David M. Good

Urinary proteomic biomarkers in coronary artery disease

Urinary proteomics is emerging as a powerful non-invasive tool for diagnosis and monitoring of variety of human diseases. We tested whether signatures of urinary polypeptides can contribute to the existing biomarkers for coronary artery disease (CAD). We examined a total of 359 urine samples from 88 patients with severe CAD and 282 controls. Spot urine was analyzed using capillary electrophoresis on-line coupled to ESI-TOF-MS enabling characterization of more than 1000 polypeptides per sample. In a first step a “training set” for biomarker definition was created. Multiple biomarker patterns clearly distinguished healthy controls from CAD patients, and we extracted 15 peptides that define a characteristic CAD signature panel. In a second step, the ability of the CAD-specific panel to predict the presence of CAD was evaluated in a blinded study using a “test set.” The signature panel showed sensitivity of 98% (95% confidence interval, 88.7–99.6) and 83% specificity (95% confidence interval, 51.6–97.4). Furthermore the peptide pattern significantly changed toward the healthy signature correlating with the level of physical activity after therapeutic intervention. Our results show that urinary proteomics can identify CAD patients with high confidence and might also play a role in monitoring the effects of therapeutic interventions. The workflow is amenable to clinical routine testing suggesting that non-invasive proteomics analysis can become a valuable addition to other biomarkers used in cardiovascular risk assessment.

Identification and Validation of Urinary Biomarkers for Differential Diagnosis and Evaluation of Therapeutic Intervention in Anti-neutrophil Cytoplasmic Antibody-associated Vasculitis

Renal activity and smoldering disease is difficult to assess in anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) because of renal scarring. Even repeated biopsies suffer from sampling errors in this focal disease especially in patients with chronic renal insufficiency. We applied capillary electrophoresis coupled to mass spectrometry toward urine samples from patients with active renal AAV to identify and validate urinary biomarkers that enable differential diagnosis of disease and assessment of disease activity. The data were compared with healthy individuals, patients with other renal and non-renal diseases, and patients with AAV in remission. 113 potential biomarkers were identified that differed significantly between active renal AAV and healthy individuals and patients with other chronic renal diseases. Of these, 58 could be sequenced. Sensitivity and specificity of models based on 18 sequenced biomarkers were validated using blinded urine samples of 40 patients with different renal diseases. Discrimination of AAV from other renal diseases in blinded samples was possible with 90% sensitivity and 86.7–90% specificity depending on the model. 10 patients with active AAV were followed for 6 months after initiation of treatment. Immunosuppressive therapy led to a change of the proteome toward “remission.” 47 biomarkers could be sequenced that underwent significant changes during therapy together with regression of clinical symptoms, normalization of C-reactive protein, and improvement of renal function. Proteomics analysis with capillary electrophoresis-MS represents a promising tool for fast identification of patients with active AAV, indication of renal relapses, and monitoring for ongoing active renal disease and remission without renal biopsy.

The human urinary proteome reveals high similarity between kidney aging and chronic kidney disease.

Aging induces morphological changes of the kidney and reduces renal function. We analyzed the low molecular weight urinary proteome of 324 healthy individuals from 2–73 years of age to gain insight on human renal aging. We observed age‐related modification of secretion of 325 out of over 5000 urinary peptides. The majority of these changes were associated with renal development before and during puberty, while 49 peptides were related to aging in adults. We therefore focussed the remainder of the study on these 49 peptides. The majority of these 49 peptides were also markers of chronic kidney disease, suggesting high similarity between aging and chronic kidney disease. Blinded evaluation of samples from healthy volunteers and diabetic nephropathy patients confirmed both the correlation of biomarkers with aging and with renal disease. Identification of a number of these aging‐related peptides led us to hypothesize that reduced proteolytic activity is involved in human renal aging. Finally, among the 324 supposedly healthy individuals, some had urinary aging‐related peptide excretion patterns typical of an individual significantly older than their actual age. In conclusion, these aging‐related biomarkers may allow noninvasive detection of renal lesions in healthy persons and show high resemblance between human aging and chronic kidney disease. This similarity has to be taken into account when searching for biomarkers of renal disease.

