Untargeted metabolomics confirms the association between plasma branched chain amino acids and residual feed intake in beef heifers.

This study explored plasma biomarkers and metabolic pathways underlying feed efficiency measured as residual feed intake (RFI) in Charolais heifers. A total of 48 RFI extreme individuals (High-RFI, n = 24; Low-RFI, n = 24) were selected from a population of 142 heifers for classical plasma metabolite and hormone quantification and plasma metabolomic profiling through untargeted LC-MS. Most efficient heifers (Low-RFI) had greater (P = 0.03) plasma concentrations of IGF-1 and tended to have (P = 0.06) a lower back fat depth compared to least efficient heifers. However, no changes were noted (P ≥ 0.10) for plasma concentrations of glucose, insulin, non-esterified fatty acids, ß-hydroxybutyrate and urea. The plasma metabolomic dataset comprised 3,457 ions with none significantly differing between RFI classes after false discovery rate correction (FDR > 0.10). Among the 101 ions having a raw P < 0.05 for the RFI effect, 13 were putatively annotated by using internal databases and 6 compounds were further confirmed with standards. Metabolic pathway analysis from these 6 confirmed compounds revealed that the branched chain amino acid metabolism was significantly (FDR < 0.05) impacted by the RFI classes. Our results confirmed for the first time in beef heifers previous findings obtained in male beef cattle and pointing to changes in branched-chain amino acids metabolism along with that of body composition as biological mechanisms related to RFI. Further studies are warranted to ascertain whether there is a cause-and-effect relationship between these mechanisms and RFI.

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

Non-targeted metabolomics analyses by mass spectrometry to explore metabolic stress after six training weeks in high level swimmers.

The objective was to compare the metabolic responses of high-level national swimmers to threshold or polarised training. 22 swimmers (n = 12 males and 10 females) participated in a 28-week cross-over intervention study consisting of 2 × 6 period weeks of training. Swimmers were assigned randomly to either training group for the first period: polarised (POL) (81% in energetic zone 1: blood lactate [La]b ≤ 2 mmol.L-1; 4% in zone 2: 2 mmol.L-1 <[La]b ≤ 4 mmol.L-1; 15% in zone 3: [La]b > 4 mmol.L-1) or threshold (THR) (65%/25%/10%). Before and after each training period, urine samples were collected for non-targeted metabolomics analysis. Mixed model analysis was performed on metabolomics data including fatigue class factors and/or training and/or interaction. Ion intensities of 6-keto-decanoylcarnitine (+31%), pregnanediol-3-glucuronide (+81%), P-cresol sulphate (+18%) were higher in the threshold group (P < 0.05) indicating higher glycogenic depletion and inflammation without alteration of the neuroendocrine stress axis. 4-phenylbutanic acid sulphate was 200% higher in less fatigued swimmers (P < 0.01) linking the anti-inflammatory activity at the cell membrane level to the subjective perception of fatigue. This research suggests the importance of replenishing glycogen stores and reducing inflammation during high thresholds training loads.

Untargeted plasma metabolomic profiles associated with overall diet in women from the SU.VI.MAX cohort.

