A simple shotgun proteomics method for rapid bacterial identification

Shotgun proteomics for the identification of yeasts responsible for pink/red discoloration in commercial dairy products

Pink/red discoloration encompasses a series of relatively common spoilage defects of commercial dairy products. In this study, we used shotgun proteomics to identify the microorganism responsible for the production of intensely red-coloured slimes found on the surface of freshly opened commercial spreadable cheese and yogurt samples. Proteome-wide characterization of microbial proteins allowed to identify 1042 and 687 gene products from Rhodotorula spp. in spreadable cheese and yogurt samples, respectively, while no significant protein scores from other microorganisms were recorded. Subsequent microbiological analyses and sequencing of the 26S rRNA gene region supported the proteomic results demonstrating that the microorganism involved was Rhodotorula mucilaginosa, a carotenoid - producing basidiomycetous that can be potentially pathogenic to humans, especially for immunocompromised individuals. This is the first time that shotgun proteomics has been used to identify a microorganism responsible for spoilage in dairy products, proposing it as a relatively fast, sensitive, and reliable alternative or complement to conventional methods for microbial identification.

Direct Identification of Urinary Tract Pathogens by MALDI-TOF/TOF Analysis and De Novo Peptide Sequencing

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy.

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

Current Status of Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry (MALDI-TOF MS) in Clinical Diagnostic Microbiology

Mass spectrometry (MS), a core technology for proteomics and metabolomics, is currently being developed for clinical applications. The identification of microorganisms in clinical samples using matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry (MALDI-TOF MS) is a representative MS-based proteomics application that is relevant to daily clinical practice. This technology has the advantages of convenience, speed, and accuracy when compared with conventional biochemical methods. MALDI-TOF MS can shorten the time used for microbial identification by about 1 day in routine workflows. Sample preparation from microbial colonies has been improved, increasing the accuracy and speed of identification. MALDI-TOF MS is also used for testing blood, cerebrospinal fluid, and urine, because it can directly identify the microorganisms in these liquid samples without prior culture or subculture. Thus, MALDI-TOF MS has the potential to improve patient prognosis and decrease the length of hospitalization and is therefore currently considered an essential tool in clinical microbiology. Furthermore, MALDI-TOF MS is currently being combined with other technologies, such as flow cytometry, to expand the scope of clinical applications.

TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples

Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.

Mass spectrometry-based microbiological testing for blood stream infection

BACKGROUND

The most successful application of mass spectrometry (MS) in laboratory medicine is identification (ID) of microorganisms using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) in blood stream infection. We describe MALDI-TOF MS-based bacterial ID with particular emphasis on the methods so far developed to directly identify microorganisms from positive blood culture bottles with MALDI-TOF MS including our own protocols. We touch upon the increasing roles of Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) as well.

MAIN BODY

Because blood culture bottles contain a variety of nonbacterial proteins that may interfere with analysis and interpretation, appropriate pretreatments are prerequisites for successful ID. Pretreatments include purification of bacterial pellets and short-term subcultures to form microcolonies prior to MALDI-TOF MS analysis. Three commercial protocols are currently available: the Sepsityper® kit (Bruker Daltonics), the Vitek MS blood culture kit (bioMerieux, Inc.), and the rapid BACpro® II kit (Nittobo Medical Co., Tokyo). Because these commercially available kits are costly and bacterial ID rates using these kits are not satisfactory, particularly for Gram-positive bacteria, various home-brew protocols have been developed: 1. Stepwise differential sedimentation of blood cells and microorganisms, 2. Combination of centrifugation and lysis procedures, 3. Lysis-vacuum filtration, and 4. Centrifugation and membrane filtration technique (CMFT). We prospectively evaluated the performance of this CMFT protocol compared with that of Sepsityper® using 170 monomicrobial positive blood cultures. Although preliminary, the performance of the CMFT was significantly better than that of Sepsityper®, particularly for Gram-positive isolates. MALDI-TOF MS-based testing of polymicrobial blood specimens, however, is still challenging. Also, its contribution to assessment of susceptibility and resistance to antibiotics is still limited. For this purpose, liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) should be more useful because this approach can identify as many as several thousand peptide sequences.

