A metabolomics approach to characterise and identify various Mycobacterium species

Behavioral interplay between mosquito and mycolactone produced by Mycobacterium ulcerans and bacterial gene expression induced by mosquito proximity

Mycolactone is a cytotoxic lipid metabolite produced by Mycobacterium ulcerans, the environmental pathogen responsible for Buruli ulcer, a neglected tropical disease. Mycobacterium ulcerans is prevalent in West Africa, particularly found in lentic environments, where mosquitoes also occur. Researchers hypothesize mosquitoes could serve as a transmission mechanism resulting in infection by M. ulcerans when mosquitoes pierce skin contaminated with M. ulcerans. The interplay between the pathogen, mycolactone, and mosquito is only just beginning to be explored. A triple-choice assay was conducted to determine the host-seeking preference of Aedes aegypti between M. ulcerans wildtype (MU, mycolactone active) and mutant (MUlac-, mycolactone inactive). Both qualitative and quantitative differences in volatile organic compounds' (VOCs) profiles of MU and MUlac- were determined by GC-MS. Additionally, we evaluated the interplay between Ae. aegypti proximity and M. ulcerans mRNA expression. The results showed that mosquito attraction was significantly greater (126.0%) to an artificial host treated with MU than MUlac-. We found that MU and MUlac produced differential profiles of VOCs associated with a wide range of biological importance from quorum sensing (QS) to human odor components. RT-qPCR assays showed that mycolactone upregulation was 24-fold greater for MU exposed to Ae. aegypti in direct proximity. Transcriptome data indicated significant induction of ten chromosomal genes of MU involved in stress responses and membrane protein, compared to MUlac- when directly having access to or in near mosquito proximity. Our study provides evidence of possible interkingdom interactions between unicellular and multicellular species that MU present on human skin is capable of interreacting with unrelated species (i.e., mosquitoes), altering its gene expression when mosquitoes are in direct contact or proximity, potentially impacting the production of its VOCs, and consequently leading to the stronger attraction of mosquitoes toward human hosts. This study elucidates interkingdom interactions between viable M. ulcerans bacteria and Ae. aegypti mosquitoes, which rarely have been explored in the past. Our finding opens new doors for future research in terms of disease ecology, prevalence, and pathogen dispersal outside of the M. ulcerans system.

Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy

As an ancient infectious disease, tuberculosis (TB) is still the leading cause of death from a single infectious agent worldwide. Latent TB infection (LTBI) has been recognized as the largest source of new TB cases and is one of the biggest obstacles to achieving the aim of the End TB Strategy. The latest data indicate that a considerable percentage of the population with LTBI and the lack of differential diagnosis between LTBI and active TB (aTB) may be potential reasons for the high TB morbidity and mortality in countries with high TB burdens. The tuberculin skin test (TST) has been used to diagnose TB for > 100 years, but it fails to distinguish patients with LTBI from those with aTB and people who have received Bacillus Calmette–Guérin vaccination. To overcome the limitations of TST, several new skin tests and interferon-gamma release assays have been developed, such as the Diaskintest, C-Tb skin test, EC-Test, and T-cell spot of the TB assay, QuantiFERON-TB Gold In-Tube, QuantiFERON-TB Gold-Plus, LIAISON QuantiFERON-TB Gold Plus test, and LIOFeron TB/LTBI. However, these methods cannot distinguish LTBI from aTB. To investigate the reasons why all these methods cannot distinguish LTBI from aTB, we have explained the concept and definition of LTBI and expounded on the immunological mechanism of LTBI in this review. In addition, we have outlined the research status, future directions, and challenges of LTBI differential diagnosis, including novel biomarkers derived from Mycobacterium tuberculosis and hosts, new models and algorithms, omics technologies, and microbiota.