PURPOSE: Dietary intakes are reflected in plasma by the presence of hundreds of exogenous metabolites and variations in endogenous metabolites. The exploration of diet-related plasma metabolic profiles could help to better understand the impact of overall diet on health. Our aim was to identify metabolomic signatures reflecting overall diet in women from the French general population. METHODS: This cross-sectional study included 160 women in the SU.VI.MAX cohort with detailed dietary data (≥ 10 24-h dietary records) selected according to their level of adherence to the French dietary recommendations, represented by the validated score mPNNS-GS; 80 women from the 10th decile of the score were matched with 80 women from the 1st decile. Plasma metabolomic profiles were acquired using untargeted UPLC-QToF mass spectrometry analysis. The associations between metabolomic profiles and the mPNNG-GS, its components and Principal Component Analyses-derived dietary patterns were investigated using multivariable conditional logistic regression models and partial correlations. RESULTS: Adherence to the dietary recommendations was positively associated with 3-indolepropionic acid and pipecolic acid (also positively associated with fruit and vegetable intake and a healthy diet)-2 metabolites linked to microbiota and inversely associated with lysophosphatidylcholine (LysoPC(17:1)), acylcarnitine C9:1 (also inversely associated with a healthy diet), acylcarnitine C11:1 and 2-deoxy-D-glucose. Increased plasma levels of piperine and Dihydro4mercapto-3(2H) furanone were observed in women who consumed a Western diet and a healthy diet, respectively. Ethyl-ß-D-glucopyranoside was positively associated with alcohol intake. Plasma levels of LysoPC(17:1), cholic acid, phenylalanine-phenylalanine and phenylalanine and carnitine C9:1 decreased with the consumption of vegetable added fat, sweetened food, milk and dairy products and fruit and vegetable intakes, respectively. CONCLUSION: This study highlighted several metabolites from both host and microbial metabolism reflecting the long-term impact of the overall diet. TRIAL REGISTRATION: SU.VI.MAX, clinicaltrials.gov NCT00272428. Registered 3 January 2006, https://clinicaltrials.gov/show/NCT00272428.

Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort.

BACKGROUND: Diet has been recognized as a modifiable risk factor for breast cancer. Highlighting predictive diet-related biomarkers would be of great public health relevance to identify at-risk subjects. The aim of this exploratory study was to select diet-related metabolites discriminating women at higher risk of breast cancer using untargeted metabolomics. METHODS: Baseline plasma samples of 200 incident breast cancer cases and matched controls, from a nested case-control study within the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, were analyzed by untargeted LC-MS. Diet-related metabolites were identified by partial correlation with dietary exposures, and best predictors of breast cancer risk were then selected by Elastic Net penalized regression. The selection stability was assessed using bootstrap resampling. RESULTS: 595 ions were selected as candidate diet-related metabolites. Fourteen of them were selected by Elastic Net regression as breast cancer risk discriminant ions. A lower level of piperine (a compound from pepper) and higher levels of acetyltributylcitrate (an alternative plasticizer to phthalates), pregnene-triol sulfate (a steroid sulfate), and 2-amino-4-cyano butanoic acid (a metabolite linked to microbiota metabolism) were observed in plasma from women who subsequently developed breast cancer. This metabolomic signature was related to several dietary exposures such as a "Western" dietary pattern and higher alcohol and coffee intakes. CONCLUSIONS: Our study suggested a diet-related plasma metabolic signature involving exogenous, steroid metabolites, and microbiota-related compounds associated with long-term breast cancer risk that should be confirmed in large-scale independent studies. IMPACT: These results could help to identify healthy women at higher risk of breast cancer and improve the understanding of nutrition and health relationship.

Plasma Metabolomic Signatures Associated with Long-term Breast Cancer Risk in the SU.VI.MAX Prospective Cohort.