CONCLUSION

MALDI-TOF MS is now an essential tool for rapid bacterial ID of pathogens that cause blood stream infection. For the purpose of assessment of susceptibility and resistance to antibiotics of the pathogens, the roles of liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) will increase in the future.

Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks

To improve the preparedness against exposure to highly pathogenic bacteria and to anticipate the wide variety of bacteria that can cause bloodstream infections (BSIs), a safe, unbiased and highly accurate identification method was developed. Our liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based method can identify highly pathogenic bacteria, their near-neighbors and bacteria that are common causes of BSIs directly from positive blood culture flasks. The developed Peptide-Based Microbe Detection Engine (http://proteome2pathogen.com) relies on a two-step workflow: a genus-level search followed by a species-level search. This strategy enables the rapid identification of microorganisms based on the analyzed proteome. This method was successfully used to identify strains of Bacillus anthracis, Brucella abortus, Brucella melitensis, Brucella suis, Burkholderia pseudomallei, Burkholderia mallei, Francisella tularensis, Yersinia pestis and closely related species from simulated blood culture flasks. This newly developed LC-MS/MS method is a safe and rapid method for accurately identifying bacteria directly from positive blood culture flasks.

Mass Spectrometry to Study the Bacterial Proteome from a Single Colony

Mass spectrometry (MS) has been widely used in recent years for bacterial identification and typing. Single bacterial colonies are regarded as pure cultures of bacteria grown from single cells. In this chapter, we describe a method for identifying bacteria at the species level with 100% accuracy using the proteomes of bacterial cultures from single colonies. In this chapter, six reference strains of gram-negative and gram-positive bacteria are analyzed, producing results of high reproducibility, as examples of bacterial identification through the application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) and a custom database. Details on sample preparation and identification of Streptococcus pneumoniae are also described.

Identification of Salmonella Taxon-Specific Peptide Markers to the Serovar Level by Mass Spectrometry

We present an LC-MS/MS pipeline to identify taxon-specific tryptic peptide markers for the identification of Salmonella at the genus, species, subspecies, and serovar levels of specificity. Salmonella enterica subsp. enterica serovars Typhimurium and its four closest relatives, Saintpaul, Heidelberg, Paratyphi B, and Muenchen, were evaluated. A decision-tree approach was used to identify peptides common to the five Salmonella proteomes for evaluation as genus-, species-, and subspecies-specific markers. Peptides identified for two or fewer Salmonella strains were evaluated as potential serovar markers. Currently, there are approximately 140 000 assembled bacterial genomes publicly available, more than 8500 of which are for Salmonella. Consequently, the specificity of each candidate peptide marker was confirmed across all publicly available protein sequences in the NCBI nonredundant (nr) database. The performance of a subset of candidate taxon-specific peptide markers was evaluated in a targeted mass-spectrometry method. The presented workflow offers a marked improvement in specificity over existing MALDI-TOF-based bacterial identification platforms for the identification of closely related Salmonella serovars.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

Identification of microorganisms grown in blood culture flasks using liquid chromatography–tandem mass spectrometry

Aim: Bloodstream infections are a common cause of disease and a fast and accurate identification of the causative agent or agents of bloodstream infections would aid the start of adequate treatment. Materials & methods: A liquid chromatography–tandem mass spectrometry (LC–MS/MS) shotgun proteomics method was developed for the identification of bacterial species directly from blood cultures that were simulated by inoculating blood culture bottles with single or multiple clinically relevant microorganisms. Results: Using LC–MS/MS, the single species were correctly identified in 100% of the blood cultures, whereas for polymicrobial infections, 78% of both species were correctly identified in blood cultures. Conclusion: The LC–MS/MS method allows for the identification of the causative agent of positive blood cultures.

Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics

Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary for accurate diagnosis and efficient treatment of infectious diseases. Bottom-up tandem mass spectrometry (MS) proteomics enables rapid characterization of large parts of the expressed genes of microorganisms. However, the generated data are highly fragmented, making downstream analyses complex. Here we present TCUP, a new computational method for typing and characterizing bacteria using proteomics data from bottom-up tandem MS. TCUP compares the generated protein sequence data to reference databases and automatically finds peptides suitable for characterization of taxonomic composition and identification of expressed antimicrobial resistance genes. TCUP was evaluated using several clinically relevant bacterial species (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae), using both simulated data generated by in silico peptide digestion and experimental proteomics data generated by liquid chromatography-tandem mass spectrometry (MS/MS). The results showed that TCUP performs correct peptide classifications at rates between 90.3 and 98.5% at the species level. The method was also able to estimate the relative abundances of individual species in mixed cultures. Furthermore, TCUP could identify expressed β-lactamases in an extended spectrum β-lactamase-producing (ESBL) E. coli strain, even when the strain was cultivated in the absence of antibiotics. Finally, TCUP is computationally efficient, easy to integrate in existing bioinformatics workflows, and freely available under an open source license for both Windows and Linux environments.

Elucidating Host-Pathogen Interactions Based on Post-Translational Modifications Using Proteomics Approaches

Microbes with the capability to survive in the host tissue and efficiently subvert its innate immune responses can cause various health hazards. There is an inherent need to understand microbial infection patterns and mechanisms in order to develop efficient therapeutics. Microbial pathogens display host specificity through a complex network of molecular interactions that aid their survival and propagation. Co-infection states further lead to complications by increasing the microbial burden and risk factors. Quantitative proteomics based approaches and post-translational modification analysis can be efficiently applied to gain an insight into the molecular mechanisms involved. The measurement of the proteome and post-translationally modified proteome dynamics using mass spectrometry, results in a wide array of information, such as significant changes in protein expression, protein abundance, the modification status, the site occupancy level, interactors, functional significance of key players, potential drug targets, etc. This mini review discusses the potential of proteomics to investigate the involvement of post-translational modifications in bacterial pathogenesis and host-pathogen interactions.

Tatumella saanichensis sp. nov., isolated from a cystic fibrosis patient

Polyphasic taxonomic analysis was performed on a clinical isolate (NML 06-3099T) from a cystic fibrosis patient, including whole-genome sequencing, proteomics, phenotypic testing, electron microscopy, chemotaxonomy and a clinical investigation. Comparative whole-genome sequence analysis and multilocus sequence analysis (MLSA) between Tatumella ptyseos ATCC 33301T and clinical isolate NML 06-3099T suggested that the clinical isolate was closely related to, but distinct from, the species T. ptyseos. By 16S rRNA gene sequencing, the clinical isolate shared 98.7 % sequence identity with T. ptyseos ATCC 33301T. A concatenate of six MLSA loci (totalling 4500 bp) revealed < 93.9 % identity between T. ptyseos ATCC 33301T, other members of the genus and the clinical isolate. A whole-genome sequence comparison between NML 06-3099T and ATCC 33301T determined that the average nucleotide identity was 76.24 %. The overall DNA G+C content of NML 06-3099T was 51.27 %, consistent with members of the genus Tatumella. By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis, NML 06-3099T had a genus-level match, but not a species-level match, to T. ptyseos. By shotgun proteomics, T. ptyseos ATCC 33301T and NML 06-3099T were found to have unique proteomes. The two strains had similar morphologies and multiple fimbriae, as observed by transmission electron microscopy, but were distinguishable by phenotypic testing. Cellular fatty acids found were typical for members of the Enterobacteriaceae. NML 06-3099T was susceptible to commonly used antibiotics. Based on these data, NML 06-3099T represents a novel species in the genus Tatumella, for which the name Tatumella saanichensis sp. nov. is proposed (type strain NML 06-3099T = CCUG 55408T = DSM 19846T).