M. tuberculosis curli pili (MTP) is associated with alterations in carbon, fatty acid and amino acid metabolism in a THP-1 macrophage infection model

The initial host-pathogen interaction is crucial for the establishment of infection. An improved understanding of the pathophysiology of Mycobacterium tuberculosis (M. tuberculosis) during macrophage infection can aid the development of intervention therapeutics against tuberculosis. M. tuberculosis curli pili (MTP) is a surface located adhesin, involved in the first point-of-contact between pathogen and host. This study aimed to better understand the role of MTP in modulating the intertwined metabolic pathways of M. tuberculosis and its THP-1 macrophage host. Metabolites were extracted from pelleted wet cell mass of THP-1 macrophages infected with M. tuberculosis wild-type V9124 (WT), Δmtp-deletion mutant and the mtp-complemented strains, respectively, via a whole metabolome extraction method using a 1:3:1 ratio of chloroform:methanol:water. Metabolites were detected by two-dimensional gas chromatography time-of-flight mass spectrometry. Significant metabolites were determined through univariate and multivariate statistical tests and online pathway databases. Relative to the WT, a total of nine and ten metabolites were significantly different in the Δmtp and complement strains, respectively. All nine significant metabolites were found in elevated levels in the Δmtp relative to the WT. Additionally, of the ten significant metabolites, eight were detected in lower levels and two were detected in higher levels in the complement relative to the WT. The absence of the MTP adhesin resulted in reduced virulence of M. tuberculosis leading to alterations in metabolites involved in carbon, fatty acid and amino acid metabolism during macrophage infection, suggesting that MTP plays an important role in the modulation of host metabolic activity. These findings support the prominent role of the MTP adhesin as a virulence factor as well as a promising biomarker for possible diagnostic and therapeutic intervention.

Detection of Mycobacterium kansasii using a combination of loop-mediated isothermal amplification (LAMP) and lateral flow biosensors

Mycobacterium kansasii is an opportunistic pathogen that causes both intrapulmonary and extrapulmonary infections. The symptoms of the pulmonary diseases caused by M. kansasii closely resemble Mycobacterium tuberculosis. Rapid and accurate differentiation of M. kansasii from M. tuberculosis, as well as other mycobacteria, is crucial for developing effective therapeutics and disease treatment. In this study, we combined loop-mediated isothermal amplification (LAMP) with lateral flow biosensors (LFB) to detect M. kansasii, by targeting the species-specific sequence of rpoB, a gene which encodes the β subunit of bacterial RNA polymerase. The assay was validated to ensure that it was highly selective by testing M. kansasii, M. tuberculosis, other species of respiratory bacteria, and other nontuberculous mycobacteria. The detection limit of the assay was 1 fg/μL of DNA and 50 CFU of bacilli in sputum. The M. kansasii-LAMP-LFB assay is a fast, cheap, and accurate method for detecting M. kansasii by constant temperature amplification and simple interpretation.

Gene network in pulmonary tuberculosis based on bioinformatic analysis

BACKGROUND

Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear.

METHODS

This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment.

RESULTS

Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy.

CONCLUSIONS

Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

Metabolite Profiling: A Tool for the Biochemical Characterisation of Mycobacterium sp

Over the last decades, the prevalence of drug-resistance in Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, has increased. These findings have rekindled interest in elucidating the unique adaptive molecular and biochemistry physiology of Mycobacterium. The use of metabolite profiling independently or in combination with other levels of "-omic" analyses has proven an effective approach to elucidate key physiological/biochemical mechanisms associated with Mtb throughout infection. The following review discusses the use of metabolite profiling in the study of tuberculosis, future approaches, and the technical and logistical limitations of the methodology.

