New approaches to identification of bacterial pathogens by surface enhanced laser desorption/ionization time of flight mass spectrometry in concert with artificial neural networks, with special reference to Neisseria gonorrhoeae

“Omic” Approaches to Bacteria and Antibiotic Resistance Identification

The quick and accurate identification of microorganisms and the study of resistance to antibiotics is crucial in the economic and industrial fields along with medicine. One of the fastest-growing identification methods is the spectrometric approach consisting in the matrix-assisted laser ionization/desorption using a time-of-flight analyzer (MALDI-TOF MS), which has many advantages over conventional methods for the determination of microorganisms presented. Thanks to the use of a multiomic approach in the MALDI-TOF MS analysis, it is possible to obtain a broad spectrum of data allowing the identification of microorganisms, understanding their interactions and the analysis of antibiotic resistance mechanisms. In addition, the literature data indicate the possibility of a significant reduction in the time of the sample preparation and analysis time, which will enable a faster initiation of the treatment of patients. However, it is still necessary to improve the process of identifying and supplementing the existing databases along with creating new ones. This review summarizes the use of “-omics” approaches in the MALDI TOF MS analysis, including in bacterial identification and antibiotic resistance mechanisms analysis.

Applications of machine learning in the chemical pathology laboratory

Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory's total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.

Detecting Biothreat Agents: From Current Diagnostics to Developing Sensor Technologies

Although a fundamental understanding of the pathogenicity of most biothreat agents has been elucidated and available treatments have increased substantially over the past decades, they still represent a significant public health threat in this age of (bio)terrorism, indiscriminate warfare, pollution, climate change, unchecked population growth, and globalization. The key step to almost all prevention, protection, prophylaxis, post-exposure treatment, and mitigation of any bioagent is early detection. Here, we review available methods for detecting bioagents including pathogenic bacteria and viruses along with their toxins. An introduction placing this subject in the historical context of previous naturally occurring outbreaks and efforts to weaponize selected agents is first provided along with definitions and relevant considerations. An overview of the detection technologies that find use in this endeavor along with how they provide data or transduce signal within a sensing configuration follows. Current "gold" standards for biothreat detection/diagnostics along with a listing of relevant FDA approved in vitro diagnostic devices is then discussed to provide an overview of the current state of the art. Given the 2014 outbreak of Ebola virus in Western Africa and the recent 2016 spread of Zika virus in the Americas, discussion of what constitutes a public health emergency and how new in vitro diagnostic devices are authorized for emergency use in the U.S. are also included. The majority of the Review is then subdivided around the sensing of bacterial, viral, and toxin biothreats with each including an overview of the major agents in that class, a detailed cross-section of different sensing methods in development based on assay format or analytical technique, and some discussion of related microfluidic lab-on-a-chip/point-of-care devices. Finally, an outlook is given on how this field will develop from the perspective of the biosensing technology itself and the new emerging threats they may face.

Proteomics of Neisseria gonorrhoeae: the treasure hunt for countermeasures against an old disease

Neisseria gonorrhoeae is an exquisitely adapted, strictly human pathogen and the causative agent of the sexually transmitted infection gonorrhea. This ancient human disease remains a serious problem, occurring at high incidence globally and having a major impact on reproductive and neonatal health. N. gonorrhoeae is rapidly evolving into a superbug and no effective vaccine exists to prevent gonococcal infections. Untreated or inadequately treated gonorrhea can lead to severe sequelae, including pelvic inflammatory disease and infertility in women, epididymitis in men, and sight-threatening conjunctivitis in infants born to infected mothers. Therefore, there is an immediate need for accelerated research toward the identification of molecular targets for development of drugs with new mechanisms of action and preventive vaccine(s). Global proteomic approaches are ideally suited to guide these studies. Recent quantitative proteomics (SILAC, iTRAQ, and ICAT) have illuminated the pathways utilized by N. gonorrhoeae to adapt to different lifestyles and micro-ecological niches within the host, while comparative 2D SDS-PAGE analysis has been used to elucidate spectinomycin resistance mechanisms. Further, high-throughput examinations of cell envelopes and naturally released membrane vesicles have unveiled the ubiquitous and differentially expressed proteins between temporally and geographically diverse N. gonorrhoeae isolates. This review will focus on these different approaches, emphasizing the role of proteomics in the search for vaccine candidates. Although our knowledge of N. gonorrhoeae has been expanded, still far less is known about this bacterium than the closely related N. meningitidis, where genomics- and proteomics-driven studies have led to the successful development of vaccines.