BACKGROUND: Breast cancer is a major cause of death in occidental women. The role of metabolism in breast cancer etiology remains unclear. Metabolomics may help to elucidate novel biological pathways and identify new biomarkers to predict breast cancer long before symptoms appear. The aim of this study was to investigate whether untargeted metabolomic signatures from blood draws of healthy women could contribute to better understand and predict the long-term risk of developing breast cancer. METHODS: A nested case-control study was conducted within the SU.VI.MAX prospective cohort (13 years of follow-up) to analyze baseline plasma samples of 211 incident breast cancer cases and 211 matched controls by LC/MS. Multivariable conditional logistic regression models were computed. RESULTS: A total of 3,565 ions were detected and 1,221 were retained for statistical analysis. A total of 73 ions were associated with breast cancer risk (P < 0.01; FDR ≤ 0.2). Notably, we observed that a lower plasma level of O-succinyl-homoserine (OR = 0.70, 95%CI = [0.55-0.89]) and higher plasma levels of valine/norvaline [1.45 (1.15-1.83)], glutamine/isoglutamine [1.33 (1.07-1.66)], 5-aminovaleric acid [1.46 (1.14-1.87)], phenylalanine [1.43 (1.14-1.78)], tryptophan [1.40 (1.10-1.79)], γ-glutamyl-threonine [1.39 (1.09-1.77)], ATBC [1.41 (1.10-1.79)], and pregnene-triol sulfate [1.38 (1.08-1.77)] were associated with an increased risk of developing breast cancer during follow-up.Conclusion: Several prediagnostic plasmatic metabolites were associated with long-term breast cancer risk and suggested a role of microbiota metabolism and environmental exposure. IMPACT: After confirmation in other independent cohort studies, these results could help to identify healthy women at higher risk of developing breast cancer in the subsequent decade and to propose a better understanding of the complex mechanisms involved in its etiology.

Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort.

SCOPE: Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome. METHODS AND RESULTS: A case-control study nested in the prospective Three-City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gender, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap-enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium-chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE-ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross-validated Area Under the Receiver Operating Curve = 75% [95% CI 70-80%] compared to 62% [95% CI 56-67%]. CONCLUSIONS: The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging.

MALDI mass spectrometry imaging and in situ microproteomics of Listeria monocytogenes biofilms.

MALDI-TOF Mass spectrometry Imaging (MSI) is a surface-sampling technology that can determine spatial information and relative abundance of analytes directly from biological samples. Human listeriosis cases are due to the ingestion of contaminated foods with the pathogenic bacteria Listeria monocytogenes. The reduction of water availability in food workshops by decreasing the air relative humidity (RH) is one strategy to improve the control of bacterial contamination. This study aims to develop and implement an MSI approach on L. monocytogenes biofilms and proof of concept using a dehumidified stress condition. MSI allowed examining the distribution of low molecular weight proteins within the biofilms subjected to a dehumidification environment, mimicking the one present in a food workshop (10 °C, 75% RH). Furthermore, a LC-MS/MS approach was made to link the dots between MSI and protein identification. Five identified proteins were assigned to registered MSI m/z, including two cold-shock proteins and a ligase involved in cell wall biogenesis. These data demonstrate how imaging can be used to dissect the proteome of an intact bacterial biofilm giving new insights into protein expression relating to a dehumidification stress adaptation. Data are available via ProteomeXchange with identifier PXD010444. BIOLOGICAL SIGNIFICANCE: The ready-to-eat food processing industry has the daily challenge of controlling the contamination of surfaces and machines with spoilage and pathogenic microorganisms. In some cases, it is a lost cause due to these microorganisms' capacity to withstand the cleaning treatments, like desiccation procedures. Such a case is the ubiquitous Gram-positive Bacterium Listeria monocytogenes. Its surface proteins have particular importance for the interaction with its environment, being important factors contributing to adaptation to stress conditions. There are few reproducibly techniques to obtain the surface proteins of Gram-positive cells. Here, we developed a workflow that enables the use of MALDI imaging on Gram-positive bacterium biofilms to study the impact of dehumidification on sessile cells. It will be of the most interest to test this workflow with different environmental conditions and potentially apply it to other biofilm-forming bacteria.

Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics.

Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87-1) and 0.94 (95% CI = 0.87-1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72-0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86-0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage.

Dietary supplementation with cysteine prevents adverse metabolic outcomes of repeated cures with paracetamol in old rats.

