Assessing Susceptibility to Age-related Macular Degeneration with Proteomic and Genomic Biomarkers

Age-related macular degeneration (AMD) is a progressive disease and major cause of severe visual loss. Toward the discovery of tools for early identification of AMD susceptibility, we evaluated the combined predictive capability of proteomic and genomic AMD biomarkers. We quantified plasma carboxyethylpyrrole (CEP) oxidative protein modifications and CEP autoantibodies by ELISA in 916 AMD and 488 control donors. CEP adducts are uniquely generated from oxidation of docosahexaenoate-containing lipids that are abundant in the retina. Mean CEP adduct and autoantibody levels were found to be elevated in AMD plasma by ∼60 and ∼30%, respectively. The odds ratio for both CEP markers elevated was 3-fold greater or more in AMD than in control patients. Genotyping was performed for AMD risk polymorphisms associated with age-related maculopathy susceptibility 2 (ARMS2), high temperature requirement factor A1 (HTRA1), complement factor H, and complement C3, and the risk of AMD was predicted based on genotype alone or in combination with the CEP markers. The AMD risk predicted for those exhibiting elevated CEP markers and risk genotypes was 2–3-fold greater than the risk based on genotype alone. AMD donors carrying the ARMS2 and HTRA1 risk alleles were the most likely to exhibit elevated CEP markers. The results compellingly demonstrate higher mean CEP marker levels in AMD plasma over a broad age range. Receiver operating characteristic curves suggest that CEP markers alone can discriminate between AMD and control plasma donors with ∼76% accuracy and in combination with genomic markers provide up to ∼80% discrimination accuracy. Plasma CEP marker levels were altered slightly by several demographic and health factors that warrant further study. We conclude that CEP plasma biomarkers, particularly in combination with genomic markers, offer a potential early warning system for assessing susceptibility to this blinding, multifactorial disease.Age-related macular degeneration (AMD)¹ is the most common cause of legal blindness in the elderly in developed countries (1). It is a complex, progressive disease involving multiple genetic and environmental factors that can result in severe visual loss. Early risk factors include the macular deposition of debris (drusen) on Bruch membrane, the extracellular matrix separating the choriocapillaris from the retinal pigment epithelium (RPE). Later stages of “dry” AMD involve the degeneration of photoreceptor and RPE cells resulting in geographic atrophy. In “wet” AMD, abnormal blood vessels grow from the choriocapillaris through Bruch membrane (choroidal neovascularization (CNV)). CNV occurs in 10–15% of AMD cases yet accounts for over 80% of debilitating visual loss in AMD. Anti-vascular endothelial growth factor treatments can effectively inhibit the progression of CNV (1), and antioxidant vitamins and zinc can slow dry AMD progression for select individuals (2). However, there are no universally effective therapies for the prevention of dry AMD or the progression from dry to wet AMD nor are there therapies to repair retinal damage in advanced AMD. The prevalence of advanced AMD in the United States is projected to increase by 50% to ∼3 million by the year 2020 largely because of the rapidly growing elderly population (3). Accordingly early identification of AMD susceptibility and implementation of preventive measures are important therapeutic strategies (1).The molecular mechanisms causing AMD remain unknown, although inflammatory processes have been implicated by the identification of AMD susceptibility genes encoding complement factors (4–10) and the presence of complement proteins in drusen (11–13). Oxidative stress has long been associated with AMD pathology as shown by the finding that smoking significantly increases the risk of AMD (14) and that antioxidant vitamins can selectively slow AMD progression (2). A direct molecular link between oxidative damage and AMD was established by the finding that carboxyethylpyrrole (CEP), an oxidative protein modification generated from docosahexaenoate (DHA)-containing phospholipids, was elevated in Bruch membrane and drusen from AMD patients (11). Subsequently CEP adducts as well as CEP autoantibodies were found to be elevated in plasma from AMD donors (15), and CEP adducts were found to stimulate neovascularization in vivo, suggesting a role in the induction of CNV (16). From such observations, oxidative protein modifications were hypothesized to serve as catalysts of AMD pathology (11, 15, 17). In support of this hypothesis, mice immunized with CEP-adducted mouse albumin develop a dry AMD-like phenotype that includes sub-RPE deposits resembling drusen and RPE lesions mimicking geographic atrophy (18).Although identified AMD susceptibility genes account for over half of AMD cases (19), many individuals carrying AMD risk genotypes may never develop the disease. Likewise only a fraction of those diagnosed with early AMD progress to advanced stage disease with severe visual loss (2). Toward the discovery of better methods to predict susceptibility to advanced AMD, we quantified CEP adducts and autoantibodies in over 1400 plasma donors and also genotyped many of these donors for AMD risk polymorphisms in complement factor H (CFH) (4–7), complement C3 (9, 10), age-related maculopathy susceptibility 2 (ARMS2; also known as LOC387715) (19–22), and high temperature requirement factor A1 (HTRA1) (23, 24). The results demonstrate that combined CEP proteomic and genomic biomarker measurements are more effective in assessing AMD risk than either method alone.     

