Exploring serological classification tree model of active pulmonary tuberculosis by magnetic beads pretreatment and MALDI-TOF MS analysis

MALDI-TOF Mass Spectroscopy Applications in Clinical Microbiology

There is a range of proteomics methods to spot and analyze bacterial protein contents such as liquid chromatography-mass spectrometry (LC-MS), two-dimensional gel electrophoresis, and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS), which give comprehensive information about the microorganisms that may be helpful within the diagnosis and coverings of infections. Microorganism identification by mass spectrometry is predicted on identifying a characteristic spectrum of every species so matched with an outsized database within the instrument. MALDI-TOF MS is one of the diagnostic methods, which is a straightforward, quick, and precise technique, and is employed in microbial diagnostic laboratories these days and may replace other diagnostic methods. This method identifies various microorganisms such as bacteria, fungi, parasites, and viruses, which supply comprehensive information. One of the MALDI-TOF MS's crucial applications is bacteriology, which helps identify bacterial species, identify toxins, and study bacterial antibiotic resistance. By knowing these cases, we will act more effectively against bacterial infections.

Serum Clusterin and Complement Factor H May Be Biomarkers Differentiate Primary Sjögren's Syndrome With and Without Neuromyelitis Optica Spectrum Disorder

Background: Neuromyelitis optica spectrum disorder (NMOSD) is a neurological complication of primary Sjögren's syndrome (pSS).

Objective: We aimed to explore potential serological differences between pSS patients with and without NMOSD.

Methods: There were 4 pSS patients with NMOSD and 8 pSS patients without NMOSD enrolled as the screening group for two-dimensional difference gel electrophoresis (DIGE) analysis. Then differential expressed protein spots between groups were identified by MALDI-TOF/TOF MS. The levels of the identified potential biomarkers were verified by ELISA in a second independent cohort including 22 pSS patients with NMOSD, 26 pSS without NMOSD and 30 NMOSD patients.

Results: Nine proteins were identified significantly differently expressed (more than 1.5-fold, p < 0.05) between these two groups. Serum levels of clusterin and complement factor H (CFH) were further verified by ELISA. Results showed that the serum clusterin was significantly higher in NMOSD with pSS than without (298.33 ± 184.52 vs. 173.49 ± 63.03 ng/ml, p < 0.01), while the levels of CFH were lower in pSS patients with NMOSD than without (24.19 ± 1.79 vs. 25.87 ± 3.98 ng/ml, p < 0.01).

Conclusion: This is the first study of serological comparative proteomics between pSS patients with and without NMOSD. Serum clusterin and CFH might be potential biomarkers for pSS patients with NMOSD and play important role in the pathogenesis of the disease but needs further verification.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.

Exploring pathogenesis of primary biliary cholangitis by proteomics: A pilot study

AIM

To explore the pathogenesis of primary biliary cholangitis (PBC) by identifying candidate autoantibodies in serum samples by proteomics and bioinformatics.

METHODS

Nine antimitochondrial antibody (AMA)-positive PBC patients and nine age- and sex-matched AMA-negative PBC patients were recruited. Antigen enrichment technology was applied to capture autoantigens of human intrahepatic biliary epithelial cells (HiBECs) that are recognized by autoantibodies from the sera of PBC patients. Candidate autoantigens were identified by label-free mass spectrometry. Bioinformatics analysis with MaxQuant software (version 1.5.2.8), DAVID platform, and Cytoscape v.3.0 allowed illustration of pathways potentially involved in the pathogenesis of PBC.

RESULTS

In total, 1081 candidate autoantigen proteins were identified from the PBC patient pool. Among them, 371 were determined to be significantly differentially expressed between AMA-positive and -negative PBC patients (P < 0.05). Fisher's exact test was performed for enrichment analysis of Gene Ontology protein annotations (biological processes, cellular components, and molecular functions) and the Kyoto Encyclopedia of Genes and Genomes pathways. Significantly different protein categories were revealed between AMA-positive and -negative PBC patients. As expected, autoantigens related to mitochondria were highly enriched in AMA-positive PBC patients. However, lower levels of AMA were also detected in AMA-negative PBC patients. In addition, autoantigens of AMA-negative PBC patients were mainly involved in B-cell activation, recognition of phagocytosis, and complement activation.

