ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction

BACKGROUND: Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139–1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing large-scale proteome data. RESULTS: To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report. CONCLUSION: AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact [email protected] if you are interested in the application. The project web page is

The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called O(i) value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (O(i)). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances. Results: The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis. Conclusion: The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.National Institute of Allergy and Infectious Diseases (NIAID) N01-AI15447National Institutes of HealthNational Science Foundation, the Welsh and Packard FoundationsInternational Human Frontier Science ProgramCenter for Systems and Synthetic Biolog

Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury

Study design: Cross-sectional validation study. Objectives: The goals of this study were to validate the use of accelerometers by means of multiple linear models (MLMs) to estimate the O2 consumption (VO2) in paraplegic persons and to determine the best placement for accelerometers on the human body. Setting: Non-hospitalized paraplegics’ community. Methods: Twenty participants (age=40.03 years, weight=75.8 kg and height=1.76 m) completed sedentary, propulsion and housework activities for 10 min each. A portable gas analyzer was used to record VO2. Additionally, four accelerometers (placed on the non-dominant chest, non-dominant waist and both wrists) were used to collect second-by-second acceleration signals. Minute-by-minute VO2 (ml kg−1 min−1) collected from minutes 4 to 7 was used as the dependent variable. Thirty-six features extracted from the acceleration signals were used as independent variables. These variables were, for each axis including the resultant vector, the percentiles 10th, 25th, 50th, 75th and 90th; the autocorrelation with lag of 1 s and three variables extracted from wavelet analysis. The independent variables that were determined to be statistically significant using the forward stepwise method were subsequently analyzed using MLMs. Results: The model obtained for the non-dominant wrist was the most accurate (VO2=4.0558−0.0318Y25+0.0107Y90+0.0051YND2−0.0061ZND2+0.0357VR50) with an r-value of 0.86 and a root mean square error of 2.23 ml kg−1 min−1. Conclusions: The use of MLMs is appropriate to estimate VO2 by accelerometer data in paraplegic persons. The model obtained to the non-dominant wrist accelerometer (best placement) data improves the previous models for this population.LM Garcia-Raffi and EA Sanchez-Perez gratefully acknowledge the support of the Ministerio de Economia y Competitividad under project #MTM2012-36740-c02-02. X Garcia-Masso is a Vali + D researcher in training with support from the Generalitat Valenciana.Garcia Masso, X.; Serra Añó, P.; García Raffi, LM.; Sánchez Pérez, EA.; Lopez Pascual, J.; González, L. (2013). Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury. Spinal Cord. 51(12):898-903. https://doi.org/10.1038/sc.2013.85S8989035112Van den Berg-Emons RJ, Bussmann JB, Haisma JA, Sluis TA, van der Woude LH, Bergen MP et al. A prospective study on physical activity levels after spinal cord injury during inpatient rehabilitation and the year after discharge. Arch Phys Med Rehabil 2008; 89: 2094–2101.Jacobs PL, Nash MS . Exercise recommendations for individuals with spinal cord injury. Sports Med 2004; 34: 727–751.Erikssen G . Physical fitness and changes in mortality: the survival of the fittest. Sports Med 2001; 31: 571–576.Warburton DER, Nicol CW, Bredin SSD . Health benefits of physical activity: the evidence. CMAJ 2006; 174: 801–809.Haennel RG, Lemire F . Physical activity to prevent cardiovascular disease. How much is enough? Can Fam Physician 2002; 48: 65–71.Manns PJ, Chad KE . Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. Arch Phys Med Rehabil 1999; 80: 1566–1571.Hetz SP, Latimer AE, Buchholz AC, Martin Ginis KA . Increased participation in activities of daily living is associated with lower cholesterol levels in people with spinal cord injury. Arch Phys Med Rehabil 2009; 90: 1755–1759.Buchholz AC, Martin Ginis KA, Bray SR, Craven BC, Hicks AL, Hayes KC et al. Greater daily leisure time physical activity is associated with lower chronic disease risk in adults with spinal cord injury. Appl Physiol Nutr Metab 2009; 34: 640–647.Slater D, Meade MA . Participation in recreation and sports for persons with spinal cord injury: review and recommendations. Neurorehabilitation 2004; 19: 121–129.Valanou EM, Bamia C, Trichopoulou A . Methodology of physical-activity and energy-expenditure assessment: a review. J Public Health 2006; 14: 58–65.Liu S, Gao RX, Freedson PS . Computational methods for estimating energy expenditure in human physical activities. Med Sci Sports Exerc 2012; 44: 2138–2146.Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M . Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40: 181–188.Riddoch CJ, Bo Andersen L, Wedderkopp N, Harro M, Klasson-Heggebø L, Sardinha LB et al. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc 2004; 36: 86–92.Hiremath SV, Ding D . Evaluation of activity monitors in manual wheelchair users with paraplegia. J Spinal Cord Med 2011; 34: 110–117.Hiremath SV, Ding D . Evaluation of activity monitors to estimate energy expenditure in manual wheelchair users. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 835–838.Washburn R, Copay A . Assessing physical activity during wheelchair pushing: validity of a portable accelerometer. Adapt Phys Activ Q 1999; 16: 290–299.Hiremath SV, Ding D . Regression equations for RT3 activity monitors to estimate energy expenditure in manual wheelchair users. Conf Proc IEEE Eng Med Biol Soc 2011; 2011: 7348–7351.Hiremath SV, Ding D, Farringdon J, Cooper RA . Predicting energy expenditure of manual wheelchair users with spinal cord injury using a multisensor-based activity monitor. Arch Phys Med Rehabil 2012; 93: 1937–1943.Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA . Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 2000; 32: S471–S480.Motl RW, Sosnoff JJ, Dlugonski D, Suh Y, Goldman M . Does a waist-worn accelerometer capture intra- and inter-person variation in walking behavior among persons with multiple sclerosis? Med Eng Phys 2010; 32: 1224–1228.Van Remoortel H, Raste Y, Louvaris Z, Giavedoni S, Burtin C, Langer D et al. Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS One 2012; 7: e39198.Macfarlane DJ . Automated metabolic gas analysis systems: a review. Sports Med 2001; 31: 841–861.Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P . An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300–1307.Daubechies I . Ten Lectures on Wavelets. SIAM, Philadelphia. 1999.Debnat I . Wavelets and Signal Processing. Birkhauser, Boston. 2003.Collins EG, Gater D, Kiratli J, Butler J, Hanson K, Langbein WE . Energy cost of physical activities in persons with spinal cord injury. Med Sci Sports Exerc 2010; 42: 691–700.Lee M, Zhu W, Hedrick B, Fernhall B . Determining metabolic equivalent values of physical activities for persons with paraplegia. Disabil Rehabil 2010; 32: 336–343.Crouter SE, Clowers KG, Bassett DR Jr . A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 2006; 100: 1324–1331