Predicting Ion Channels Genes and Their Types With Machine Learning Techniques

Motivation: The number of ion channels is increasing rapidly. As many of them are associated with diseases, they are the targets of more than 700 drugs. The discovery of new ion channels is facilitated by computational methods that predict ion channels and their types from protein sequences. Methods: We used the SVMProt and the k-skip-n-gram methods to extract the feature vectors of ion channels, and obtained 188- and 400-dimensional features, respectively. The 188- and 400-dimensional features were combined to obtain 588-dimensional features. We then employed the maximum-relevance-maximum-distance method to reduce the dimensions of the 588-dimensional features. Finally, the support vector machine and random forest methods were used to build the prediction models to evaluate the classification effect. Results: Different methods were employed to extract various feature vectors, and after effective dimensionality reduction, different classifiers were used to classify the ion channels. We extracted the ion channel data from the Universal Protein Resource (UniProt, http://www.uniprot.org/) and Ligand-Gated Ion Channel databases (http://www.ebi.ac.uk/compneur-srv/LGICdb/LGICdb.php), and then verified the performance of the classifiers after screening. The findings of this study could inform the research and development of drugs.

The application of metabolomics toward pulmonary tuberculosis research

In the quest to identify novel biomarkers for pulmonary tuberculosis (TB), high-throughput systems biology approaches such as metabolomics has become increasingly widespread. Such biomarkers have not only successfully been used for better disease characterization, but have also provided new insights toward the future development of improved diagnostic and therapeutic approaches. In this review, we give a summary of the metabolomics studies done to date, with a specific focus on those investigating various aspects of pulmonary TB, and the infectious agent responsible, Mycobacterium tuberculosis. These studies, done on a variety of sample matrices, including bacteriological culture, sputum, blood, urine, tissue, and breath, are discussed in terms of their intended research outcomes or future clinical applications. Additionally, a summary of the research model, sample cohort, analytical apparatus and statistical methods used for biomarker identification in each of these studies, is provided.

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

Panomics for Precision Medicine

Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.

Metabolomics biomarkers for tuberculosis diagnostics: current status and future objectives

Numerous studies have contributed to our current understanding of the complex biology of pulmonary tuberculosis and subsequently provided solutions to its control or eradication. Metabolomics, a newcomer to the Omics research domain, has significantly contributed to this understanding by identifying biomarkers originating from the disease-associated metabolome adaptations of both the microbe and host. These biomarkers have shed light on previously unknown disease mechanisms, many of which have been implemented toward the development of improved diagnostic strategies. In this review, we will discuss the role that metabolomics has played in tuberculosis research to date, with a specific focus on new biomarker identification, and how these have contributed to improved disease characterization and diagnostics, and their potential clinical applications.

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis

To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.

Direct detection of Mycobacterium tuberculosis in sputum: A validation study using solid phase extraction-gas chromatography-mass spectrometry

Tuberculosis (TB) remains a worldwide health problem, especially in developing countries. Correct identification of Mycobacterium tuberculosis (MTB) infection is extremely important for providing appropriate treatment and care to patients. Here we describe a solid phase extraction-gas chromatography-mass spectrometry method (SPE-THM-GC-MS) for the detection of five biomarkers for M. tuberculosis. The method for classification is developed and validated through the analysis of 112 sputum samples from patients suspected of having TB. Twenty of twenty-five MTB culture-positive sputum samples were correctly classified as positive by our improved SPE-THM-GC-MS method. Eighty-five of eighty-seven MTB culture-negative samples were also negative by SPE-THM-GC-MS. The overall sensitivity of the new SPE-THM-GC-MS method is 80% (20/25) and the specificity is 98% (85/87) compared with culture. The method proved to be reliable and, although complex in principle, easy to operate due to the high degree of automation.

New insights into the survival mechanisms of rifampicin-resistant Mycobacterium tuberculosis

OBJECTIVES

Rifampicin is considered the most important antibiotic for treating TB, but unfortunately Mycobacterium tuberculosis is rapidly developing resistance to this drug. Despite the fervent research efforts to date, TB is still a major global problem, and hence new approaches are necessary to better characterize this disease, especially the mechanisms relating to drug resistance.

METHODS

Using a two-dimensional GC-coupled time-of-flight MS metabolomics approach, the most important metabolite markers characterizing rifampicin-resistant M. tuberculosis were identified.