MALDI TOF MS profiling of bacteria at the strain level: a review

Since the advent of the use of matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF MS) as a tool for microbial characterization, efforts to increase the taxonomic resolution of the approach have been made. The rapidity and efficacy of the approach have suggested applications in counter-bioterrorism, prevention of food contamination, and monitoring the spread of antibiotic-resistant bacteria. Strain-level resolution has been reported with diverse bacteria, using library-based and bioinformatics-enabled approaches. Three types of characterization at the strain level have been reported: strain categorization, strain differentiation, and strain identification. Efforts to enhance the library-based approach have involved sample pre-treatment and data reduction strategies. Bioinformatics approaches have leveraged the ever-increasing amount of publicly available genomic and proteomic data to attain strain-level characterization. Bioinformatics-enabled strategies have facilitated strain characterization via intact biomarker identification, bottom-up, and top-down approaches. Rigorous quantitative and advanced statistical analyses have fostered success at the strain level with both approaches. Library-based approaches can be limited by effects of sample preparation and culture conditions on reproducibility, whereas bioinformatics-enabled approaches are typically limited to bacteria, for which genetic and/or proteomic data are available. Biological molecules other than proteins produced in strain-specific manners, including lipids and lipopeptides, might represent other avenues by which strain-level resolution might be attained. Immunological and lectin-based chemistries have shown promise to enhance sensitivity and specificity. Whereas the limits of the taxonomic resolution of MALDI TOF MS profiling of bacteria appears bacterium-specific, recent data suggest that these limits might not yet have been reached.

Advances in mass spectrometry for the identification of pathogens

Mass spectrometry (MS) has become an important technique to identify microbial biomarkers. The rapid and accurate MS identification of microorganisms without any extensive pretreatment of samples is now possible. This review summarizes MS methods that are currently utilized in microbial analyses. Affinity methods are effective to clean, enrich, and investigate microorganisms from complex matrices. Functionalized magnetic nanoparticles might concentrate traces of target microorganisms from sample solutions. Therefore, nanoparticle-based techniques have a favorable detection limit. MS coupled with various chromatographic techniques, such as liquid chromatography and capillary electrophoresis, reduces the complexity of microbial biomarkers and yields reliable results. The direct analysis of whole pathogenic microbial cells with matrix-assisted laser desorption/ionization MS without sample separation reveals specific biomarkers for taxonomy, and has the advantages of simplicity, rapidity, and high-throughput measurements. The MS detection of polymerase chain reaction (PCR)-amplified microbial nucleic acids provides an alternative to biomarker analysis. This review will conclude with some current applications of MS in the identification of pathogens.

Sample preparation methods for MALDI-MS profiling of bacteria

Direct matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) bacterial cell or lysate analysis appears to meet all the criteria required for a rapid and reliable analytical microorganism identification and taxonomical classification tool. Few-minute analytical procedure providing information extending up to sub-species level underlines the potential of the MALDI-MS profiling in comparison with other methods employed in the field. However, the quality of MALDI-MS profiles and consequently the performance of the method are influenced by numerous factors, which involve particular steps of the sample preparation procedure. This review is aimed at advances in development and optimization of the MALDI-MS profiling methodology. Approaches improving the quality of the MALDI-MS profiles and universal feasibility of the method are discussed.

Is SELDI-TOF a valid tool for diagnostic biomarkers?

The genome revolution is providing fresh insights into host and parasite genomes, and new tools are becoming available for examining host-parasite interactions at the proteome level. Technologies such as surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) can be applied to discover biomarkers (alterations in both host and parasite proteomes) associated with parasitic diseases. Such biomarkers can represent host proteins, fragments of host proteins or parasite proteins that appear in body fluids or tissues following infection. Individual biomarkers or biomarker patterns not only have diagnostic utility (e.g. in active disease, prognosis, tests of cure) but can also provide unique insights into the mechanisms underlying host responses and pathogenesis.

