Determination of latent tuberculosis infection from plasma samples via label-free SERS sensors and machine learning.

Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS). We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 × 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database. We demonstrated that SERS analysis with machine learning is a potential diagnostic tool for LTBI screening.

MALDI-TOF MS analysis of Burkholderia pseudomallei and closely related species isolated from soils and water in Khon Kaen, Thailand.

Melioidosis is caused by Burkholderia pseudomallei (Bp) acquired from the environment. Conventional identification methods for environmental Bp are challenging due to the presence of closely related species. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is accurate for bacterial identification, but has been little used to identify Bp from environmental samples. This study aims to evaluate MALDI-TOF MS for the identification of Bp and closely related species isolated from environmental samples in Thailand using whole-genome sequencing (WGS) as the gold standard, including determining the best sample preparation method for this purpose. We identified Bp (n = 22), Burkholderia spp. (n = 28), and other bacterial species (n = 32) using WGS. MALDI-TOF analysis of all Bp isolates yielded results consistent with WGS. A decision-tree algorithm identified 16 important variable peaks, using the protein extraction method (PEM), demonstrating distinct MALDI-TOF profiles for the three categories (Bp, Burkholderia spp. and "other bacterial species"). Three biomarker peaks (4060, 5196, and 6553 Da) could discriminate Bp from other Burkholderia and closely related species with 100% sensitivity and specificity. Hence, the MALDI-TOF technique has shown its potential as a species discriminatory tool, providing results comparable to WGS for classification and surveillance of environmental Bp.

Proteomic Profiles and Protein Network Analysis of Primary Human Leukocytes Revealed Possible Clearance Biomarkers for Staphylococcus aureus Infection.

Staphylococcus aureus is a serious pathogen that can survive within host cells after a typical course of treatment completion, leading to chronic infection. Knowledge of host proteomic patterns after clearance of this pathogen from cells is limited. Here, we looked for S. aureus clearance biomarkers produced by in vitro-infected leukocytes. Extracellular proteins from primary human leukocytes infected with S. aureus ATCC 25923 were investigated as possible treatment-monitoring clearance biomarkers by applying a proteomics approach combining liquid chromatography with tandem mass spectrometry (LC-MS/MS) and protein interaction network analysis. It was found that the expression patterns of proteins secreted by S. aureus-infected leukocytes differed among stages of infection. Proteomic profiles showed that an ATPase, aminophospholipid transporter-like, Class I, type 8A, member 2 (ATP8A2) was expressed in the clearance stage and was not detected at any earlier stage or in uninfected controls. Protein network analysis showed that TERF2 (telomeric repeat-binding factor 2), ZNF440 (zinc finger protein 440), and PPP1R14A (phosphatase 1 regulatory subunit 14A) were up-regulated, while GLE1, an essential RNA-export mediator, was suppressed in both infection and clearance stages, suggesting their potential roles in S. aureus infection and clearance. These findings are the first to report that the ATP8A2 has potential as a clearance biomarker for S. aureus infection.

Serum proteomics analysis for differentiation among Mycobacterium tuberculosis infection categories.

Inhalation of Mycobacterium tuberculosis (Mtb) bacilli can lead to a range of TB categories including early clearance (EC), latent TB infection (LTBI) and active TB (ATB). There are few biomarkers available to differentiate among these TB categories: effective new biomarkers are badly needed. Here, we analyzed the serum proteins from 26 ATB cases, 20 LTBI cases, 34 EC cases and 38 healthy controls (HC) using label-free LC-MS/MS. The results were analyzed using MaxQuant software and matched to three different bacterial proteomics databases, including Mtb, Mycobacterium spp. and normal lung flora. PCA of protein candidates using the three proteomics databases revealed 44.5% differentiation power to differentiate among four TB categories. There were 289 proteins that showed potential for distinguishing between each pair of groups among TB categories. There were 50 candidate protein markers specifically found in ATB and LTBI but not in HC and EC groups. Decision trees using the top five candidate biomarkers (A0A1A2RWZ9, A0A1A3FMY8, A0A1A3KIY2, A0A5C7MJH5 and A0A1X0XYR3) had 92.31% accuracy to differentiate among TB categories and the accuracy was increased to 100% when using 10 candidate biomarkers. Our study shows that proteins expressed from Mycobacterium spp. have the potential to be used to differentiate among TB categories.

Metabolomic analysis of Mycobacterium tuberculosis reveals metabolic profiles for identification of drug-resistant tuberculosis.