Differential Proteomic Analysis of Mammalian Tissues Using SILAM

Differential expression of proteins between tissues underlies organ-specific functions. Under certain pathological conditions, this may also lead to tissue vulnerability. Furthermore, post-translational modifications exist between different cell types and pathological conditions. We employed SILAM (Stable Isotope Labeling in Mammals) combined with mass spectrometry to quantify the proteome between mammalian tissues. Using 15N labeled rat tissue, we quantified 3742 phosphorylated peptides in nuclear extracts from liver and brain tissue. Analysis of the phosphorylation sites revealed tissue specific kinase motifs. Although these tissues are quite different in their composition and function, more than 500 protein identifications were common to both tissues. Specifically, we identified an up-regulation in the brain of the phosphoprotein, ZFHX1B, in which a genetic deletion causes the neurological disorder Mowat–Wilson syndrome. Finally, pathway analysis revealed distinct nuclear pathways enriched in each tissue. Our findings provide a valuable resource as a starting point for further understanding of tissue specific gene regulation and demonstrate SILAM as a useful strategy for the differential proteomic analysis of mammalian tissues

Survival and major neurodevelopmental impairment in extremely low gestational age newborns born 1990–2000: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>It is important to determine if rates of survival and major neurodevelopmental impairment in extremely low gestational age newborns (ELGANs; infants born at 23–27 weeks gestation) are changing over time.</p> <p>Methods</p> <p>Study infants were born at 23 to 27 weeks of gestation without congenital anomalies at a tertiary medical center between July 1, 1990 and June 30, 2000, to mothers residing in a thirteen-county region in North Carolina. Outcomes at one year adjusted age were compared for two epochs of birth: epoch 1, July 1, 1990 to June 30, 1995; epoch 2, July 1, 1995 to June 30, 2000. Major neurodevelopmental impairment was defined as cerebral palsy, Bayley Scales of Infant Development Mental Developmental Index more than two standard deviations below the mean, or blindness.</p> <p>Results</p> <p>Survival of ELGANs, as a percentage of live births, was 67% [95% confidence interval: (61, 72)] in epoch 1 and 71% (65, 75) in epoch 2. Major neurodevelopmental impairment was present in 20% (15, 27) of survivors in epoch 1 and 14% (10, 20) in epoch 2. When adjusted for gestational age, survival increased [odds ratio 1.5 (1.0, 2.2), p = .03] and major neurodevelopmental impairment decreased [odds ratio 0.54 (0.31, 0.93), p = .02] from epoch 1 to epoch 2.</p> <p>Conclusion</p> <p>The probability of survival increased while that of major neurodevelopmental impairment decreased during the 1990's in this regionally based sample of ELGANs.</p

