Differential mass spectrometry of rat plasma reveals proteins that are responsive to 17beta-estradiol and a selective estrogen receptor modulator PPT

Proteomic approach to discover human cancer viruses from formalin-fixed tissues

The challenge of discovering a completely new human tumor virus of unknown phylogeny or sequence depends on detecting viral molecules and differentiating them from host molecules in the virus-associated neoplasm. We developed differential peptide subtraction (DPS) using differential mass spectrometry (dMS) followed by targeted analysis to facilitate this discovery. We validated this approach by analyzing Merkel cell carcinoma (MCC), an aggressive human neoplasm, in which ~80% of cases are caused by the human Merkel cell polyomavirus (MCV). Approximately 20% of MCC have a high mutational burden and are negative for MCV, but are microscopically indistinguishable from virus positive cases. Using 23 (12 MCV⁺, 11 MCV^–) formalin-fixed MCC, DPS identified both viral and human biomarkers (MCV large T antigen, CDKN2AIP, SERPINB5, and TRIM29) that discriminate MCV⁺ and MCV^– MCC. Statistical analysis of 498,131 dMS features not matching the human proteome by DPS revealed 562 (0.11%) to be upregulated in virus-infected samples. Remarkably, 4 (20%) of the top 20 candidate MS spectra originated from MCV T oncoprotein peptides and confirmed by reverse translation degenerate oligonucleotide sequencing. DPS is a robust proteomic approach to identify potentially novel viral sequences in infectious tumors when nucleic acid–based methods are not feasible.

Differential peptide subtraction (DPS) is an application of differential mass-spectrometry followed by targeted analysis that can facilitate tumor virus and biomarker discovery.

High Resolution Discovery Proteomics Reveals Candidate Disease Progression Markers of Alzheimer's Disease in Human Cerebrospinal Fluid

Disease modifying treatments for Alzheimer’s disease (AD) constitute a major goal in medicine. Current trends suggest that biomarkers reflective of AD neuropathology and modifiable by treatment would provide supportive evidence for disease modification. Nevertheless, a lack of quantitative tools to assess disease modifying treatment effects remains a major hurdle. Cerebrospinal fluid (CSF) biochemical markers such as total tau, p-tau and Ab42 are well established markers of AD; however, global quantitative biochemical changes in CSF in AD disease progression remain largely uncharacterized. Here we applied a high resolution open discovery platform, dMS, to profile a cross-sectional cohort of lumbar CSF from post-mortem diagnosed AD patients versus those from non-AD/non-demented (control) patients. Multiple markers were identified to be statistically significant in the cohort tested. We selected two markers SME-1 (p<0.0001) and SME-2 (p = 0.0004) for evaluation in a second independent longitudinal cohort of human CSF from post-mortem diagnosed AD patients and age-matched and case-matched control patients. In cohort-2, SME-1, identified as neuronal secretory protein VGF, and SME-2, identified as neuronal pentraxin receptor-1 (NPTXR), in AD were 21% (p = 0.039) and 17% (p = 0.026) lower, at baseline, respectively, than in controls. Linear mixed model analysis in the longitudinal cohort estimate a decrease in the levels of VGF and NPTXR at the rate of 10.9% and 6.9% per year in the AD patients, whereas both markers increased in controls. Because these markers are detected by mass spectrometry without the need for antibody reagents, targeted MS based assays provide a clear translation path for evaluating selected AD disease-progression markers with high analytical precision in the clinic.

Quantitative analysis of apolipoproteins in human HDL by top-down differential mass spectrometry

The field of quantitative, label-free proteomics has evolved significantly over time, with most experiments performed "bottom-up" using proteolyzed protein mixtures. In these experiments, statistically significant peptide abundance differences between two or more experimental conditions are determined, and their corresponding proteins later identified. Recently, the rationale for extending this experimental design to mixtures of intact proteins has become clear, as analysis at the protein level allows for the independent detection of each protein form present, including those modified posttranslationally. This provides a level of specificity lost in bottom-up experiments. As such, the application of label-free top-down differential mass spectrometry has provided a means for understanding the subtle protein changes that define a particular phenotype. Described here is an approach for the top-down label-free quantitative analysis of the proteins which constitute human high-density lipoprotein particles. The methodology is conceptually very straightforward; however, it does require a level of rigor and consistency typically not addressed by more conventional proteomics experiments.

An exclusion list based label-free proteome quantification approach using an LTQ Orbitrap

RATIONALE

Label-based mass spectrometry is a powerful tool for large-scale protein identification and quantification. However, it requires the chemical or metabolic incorporation of the labeled compound(s) which can be difficult to attain, e.g. for non-cultivable organisms or scarce sample, such as biopsies. Therefore, we set out to develop and validate an efficient label-free liquid chromatography/tandem mass spectrometry (LC/MS/MS) workflow based on optimized instrument settings and incremental exclusion lists.

METHODS

To increase the number of quantified peptides an incremental exclusion list was incorporated along with optimized instrument settings for the used LTQ Orbitrap. As a proof of concept, label-free quantification data from this optimized approach were compared to the results of control measurements without exclusion lists and of an in vivo metabolic labeling GeLC/MS/MS experiment. The data were drawn from Staphylococcus aureus whole cell lysates of non-stressed and nitric oxide (NO)-stressed cells.

RESULTS

Compared to MS analysis without exclusion lists the new approach resulted in an increased number of identified peptides, enabling label-free quantification of more than 990 S. aureus proteins. With respect to the number of quantified proteins and differences in protein levels between the control and NO-treated samples the results of the new method were consistent with those of the GeLC/MS/MS experiment.

CONCLUSIONS

The application of exclusion lists and optimized instrument settings in LC/MS/MS analysis significantly enhances the sensitivity and resolution of label-free protein identification and quantification. Therefore, the new workflow is a powerful alternative to label-based quantification methods.