Cysteine (Cys), a conditionally indispensable amino acid, is required for the detoxification of paracetamol (acetaminophen, N-acetyl-para-aminophenol, 4-hydroxy-acetanilide, APAP), a drug of widespread use in older persons. We recently reported that repeated APAP cures could worsen sarcopenia in old rats, likely to be due to the impairment of Cys/GSH homoeostasis. The aim of the study was to evaluate whether a dietary Cys supplementation during APAP cures could improve Cys/GSH homoeostasis and thus preserve skeletal muscle. Male 21·5-month-old Wistar rats received three 2-week-long cures of APAP (1 % of diet) alone or with extra Cys (0·5 % of diet), intercalated with washout periods of 2 weeks (APAP and APAP-Cys groups, respectively). They were compared with untreated control rats (CT group). CT and APAP-Cys groups were pair-fed to the APAP group. Dietary Cys supplementation was efficient to prevent increase in liver mass (P<0·0001), decrease in liver GSH (P<0·0001), increase in blood GSH concentration (P<0·0001), and to some extent, decrease in plasma free Cys concentration (P<0·05), all induced by repeated APAP cures. The addition of Cys to APAP cures decreased plasma alanine transaminase (P<0·05), the fractional synthesis rate of liver proteins (P<0·01), and increased masses of extensor digitorum longus (P<0·01), and soleus (P<0·05), compared with the APAP group. Cys supplementation prevented alteration in Cys/GSH homoeostasis and increased some muscle masses in old rats under repeated cures with a non-toxic dose of APAP.

Myofiber metabolic type determination by mass spectrometry imaging.

Matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging is a powerful tool that opens new research opportunities in the field of biology. In this work, predictive model was developed to discriminate metabolic myofiber types using the MALDI spectral data. Rat skeletal muscles are constituted of type I and type IIA fiber, which have an oxidative metabolism for glycogen degradation, and type IIX and type IIB fiber which have a glycolytic metabolism, present in different proportions according to the muscle function and physiological state. So far, myofiber type is determined by histological methods that are time consuming. Thanks to the predictive model, we were able to predict not only the metabolic fiber type but also their location, on the same muscle section that was used for MALDI imaging. Copyright © 2017 John Wiley & Sons, Ltd.

A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF.

Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF workflow to link the results from MALDI-MSI with potential peak identification and label-free quantitation, using only one tissue section. At first, we studied the impact of matrix deposition and laser ablation on protein extraction from the tissue section. Then, we did a back-correlation of the m/z of the proteins detected by MALDI-MSI to those identified by label-free quantitation. This allowed us to compare the label-free quantitation of proteins obtained in LC-MS/MS with the peak intensities observed in MALDI-MSI. We managed to link identification to nine peaks observed by MALDI-MSI. The results showed that the MSI~LC-MS/MS-LF workflow (i) allowed us to study a representative muscle proteome compared to a classical bottom-up workflow; and (ii) was sparsely impacted by matrix deposition and laser ablation. This workflow, performed as a proof-of-concept, suggests that a single tissue section can be used to perform MALDI-MSI and protein extraction, identification, and relative quantitation.

Assessment of protein modifications in liver of rats under chronic treatment with paracetamol (acetaminophen) using two complementary mass spectrometry-based metabolomic approaches.

Liver protein can be altered under paracetamol (APAP) treatment. APAP-protein adducts and other protein modifications (oxidation/nitration, expression) play a role in hepatotoxicity induced by acute overdoses, but it is unknown whether liver protein modifications occur during long-term treatment with non-toxic doses of APAP. We quantified APAP-protein adducts and assessed other protein modifications in the liver from rats under chronic (17 days) treatment with two APAP doses (0.5% or 1% of APAP in the diet w/w). A targeted metabolomic method was validated and used to quantify APAP-protein adducts as APAP-cysteine adducts following proteolytic hydrolysis. The limit of detection was found to be 7ng APAP-cysteine/mL hydrolysate i.e. an APAP-Cys to tyrosine ratio of 0.016‰. Other protein modifications were assessed on the same protein hydrolysate by untargeted metabolomics including a new strategy to process the data and identify discriminant molecules. These two complementary mass spectrometry (MS)-based metabolic approaches enabled the assessment of a wide range of protein modifications induced by chronic treatment with APAP. BIOLOGICAL SIGNIFICANCE: APAP-protein adducts were detected even in the absence of glutathione depletion and hepatotoxicity, i.e. in the 0.5% APAP group, and increased by 218% in the 1% APAP group compared to the 0.5% APAP group. At the same time, the untargeted metabolomic method revealed a decrease in the binding of cysteine, cysteinyl-glycine and GSH to thiol groups of protein cysteine residues, an increase in the oxidation of tryptophan and proline residues and a modification in protein expression. This wide range of modifications in liver proteins occurred in rats under chronic treatment with APAP that did not induce hepatotoxicity.

