Rapid Identification of Protein Biomarkers of Escherichia coil O157:H7 by Matrix-Assisted Laser Desorption Ionization-Time-of-Flight-Time-of-Flight Mass Spectrometry and Top-Down Proteomics
ABSTRACT Six protein biomarkers from two strains of Escherichia coli O157:H7 and one non-O157:H7, nonpathogenic strain of E. coli have been identified by matrix-assisted laser desorption ionization time-of-flight-time-of-flight tandem mass spectrometry (MALDI-TOF-TOF-MS/MS) and top-down proteomics. Proteins were extracted from bacterial cell lysates, ionized by MALDI, and analyzed by MS/MS. Protein biomarker ions were identified from their sequence-specific fragment ions by comparison to a database of in silico fragment ions derived from bacterial protein sequences. Web-based software, developed in-house, was used to rapidly compare the mass-to-charge (m/z) of MS/MS fragment ions to the m/z of in silico fragment ions derived from hundreds of bacterial protein sequences. A peak matching algorithm and a p-value algorithm were used to independently score and rank identifications on the basis of the number of MS/MS-in silico matches. The six proteins identified were the acid stress chaperone-like proteins, HdeA and HdeB; the cold shock protein, CspC; the YbgS (or homeobox protein); the putative stress-response protein YjbJ (or CsbD family protein); and a protein of unknown function, YahO. HdeA, HdeB, YbgS, and YahO proteins were found to be modified post-translationally with removal of an N-terminal signal peptide. Gene sequencing of hdeA, hdeB, cspC, ybgS, yahO, and yjbJ for 11 strains of E. coli O157:H7 and 7 strains of the "near-neighbor" serotype O55:H7 revealed a high degree sequence homology between these two serotypes. Although it was not possible to distinguish O157:H7 from O55:H7 from these six biomarkers, it was possible to distinguish E. coli O157:H7 from a nonpathogenic E. coli by top-down proteomics of the YahO and YbgS. In the case of the YahO protein, a single amino acid residue substitution in its sequence (resulting in a molecular weight difference of only 1 Da) was sufficient to distinguish E. coli O157:H7 from a non-O157:H7, nonpathogenic E. coli by MALDI-TOF-TOF-MS/MS, whereas this would be difficult to distinguish by MALDI-TOF-MS. Finally, a protein biomarker ion at m/z approximately 9060 observed in the MS spectra of non-O157:H7 E. coli strains but absent from MS spectra of E. coli O157:H7 strains was identified by top-down analysis to be the HdeB acid stress chaperone-like protein consistent with previous identifications by gene sequencing and bottom-up proteomics.
SourceAvailable from: PubMed Central[Show abstract] [Hide abstract]
ABSTRACT: Enterohemorrhagic Escherichia coli (EHEC), causes a potentially life-threatening infection in humans worldwide. Serovar O157:H7, and to a lesser extent serovars O26 and O111, are the most commonly reported EHEC serovars responsible for a large number of outbreaks. We have established a rapid discrimination method for E. coli serovars O157, O26 and O111 from other E. coli serovars, based on the pattern matching of mass spectrometry (MS) differences and the presence/absence of biomarker proteins detected in matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOF MS). Three biomarkers, ribosomal proteins S15 and L25, and acid stress chaperone HdeB, with MS m/z peaks at 10138.6/10166.6, 10676.4/10694.4 and 9066.2, respectively, were identified as effective biomarkers for O157 discrimination. To distinguish serovars O26 and O111 from the others, DNA-binding protein H-NS, with an MS peak at m/z 15409.4/15425.4 was identified. Sequence analysis of the O157 biomarkers revealed that amino acid changes: Q80R in S15, M50I in L25 and one mutation within the start codon ATG to ATA in the encoded HdeB protein, contributed to the specific peak pattern in O157. We demonstrated semi-automated pattern matching using these biomarkers and successfully discriminated total 57 O157 strains, 20 O26 strains and 6 O111 strains with 100% reliability by conventional MALDI-TOF MS analysis, regardless of the sample conditions. Our simple strategy, based on the S10-spc-alpha operon gene-encoded ribosomal protein mass spectrum (S10-GERMS) method, therefore allows for the rapid and reliable detection of this pathogen and may prove to be an invaluable tool both clinically and in the food industry.PLoS ONE 11/2014; 9(11):e113458. DOI:10.1371/journal.pone.0113458 · 3.53 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: RATIONALEThe identification of bacteria based on mass spectra produced by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) has become routine since its introduction in 1996. The major drawback is that bacterial patterns produced by MALDI are dependent on sample preparation prior to analysis. This results in poor reproducibility in identifying bacterial types and between laboratories. The need for a more broadly applicable and useful sample handling procedure is warranted.METHODS Thymol was added to the suspension solvent of bacteria prior to MALDI analysis. The suspension solvent consisted of ethanol, water and TFA. The bacterium was added to the thymol suspension solvent and heated. An aliquot of the bacterial suspension was mixed directly with the matrix solution at a 9:1 ratio, matrix/bacteria solution, respectively. The mixture was then placed on the MALDI plate and allowed to air dry before MALDI analysis.RESULTSThe thymol method improved the quality of spectra and number of peaks when compared to other sample preparation procedures studied. The bacterium-identifying biomarkers assigned to four strains of E. coli were statistically 95% reproducible analyzed on three separate days. The thymol method successfully differentiated between the four E. coli strains. In addition, the thymol procedure could identify nine out of ten S. enterica serovars over a 3-day period and nine S. Typhimurium strains from the other ten serovars 90% of the time over the same period.CONCLUSIONS The thymol method can identify certain bacteria at the sub-species level and yield reproducible results over time. It improves the quality of spectra by increasing the number of peaks when compared to the other sample preparation methods assessed in this study. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.Rapid Communications in Mass Spectrometry 12/2014; 28(23). DOI:10.1002/rcm.7060 · 2.64 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: We have measured the relative abundance of the B-subunits and mRNA transcripts of two Stx2 subtypes present in Shiga toxin-producing Escherichia coli (STEC) O157:H- strain E32511 using matrix-assisted laser desorption/ionization time-of-flight-time-of-flight tandem mass spectrometry (MALDI-TOF-TOF-MS/MS) with post source decay (PSD) and real time-quantitative polymerase chain reaction (RT-qPCR). Stx2a and Stx2c in STEC strain E32511 were quantified from the integrated peak area of their singly charged disulfide-intact B-subunit ions at m/z ~7819 and m/z ~7774, respectively. We found that the Stx2a subtype was 21-fold more abundant than the Stx2c subtype. The two amino acid substitutions (16D ↔ 16 N and 24D ↔ 24A) that distinguish Stx2a from Stx2c not only result in a mass difference of 45 Da between their respective B-subunits but also result in distinctly different fragmentation channels by MS/MS-PSD because both substitutions involve an aspartic acid (D) residue. Importantly, these two substitutions have also been linked to differences in subtype toxicity. We measured the relative abundances of mRNA transcripts using RT-qPCR and determined that the stx2a transcript is 13-fold more abundant than stx2c transcript. In silico secondary structure analysis of the full mRNA operons of stx2a and stx2c suggest that transcript structural differences may also contribute to a relative increase of Stx2a over Stx2c. In consequence, toxin expression may be under both transcriptional and post-transcriptional control.Journal of the American Society for Mass Spectrometry 02/2015; DOI:10.1007/s13361-015-1076-3 · 3.19 Impact Factor