Mass Spectrometry Proteotyping-Based Detection and Identification of Staphylococcus aureus, Escherichia coli, and Candida albicans in Blood

Plasma proteomics in pediatric patients with sepsis- hopes and challenges

One of the main causes of morbidity and death in pediatric patients is sepsis. Of the 48.9 million cases of sepsis reported globally, 41.5% involve children under the age of five, with 2.9 million deaths associated with the disease. Clinicians must identify and treat patients at risk of sepsis or septic shock before late-stage organ dysfunction occurs since diagnosing sepsis in young patients is more difficult than in adult patients. As of right now, omics technologies that possess adequate diagnostic sensitivity and specificity can assist in locating biomarkers that indicate how the disease will progress clinically and how the patient will react to treatment. By identifying patients who are at a higher risk of dying or experiencing persistent organ dysfunction, risk stratification based on biomarkers generated from proteomics can enhance prognosis. A potentially helpful method for determining the proteins that serve as biomarkers for sepsis and formulating theories on the pathophysiological mechanisms behind complex sepsis symptoms is plasma proteomics.

Proteomic Diversity in Bacteria: Insights and Implications for Bacterial Identification

Mass spectrometry-based proteomics has revolutionized bacterial identification and elucidated many molecular mechanisms underlying bacterial growth, community formation, and drug resistance. However, most research has been focused on a few model bacteria, overlooking bacterial diversity. In this study, we present the most extensive bacterial proteomic resource to date, covering 303 species, 119 genera, and five phyla with over 636,000 unique expressed proteins, confirming the existence of over 38,700 hypothetical proteins. Accessible via the public resource ProteomicsDB, this dataset enables quantitative exploration of proteins within and across species. Additionally, we developed MS2Bac, a bacterial identification algorithm that queries NCBI's bacterial proteome space in two iterations. MS2Bac achieved over 99% species-level and 89% strain-level accuracy, surpassing methods like MALDI-TOF and FTIR, as demonstrated with food-derived bacterial isolates. MS2Bac also effectively identified bacteria in clinical samples, highlighting the potential of MS-based proteomics as a routine diagnostic tool.

Detection of Salmonella enterica in food using targeted mass spectrometry

The high prevalence of Salmonella enterica necessitates rapid and efficient detection methods. Targeted mass spectrometry (MS) using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) has become a promising technique with improved specificity and sensitivity. We develop a novel targeted MS method for detecting S. enterica in food based on peptide biomarkers. Using a combination of four peptide biomarkers, this newly developed method could accurately distinguish S. enterica from other conventional food-borne pathogens. When combined with buoyant density centrifugation (BDC), Salmonella was efficiently separated from food matrices. Based on this discovery, this method was successfully applied to detect S. enterica in both artificially and naturally contaminated food samples, comparable to the culture method. These results demonstrate the potential of the targeted MS method in various food categories and are expected to be an alternative approach for S. enterica detection in food.

The role of metagenomic next-generation sequencing in diagnosing and managing post-kidney transplantation infections

Kidney transplantation (KT) is a life-saving treatment for patients with end-stage renal disease, but post-transplant infections remain one of the most significant challenges. These infections, caused by a variety of pathogens, can lead to prolonged hospitalization, graft dysfunction, and even mortality, particularly in immunocompromised patients. Traditional diagnostic methods often fail to identify the causative organisms in a timely manner, leading to delays in treatment and poorer patient outcomes. This review explores the application of metagenomic next-generation sequencing (mNGS) in the diagnosis of post-KT infections. mNGS allows for the rapid, comprehensive detection of a wide range of pathogens, including bacteria, viruses, fungi, and parasites, without the need for culture-based techniques. We discuss the advantages of mNGS in early and accurate pathogen identification, its role in improving patient management, and the potential challenges in its clinical implementation. Additionally, we consider the future prospects of mNGS in overcoming current diagnostic limitations and its potential for guiding targeted therapies, particularly in detecting antimicrobial resistance and emerging pathogens. This review emphasizes the promise of mNGS as an essential tool in improving the diagnosis and treatment of infections in KT recipients.

Development and characterization of a portable electrochemical aptasensor for IsdA protein and Staphylococcus aureus detection

Staphylococcus aureus (S. aureus) is recognized as one of the most common causes of gastroenteritis worldwide. This pathogen is a major foodborne pathogen that can cause many different types of various infections, from minor skin infections to lethal blood infectious diseases. Iron-regulated surface determinant protein A (IsdA) is an important protein on the S. aureus surface. It is responsible for iron scavenging via interaction with hemoglobin, haptoglobin, and hemoglobin-haptoglobin complexes. This study develops a portable aptasensor for IsdA and S. aureus detection using aptamer-modified gold nanoparticles (AuNPs) integrated into screen-printed carbon electrodes (SPCEs). The electrode system was made of three parts, including a carbon counter electrode, an AuNPs/carbon working electrode, and a silver reference electrode. The aptamer by Au-S bonding was conjugated on the electrode surface to create the aptasensor platform. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were utilized to investigate the binding interactions between the aptasensor and the IsdA protein. CV studies showed a linear correlation between varying S. aureus concentrations within the range of 101 to 106 CFU/mL, resulting in a limit of detection (LOD) of 0.2 CFU/mL. The results demonstrated strong reproducibility, selectivity, and sensitivity of the aptasensor for enhanced detection of IsdA, along with about 93% performance stability after 30 days. The capability of the aptasensor to directly detect S. aureus via the IsdA surface protein binding was further investigated in a food matrix. Overall, the aptasensor device showed the potential for rapid detection of S. aureus, serving as a robust approach to developing real-time aptasensors to identify an extensive range of targets of foodborne pathogens and beyond.