LC/MS-Based Quantitative Proteomic Analysis of Paraffin-Embedded Archival Melanomas Reveals Potential Proteomic Biomarkers Associated with Metastasis

Background

Melanoma metastasis status is highly associated with the overall survival of patients; yet, little is known about proteomic changes during melanoma tumor progression. To better understand the changes in protein expression involved in melanoma progression and metastasis, and to identify potential biomarkers, we conducted a global quantitative proteomic analysis on archival metastatic and primary melanomas.

Methodology and Findings

A total of 16 metastatic and 8 primary cutaneous melanomas were assessed. Proteins were extracted from laser captured microdissected formalin fixed paraffin-embedded archival tissues by liquefying tissue cells. These preparations were analyzed by a LC/MS-based label-free protein quantification method. More than 1500 proteins were identified in the tissue lysates with a peptide ID confidence level of >75%. This approach identified 120 significant changes in protein levels. These proteins were identified from multiple peptides with high confidence identification and were expressed at significantly different levels in metastases as compared with primary melanomas (q-Value<0.05).

Conclusions and Significance

The differentially expressed proteins were classified by biological process or mapped into biological system networks, and several proteins were implicated by these analyses as cancer- or metastasis-related. These proteins represent potential biomarkers for tumor progression. The study successfully identified proteins that are differentially expressed in formalin fixed paraffin-embedded specimens of metastatic and primary melanoma.     

Proteomic Profiling of Exosomes Leads to the Identification of Novel Biomarkers for Prostate Cancer

Background

Current markers for prostate cancer, such as PSA lack specificity. Therefore, novel biomarkers are needed. Unfortunately, the complexity of body fluids often hampers biomarker discovery. An attractive alternative approach is the isolation of small vesicles, i.e. exosomes, ∼100 nm, which contain proteins that are specific to the tissue from which they are derived and therefore can be considered as treasure chests for disease-specific biomarker discovery.

Materials and Methods

Exosomes were isolated from 2 immortalized primary prostate epithelial cells (PNT2C2 and RWPE-1) and 2 PCa cell lines (PC346C and VCaP) by ultracentrifugation. After tryptic digestion, proteomic analyses utilized a nanoLC coupled with an LTQ-Orbitrap operated in tandem MS (MS/MS) mode. Accurate Mass and Time (AMT) tag approach was employed for peptide identification and quantitation. Candidate biomarkers were validated by Western blotting and Immunohistochemistry.

Results

Proteomic characterization resulted in the identification of 248, 233, 169, and 216 proteins by at least 2 peptides in exosomes from PNT2C2, RWPE-1, PC346C, and VCaP, respectively. Statistical analyses revealed 52 proteins differently abundant between PCa and control cells, 9 of which were more abundant in PCa. Validation by Western blotting confirmed a higher abundance of FASN, XPO1 and PDCD6IP (ALIX) in PCa exosomes.

Conclusions

Identification of exosomal proteins using high performance LC-FTMS resulted in the discovery of PDCD6IP, FASN, XPO1 and ENO1 as new candidate biomarkers for prostate cancer.     

Proteomic Profiling in Multiple Sclerosis Clinical Courses Reveals Potential Biomarkers of Neurodegeneration