Post-acquisition ETD spectral processing for increased peptide identifications

Tandem mass spectra (MS/MS) produced using electron transfer dissociation (ETD) differ from those derived from collision-activated dissociation (CAD) in several important ways. Foremost, the predominant fragment ion series are different: c- and z·-type ions are favored in ETD spectra while b- and y-type ions comprise the bulk of the fragments in CAD spectra. Additionally, ETD spectra possess charge-reduced precursors and unique neutral losses. Most database search algorithms were designed to analyze CAD spectra, and have only recently been adapted to accommodate c- and z·-type ions; therefore, inclusion of these additional spectral features can hinder identification, leading to lower confidence scores and decreased sensitivity. Because of this, it is important to pre-process spectral data before submission to a database search to remove those features that cause complications. Here, we demonstrate the effects of removing these features on the number of unique peptide identifications at a 1% false discovery rate (FDR) using the open mass spectrometry search algorithm (OMSSA). When analyzing two biologic replicates of a yeast protein extract in three total analyses, the number of unique identifications with a ∼1% FDR increased from 4611 to 5931 upon spectral pre-processing—an increase of ∼28. 6%. We outline the most effective pre-processing methods, and provide free software containing these algorithms.

Advancing proteomics with ion/ion chemistry.

Mass spectrometers, instruments that use electric and/or magnetic fields to measure a gas-phase ions mass-to-charge ratio (m/z), are used in a wide variety of applications--with the field having a reputation for providing good sensitivity and high-informing power. Protein analysis (proteomics) is a relatively recent affair for the field and was enabled in the late 1980s with the advent of biomolecule ionization methods such as electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI). Today, the area of protein analysis garners considerable attention from many in the mass spectrometry (MS) field; given the myriad of possible protein forms and their broad dynamic range (abundance) in the cell, the analytical challenge is paramount. Here we discuss a developing technology--ion/ion chemical reactions--that promises to transform how we think about and conduct protein sequence analysis via MS.

A New Probabilistic Database Search Algorithm for ETD Spectra

Peptide characterization using electron transfer dissociation (ETD) is an important analytical tool for protein identification. The fragmentation observed in ETD spectra is complementary to that seen when using the traditional dissociation method, collision activated dissociation (CAD). Applications of ETD enhance the scope and complexity of the peptides that can be studied by mass spectrometry-based methods. For example, ETD is shown to be particularly useful for the study of post-translationally modified peptides. To take advantage of the power provided by ETD, it is important to have an ETD-specific database search engine, an integral tool of mass spectrometry-based analytical proteomics. In this paper, we report on our development of a database search engine using ETD spectra and protein sequence databases to identify peptides. The search engine is based on the probabilistic modeling of shared peaks count and shared peaks intensity between the spectra and the peptide sequences. The shared peaks count accounts for the cumulative variations from amino acid sequences, while shared peaks intensity models the variations between the candidate sequence and product ion intensities. To demonstrate the utility of this algorithm for searching real-world data, we present the results of applications of this model to two high-throughput data sets. Both data sets were obtained from yeast whole cell lysates. The first data set was obtained from a sample digested by Lys-C, and the second data set was obtained by a digestion using trypsin. We searched the data sets against a combined forward and reversed yeast protein database to estimate false discovery rates. We compare the search results from the new methods with the results from a search engine often employed for ETD spectra, OMSSA. Our findings show that overall the new model performs comparably to OMSSA for low false discovery rates. At the same time, we demonstrate that there are substantial differences with OMSSA for results on subsets of data. Therefore, we conclude the new model can be considered as being complementary to previously developed models.

Adapting mass spectrometry-based platforms for clinical proteomics applications: The capillary electrophoresis coupled mass spectrometry paradigm

Single biomarker detection is common in clinical laboratories due to the currently available method spectrum. For various diseases, however, no specific single biomarker could be identified. A strategy to overcome this diagnostic void is to shift from single analyte detection to multiplexed biomarker profiling. Mass spectrometric methods were employed for biomarker discovery in body fluids. The enormous complexity of biofluidic proteome compartments implies upstream fractionation. For this reason, mass spectrometry (MS) was coupled to two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization, or capillary electrophoresis (CE). Differences in performance and operating characteristics make them differentially suited for routine laboratory applications. Progress in the field of clinical proteomics relies not only on the use of an adequate technological platform, but also on a fast and efficient proteomic workflow including standardized sample preparation, proteomic data processing, statistical validation of biomarker selection, and sample classification. Based on CE-MS analysis, we describe how proteomic technology can be implemented in a clinical laboratory environment. In the last part of this review, we give an overview of CE-MS-based clinical studies and present information on identity and biological significance of the identified peptide biomarkers providing evidence of disease-induced changes in proteolytic processing and posttranslational modification.

