Table_1_A Novel Iron Transporter SPD_1590 in Streptococcus pneumoniae Contributing to Bacterial Virulence Properties.DOCX

<p>Streptococcus pneumoniae, a Gram-positive human pathogen, has evolved three main transporters for iron acquisition from the host: PiaABC, PiuABC, and PitABC. Our previous study had shown that the mRNA and protein levels of SPD_1590 are significantly upregulated in the ΔpiuA/ΔpiaA/ΔpitA triple mutant, suggesting that SPD_1590 might be a novel iron transporter in S. pneumoniae. In the present study, using spd1590-knockout, -complemented, and -overexpressing strains and the purified SPD_1590 protein, we show that SPD_1590 can bind hemin, probably supplementing the function of PiuABC, to provide the iron necessary for the bacterium. Furthermore, the results of iTRAQ quantitative proteomics and cell-infection studies demonstrate that, similarly to other metal-ion uptake proteins, SPD_1590 is important for bacterial virulence properties. Overall, these results provide a better understanding of the biology of this clinically important bacterium.</p

Binomial Probability Distribution Model-Based Protein Identification Algorithm for Tandem Mass Spectrometry Utilizing Peak Intensity Information

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/

Protein biogenesis under oxidative stress.

<p>(A) Polysome profiles under normal and oxidative stress conditions. (B) SufI synthesis in an <i>in vitro</i> translation system, with or without oxidative stress. The SufI gene fused with a C-terminal His₆-tag sequence constructed in a pET-28b plasmid (Novagen) was used as a template in an <i>in vitro</i> translation reaction. The protein product was detected by western blotting with an anti-His₆ antibody. (C) Detergent-insoluble proteins under normal (N) and oxidative stress (OS) conditions. Insoluble proteins extracted from <i>E</i>. <i>coli</i> BL21(DE3) and wild-type BW25113 cells grown at 47°C for 10 min were used as positive controls (Heat). Soluble proteins (Sup) were loaded on the same gel as controls. (D) Western blot analysis of the molecular chaperones GroEL and DnaK in <i>E</i>. <i>coli</i> BL21(DE3) and wild-type BW25113 cells grown under normal (N) conditions, oxidative stress (OS) condition, or heat stress (heat) at 47°C for 30 min.</p

Binomial Probability Distribution Model-Based Protein Identification Algorithm for Tandem Mass Spectrometry Utilizing Peak Intensity Information

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/

Protein synthesis measurement under normal condition and oxidative stress using ¹⁵N metabolic labeling and mass spectrometry.

<p>(A) Growth rate constant of <i>E</i>. <i>coli</i> BL21(DE3) cells grown under normal conditions in M9 minimal medium containing ¹⁴NH₄Cl (¹⁴N) or ¹⁵NH₄Cl (¹⁵N). (B) Experimental design for pulse labeling with ¹⁵N under normal and oxidative stress conditions. (C) Bacterial cells were transferred to M9 medium containing ¹⁵N, after which the ¹⁴N/¹⁵N ratio of the <i>E</i>. <i>coli</i> BL21(DE3) proteome was determined by mass spectrometry under normal conditions (upper plot) and oxidative stress induced by exposure to 0.5 mM H₂O₂ (lower plot). The green line indicates the best linear fit of the dataset. The Pearson <i>r</i> correlation coefficient and <i>P</i>-value of the fit are indicated in green text. (D) The same experiment represented in (C) was repeated using the wild-type <i>E</i>. <i>coli</i> BW25113 strain. (E) Examples showing changes in the ¹⁴N/¹⁵N ratio of 4 randomly selected proteins (AtpA, GpmA, Eda, and PurA) in BL21(DE3) cells over time. The fit was determined according to <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005302#pgen.1005302.e008" target="_blank">Eq 6</a> (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005302#sec010" target="_blank">Materials and Methods</a> section). The slope of the linear fit is the synthesis rate constant (<i>k</i>_syn). The black dots indicate the actual data points following protein quantification under normal condition, while the red dots indicate the data points measured under oxidative stress. Linear fits were determined for each dataset, and the Pearson correlation coefficients (<i>r</i>) are indicated in the diagram. (F) The Pearson correlation coefficients (<i>r</i>) values of all proteins were fitted using <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005302#pgen.1005302.e008" target="_blank">Eq 6</a>, under normal condition and oxidative stress. Data generated using the BL21(DE3) and wild-type BW25113 strains are shown. (G) <i>P</i> values (in -log₁₀ scale) of linear fits of all proteins using <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005302#pgen.1005302.e008" target="_blank">Eq 6</a> under normal and oxidative stress conditions. The green line denotes the significance threshold (<i>P</i> = 0.05). The data generated using the BL21(DE3) and wild-type BW25113 strains are shown. (H) Histograms of protein half-lives under normal (blue) and oxidative stress (red) conditions. (I) Comparison of protein half-lives measured under normal and oxidative stress conditions. The grey line is at a 45° angle, which demarks the positions where the protein half-lives were unchanged. Half-lives longer than 500 min are shown as 500 min.</p