The aim of our project was to perform an exploratory analysis of the cerebrospinal fluid (CSF) proteomic profiles of Multiple Sclerosis (MS) patients, collected in different phases of their clinical course, in order to investigate the existence of peculiar profiles characterizing the different MS phenotypes. The study was carried out on 24 Clinically Isolated Syndrome (CIS), 16 Relapsing Remitting (RR) MS, 11 Progressive (Pr) MS patients. The CSF samples were analysed using the Matrix Assisted Laser Desorption Ionisation Time Of Flight (MALDI-TOF) mass spectrometer in linear mode geometry and in delayed extraction mode (m/z range: 1000–25000 Da). Peak lists were imported for normalization and statistical analysis. CSF data were correlated with demographic, clinical and MRI parameters. The evaluation of MALDI-TOF spectra revealed 348 peak signals with relative intensity ≥1% in the study range. The peak intensity of the signals corresponding to Secretogranin II and Protein 7B2 were significantly upregulated in RRMS patients compared to PrMS (p<0.05), whereas the signals of Fibrinogen and Fibrinopeptide A were significantly downregulated in CIS compared to PrMS patients (p<0.04). Additionally, the intensity of the Tymosin β4 peak was the only signal to be significantly discriminated between the CIS and RRMS patients (p = 0.013). Although with caution due to the relatively small size of the study populations, and considering that not all the findings remained significant after adjustment for multiple comparisons, in our opinion this mass spectrometry evaluation confirms that this technique may provide useful and important information to improve our understanding of the complex pathogenesis of MS.     

Identification of Potential Serum Proteomic Biomarkers for Clear Cell Renal Cell Carcinoma

Objective

To investigate discriminating protein patterns and serum biomarkers between clear cell renal cell carcinoma (ccRCC) patients and healthy controls, as well as between paired pre- and post-operative ccRCC patients.

Methods

We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with ccRCC. A total of 162 serum samples were analyzed in this study, among which there were 58 serum samples from ccRCC patients, 40 from additional paired pre- and post-operative ccRCC patients (n = 20), and 64 from healthy volunteers as healthy controls. ClinProTools software identified several distinct markers between ccRCC patients and healthy controls, as well as between pre- and post-operative patients.

Results

Patients with ccRCC could be identified with a mean sensitivity of 88.38% and a mean specificity of 91.67%. Of 67 m/z peaks that differed among the ccRCC, healthy controls, pre- and post-operative ccRCC patients, 24 were significantly different (P<0.05). Three candidate peaks, which were upregulated in ccRCC group and showed a tendency to return to healthy control values after surgery, were identified as peptide regions of RNA-binding protein 6 (RBP6), tubulin beta chain (TUBB), and zinc finger protein 3 (ZFP3) with the m/z values of 1466.98, 1618.22, and 5905.23, respectively.

Conclusion

MB-MALDI-TOF-MS method could generate serum peptidome profiles of ccRCC, and provide a new approach to identify potential biomarkers for diagnosis as well as prognosis of this malignancy.     

Proteomic Characterization of Colorectal Cancer Tissue from Patients Identifies Novel Putative Protein Biomarkers

Colorectal cancer (CRC) is one of the leading causes of cancer-related death over the world. There is a great need for biomarkers capable of early detection and as targets for treatment. Differential protein expression was investigated with two-dimensional gel electrophoresis (2D-PAGE) followed by identification with liquid chromatography–tandem mass spectrometry (LC-MS/MS) in CRC patient tissue from (i) the peripheral part of the tumor, (ii) the central part of the tumor as well as from (iii) a non-involved part of the colorectal tissue. The expression patterns of six identified proteins were further evaluated by one-dimensional Western blot (1D-WB) analysis of the CRC tissue. Proteins that were perturbed in expression level in the peripheral or in the central part of the tumor as compared with the non-involved part included S100A11, HNRNPF, HNRNPH1 or HNRNPH2, GSTP1, PKM and FABP1. These identified markers may have future diagnostic potential or may be novel treatment targets after further evaluation in larger patient cohorts.     

Addressing the Grand Challenge of atmospheric carbon dioxide: geologic sequestration vs. biological recycling

On February 15, 2008, the National Academy of Engineering unveiled their list of 14 Grand Challenges for Engineering. Building off of tremendous advancements in the past century, these challenges were selected for their role in assuring a sustainable existence for the rapidly increasing global community. It is no accident that the first five Challenges on the list involve the development of sustainable energy sources and management of environmental resources. While the focus of this review is to address the single Grand Challenge of "develop carbon sequestration methods", is will soon be clear that several other Challenges are intrinsically tied to it through the principles of sustainability. How does the realm of biological engineering play a role in addressing these Grand Challenges?     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

Defining a Conformational Consensus Motif in Cotransin-Sensitive Signal Sequences: A Proteomic and Site-Directed Mutagenesis Study