Salt-stress induced changes in the leaf proteome of diploid and tetraploid mandarins with contrasting Na+ and Cl- accumulation behaviour.

To understand the genotypic variation of citrus to mild salt stress, a proteomic approach has been carried out in parallel on two citrus genotypes ('Cleopatra' and 'Willow leaf' mandarins), which differ for Na(+) and Cl(-) accumulation, and their cognate autotetraploids (4×). Using two-dimensional electrophoresis approximately 910 protein spots were reproducibly detected in control and salt-stressed leaves of all genotypes. Among them, 44 protein spots showing significant variations at least in one genotype were subjected to mass spectrometry analysis for identification. Salt-responsive proteins were involved in several functions, including photosynthetic processes, ROS scavenging, stress defence, and signalling. Genotype factors affect the salt-responsive pattern, especially that of carbon metabolism. The no ion accumulator 'Cleopatra' mandarin genotype showed the highest number of salt-responsive proteins, and up-regulation of Calvin cycle-related proteins. Conversely the ion accumulator 'Willow leaf' mandarin showed high levels of several photorespiration-related enzymes. A common set of proteins (twelve spots) displayed higher levels in salt-stressed leaves of 2× and 4× 'Cleopatra' and 4× 'Willow leaf' mandarin. Interestingly, antioxidant enzymes and heat shock proteins showed higher constitutive levels in 4× 'Cleopatra' mandarin and 4× 'Willow leaf' mandarin compared with the cognate 2× genotype. This work provides for the first time information on the effect of 8 weeks of salt stress on citrus genotypes contrasting for ion accumulation and their cognate autotetraploids. Results underline that genetic factors have a predominant effect on the salt response, although a common stress response independent from genotype was also found.

Exocytosis and protein secretion in Trypanosoma.

BACKGROUND: Human African trypanosomiasis is a lethal disease caused by the extracellular parasite Trypanosoma brucei. The proteins secreted by T. brucei inhibit the maturation of dendritic cells and their ability to induce lymphocytic allogenic responses. To better understand the pathogenic process, we combined different approaches to characterize these secreted proteins. RESULTS: Overall, 444 proteins were identified using mass spectrometry, the largest parasite secretome described to date. Functional analysis of these proteins revealed a strong bias toward folding and degradation processes and to a lesser extent toward nucleotide metabolism. These features were shared by different strains of T. brucei, but distinguished the secretome from published T. brucei whole proteome or glycosome. In addition, several proteins had not been previously described in Trypanosoma and some constitute novel potential therapeutic targets or diagnostic markers. Interestingly, a high proportion of these secreted proteins are known to have alternative roles once secreted. Furthermore, bioinformatic analysis showed that a significant proportion of proteins in the secretome lack transit peptide and are probably not secreted through the classical sorting pathway. Membrane vesicles from secretion buffer and infested rat serum were purified on sucrose gradient and electron microscopy pictures have shown 50- to 100-nm vesicles budding from the coated plasma membrane. Mass spectrometry confirmed the presence of Trypanosoma proteins in these microvesicles, showing that an active exocytosis might occur beyond the flagellar pocket. CONCLUSIONS: This study brings out several unexpected features of the secreted proteins and opens novel perspectives concerning the survival strategy of Trypanosoma as well as possible ways to control the disease. In addition, concordant lines of evidence support the original hypothesis of the involvement of microvesicle-like bodies in the survival strategy allowing Trypanosoma to exchange proteins at least between parasites and/or to manipulate the host immune system.