CONCLUSION

AMA-negative PBC individuals may not exist, but rather, those patients exhibit pathogenesis pathways different from those of AMA-positive PBC. Comprehensive research is needed to confirm these observations.

Identification of immune correlates of protection in Shigella infection by application of machine learning

BACKGROUND

Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model.

METHODS

The proposed methodology utilizes the Random Forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models.

RESULTS

The results demonstrate that the combination of Classification and Regression Trees and Random Forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method.

CONCLUSION

Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines.

Use of MALDI-TOF mass spectrometry in the battle against bacterial infectious diseases: recent achievements and future perspectives

INTRODUCTION

Advancements in microbial identification occur increasingly faster as more laboratories explore, refine and extend the use of mass spectrometry in the field of microbiology. Areas covered: This review covers the latest knowledge found in the literature for quick identification of various classes of bacterial pathogens known to cause human infection by the use of MALDI-TOF MS technology. Except for identification of bacterial strains, more researchers try to 'battle time' in favor of the patient. These novel approaches to identify bacteria directly from clinical samples and even determine antibiotic resistance are extensively revised and discussed. Expert commentary: Mass spectrometry is the future of bacterial identification and creates a new era in modern microbiology. Its incorporation in routine practice seems to be not too far, providing a valuable alternative, especially in terms of time, to conventional techniques. If the technology further advances, quick bacterial identification and probable identification of common antibiotic resistance might guide patient decision-making regarding bacterial infectious diseases in the near future.

Pulmonary Tuberculosis Diagnosis: Where We Are?

In recent years, in spite of medical advancement, tuberculosis (TB) remains a worldwide health problem. Although many laboratory methods have been developed to expedite the diagnosis of TB, delays in diagnosis remain a major problem in the clinical practice. Because of the slow growth rate of the causative agent Mycobacterium tuberculosis, isolation, identification, and drug susceptibility testing of this organism and other clinically important mycobacteria can take several weeks or longer. During the past several years, many methods have been developed for direct detection, species identification, and drug susceptibility testing of TB. A good understanding of the effectiveness and practical limitations of these methods is important to improve diagnosis. This review summarizes the currently-used advances in nonmolecular and molecular diagnostics.

Establishment of a decision tree model for diagnosis of early rheumatoid arthritis by proteomic fingerprinting

AIM

The objective of this study was to identify proteomic biomarkers specific for rheumatoid arthritis (RA) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in combination with weak cationic exchange (WCX) magnetic beads.

METHODS

Serum samples from 50 patients with RA and 110 disease controls (50 SLE and 60 SS) and 51 healthy individuals were analyzed. The samples were randomly divided into a training set or test set to develop a diagnostic model for RA.

RESULTS

A total of 83 protein peaks were identified to be related with RA, in which four of the peaks with mass-charge ratio (m/z) at 8133.85, 5844.60, 13 541.3 and 14 029.0 were selected to establish a model for diagnosis of RA. This classification model could separate patients with RA from diseased and healthy controls with sensitivity of 84.0% and specificity of 92.5%, and its accuracy was confirmed in the blind testing set with high sensitivity and specificity of 80.0% and 93.3%, respectively.

CONCLUSIONS

This study suggested that potential serum biomarkers for RA diagnosis could be discovered by MALDI-TOF-MS. The classification tree model set up in this study might be used as a novel diagnostic tool for RA.