The cyclodepsipeptide cotransin was described to inhibit the biosynthesis of a small subset of proteins by a signal sequence-discriminatory mechanism at the Sec61 protein-conducting channel. However, it was not clear how selective cotransin is, i.e. how many proteins are sensitive. Moreover, a consensus motif in signal sequences mediating cotransin sensitivity has yet not been described. To address these questions, we performed a proteomic study using cotransin-treated human hepatocellular carcinoma cells and the stable isotope labelling by amino acids in cell culture technique in combination with quantitative mass spectrometry. We used a saturating concentration of cotransin (30 micromolar) to identify also less-sensitive proteins and to discriminate the latter from completely resistant proteins. We found that the biosynthesis of almost all secreted proteins was cotransin-sensitive under these conditions. In contrast, biosynthesis of the majority of the integral membrane proteins was cotransin-resistant. Cotransin sensitivity of signal sequences was neither related to their length nor to their hydrophobicity. Instead, in the case of signal anchor sequences, we identified for the first time a conformational consensus motif mediating cotransin sensitivity.     

Assessment of Metabolomic and Proteomic Biomarkers in Detection and Prognosis of Progression of Renal Function in Chronic Kidney Disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m²; n = 10) or advanced (8.9±4.5 mL/min/1.73 m²; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.     

Identification of Novel Translational Urinary Biomarkers for Acetaminophen-Induced Acute Liver Injury Using Proteomic Profiling in Mice

Drug-induced liver injury (DILI) is the leading cause of acute liver failure. Currently, no adequate predictive biomarkers for DILI are available. This study describes a translational approach using proteomic profiling for the identification of urinary proteins related to acute liver injury induced by acetaminophen (APAP). Mice were given a single intraperitoneal dose of APAP (0–350 mg/kg bw) followed by 24 h urine collection. Doses of ≥275 mg/kg bw APAP resulted in hepatic centrilobular necrosis and significantly elevated plasma alanine aminotransferase (ALT) values (p<0.0001). Proteomic profiling resulted in the identification of 12 differentially excreted proteins in urine of mice with acute liver injury (p<0.001), including superoxide dismutase 1 (SOD1), carbonic anhydrase 3 (CA3) and calmodulin (CaM), as novel biomarkers for APAP-induced liver injury. Urinary levels of SOD1 and CA3 increased with rising plasma ALT levels, but urinary CaM was already present in mice treated with high dose of APAP without elevated plasma ALT levels. Importantly, we showed in human urine after APAP intoxication the presence of SOD1 and CA3, whereas both proteins were absent in control urine samples. Urinary concentrations of CaM were significantly increased and correlated well with plasma APAP concentrations (r = 0.97; p<0.0001) in human APAP intoxicants, who did not present with elevated plasma ALT levels. In conclusion, using this urinary proteomics approach we demonstrate CA3, SOD1 and, most importantly, CaM as potential human biomarkers for APAP-induced liver injury.     

Web-Based Software for Rapid Top-Down Proteomic Identification of Protein Biomarkers,with Implications for Bacterial Identification

