. Three commercially and open-source available peak list generation software tools were compared based on missing reporter ions, peptide identification and quantification.

RESULTS

We found that each tool discarded lower-intensity reporter ions, when they followed a higher intensity reporter ion, due to the implemented de-isotoping algorithms. By using the non-de-isotoping setting within TS2Mascot, we found that all reporter ions are exported, yet less peptides were identified with Mascot. Therefore, we developed a strategy merging the de-isotoped and non-de-isotoped outputs from TS2Mascot using the Perl script RICmerge.pl.

CONCLUSIONS

With this approach, we correctly quantified all labeled peptides that were identified within the 15-protein standard. This strategy allows improved annotation of isobaric tag labeled peptide MS/MS spectra and improves downstream peptide and protein quantification in proteomics studies.

Mass spectrometry-based proteomics strategies for protease cleavage site identification

Protease-catalyzed hydrolysis of peptide bonds is one of the most pivotal post-translational modifications fulfilling manifold functions in the regulation of cellular processes. Therefore, dysregulation of proteolytic reactions plays a central role in many pathophysiological events. For this reason, understanding the molecular mechanisms in proteolytic reactions, in particular the knowledge of proteases involved in complex processes, expression levels and activity of protease and knowledge of the targeted substrates are an indispensable prerequisite for targeted drug development. The present review focuses on mass spectrometry-based proteomic methods for the analysis of protease cleavage sites, including the identification of the hydrolyzed bonds as well as of the surrounding sequence. Peptide- and protein-centric approaches and bioinformatic tools for experimental data interpretation will be presented and the major advantages and drawbacks of the different approaches will be addressed. The recent applications of these approaches for the analysis of biological function of different protease classes and potential future directions will be discussed.

Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data

The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek to draw meaningful biological conclusions from their microarray results. Gene annotation enrichment analysis is a functional analysis technique that has gained widespread attention and for which many tools have been developed. Unfortunately, most of these tools have limited support for agricultural species. Here, we evaluate and compare four publicly available computational tools (Onto-Express, EasyGO, GOstat, and DAVID) that support analysis of gene expression datasets in agricultural species. We use AgBase as the functional annotation reference for agricultural species. The selected tools were evaluated based on i) available features, usage and accessibility, ii) implemented statistical computational methods, and iii) annotation and enrichment performance analysis. Annotation was assessed using a randomly selected test gene annotation set and an experimental differentially expressed gene-set – both from chicken. The experimental set was also used to evaluate identification of enriched functional groups.

Comparison of the tools shows that they produce different sets of annotations for the two datasets and different functional groups for the experimental dataset. While DAVID, GOstat and Onto-Express annotate comparable numbers of genes, DAVID provides by far the most annotations per gene. However, many of DAVID's annotations appear to be redundant or are at very high levels in the GO hierarchy. The GOSlim distribution of annotations shows that GOstat, Onto-Express and EasyGO provide similar GO distributions to those found in AgBase while annotations from DAVID show a different GOSlim distribution, again probably due to duplication and many non-specific terms. No consistent trends were found in results of GO term over/under representation analysis applied to the experimental data using different tools. While GOstat, David and Onto-Express could retrieve some significantly enriched terms, EasyGO did not show any significantly enriched terms. There was little agreement about the enriched terms identified by the tools.

Conclusion

Different tools for functionally annotating gene sets and identifying significantly enriched GO categories differ widely in their results when applied to a test annotation gene set and an experimental dataset from chicken. These results emphasize the need for care when interpreting the results of such analysis and the lack of standardization of approaches.

Non-electrophoretic differential detergent fractionation proteomics using frozen whole organs

Non-electrophoretic methods based on two-dimensional liquid chromatography followed directly by tandem mass spectrometry (2D-LC/MS(2)) have become the preferred method for high-throughput expression proteomics and are widely applied to fresh tissues. Pre-fractionation techniques are also used in combination with 2D-LC/MS(2) to both increase the proteome size and to assign cellular locations. Data from such experiments have become central to systems biology analyses. Here we apply a differential detergent (pre)fractionation (DDF) followed by 2D-LC/MS(2) to frozen archival tissues. Our results show that by using frozen archival tissues, we do not lose proteome coverage or the ability to assign proteins to cellular compartments. In addition, we were able to assign 'biological process' Gene Ontology (GO) annotations, which will facilitate systems biological modeling of our proteomics data.

Differential detergent fractionation for non-electrophoretic eukaryote cell proteomics

Differential detergent fractionation (DDF), which relies on detergents to sequentially extract proteins from eukaryotic cells, has been used to increase proteome coverage of 2D-PAGE. Here, we used DDF extraction in conjunction with the nonelectrophoretic proteomics method of liquid chromatography and electrospray ionization tandem mass spectrometry. We demonstrate that DDF can be used with 2D-LC ESI MS2 for comprehensive cellular proteomics, including a large proportion of membrane proteins. Compared to some published methods designed to isolate membrane proteins specifically, DDF extraction yields comprehensive proteomes which include twice as many membrane proteins. Two-thirds of these membrane proteins have more than one trans-membrane domain. Since DDF separates proteins based upon their physicochemistry and subcellular localization, this method also provides data useful for functional genome annotation. As more genome sequences are completed, methods which can aid in functional annotation will become increasingly important.