Toxicity of metal oxide nanoparticles: mechanisms, characterization, and avoiding experimental artefacts

Metal oxide nanomaterials are widely used in practical applications and represent a class of nanomaterials with the highest global annual production. Many of those, such as TiO2 and ZnO, are generally considered non-toxic due to the lack of toxicity of the bulk material. However, these materials typically exhibit toxicity to bacteria and fungi, and there have been emerging concerns about their ecotoxicity effects. The understanding of the toxicity mechanisms is incomplete, with different studies often reporting contradictory results. The relationship between the material properties and toxicity appears to be complex and diifficult to understand, which is partly due to incomplete characterization of the nanomaterial, and possibly due to experimental artefacts in the characterization of the nanomaterial and/or its interactions with living organisms. This review discusses the comprehensive characterization of metal oxide nanomaterials and the mechanisms of their toxicity.

Reprint of "Assessment of marker proteins identified in whole cell extracts for bacterial speciation using liquid chromatography electrospray ionization tandem mass spectrometry"

Staphylococcal strains (CoNS) were speciated in this study. Digests of proteins released from whole cells were converted to tryptic peptides for analysis. Liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI MS/MS, Orbitrap) was employed for peptide analysis. Data analysis was performed employing the open-source software X!Tandem which uses sequenced genomes to generate a virtual peptide database for comparison to experimental data. The search database was modified to include the genomes of the 11 Staphylococcus species most commonly isolated from man. The number of total peptides matching each protein along with the number of peptides specifically matching to the homologue (or homologues) for strains of the same species were assessed. Any peptides not matching to the species examined were considered conflict peptides. The proteins typically identified with the largest percentage of sequence coverage, number of matched peptides and number of peptides corresponding to only the correct species were elongation factor Tu (EF Tu) and enolase (Enol). Additional proteins with consistently observed peptides as well as peptides matching only homologues from the same species were citrate synthase (CS) and 1-pyrroline-5-carboxylate dehydrogenase (1P5CD). Protein markers, previously identified from gel slices, (aconitate hydratase and oxoglutarate dehydrogenase) were found to provide low confidence scores when employing whole cell digests. The methodological approach described here provides a simple yet elegant way of identification of staphylococci. However, perhaps more importantly the technology should be applicable universally for identification of any bacterial species.

Assessment of marker proteins identified in whole cell extracts for bacterial speciation using liquid chromatography electrospray ionization tandem mass spectrometry

Staphylococcal strains (CoNS) were speciated in this study. Digests of proteins released from whole cells were converted to tryptic peptides for analysis. Liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI MS/MS, Orbitrap) was employed for peptide analysis. Data analysis was performed employing the open-source software X!Tandem which uses sequenced genomes to generate a virtual peptide database for comparison to experimental data. The search database was modified to include the genomes of the 11 Staphylococcus species most commonly isolated from man. The number of total peptides matching each protein along with the number of peptides specifically matching to the homologue (or homologues) for strains of the same species were assessed. Any peptides not matching to the species examined were considered conflict peptides. The proteins typically identified with the largest percentage of sequence coverage, number of matched peptides and number of peptides corresponding to only the correct species were elongation factor Tu (EF Tu) and enolase (Enol). Additional proteins with consistently observed peptides as well as peptides matching only homologues from the same species were citrate synthase (CS) and 1-pyrroline-5-carboxylate dehydrogenase (1P5CD). Protein markers, previously identified from gel slices, (aconitate hydratase and oxoglutarate dehydrogenase) were found to provide low confidence scores when employing whole cell digests. The methodological approach described here provides a simple yet elegant way of identification of staphylococci. However, perhaps more importantly the technology should be applicable universally for identification of any bacterial species.