Development of a method based on surface enhanced laser desorption and ionization time of flight mass spectrometry for rapid identification of Klebsiella pneumoniae

A method based on surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF MS) was developed for the rapid identification of Klebsiella pneumoniae by directly applying bacterial colonies without further protein extraction. A total of 40 K. pneumoniae and 114 other related microorganisms isolated clinically were analyzed by SELDI-TOF MS. An identification model for K. pneumoniae was established by artificial neural networks (ANNs) with classification accuracy of 100%. The model was blindly tested with 43 K. pneumoniae and 53 control bacteria again. The results showed that the model was successful with accuracy of 96.9%, sensitivity of 100% and specificity of 943%. This strategy is potential for rapid identification of K. pneumoniae.

Rapid identification of Staphylococcus aureus by surface enhanced laser desorption and ionization time of flight mass spectrometry

Staphylococcus aureus (S. aureus), a vital nosocomial pathogen, is responsible for several diseases. With the increasing isolation rate in clinical specimens, rapid identification of this bacterial species is required. But present identification via conventional methods is time-consuming and lacks accuracy. The purpose of the current study was to evaluate the use of surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) for rapid identification of S. aureus. A total of 120 clinical isolates of S. aureus and 153 non-S. aureus species were identified by conventional methods, and the species nature of all staphylococci was further confirmed by 16S rDNA sequencing. All strains observed were analyzed by SELDI-TOF MS. An identification model for S. aureus was developed and validated by an artificial neural network. The model based on 6 protein peaks exhibited a sensitivity of 98.4% and specificity of 98.6%. This strategy has the potential for rapid identification of S. aureus.

Direct bacterial profiling by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry for identification of pathogenic Neisseria

The present study investigates the suitability of direct bacterial profiling as a tool for the identification and subtyping of pathogenic Neisseria. The genus Neisseria includes two human pathogens, Neisseria meningitidis and Neisseria gonorrhoeae, as well as several nonpathogenic Neisseria species. Here, a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling protocol was optimized using a laboratory strain of E. coli DH5alpha to guarantee high quality and reproducible results. Subsequently, mass spectra for both laboratory and clinical strains of N. gonorrhoeae, N. meningitidis, and several nonpathogenic Neisseria species were collected. Significant interspecies differences but little intraspecies diversity were revealed by means of a visual inspection and bioinformatics examination using the MALDI BioTyper software. Cluster analysis successfully separated mass spectra collected from three groups that corresponded to N. gonorrhoeae, N. meningitidis, and nonpathogenic Neisseria isolates. Requiring only one bacterial colony for testing and using a fast and easy measuring protocol, this approach represents a powerful tool for the rapid identification of pathogenic Neisseria and can be adopted for other microorganisms.

Recent Advances in Real-time Mass Spectrometry Detection of Bacteria

The analysis of bio-aerosols poses a technology challenge, particularly when sampling and analysis are done in situ. Mass spectrometry laboratory technology has been modified to achieve quick bacteria typing of aerosols in the field. Initially, aerosol material was collected and subjected off-line to minimum sample treatment and mass spectrometry analysis. More recently, sampling and analysis were combined in a single process for the real-time analysis of bio-aerosols in the field. This chapter discusses the development of technology for the mass spectrometry of bio-aerosols, with a focus on bacteria aerosols. Merits and drawbacks of the various technologies and their typing signatures are discussed. The chapter concludes with a brief view of future developments in bio-aerosol mass spectrometry.

Optimized application of surface-enhanced laser desorption/ionization time-of-flight MS to differentiateFrancisella tularensisat the level of subspecies and individual strains

Francisella tularensis, the causative agent of tularaemia, is a potential agent of bioterrorism. The phenotypic discrimination of the closely related F. tularensis subspecies and individual strains with traditional methods is difficult and time consuming, often producing ambiguous results. Surface-enhanced laser desorption/ionization time-of-flight MS (SELDI-TOF MS) was used in this study to discriminate the different species and subspecies of the genus Francisella. We tested 18 Francisella strains including at least one representative of each species/subspecies on four different types of chromatographic chip surfaces. Multivariate analysis (hierarchical clustering and principal component analysis) allowed grouping of the strains according to their designated subspecies. Furthermore, single strains within F. tularensis subspecies could be discriminated.