Antenatal corticosteroids and cardio-metabolic outcomes in adolescents born with very low birth weight

Exposure to antenatal corticosteroids (ANCS) is associated with adverse cardio-metabolic outcomes in animal models; however long-term outcomes in clinical studies are not well characterized. We hypothesized that exposure to ANCS would be associated with markers of increased cardio-metabolic risk in adolescents born with very low birth weight (VLBW)

Molecular Targets for 17α-Ethynyl-5-Androstene-3β,7β,17β-Triol, an Anti-Inflammatory Agent Derived from the Human Metabolome

HE3286, 17α-ethynyl-5-androstene-3β, 7β, 17β-triol, is a novel synthetic compound related to the endogenous sterol 5-androstene-3β, 7β, 17β-triol (β-AET), a metabolite of the abundant adrenal steroid dehydroepiandrosterone (DHEA). HE3286 has shown efficacy in clinical studies in impaired glucose tolerance and type 2 diabetes, and in vivo models of types 1 and 2 diabetes, autoimmunity, and inflammation. Proteomic analysis of solid-phase HE3286-bound bead affinity experiments, using extracts from RAW 264.7 mouse macrophage cells, identified 26 binding partners. Network analysis revealed associations of these HE3286 target proteins with nodes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for type 2 diabetes, insulin, adipokine, and adipocyte signaling. Binding partners included low density lipoprotein receptor-related protein (Lrp1), an endocytic receptor; mitogen activated protein kinases 1 and 3 (Mapk1, Mapk3), protein kinases involved in inflammation signaling pathways; ribosomal protein S6 kinase alpha-3 (Rsp6ka3), an intracellular regulatory protein; sirtuin-2 (Sirt2); and 17β-hydroxysteroid dehydrogenase 1 (Hsd17β4), a sterol metabolizing enzyme

Transgenerational Effects of Heavy Metal Pollution on Immune Defense of the Blow Fly Protophormia terraenovae

Recently environmental conditions during early parental development have been found to have transgenerational effects on immunity and other condition-dependent traits. However, potential transgenerational effects of heavy metal pollution have not previously been studied. Here we show that direct exposure to heavy metal (copper) upregulates the immune system of the blow fly, Protophormia terraenovae, reared in copper contaminated food. In the second experiment, to test transgenerational effects of heavy metal, the parental generation of the P. terraenovae was reared in food supplemented with copper, and the immunocompetence of their offspring, reared on uncontaminated food, was measured. Copper concentration used in this study was, in the preliminary test, found to have no effect on mortality of the flies. Immunity was tested on the imago stage by measuring encapsulation response against an artificial antigen, nylon monofilament. We found that exposure to copper during the parental development stages through the larval diet resulted in immune responses that were still apparent in the next generation that was not exposed to the heavy metal. We found that individuals reared on copper-contaminated food developed more slowly compared with those reared on uncontaminated food. The treatment groups did not differ in their dry body mass. However, parental exposure to copper did not have an effect on the development time or body mass of their offspring. Our study suggests that heavy metal pollution has positive feedback effect on encapsulation response through generations which multiplies the harmful effects of heavy metal pollution in following generations

Proteomic Analyses of Host and Pathogen Responses during Bovine Mastitis

The pursuit of biomarkers for use as clinical screening tools, measures for early detection, disease monitoring, and as a means for assessing therapeutic responses has steadily evolved in human and veterinary medicine over the past two decades. Concurrently, advances in mass spectrometry have markedly expanded proteomic capabilities for biomarker discovery. While initial mass spectrometric biomarker discovery endeavors focused primarily on the detection of modulated proteins in human tissues and fluids, recent efforts have shifted to include proteomic analyses of biological samples from food animal species. Mastitis continues to garner attention in veterinary research due mainly to affiliated financial losses and food safety concerns over antimicrobial use, but also because there are only a limited number of efficacious mastitis treatment options. Accordingly, comparative proteomic analyses of bovine milk have emerged in recent years. Efforts to prevent agricultural-related food-borne illness have likewise fueled an interest in the proteomic evaluation of several prominent strains of bacteria, including common mastitis pathogens. The interest in establishing biomarkers of the host and pathogen responses during bovine mastitis stems largely from the need to better characterize mechanisms of the disease, to identify reliable biomarkers for use as measures of early detection and drug efficacy, and to uncover potentially novel targets for the development of alternative therapeutics. The following review focuses primarily on comparative proteomic analyses conducted on healthy versus mastitic bovine milk. However, a comparison of the host defense proteome of human and bovine milk and the proteomic analysis of common veterinary pathogens are likewise introduced