Peter Horvatovich

Comparative proteomic analysis of Lactobacillus plantarum for the identification of key proteins in bile tolerance

BackgroundLactic acid bacteria are commonly marketed as probiotics based on their putative or proven health-promoting effects. These effects are known to be strain specific but the underlying molecular mechanisms remain poorly understood. Therefore, unravelling the determinants behind probiotic features is of particular interest since it would help select strains that stand the best chance of success in clinical trials. Bile tolerance is one of the most crucial properties as it determines the ability of bacteria to survive in the small intestine, and consequently their capacity to play their functional role as probiotics. In this context, the objective of this study was to investigate the natural protein diversity within the Lactobacillus plantarum species with relation to bile tolerance, using comparative proteomics.ResultsBile tolerance properties of nine L. plantarum strains were studied in vitro. Three of them presenting different bile tolerance levels were selected for comparative proteomic analysis: L. plantarum 299 V (resistant), L. plantarum LC 804 (intermediate) and L. plantarum LC 56 (sensitive). Qualitative and quantitative differences in proteomes were analyzed using two-dimensional electrophoresis (2-DE), tryptic digestion, liquid chromatography-mass spectrometry analysis and database search for protein identification. Among the proteins correlated with differences in the 2-DE patterns of the bacterial strains, 15 have previously been reported to be involved in bile tolerance processes. The effect of a bile exposure on these patterns was investigated, which led to the identification of six proteins that may be key in the bile salt response and adaptation in L. plantarum: two glutathione reductases involved in protection against oxidative injury caused by bile salts, a cyclopropane-fatty-acyl-phospholipid synthase implicated in maintenance of cell envelope integrity, a bile salt hydrolase, an ABC transporter and a F0F1-ATP synthase which participate in the active removal of bile-related stress factors.ConclusionsThese results showed that comparative proteomic analysis can help understand the differential bacterial properties of lactobacilli. In the field of probiotic studies, characteristic proteomic profiles can be identified for individual properties that may serve as bacterial biomarkers for the preliminary selection of strains with the best probiotic potential.

2-DE and MS analysis of key proteins in the adhesion of Lactobacillus plantarum, a first step toward early selection of probiotics based on bacterial biomarkers

The identification of cell components involved in probiotic activities is a challenge in current probiotic research. In this work, a new approach based on proteomics as an analytical tool for the identification of characteristic protein profiles related to adhesion to mucin as a model probiotic property was used. Three Lactobacillus plantarum strains with different adhesion rates were used for proteomic analysis: L. plantarum WHE 92 (15.9%), L. plantarum 299 v (9.1%) and L. plantarum CECT 4185 (1.4%). Cell wall extracts were subjected to proteomic analysis of differential protein expression using 2‐DE, tryptic digestion, chip‐LC‐QTOF mass analysis and protein identification using database search. Several proteins, previously reported to be involved in bacterial adhesion: elongation factor EF‐Tu, GroEL chaperonin, molecular chaperone DnaK and glyceraldehyde‐3‐phosphate dehydrogenase were found to be overexpressed in the cell wall proteome of the highly adhesive strain L. plantarum WHE 92. The overexpression of two spots containing GroES co‐chaperonin in the most adhesive strain also suggested the involvement of this protein in the adhesion process. The association of proteomic profiles and proteins with particular probiotic properties opens the way for the use of such profiles and proteins as bacterial biomarkers for the properties of bacteria but probably also for their potential health effects.

Multidimensional chromatography coupled to mass spectrometry in analysing complex proteomics samples

Multidimensional chromatography coupled to mass spectrometry (LC(n)-MS) provides more separation power and an extended measured dynamic concentration range to analyse complex proteomics samples than one dimensional liquid chromatography coupled to mass spectrometry (1D-LC-MS). This review gives an overview of the most important aspects of LC(n)-MS with respect to optimizing peak capacity and evaluate orthogonality. We review recent developments in LC(n)-MS to analyse proteomics samples from the analyst point of view and give an overview over methods and future developments to process LC(n)-MS data for comprehensive differential protein expression profiling. Examples from our research, such as combining protein fractionation using high temperature reverse phase (RP) columns followed by analysis of the trypsin-digested fractions by RP LC-MS, serve to highlight possibilities and shortcomings of present-day approaches. Other LC(n)-MS systems that have been used to analyse highly complex shotgun proteomic samples, such as the combination of RP columns using low and high pH eluents or the combination of hydrophilic interaction liquid chromatography (HILIC) with RP-MS is discussed in detail.