Peptide mass fingerprinting: identification of proteins by MALDI-TOF.

MALDI-TOF peptide mass fingerprinting (PMF) is the fastest and cheapest method of protein identification; the studied genome is sequenced and annotated, and the protein is amenable to separation and detection in 2D gel electrophoresis. In plant proteomics there are two main difficulties: few plant genomes are sequenced, and major contaminants are non-plant specific. This chapter describes the classical "bottom-up" method (i.e., from peptide to protein identification) of gel cutting, in-gel digestion, peptide recovery and purification, MALDI-TOF mass spectrometry, and critical survey of protein database queries.

Different impact of staining procedures using visible stains and fluorescent dyes for large-scale investigation of proteomes by MALDI-TOF mass spectrometry.

Replicate 2-D gels were stained with four visible or fluorescent dyes using published procedures, and 48 co-detected spots were selected for contrasting values in abundance, M(r) and pI. Success rate of identification and sequence coverage were affected in a dye-dependent manner by the three parameters. Frequency of missed cleavages and recovery of sulfur-containing peptides also depended on the dye. Finally, the dataset was used to predict the number of proteins identifiable when integrating the differential contribution of each parameter. Sypro Ruby appeared to combine several favorable features: no dependence of the identification rate upon the physicochemical properties of proteins, no impact on frequency of missed cleavages, and a higher predicted identification rate.

MS characterization of multiple forms of alpha-amylase in human saliva.

Alpha-amylase is a major and well-characterized component of human saliva. Recent proteomic studies suggested that this protein could be observed in more than twenty spots on 2-D gels of salivary proteins. The aim of this work was to investigate this unexpected redundancy. 2-D gel electrophoresis was combined with systematic MALDI-TOF MS analysis. More than 140 protein spots identifying the alpha-amylase were shown to constitute a stable but very complex pattern. Careful analysis of mass spectra and simultaneous hierarchical clustering of the observed peptides and of the electrophoretic features of spots allowed one to define three major groups. A main class grouping 90 spots was shown to correspond to full length alpha-amylases that can be assumed to include isoforms and post-translationally modified forms, a subset of this class being demonstrated to be N-glycosylated. A second group included short alpha-amylases that are differently truncated in a non-random manner, very likely in the oral cavity. The last class grouped alpha-amylase forms showing both the N- and C-terminal sequences of the enzyme but displaying a molecular weight that was up to 50% lower than that of the native protein. It is speculated that the last group of alpha-amylase spots could correspond to proteins submitted to internal deletions prior to the secretion.

Characterisation of the katA gene encoding a catalase and evidence for at least a second catalase activity in Staphylococcus xylosus, bacteria used in food fermentation.

The catalase gene katA of Staphylococcus xylosus was cloned. It encodes a protein of 494 amino acids with a molecular mass of 56.9 kDa, closely related to monofunctional catalases. A katA mutant still showed a relatively high catalase activity demonstrating that S. xylosus possesses more than one enzyme. By Southern blot analysis using a katA probe, a second genetic locus distinct from katA was detected that probably contained the additional catalase gene. To analyse katA expression, a transcriptional fusion of the katA promoter region to a promoterless beta-galactosidase gene was integrated into the genome of S. xylosus. katA expression is induced upon entry into stationary phase, by oxygen and hydrogen peroxide. Iron and manganese depletion induced katA transcription. Comparing the resistance of S. xylosus wild-type and the katA mutant strain to hydrogen peroxide clearly showed that KatA is essential for S. xylosus to cope with hydrogen peroxide stress. Therefore, S. xylosus has at least two differentially expressed catalases.