Methionine Deprivation Induces a Targetable Vulnerability in Triple-negative Breast Cancer Cells by Enhancing TRAIL Receptor-2 Expression

Purpose: Many neoplasms are vulnerable to methionine deficiency by mechanisms that are poorly understood. Because gene profiling studies have revealed that methionine depletion increases TNF-related apoptosis-inducing ligand receptor-2 (TRAIL-R2) mRNA, we postulated that methionine stress sensitizes breast cancer cells to proapoptotic TRAIL-R2 agonists. Experimental Design: Human triple (ER/PR/HER2)-negative breast carcinoma cell lines were cultured in control or methionine-free media. The effects of methionine depletion on TRAIL receptor expression and sensitivity to chemotherapy or a humanized agonistic TRAIL-R2 monoclonal antibody (lexatumumab) were determined. The melanoma-associated antigen MAGED2 was silenced to delineate its functional role in sensitizing TNBC cells to methionine stress. An orthotopic TNBC model was utilized to evaluate the effects of dietary methionine deficiency, lexatumumab, or the combination. Results: Methionine depletion sensitized TNBC cells to lexatumumab-induced caspase activation and apoptosis by increasing TRAIL-R2 mRNA and cell surface expression. MCF-10A cells transformed by oncogenic H-Ras, but not untransformed cells, and matrix-detached TNBC cells were highly sensitive to the combination of lexatumumab and methionine depletion. Proteomics analyses revealed that MAGED2, which has been reported to reduce TRAIL-R2 expression, was suppressed by methionine stress. Silencing MAGED2 recapitulated features of methionine deprivation, including enhanced mRNA and cell surface expression of TRAIL receptors and increased sensitivity to TRAIL receptor agonists. Dietary methionine deprivation enhanced the antitumor effects of lexatumumab in an orthotopic metastatic TNBC model. Conclusions: Methionine depletion exposes a targetable defect in TNBC cells by increasing TRAIL-R2 expression. Our findings provide the foundation for a clinical trial combining dietary methionine restriction and TRAIL-R2 agonists. Clin Cancer Res; 21(12); 2780–91. ©2015 AACR.

Increased Throughput of Proteomics Analysis by Multiplexing High-Resolution Tandem Mass Spectra

High-resolution and high-accuracy Fourier transform mass spectrometry (FTMS) is becoming increasingly attractive due to its specificity. However, the speed of tandem FTMS analysis severely limits the competitive advantage of this approach relative to faster low-resolution quadrupole ion trap MS/MS instruments. Here we demonstrate an entirely FTMS-based analysis method with a 2.5-3.0-fold greater throughput than a conventional FT MS/MS approach. The method consists of accumulating together the MS/MS fragments ions from multiple precursors, with subsequent high-resolution analysis of the mixture. Following acquisition, the multiplexed spectrum is deconvoluted into individual MS/MS spectra which are then combined into a single concatenated file and submitted for peptide identification to a search engine. The method is tested both in silico using a database of MS/MS spectra as well as in situ using a modified LTQ Orbitrap mass spectrometer. The performance of the method in the experiment was consistent with theoretical expectations.

Drug target identification from protein dynamics using quantitative pathway analysis.

Dynamic proteomics promises to greatly facilitate identification of target proteins for drug molecules. Cohen et al. [Science, 2008, 322 (5907), 1511-1516] illustrated this potential, with the responses of 812 fluorescently tagged proteins to camptothecin administration monitored over 48 h. Directly from this data, one can restrict the list of candidate targets to 52 proteins. However, this approach has numerous limitations: equipment, labor (tagging and analyzing ≥1 colony/protein), and data analysis (aggregating individual cell data into population-relevant data sets). Furthermore, analytical success requires both explicit knowledge of drug target time-course evolution and, most importantly, monitoring of the target, itself. To address these issues, we developed a quantitative pathway analysis (qPA) technique, which employs well-annotated signaling pathways and elucidates putative drug targets and other molecules of interest. qPA, using more general assumptions and only 3 out of 144 available time points, identified the single known camptothecin target, TOPI, among only a handful of putative targets. Importantly, identification was possible without containing TOPI within the input data. These results demonstrate the potential of qPA in drug target discovery and highlight the importance of systems biology approaches for analysis of proteomics data.