Quantitative statistical analysis of standard and human blood proteins from liquid chromatography, electrospray ionization, and tandem mass spectrometry

It will be important to determine if the parent and fragment ion intensity results of liquid chromatography, electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) experiments have been randomly and independently sampled from a normal population for the purpose of statistical analysis by general linear models and ANOVA. The tryptic parent peptide and fragment ion m/z and intensity data in the mascot generic files from LC-ESI-MS/MS of purified standard proteins, and human blood protein fractionated by partition chromatography, were parsed into a Structured Query Language (SQL) database and were matched with protein and peptide sequences provided by the X!TANDEM algorithm. The many parent and/or fragment ion intensity values were log transformed, tested for normality, and analyzed using the generic Statistical Analysis System (SAS). Transformation of both parent and fragment intensity values by logarithmic functions yielded intensity distributions that closely approximate the log-normal distribution. ANOVA models of the transformed parent and fragment intensity values showed significant effects of treatments, proteins, and peptides, as well as parent versus fragment ion types, with a low probability of false positive results. Transformed parent and fragment intensity values were compared over all sample treatments, proteins or peptides by the Tukey-Kramer Honestly Significant Difference (HSD) test. The approach provided a complete and quantitative statistical analysis of LC-ESI-MS/MS data from human blood.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Application of an end-to-end biomarker discovery platform to identify target engagement markers in cerebrospinal fluid by high resolution differential mass spectrometry

The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This approach, differential mass spectrometry (dMS), is based on the analysis of full scan mass spectrometry data. The dMS workflow does not require complex mixing and pooling strategies, or isotope labeling techniques. Accordingly, clinical samples can be analyzed individually, allowing the use of longitudinal designs and within-subject data analysis in which each subject acts as its own control. As a proof of concept, we performed multifactorial dMS analyses on CSF samples drawn at 6 time points from n = 6 cisterna magna ported (CMP) rhesus monkeys treated with 2 potent gamma secretase inhibitors (GSI) or comparable vehicle in a 3-way crossover study that included a total of 108 individual CSF samples. Using analysis of variance and statistical filtering on the aligned and normalized LC-MS data sets, we detected 26 features that were significantly altered in CSF by drug treatment. Of those 26 features, which belong to 10 distinct isotopic distributions, 20 were identified by MS/MS as 7 peptides from CD99, a cell surface protein. Six features from the remaining 3 isotopic distributions were not identified. A subsequent analysis showed that the relative abundance of these 26 features showed the same temporal profile as the ELISA measured levels of CSF A beta 42 peptide, a known pharmacodynamic marker for gamma-secretase inhibition. These data demonstrate that dMS is a promising approach for the discovery, quantification, and identification of candidate target engagement biomarkers in CSF.

Quantitative analysis of intact apolipoproteins in human HDL by top-down differential mass spectrometry

Top-down mass spectrometry holds tremendous potential for the characterization and quantification of intact proteins, including individual protein isoforms and specific posttranslationally modified forms. This technique does not require antibody reagents and thus offers a rapid path for assay development with increased specificity based on the amino acid sequence. Top-down MS is efficient whereby intact protein mass measurement, purification by mass separation, dissociation, and measurement of product ions with ppm mass accuracy occurs on the seconds to minutes time scale. Moreover, as the analysis is based on the accurate measurement of an intact protein, top-down mass spectrometry opens a research paradigm to perform quantitative analysis of "unknown" proteins that differ in accurate mass. As a proof of concept, we have applied differential mass spectrometry (dMS) to the top-down analysis of apolipoproteins isolated from human HDL(3). The protein species at 9415.45 Da demonstrates an average fold change of 4.7 (p-value 0.017) and was identified as an O-glycosylated form of apolipoprotein C-III [NANA-(2 --> 3)-Gal-beta(1 --> 3)-GalNAc, +656.2037 Da], a protein associated with coronary artery disease. This work demonstrates the utility of top-down dMS for quantitative analysis of intact protein mixtures and holds potential for facilitating a better understanding of HDL biology and complex biological systems at the protein level.

Proteomic Study of Hepatic Nuclear Extracts in an Adaptive Acetaminophen Tolerance Model

Introduction

Variability in response to acetaminophen (APAP)-induced aseptic inflammation and tolerance to the impending hepatic damage has been described. To understand the mechanism of adaptive tolerance, we investigated the proteomic profiles of crude nuclear lysates in a mouse model. We hypothesized that pretreatment with low doses of APAP prior to a toxic dose results in differential protein expression.

Materials and Methods

Mice (BALB/C) were separated into three groups: the pretreated (PT) group received incremental doses of APAP while the last dose only (LD) and naïve groups were given saline vehicle. A toxic dose of APAP was administered on the seventh day to the PT and LD animals only and all groups were euthanized 3 h postdose. Total protein from crude hepatic nuclear lysates were applied to protein arrays and analyzed by immunoaffinity mass spectrometry.

Results and Discussion

Comparative data analyses of protein peaks revealed a protein that was significantly increased at m/z of 60,030 (p60) in the LD animals vs the other two groups. The closest match for the preliminary identification of the p60 protein based on a Swiss-Prot/TagIdent database search using the approximate isoelectric point and molecular weight information was Ccr4–Not complex subunit-2. This protein is a subunit of a multiprotein complex and serves as a transcriptional suppressor involved in controlling mRNA synthesis and degradation. Preliminary identification was also supported by Western blot analysis using anti-CNOT2 antibody.

Conclusion

Considering the APAP tolerance model, we conclude that toxicogenomic approaches such as nuclear profiling are useful tools in assessing differential expression of transcriptional factors involved in inflammatory response and adaptive tolerance to toxins.