RESULTS

The metabolite markers identified indicate instability in rifampicin-resistant M. tuberculosis mRNA, induced by the rpoB mutation. This results in a total depletion of aconitic acid, due to a shift in aconitase functionality towards mRNA binding and stability, and away from energy production and growth, and a subsequent increased dependency on alternative energy sources, fatty acids in particular. A number of other metabolic changes were observed, confirming an additional survival response for maintaining/remodelling the cell wall.

CONCLUSIONS

This study shows the value of a metabolomics approach to biological investigations in a quest to better understand disease-causing organisms and their tolerance to existing medications, which would in the future undoubtedly assist in the development of alternative treatment approaches.

Metabolomics and Personalized Medicine

Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine.

Metabolomic Profiling of Plasma from Patients with Tuberculosis by Use of Untargeted Mass Spectrometry Reveals Novel Biomarkers for Diagnosis

Although tuberculosis (TB) is a reemerging disease that affects people in developing countries and immunocompromised populations in developed countries, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used for studies on infectious diseases. We performed metabolome profiling of plasma samples to identify potential biomarkers for diagnosing TB. We compared the plasma metabolome profiles of TB patients (n = 46) with those of community-acquired pneumonia (CAP) patients (n = 30) and controls without active infection (n = 30) using ultrahigh-performance liquid chromatography-electrospray ionization-quadrupole time of flight mass spectrometry (UHPLC-ESI-QTOFMS). Using multivariate and univariate analyses, four metabolites, 12R-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid [12(R)-HETE], ceramide (d18:1/16:0), cholesterol sulfate, and 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol, were identified and found to have significantly higher levels in TB patients than those in CAP patients and controls. In a comparison of TB patients and controls, the four metabolites demonstrated area under the receiver operating characteristic curve (AUC) values of 0.914, 0.912, 0.905, and 0.856, sensitivities of 84.8%, 84.8%, 87.0%, and 89.1%, specificities of 90.0%, 86.7%, 86.7%, and 80.0%, and fold changes of 4.19, 26.15, 6.09, and 1.83, respectively. In a comparison of TB and CAP patients, the four metabolites demonstrated AUC values of 0.793, 0.717, 0.802, and 0.894, sensitivities of 89.1%, 71.7%, 80.4%, and 84.8%, specificities of 63.3%, 66.7%, 70.0%, and 83.3%, and fold changes of 4.69, 3.82, 3.75, and 2.16, respectively. 4α-Formyl-4β-methyl-5α-cholesta-8-en-3β-ol combined with 12(R)-HETE or cholesterol sulfate offered ≥70% sensitivity and ≥90% specificity for differentiating TB patients from controls or CAP patients. These novel plasma biomarkers, especially 12(R)-HETE and 4α-formyl-4β-methyl-5α-cholesta-8-en-3β-ol, alone or in combination, are potentially useful for rapid and noninvasive diagnosis of TB. The present findings may offer insights into the pathogenesis and host response in TB.

Neglected diseases prioritized in Brazil under the perspective of metabolomics: A review

This review article compiles in a critical manner literature publications regarding seven neglected diseases (ND) prioritized in Brazil (Chagas disease, dengue, leishmaniasis, leprosy, malaria, schistosomiasis, and tuberculosis) under the perspective of metabolomics. Both strategies, targeted and untargeted metabolomics, were considered in the compilation. The majority of studies focused on biomarker discovery for diagnostic purposes, and on the search of novel or alternative therapies against the ND under consideration, although temporal progression of the infection at metabolic level was also addressed. Tuberculosis, followed by schistosomiasis, malaria and leishmaniasis are the diseases that received larger attention in terms of number of publications. Dengue and leprosy were the least studied and Chagas disease received intermediate attention. NMR and HPLC-MS technologies continue to predominate among the analytical platforms of choice in the metabolomic studies of ND. A plethora of metabolites were identified in the compiled studies, with expressive predominancy of amino acids, organic acids, carbohydrates, nucleosides, lipids, fatty acids, and derivatives.