The detection of pre-extensively (pre-XDR) and extensively drug-resistant tuberculosis (XDR-TB) is challenging. Drug-susceptibility tests for some anti-TB drugs, especially ethambutol (ETH) and ethionamide (ETO), are problematic due to overlapping thresholds to differentiate between susceptible and resistant phenotypes. We aimed to identify possible metabolomic markers to detect Mycobacterium tuberculosis (Mtb) strains causing pre-XDR and XDR-TB. The metabolic patterns of ETH- and ETO-resistant Mtb isolates were also investigated. Metabolomics of 150 Mtb isolates (54 pre-XDR, 63 XDR-TB and 33 pan-susceptible; pan-S) were investigated. Metabolomics of ETH and ETO phenotypically resistant subgroups were analyzed using UHPLC-ESI-QTOF-MS/MS. Orthogonal partial least-squares discriminant analysis revealed distinct separation in all pairwise comparisons among groups. Two metabolites (meso-hydroxyheme and itaconic anhydride) were able to differentiate the pre-XDR and XDR-TB groups from the pan-S group with 100% sensitivity and 100% specificity. In comparisons of the ETH and ETO phenotypically resistant subsets, sets of increased (ETH = 15, ETO = 7) and decreased (ETH = 1, ETO = 6) metabolites specific for the resistance phenotype of each drug were found. We demonstrated the potential for metabolomics of Mtb to differentiate among types of DR-TB as well as between isolates that were phenotypically resistant to ETO and ETH. Thus, metabolomics might be further applied for DR-TB diagnosis and patient management.

Characterisation of secretome-based immune responses of human leukocytes infected with various Mycobacterium tuberculosis lineages.

BACKGROUND: Differences in immune responses against different lineages of Mycobacterium tuberculosis (Mtb), and by different types of immune cell, are still poorly understood. We aimed to compare the secretome-based immune responses among three Mtb lineages and among immune-cell types. The immune responses were also investigated during infection and when the bacilli had been eliminated from the immune cells. METHODS: Human primary leukocytes were infected with strains representing three lineages of Mtb (East-Asian, Indo-Oceanic and Euro-American). Label-free GeLC MS/MS proteomic analysis of secretomes was performed. The response of each immune-cell type was compared with the appropriate interactome database for each. RESULTS: The expression pattern of proteins secreted by Mtb-infected leukocytes differed among Mtb lineages. The ancestral lineage (IO lineage) had a greater ability to activate MMP14 (associated with leukocyte migration) than did the more recent lineages (EA and EuA). During infection, proteins secreted by macrophages, dendritic cells, neutrophils and B-cells were associated with cell proliferation. Following clearance of Mtb, proteins associated with interferon signaling were found in macrophages, dendritic cells and neutrophils: proteins associated with antigen processing were found in B-cells and regulatory T-cells. Expression of immune response-related proteins from many immune-cell types might be suppressed by Mtb infection. Our study has provided a better insight into the host-pathogen interaction and immune response against different Mtb lineages.

Proteomic analysis of infected primary human leucocytes revealed PSTK as potential treatment-monitoring marker for active and latent tuberculosis.

Markers for monitoring clearance of Mycobacterium tuberculosis (Mtb) infection during anti-TB drug treatment could facilitate management of tuberculosis (TB) treatment, but are lacking. We aimed to screen for Mtb clearance markers from in-vitro-infected leucocytes and to evaluate these markers in followed-up active TB (ATB) patients and latent TB (LTBI) cases after anti-TB drug treatment. Extracellular proteins from primary leucocytes infected with each of the Mtb lineages (East-Asian, Indo-Oceanic, Euro-American and the laboratory strain H37Rv) were screened as possible clearance markers. Leucocytes infected with Staphylococcus aureus acted as controls. The proteomic analysis was performed using GeLC-MS/MS. Several quantitative and qualitative candidate clearance markers were found. These proteins were suppressed during the infection stage of all Mtb lineages and re-expressed after bacillary clearance. PSTK, FKBP8 and MGMT were common clearance markers among the four Mtb lineages in our model. Only PSTK was a potential clearance marker based on western blot validation analysis from culture supernatants. The PSTK marker was further validated with western blot analysis using serum samples (n = 6) from ATB patients and LTBI cases during anti-TB drug treatment, and from healthy controls (n = 3). Time-dependent increase of PSTK was found both in ATB and LTBI patients during the course of anti-TB drug treatment, but not in healthy controls. We have demonstrated that PSTK is a potential treatment-monitoring marker for active and latent TB.

Diagnosis of active tuberculosis and latent tuberculosis infection based on Raman spectroscopy and surface-enhanced Raman spectroscopy.