We have developed web-based software for the rapid identification of protein biomarkers of bacterial microorganisms. Proteins from bacterial cell lysates were ionized by matrix-assisted laser desorption ionization (MALDI), mass isolated, and fragmented using a tandem time of flight (TOF-TOF) mass spectrometer. The sequence-specific fragment ions generated were compared to a database of in silico fragment ions derived from bacterial protein sequences whose molecular weights are the same as the nominal molecular weights of the protein biomarkers. A simple peak-matching and scoring algorithm was developed to compare tandem mass spectrometry (MS-MS) fragment ions to in silico fragment ions. In addition, a probability-based significance-testing algorithm (P value), developed previously by other researchers, was incorporated into the software for the purpose of comparison. The speed and accuracy of the software were tested by identification of 10 protein biomarkers from three Campylobacter strains that had been identified previously by bottom-up proteomics techniques. Protein biomarkers were identified using (i) their peak-matching scores and/or P values from a comparison of MS-MS fragment ions with all possible in silico N and C terminus fragment ions (i.e., ions a, b, b-18, y, y-17, and y-18), (ii) their peak-matching scores and/or P values from a comparison of MS-MS fragment ions to residue-specific in silico fragment ions (i.e., in silico fragment ions resulting from polypeptide backbone fragmentation adjacent to specific residues [aspartic acid, glutamic acid, proline, etc.]), and (iii) fragment ion error analysis, which distinguished the systematic fragment ion error of a correct identification (caused by calibration drift of the second TOF mass analyzer) from the random fragment ion error of an incorrect identification.Food-borne illness is a serious and continuing problem, with an estimated 76 million cases in the United States per year (http://www.cdc.gov). It is often caused by bacteria and viruses that are often ubiquitous in the environment and are difficult to eliminate due to their ability to adapt. In addition to the resulting morbidity, food-borne illness also has enormous societal costs, including losses in worker productivity due to illness, recall of food products determined (or suspected) to be contaminated, etc. Consequently, there is a critical need to develop rapid and sensitive methods for detection and accurate identification of food-borne pathogens.A number of techniques have been developed for detection and identification of food-borne pathogens. A relatively recent technique for bacterial identification involves the use of mass spectrometry (MS). Because of its sensitivity and high specificity, MS has become a popular technique for chemicotaxonomic classification of microorganisms (16, 27). The use of MS in the analysis of microorganisms is a relatively recent application that was dramatically accelerated by the development of two ionization techniques in the late 1980s and early 1990s: electrospray ionization (15) and matrix-assisted laser desorption ionization (MALDI) (24, 37). When coupled with time of flight (TOF) MS, MALDI has been demonstrated to be a powerful tool for “fingerprinting” microorganisms by ionization and detection of proteins from intact bacterial cells or extracts resulting from bacterial cell lysis (1, 2, 3, 8-12, 19, 21, 25, 26, 29, 34, 40, 41, 42). Typically, MALDI-TOF MS “fingerprinting” of microorganisms involves analysis using either pattern recognition or bioinformatic algorithms.Pattern recognition analysis compares MALDI-TOF MS spectra of samples of unknown microorganisms to spectra of known microorganisms. A high degree of similarity between the MS spectrum of an unknown microorganism and an MS spectrum of a known microorganism strongly suggests the identity of the unknown microorganism (22, 39, 43). It should be noted that pattern recognition analysis does not rely on actual identification of the biomarker ion peaks in an MS spectrum. It is the pattern generated by multiple ion peaks that constitutes a microorganism''s “fingerprint.” The actual identities of individual ion peaks are not specified, and the peaks could be peaks for any of a number of possible biological molecules generated by a microorganism, including proteins, nucleic acids, lipids, etc.Microorganism identification by bioinformatic analysis of MALDI-TOF MS data involves using the protein molecular weights (MWs) in bacterial genomic databases to assign biomarker ion peaks in a mass spectrum to specific proteins (4, 5, 32, 33, 45). If a significant number of biomarker ion peaks in a mass spectrum correspond to protein MWs for the open reading frames of a microorganism''s genome, then the microorganism is considered identified. Such an analysis has also incorporated the simplest and most common posttranslational modification (PTM) observed for bacterial proteins, N-terminal methionine cleavage (5). It should be noted, however, that “identification” of a microorganism relies solely on a sufficient number of protein MWs derived from open reading frames of its genome corresponding to the m/z of biomarker ions in a MALDI-TOF MS spectrum. However, the protein MW alone is not sufficient to definitively identify a biomarker ion as a specific protein. Protein biomarkers are considered to be tentatively assigned instead of definitively identified.Analysis of samples containing multiple bacterial organisms presents increased challenges for MALDI-TOF MS when protein MW is the sole criterion for protein biomarker identification. Clearly, it would be advantageous if researchers could obtain more information about a biomarker in addition to its MW. In the case of protein biomarkers, this can be accomplished by enzymatically digesting a protein in solution and analyzing its tryptic peptides by MS (peptide mass mapping) or by tandem MS (MS-MS) (sequence tags) (45). Alternatively, it is possible to fragment mature, intact proteins (without digestion) in the gas phase to obtain sequence-specific and PTM information. This approach is referred to as top-down proteomics. Until recently, top-down proteomics was possible only if Fourier transform ion cyclotron resonance MS involving complicated gas phase ion dissociation techniques was used (6, 23).Although not originally designed for top-down proteomics, recently developed MALDI-tandem TOF (MALDI-TOF-TOF) MS was shown to fragment small or modest-size proteins (5 kDa > molecular mass < 15 kDa) without prior digestion (28). Demirev and coworkers (7) identified Bacillus atrophaeus and Bacillus cereus spores by fragmenting their protein biomarkers using a MALDI tandem mass spectrometer and analyzing the sequence-specific fragment ions generated by comparison to in silico fragment ions derived from protein amino acid sequences from genomic databases. Protein and microorganism identities were determined using a probability-based significance-testing algorithm (P value). The P value algorithm calculates the probability that a protein or microorganism identification occurred randomly. The smaller the P value, the lower the probability that an identification occurred randomly. The data analysis was performed using software developed in house (7).In the current study, web-based software and databases, developed in house at the U.S. Department of Agriculture (USDA), were used to identify 10 protein biomarkers from three pure strains of Campylobacter by sequence-specific fragmentation using a MALDI-TOF-TOF mass spectrometer. Many of the protein biomarkers had been identified previously by bottom-up proteomics techniques (9, 11, 12), which provided an excellent data set to test the accuracy and performance of the algorithms incorporated into the software. MALDI-TOF-TOF MS-MS fragment ions were compared with a database of in silico fragment ions derived from bacterial protein sequences. The sequence-specific MS-MS fragment ions were used to identify a protein and thus the source microorganism. A simple peak-matching mathematical algorithm, incorporated into the software, was used to score and rank protein and microorganism identifications. In addition, the P value algorithm of Demirev and coworkers (7) was also incorporated into the USDA software (available with execution of appropriate control usage agreement) for comparison to the peak-matching algorithm. The peak-matching algorithm correctly identified a protein biomarker among as many as ∼1,400 possible bacterial proteins and gave rankings for protein identification comparable to the rankings obtained by more complicated and computationally intensive P value calculation. We often observed enhancement of the score for correct identification when results for MS-MS fragment ions were compared to results for residue-specific in silico fragment ions compared to non-residue-specific in silico fragment ions. In addition, the correctness of the algorithm''s identification was, in certain cases, further confirmed by fragment ion error analysis which compared random error caused by false matches between MS-MS fragment ions and in silico fragment ions with the systematic error observed for correct matches due to drift in the calibration of the TOF mass analyzer (38).(Portions of this work were presented at the 121st AOAC Conference [13] and at the 55th American Society of Mass Spectrometry Conference [14].)     