Host Protein Biomarkers Identify Active Tuberculosis in HIV Uninfected and Co-infected Individuals

Biomarkers for active tuberculosis (TB) are urgently needed to improve rapid TB diagnosis. The objective of this study was to identify serum protein expression changes associated with TB but not latent Mycobacterium tuberculosis infection (LTBI), uninfected states, or respiratory diseases other than TB (ORD). Serum samples from 209 HIV uninfected (HIV⁻) and co-infected (HIV⁺) individuals were studied. In the discovery phase samples were analyzed via liquid chromatography and mass spectrometry, and in the verification phase biologically independent samples were analyzed via a multiplex multiple reaction monitoring mass spectrometry (MRM-MS) assay. Compared to LTBI and ORD, host proteins were significantly differentially expressed in TB, and involved in the immune response, tissue repair, and lipid metabolism. Biomarker panels whose composition differed according to HIV status, and consisted of 8 host proteins in HIV⁻ individuals (CD14, SEPP1, SELL, TNXB, LUM, PEPD, QSOX1, COMP, APOC1), or 10 host proteins in HIV⁺ individuals (CD14, SEPP1, PGLYRP2, PFN1, VASN, CPN2, TAGLN2, IGFBP6), respectively, distinguished TB from ORD with excellent accuracy (AUC = 0.96 for HIV⁻ TB, 0.95 for HIV⁺ TB). These results warrant validation in larger studies but provide promise that host protein biomarkers could be the basis for a rapid, blood-based test for TB.

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Active tuberculosis leads to the differential expression of serum proteins involved in associated host processes.

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Serum protein expression changes in tuberculosis involve the immune response, tissue repair, and lipid metabolism.

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Panels of 8–10 host proteins can distinguish active tuberculosis from latent infection, and other respiratory diseases.

Accurate biomarkers for active tuberculosis (TB) are urgently needed to improve rapid diagnosis. Current diagnostics for TB rely on microbiologic or molecular confirmation of M. tuberculosis, and are therefore dependent on a specimen from the site of disease which is not always accessible. This study demonstrates that human host proteins are differentially expressed in TB compared to latent M. tuberculosis infection, or respiratory diseases other than TB. Our data thus provide promise that host proteins have the potential to become the basis of rapid blood tests that do not require a sample from the site of disease.

A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection

A proteomic analysis was performed to screen the potential latent tuberculosis infection (LTBI) biomarkers. A training set of spectra was used to generate diagnostic models, and a blind testing set was used to determine the accuracy of the models. Candidate peptides were identified using nano-liquid chromatography-electrospray ionization–tandem mass spectrometry. Based on the training set results, 3 diagnostic models recognized LTBI subjects with good cross-validation accuracy. In the blind testing set, LTBI subjects could be identified with sensitivities and specificities of 85.20% to 88.90% and 85.7% to 100%, respectively. Additionally, 14 potential LTBI biomarkers were identified, and all proteins were identified for the first time through proteomics in the plasma of healthy, latently infected individuals. In all, proteomic pattern analyses can increase the accuracy of LTBI diagnosis, and the data presented here provide novel insights into potential mechanisms involved in LTBI.

Establishing serological classification tree model in rheumatoid arthritis using combination of MALDI-TOF-MS and magnetic beads

To establish a serological classification tree model for rheumatoid arthritis (RA), protein/peptide profiles of serum were detected by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cationic exchange (WCX) from Cohort 1, including 65 patients with RA and 41 healthy controls (HC). The samples were randomly divided into a training set and a test set. Twenty-four differentially expressed peaks (P < 0.05) were identified in the training set and 4 of them, namely m/z 3,939, 5,906, 8,146, and 8,569 were chosen to set up our model. This model exhibited a sensitivity of 100.0% and a specificity of 96.0% for differentiating RA patients from HC. The test set reproduced these high levels of sensitivity and specificity, which were 100.0 and 81.2%, respectively. Cohort 2, which include 228 RA patients, was used to further verify the classification efficiency of this model. It came out that 97.4% of them were classified as RA by this model. In conclusion, MALDI-TOF-MS combined with WCX magnetic beads was a powerful method for constructing a classification tree model for RA, and the model we established was useful in recognizing RA.