The Effect of Preanalytical Factors on Stability of the Proteome and Selected Metabolites in Cerebrospinal Fluid (CSF)

To standardize the use of cerebrospinal fluid (CSF) for biomarker research, a set of stability studies have been performed on porcine samples to investigate the influence of common sample handling procedures on proteins, peptides, metabolites and free amino acids. This study focuses at the effect on proteins and peptides, analyzed by applying label-free quantitation using microfluidics nanoscale liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (chipLC-MS) as well as matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FT-ICR-MS) and Orbitrap LC-MS/MS to trypsin-digested CSF samples. The factors assessed were a 30 or 120 min time delay at room temperature before storage at -80 degrees C after the collection of CSF in order to mimic potential delays in the clinic (delayed storage), storage at 4 degrees C after trypsin digestion to mimic the time that samples remain in the cooled autosampler of the analyzer, and repeated freeze-thaw cycles to mimic storage and handling procedures in the laboratory. The delayed storage factor was also analyzed by gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) for changes of metabolites and free amino acids, respectively. Our results show that repeated freeze/thawing introduced changes in transthyretin peptide levels. The trypsin digested samples left at 4 degrees C in the autosampler showed a time-dependent decrease of peak areas for peptides from prostaglandin D-synthase and serotransferrin. Delayed storage of CSF led to changes in prostaglandin D-synthase derived peptides as well as to increased levels of certain amino acids and metabolites. The changes of metabolites, amino acids and proteins in the delayed storage study appear to be related to remaining white blood cells. Our recommendations are to centrifuge CSF samples immediately after collection to remove white blood cells, aliquot, and then snap-freeze the supernatant in liquid nitrogen for storage at -80 degrees C. Preferably samples should not be left in the autosampler for more than 24 h and freeze/thaw cycles should be avoided if at all possible.

TiO2 photocatalysis damages lipids and proteins in Escherichia coli

ABSTRACT This study investigates the mechanisms of UV-A (315 to 400 nm) photocatalysis with titanium dioxide (TiO2) applied to the degradation of Escherichia coli and their effects on two key cellular components: lipids and proteins. The impact of TiO2 photocatalysis on E. coli survival was monitored by counting on agar plate and by assessing lipid peroxidation and performing proteomic analysis. We observed through malondialdehyde quantification that lipid peroxidation occurred during the photocatalytic process, and the addition of superoxide dismutase, which acts as a scavenger of the superoxide anion radical (O2·−), inhibited this effect by half, showing us that O2·− radicals participate in the photocatalytic antimicrobial effect. Qualitative analysis using two-dimensional electrophoresis allowed selection of proteins for which spot modifications were observed during the applied treatments. Two-dimensional electrophoresis highlighted that among the selected protein spots, 7 and 19 spots had already disappeared in the dark in the presence of 0.1 g/liter and 0.4 g/liter TiO2, respectively, which is accounted for by the cytotoxic effect of TiO2. Exposure to 30 min of UV-A radiation in the presence of 0.1 g/liter and 0.4 g/liter TiO2 increased the numbers of missing spots to 14 and 22, respectively. The proteins affected by photocatalytic oxidation were strongly heterogeneous in terms of location and functional category. We identified several porins, proteins implicated in stress response, in transport, and in bacterial metabolism. This study reveals the simultaneous effects of O2·− on lipid peroxidation and on the proteome during photocatalytic treatment and therefore contributes to a better understanding of molecular mechanisms in antibacterial photocatalytic treatment.

A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics

In this paper, we compare the performance of six different feature selection methods for LC-MS-based proteomics and metabolomics biomarker discovery—t test, the Mann–Whitney–Wilcoxon test (mww test), nearest shrunken centroid (NSC), linear support vector machine–recursive features elimination (SVM-RFE), principal component discriminant analysis (PCDA), and partial least squares discriminant analysis (PLSDA)—using human urine and porcine cerebrospinal fluid samples that were spiked with a range of peptides at different concentration levels. The ideal feature selection method should select the complete list of discriminating features that are related to the spiked peptides without selecting unrelated features. Whereas many studies have to rely on classification error to judge the reliability of the selected biomarker candidates, we assessed the accuracy of selection directly from the list of spiked peptides. The feature selection methods were applied to data sets with different sample sizes and extents of sample class separation determined by the concentration level of spiked compounds. For each feature selection method and data set, the performance for selecting a set of features related to spiked compounds was assessed using the harmonic mean of the recall and the precision (f-score) and the geometric mean of the recall and the true negative rate (g-score). We conclude that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes (n = 6), but their performance improves markedly with increasing sample size up to a point (n > 12) at which they outperform the other methods. PCDA and PLSDA select small feature sets with high precision but miss many true positive features related to the spiked peptides. NSC strikes a reasonable compromise between recall and precision for all data sets independent of spiking level and number of samples. Linear SVM-RFE performs poorly for selecting features related to the spiked compounds, even though the classification error is relatively low.