Neonatal bacterial sepsis

Neonatal sepsis remains one of the key challenges of neonatal medicine, and together with preterm birth, causes almost 50% of all deaths globally for children younger than 5 years. Compared with advances achieved for other serious neonatal and early childhood conditions globally, progress in reducing neonatal sepsis has been much slower, especially in low-resource settings that have the highest burden of neonatal sepsis morbidity and mortality. By contrast to sepsis in older patients, there is no universally accepted neonatal sepsis definition. This poses substantial challenges in clinical practice, research, and health-care management, and has direct practical implications, such as diagnostic inconsistency, heterogeneous data collection and surveillance, and inappropriate treatment, health-resource allocation, and education. As the clinical manifestation of neonatal sepsis is frequently non-specific and the current diagnostic standard blood culture has performance limitations, new improved diagnostic techniques are required to guide appropriate and warranted antimicrobial treatment. Although antimicrobial therapy and supportive care continue as principal components of neonatal sepsis therapy, refining basic neonatal care to prevent sepsis through education and quality improvement initiatives remains paramount.

Multiplexing the Identification of Microorganisms via Tandem Mass Tag Labeling Augmented by Interference Removal through a Novel Modification of the Expectation Maximization Algorithm

Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the "three-in-one" SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm-1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL-1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL-1 to 2.15 × 105 CFU mL-1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

Optimization and Testing of a Commercial Viability PCR Protocol to Detect Escherichia coli in Whole Blood

Bacteremia, specifically if progressed to sepsis, poses a time-sensitive threat to human and animal health. Escherichia coli is a main causative agent of sepsis in humans. The objective was to evaluate a propidium monoazide (PMA)-based viability PCR (vPCR) protocol to detect and quantify live E. coli from whole blood. We optimized the protocol by adding a eukaryotic-specific lysis step prior to PMA exposure, then used spiking experiments to determine the lower limit of detection (LOD) and linear range of quantification. We also compared the vPCR quantification method to standard colony count of spiked inoculum. Lastly, we calculated percent viability in spiked samples containing 50% live cells or 0% live cells. The LOD was 102 CFU/mL for samples containing live cells only and samples with mixed live and heat-killed cells. The linear range of quantification was 102 CFU/mL to 108 CFU/mL (R2 of 0.997) in samples containing only live cells and 103 CFU/mL to 108 CFU/mL (R2 of 0.998) in samples containing live plus heat-killed cells. A Bland-Altman analysis showed that vPCR quantification overestimates compared to standard plate count of the spiked inoculum, with an average bias of 1.85 Log10 CFU/mL across the linear range when only live cells were present in the sample and 1.98 Log10 CFU/mL when live plus heat-killed cells were present. Lastly, percent viability calculations showed an average 89.5% viable cells for samples containing 50% live cells and an average 19.3% for samples containing 0% live cells. In summary, this optimized protocol can detect and quantify viable E. coli in blood in the presence of heat-killed cells. Additionally, the data presented here provide the groundwork for further development of vPCR to detect and quantify live bacteria in blood in clinical settings.

MiCId GUI: The Graphical User Interface for MiCId, a Fast Microorganism Classification and Identification Workflow with Accurate Statistics and High Recall

Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment. Another paramount factor to consider is the necessity of accurate statistical significance to properly control the proportion of false discoveries among the identified microorganisms, and antimicrobial-resistant proteins, and to provide robust biomass estimation. Recently, we have developed Microorganism Classification and Identification (MiCId) workflow that assigns accurate statistical significance to identified microorganisms, antimicrobial-resistant proteins, and biomass estimation. MiCId's workflow is also computationally efficient, taking about 6-17 minutes to process a tandem mass-spectrometry (MS/MS) experiment using computer resources that are available in most laptop and desktop computers, making it a portable workflow. To make data analysis accessible to a broader range of users, beyond users familiar with the Linux environment, we have developed a graphical user interface (GUI) for MiCId's workflow. The GUI brings to users all the functionality of MiCId's workflow in a friendly interface along with tools for data analysis, visualization, and to export results.

Metataxonomic and metaproteomic profiling of the oral microbiome in oral lichen planus - a pilot study

Background

A growing body of evidence demonstrates a different bacterial composition in the oral cavity of patients with oral lichen planus (OLP).

Patients and methods

Buccal swab samples were collected from affected and non-affected sites of six patients with reticular OLP and the healthy oral mucosa of six control subjects. 16S rRNA gene MiSeq sequencing and mass spectrometry-based proteomics were utilised to identify the metataxonomic and metaproteomic profiles of the oral microbiome in both groups.

Results

From the metataxonomic analysis, the most abundant species in the three subgroups were Streptococcus oralis and Pseudomonas aeruginosa, accounting for up to 70% of the total population. Principal Coordinates Analysis showed differential clustering of samples from the healthy and OLP groups. ANCOM-BC compositional analysis revealed multiple species (including P. aeruginosa and several species of Veillonella, Prevotella, Streptococcus and Neisseria) significantly over-represented in the control group and several (including Granulicatella elegans, Gemella haemolysans and G. parahaemolysans) in patients with OLP. The metaproteomic data were generally congruent and revealed that several Gemella haemolysans-belonging peptidases and other proteins with inflammatory and virulence potential were present in OLP lesions.

Conclusion

Our data suggest that several bacterial species are associated with OLP. Future studies with larger cohorts should be conducted to determine their role in the aetiology of OLP and evaluate their potential as disease biomarkers.

Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach

Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId's workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId's workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6-17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId's workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId's workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId's conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.