Metabolomics Analysis Reveals Specific Novel Tetrapeptide and Potential Anti-Inflammatory Metabolites in Pathogenic Aspergillus species

Infections related to Aspergillus species have emerged to become an important focus in infectious diseases, as a result of the increasing use of immunosuppressive agents and high fatality associated with invasive aspergillosis. However, laboratory diagnosis of Aspergillus infections remains difficult. In this study, by comparing the metabolomic profiles of the culture supernatants of 30 strains of six pathogenic Aspergillus species (A. fumigatus, A. flavus, A. niger, A. terreus, A. nomius and A. tamarii) and 31 strains of 10 non-Aspergillus fungi, eight compounds present in all strains of the six Aspergillus species but not in any strain of the non-Aspergillus fungi were observed. One of the eight compounds, Leu–Glu–Leu–Glu, is a novel tetrapeptide and represents the first linear tetrapeptide observed in Aspergillus species, which we propose to be named aspergitide. Two other closely related Aspergillus-specific compounds, hydroxy-(sulfooxy)benzoic acid and (sulfooxy)benzoic acid, may possess anti-inflammatory properties, as 2-(sulfooxy)benzoic acid possesses a structure similar to those of aspirin [2-(acetoxy)benzoic acid] and salicylic acid (2-hydroxybenzoic acid). Further studies to examine the potentials of these Aspergillus-specific compounds for laboratory diagnosis of aspergillosis are warranted and further experiments will reveal whether Leu–Glu–Leu–Glu, hydroxy-(sulfooxy)benzoic acid and (sulfooxy)benzoic acid are virulent factors of the pathogenic Aspergillus species.

Metabolomic profiling of Burkholderia pseudomallei using UHPLC-ESI-Q-TOF-MS reveals specific biomarkers including 4-methyl-5-thiazoleethanol and unique thiamine degradation pathway

BACKGROUND

Burkholderia pseudomallei is an emerging pathogen that causes melioidosis, a serious and potentially fatal disease which requires prolonged antibiotics to prevent relapse. However, diagnosis of melioidosis can be difficult, especially in culture-negative cases. While metabolomics represents an uprising tool for studying infectious diseases, there were no reports on its applications to B. pseudomallei. To search for potential specific biomarkers, we compared the metabolomics profiles of culture supernatants of B. pseudomallei (15 strains), B. thailandensis (3 strains), B. cepacia complex (14 strains), P. aeruginosa (4 strains) and E. coli (3 strains), using ultra-high performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry (UHPLC-ESI-Q-TOF-MS). Multi- and univariate analyses were used to identify specific metabolites in B. pseudomallei.

RESULTS

Principal component and partial-least squares discrimination analysis readily distinguished the metabolomes between B. pseudomallei and other bacterial species. Using multi-variate and univariate analysis, eight metabolites with significantly higher levels in B. pseudomallei were identified. Three of the eight metabolites were identified by MS/MS, while five metabolites were unidentified against database matching, suggesting that they may be potentially novel compounds. One metabolite, m/z 144.048, was identified as 4-methyl-5-thiazoleethanol, a degradation product of thiamine (vitamin B1), with molecular formula C6H9NOS by database searches and confirmed by MS/MS using commercially available authentic chemical standard. Two metabolites, m/z 512.282 and m/z 542.2921, were identified as tetrapeptides, Ile-His-Lys-Asp with molecular formula C22H37N7O7 and Pro-Arg-Arg-Asn with molecular formula C21H39N11O6, respectively. To investigate the high levels of 4-methyl-5-thiazoleethanol in B. pseudomallei, we compared the thiamine degradation pathways encoded in genomes of B. pseudomallei and B. thailandensis. While both B. pseudomallei and B. thailandensis possess thiaminase I which catalyzes degradation of thiamine to 4-methyl-5-thiazoleethanol, thiM, which encodes hydroxyethylthiazole kinase responsible for degradation of 4-methyl-5-thiazoleethanol, is present and expressed in B. thailandensis as detected by PCR/RT-PCR, but absent or not expressed in all B. pseudomallei strains. This suggests that the high 4-methyl-5-thiazoleethanol level in B. pseudomallei is likely due to the absence of hydroxyethylthiazole kinase and hence reduced downstream degradation.