Current tools for screening LTBI are limited due to the long turnaround time required, cross-reactivity of tuberculin skin test to BCG vaccine and the high cost of interferon gamma release assay (IGRA) tests. We evaluated Raman spectroscopy (RS) for serum-protein fingerprinting from 26 active TB (ATB) cases, 20 LTBI cases, 34 early clearance (EC; TB-exposed persons with undetected infection) and 38 healthy controls (HC). RS at 532 nm using candidate peaks provided 92.31% sensitivity and 90.0% to distinguish ATB from LTBI, 84.62% sensitivity and 89.47% specificity to distinguish ATB from HC and 87.10% sensitivity and 85.0% specificity to distinguish LTBI from EC. RS at 532 nm with the random forest model provided 86.84% sensitivity and 65.0% specificity to distinguish LTBI from HC and 94.74% sensitivity and 87.10% specificity to distinguish EC from HC. Using preliminary sample sets (n = 5 for each TB-infection category), surface-enhanced Raman spectroscopy (SERS) showed high potential diagnostic performance, distinguishing very clearly among all TB-infection categories with 100% sensitivity and specificity. With lower cost, shorter turnaround time and performance comparable to that of IGRAs, our study demonstrated RS and SERS to have high potential for ATB and LTBI diagnosis.

Dataset of serum proteomic spectra from tuberculosis patients detected by Raman spectroscopy and surface-enhanced Raman spectroscopy.

In this data article, we present Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) data obtained using an InVia Reflex confocal Raman microscope (Renishaw; Wotton-under-Edge, UK) and processed using WiRE™ 4.2 software. The data include RS and SERS spectra detected, after removal of albumin, from the serum proteome of tuberculosis (TB) patient categories and controls (active tuberculosis; ATB, latent tuberculosis; LTBI, TB-exposed persons with undetected infection; EC, healthy controls; HC) using 532 nm and 785 nm laser wavelengths for RS and 785 nm for SERS. The RS and SERS data had high reproducibility (SERS; R2 = 0.988, RS at 785 nm; R2 = 0.972, RS at 532 nm; R2 = 0.9150). This data can be used for analysis of proteomic spectra based on RS and SERS for TB diagnosis and can also be compared to other populations. The spectral dataset based on normal, healthy control groups might be used as the control data for analysis of other diseases using RS and SERS approaches.

Tuberculosis determination using SERS and chemometric methods.

Nanostructures have been multiplying the advantages of Raman spectroscopy and further amplify the advantages of Raman spectroscopy is a continuous effort focused on the appropriate design of nanostructures. Herein, we designed different shapes of plasmonic nanostructures such as Vertical, Zig Zag, Slant nanorods and Spherical nanoparticles employing the DC magnetron sputtering system as SERS-active substrates for ultrasensitive detection of target molecules. The fabricated plasmonic nanostructures sensitivity and uniformity were exploited by reference dye analyte. These nanostructures were utilized in the label free detection of infectious disease, Tuberculosis (TB). For the first time, TB detection from serum samples using SERS has been demonstrated. Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples. The results demonstrate the performance of the SERS spectra, chemometric methods and potential of the method in clinical diagnosis.

Comparative Proteomics of Activated THP-1 Cells Infected with Mycobacterium tuberculosis Identifies Putative Clearance Biomarkers for Tuberculosis Treatment.

Biomarkers for determining clearance of Mycobacterium tuberculosis (Mtb) infection during anti-tuberculosis therapy or following exposure could facilitate enhanced monitoring and treatment. We screened for biomarkers indicating clearance of Mtb infection in vitro. A comparative proteomic analysis was performed using GeLC MSI/MS. Intracellular and secreted proteomes from activated THP-1 cells infected with the Mtb H37Rv strain (MOI = 1) and treated with isoniazid and rifampicin for 1 day (infection stage) and 5 days (clearance stage) were analyzed. Host proteins associated with early infection (n = 82), clearance (n = 121), sustained in both conditions (n = 34) and suppressed by infection (n = 46) were elucidated. Of the potential clearance markers, SSFA2 and CAECAM18 showed the highest and lowest protein intensities, respectively. A western blot of CAECAM18 validated the LC MS/MS result. For three clearance markers (SSFA2, PARP14 and PSME4), in vivo clinical validation was concordantly reported in previous patient cohorts. A network analysis revealed that clearance markers were enriched amongst four protein interaction networks centered on: (i) CD44/CCND1, (ii) IFN-ß1/NF-κB, (iii) TP53/TGF-ß and (iv) IFN-γ/CCL2. After infection, proteins associated with proliferation, and recruitment of immune cells appeared to be enriched possibly reflecting recruitment of defense mechanisms. Counteracting proteins (CASP3 vs. Akt and NF-κB vs. TP53) associated with apoptosis regulation and its networks were enriched among the early and sustained infection biomarkers, indicating host-pathogen competition. The BRCA1/2 network was suppressed during infection, suggesting that cell proliferation suppression is a feature of Mtb survival. Our study provides insights into the mechanisms of host-Mtb interaction by comparing the stages of infection clearance. The identified clearance biomarkers may be useful in monitoring tuberculosis treatment.