Supercritical fluid extraction of hydrocarbons and 2-alkylcyclobutanones for the detection of irradiated foodstuffs

Supercritical carbon dioxide can be used to carry out a selective and fast extraction (30 min) of volatile hydrocarbons and 2-alkylcyclobutanones contained in irradiated foods. After elimination of the traces of triglycerides still contained in the extracts on a silica column, the compounds were analysed by gas chromatography-mass spectroscopy (2-alkylcyclobutanones) and gas chromatography-flame ionization detection (volatile hydrocarbons). The present method was applied successfully to freeze-dried samples (1 g or less) of cheese, chicken, avocados and to various ingredients (chocolate, liquid whole eggs) included in non-irradiated cookies. It was faster (4-5 h) than the reference methods EN 1784 (volatile hydrocarbons) and EN 1785 (2-alkylcyclobutanones), which take 1.5 days each. The minimal dose detectable by this method is, in addition, slightly lower than those of the reference methods.

The Impact of Delayed Storage on the Measured Proteome and Metabolome of Human Cerebrospinal Fluid

BACKGROUND Because cerebrospinal fluid (CSF) is in close contact with diseased areas in neurological disorders, it is an important source of material in the search for molecular biomarkers. However, sample handling for CSF collected from patients in a clinical setting might not always be adequate for use in proteomics and metabolomics studies. METHODS We left CSF for 0, 30, and 120 min at room temperature immediately after sample collection and centrifugation/removal of cells. At 2 laboratories CSF proteomes were subjected to tryptic digestion and analyzed by use of nano-liquid chromatography (LC) Orbitrap mass spectrometry (MS) and chipLC quadrupole TOF-MS. Metabolome analysis was performed at 3 laboratories by NMR, GC-MS, and LC-MS. Targeted analyses of cystatin C and albumin were performed by LC-tandem MS in the selected reaction monitoring mode. RESULTS We did not find significant changes in the measured proteome and metabolome of CSF stored at room temperature after centrifugation, except for 2 peptides and 1 metabolite, 2,3,4-trihydroxybutanoic (threonic) acid, of 5780 identified peptides and 93 identified metabolites. A sensitive protein stability marker, cystatin C, was not affected. CONCLUSIONS The measured proteome and metabolome of centrifuged human CSF is stable at room temperature for up to 2 hours. We cannot exclude, however, that changes undetectable with our current methodology, such as denaturation or proteolysis, might occur because of sample handling conditions. The stability we observed gives laboratory personnel at the collection site sufficient time to aliquot samples before freezing and storage at -80 °C.

Two-dimensional method for time aligning liquid chromatography-mass spectrometry data.

We describe a new time alignment method that takes advantage of both dimensions of LC-MS data to resolve ambiguities in peak matching while remaining computationally efficient. This approach, Warp2D, combines peak extraction with a two-dimensional correlation function to provide a reliable alignment scoring function that is insensitive to spurious peaks and background noise. One-dimensional alignment methods are often based on the total-ion-current elution profile of the spectrum and are unable to distinguish peaks of different masses. Our approach uses one-dimensional alignment in time, but with a scoring function derived from the overlap of peaks in two dimensions, thereby combining the specificity of two-dimensional methods with the computational performance of one-dimensional methods. The peaks are approximated as two-dimensional Gaussians of varying width. This approximation allows peak overlap (the measure of alignment quality) to be calculated analytically, without computationally intensive numerical integration in two dimensions. To demonstrate the general applicability of Warp2D, we chose a variety of complex samples that have substantial biological and analytical variability, including human serum and urine. We show that Warp2D works well with these diverse sample sets and with minimal tuning of parameters, based on the reduced standard deviation of peak elution times after warping. The combination of high computational speed, robustness with complex samples, and lack of need for detailed tuning makes this alignment method well suited to high-throughput LC-MS studies.

Multi-omic data analysis using Galaxy.

[Extract] Comprehensive multi-omic data acquisition has become a reality, largely driven by the availability of high-throughput sequencing technologies for genomes and transcriptomes1, and high-resolution mass spectrometry (MS)2,3 for the in-depth characterization of proteomes and metabolomes. Integrating genomic and proteomic data enables proteogenomic 4 and metaproteomic approaches 4, whereas integrating metabolomic and transcriptomic or proteomic data links biochemical activity profiles to expressed genes and proteins 6. Despite the potential for new discoveries, integrated analysis of raw multi-omic data is an often overlooked challenge 7, demanding the use of disparate software programs and requiring computational resources beyond the capacity of most biological research laboratories. For these reasons, multi-omic approaches remain out of reach for many. Here, we describe how Galaxy 8 can be used as one solution to this problem.