Binomial Probability Distribution Model-Based Protein Identification Algorithm for Tandem Mass Spectrometry Utilizing Peak Intensity Information

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/

Under oxidative stress, tRNAs are degraded <i>in vivo</i>, but not in the cell-free <i>in vitro</i> translation system.

<p>(A) Percentages of cleaved tRNA reads. X-axes indicate the number of bases cleaved from the 3′-termini. The Y-axes denote the fraction of such tRNA reads among all tRNA reads relative to each specific tRNA species. Three tRNAs are shown as examples. The observed distributions of the cleavage lengths were compared between normal (blue bars) and oxidative stress conditions (red bars). (B,C) The degradation of full-length tRNA in <i>in vitro</i> translation system. Eleven tRNAs were randomly selected and quantified by qRT-PCR. The difference observed between the oxidative stress and normal conditions are expressed in terms of ΔΔC_T values, normalized using spike-in RNA (B) or 5S rRNA (C). The data are shown as the mean ± SD.</p

Optimization of the oxidative stress conditions.

<p>(A) Cell growth in the presence of different concentrations of H₂O₂. <i>E</i>. <i>coli</i> BL21(DE3) cells were cultivated in M9 minimal medium. Cell growth in the presence of 0.25–1 mM H₂O₂ was monitored by measuring OD₆₀₀ values. (B) Cell viability assay using propidium iodide staining and flow cytometry. Histograms showing cells exposed to 0.5 mM H₂O₂ for 15, 30, 60, or 90 min were obtained (cyan lines). Cells grown under normal condition was used as a positive control (black line), and cells killed by incubation at 65°C for 15 min were used as a negative control (red line).</p

Decrease of full-length tRNAs under oxidative stress.

<p>The tRNAs are displayed in alphabetical order and named according to their amino acids and anticodons. (A) Decreased tRNA levels following 15 min of H₂O₂-induced oxidative stress, relative to tRNA levels observed under normal conditions. The data were generated by next-generation sequencing. (B) qRT-PCR validation of the tRNA decreases induced by 15 min of oxidative stress, using independent biological replicates. The data are shown as the mean ± SD. (C) Decreased tRNA levels following 30 min of OS, measured by next-generation sequencing. (D) Agarose gel electrophoresis of the total RNA extracted from the same number of cells grown under normal conditions (N) or under 15 min of oxidative stress (OS). (E) Polyacrylamide gel electrophoresis of the sample represented in panel (D) to resolve the tRNAs. The size range of tRNAs is indicated. (F) Growth curves before and after the application of oxidative stress. The red line shows the moving average of the OD₆₃₅ observed over 5 min. (G) The tRNA concentration after 90 min of oxidative stress, after which the bacteria had fully adapted to the stress and growth was resumed. (H) qRT-PCR validation of the results shown in panel (G), using an independent biological replicate. The data are shown as the mean ± SD.</p

Binomial Probability Distribution Model-Based Protein Identification Algorithm for Tandem Mass Spectrometry Utilizing Peak Intensity Information

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/