Serum Proteomic Changes after Randomized Prolonged Erythropoietin Treatment and/or Endurance Training: Detection of Novel Biomarkers

IntroductionDespite implementation of the biological passport to detect erythropoietin abuse, a need for additional biomarkers remains. We used a proteomic approach to identify novel serum biomarkers of prolonged erythropoiesis-stimulating agent (ESA) exposure (Darbepoietin-α) and/or aerobic training.MethodsSerum proteins were separated according to charge and molecular mass (2D-gel electrophoresis). The identity of proteins from spots exhibiting altered intensity was determined by mass spectrometry.ResultsSix protein spots changed in response to Darbepoietin-α treatment. Comparing all 4 experimental groups, two protein spots (serotransferrin and haptoglobin/haptoglobin related protein) showed a significant response to Darbepoietin-α treatment. The haptoglobin/haptoglobin related protein spot showed a significantly lower intensity in all subjects in the training-ESA group during the treatment period and increased during the washout period.ConclusionAn isoform of haptoglobin/haptoglobin related protein could be a new anti-doping marker and merits further research.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01320449","term_id":"NCT01320449"}}NCT01320449     

Quantitative Proteomic Analysis of Niemann-Pick Disease,Type C1 Cerebellum Identifies Protein Biomarkers and Provides Pathological Insight

Niemann-Pick disease, type C1 (NPC1) is a fatal, neurodegenerative disorder for which there is no definitive therapy. In NPC1, a pathological cascade including neuroinflammation, oxidative stress and neuronal apoptosis likely contribute to the clinical phenotype. While the genetic cause of NPC1 is known, we sought to gain a further understanding into the pathophysiology by identifying differentially expressed proteins in Npc1 mutant mouse cerebella. Using two-dimensional gel electrophoresis and mass spectrometry, 77 differentially expressed proteins were identified in Npc1 mutant mice cerebella compared to controls. These include proteins involved in glucose metabolism, detoxification/oxidative stress and Alzheimer disease-related proteins. Furthermore, members of the fatty acid binding protein family, including FABP3, FABP5 and FABP7, were found to have altered expression in the Npc1 mutant cerebellum relative to control. Translating our findings from the murine model to patients, we confirm altered expression of glutathione s-transferase α, superoxide dismutase, and FABP3 in cerebrospinal fluid of NPC1 patients relative to pediatric controls. A subset of NPC1 patients on miglustat, a glycosphingolipid synthesis inhibitor, showed significantly decreased levels of FABP3 compared to patients not on miglustat therapy. This study provides an initial report of dysregulated proteins in NPC1 which will assist with further investigation of NPC1 pathology and facilitate implementation of therapeutic trials.