CONCLUSION

Eight novel biomarkers, including 4-methyl-5-thiazoleethanol and two tetrapeptides, were identified in the culture supernatant of B. pseudomallei.

Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition

Mycobacterium tuberculosis is a bacterium that causes tuberculosis, one of the most prevalent infectious diseases.

Identification of specific metabolites in culture supernatant of Mycobacterium tuberculosis using metabolomics: exploration of potential biomarkers

Although previous studies have reported the use of metabolomics for Mycobacterium species differentiation, little is known about the potential of extracellular metabolites of Mycobacterium tuberculosis (MTB) as specific biomarkers. Using an optimized ultrahigh performance liquid chromatography-electrospray ionization-quadruple time of flight-mass spectrometry (UHPLC-ESI-Q-TOF-MS) platform, we characterized the extracellular metabolomes of culture supernatant of nine MTB strains and nine non-tuberculous Mycobacterium (NTM) strains (four M. avium complex, one M. bovis Bacillus Calmette-Guérin (BCG), one M. chelonae, one M. fortuitum and two M. kansasii). Principal component analysis readily distinguished the metabolomes between MTB and NTM. Using multivariate and univariate analysis, 24 metabolites with significantly higher levels in MTB were identified. While seven metabolites were identified by tandem mass spectrometry (MS/MS), the other 17 metabolites were unidentified by MS/MS against database matching, suggesting that they may be potentially novel compounds. One metabolite was identified as dexpanthenol, the alcohol analog of pantothenic acid (vitamin B5), which was not known to be produced by bacteria previously. Four metabolites were identified as 1-tuberculosinyladenosine (1-TbAd), a product of the virulence-associated enzyme Rv3378c, and three previously undescribed derivatives of 1-TbAd. Two derivatives differ from 1-TbAd by the ribose group of the nucleoside while the other likely differs by the base. The remaining two metabolites were identified as a tetrapeptide, Val-His-Glu-His, and a monoacylglycerophosphoglycerol, phosphatidylglycerol (PG) (16∶0/0∶0), respectively. Further studies on the chemical structure and biosynthetic pathway of these MTB-specific metabolites would help understand their biological functions. Studies on clinical samples from tuberculosis patients are required to explore for their potential role as diagnostic biomarkers.

Mycobacterium tuberculosis metabolism

Metabolism underpins the physiology and pathogenesis of Mycobacterium tuberculosis. However, although experimental mycobacteriology has provided key insights into the metabolic pathways that are essential for survival and pathogenesis, determining the metabolic status of bacilli during different stages of infection and in different cellular compartments remains challenging. Recent advances-in particular, the development of systems biology tools such as metabolomics-have enabled key insights into the biochemical state of M. tuberculosis in experimental models of infection. In addition, their use to elucidate mechanisms of action of new and existing antituberculosis drugs is critical for the development of improved interventions to counter tuberculosis. This review provides a broad summary of mycobacterial metabolism, highlighting the adaptation of M. tuberculosis as specialist human pathogen, and discusses recent insights into the strategies used by the host and infecting bacillus to influence the outcomes of the host-pathogen interaction through modulation of metabolic functions.

Identification of mycobacteria based on spectroscopic analyses of mycolic acid profiles

This report examines lipophilic extracts containing mycolic acids isolated from tuberculosis (MTB) and non-tuberculosis (NTM) mycobacterial strains using chromatography, mass spectrometry (MS), nuclear magnetic resonance (NMR), and Raman spectroscopy. Gas chromatography-MS was used to identify major fatty acid mycolate components, while proton NMR confirmed the presence of characteristic cis- and trans-cyclopropane rings within different mycolic acid sub-types. Surface-enhanced Raman (SERS) spectra were obtained from the mycolic acids extracted from the bacterial cell envelopes of the MTB or NTM mycobacterial species. The Raman spectral profiles were used to develop a classification method based on chemometrics for identification of the mycobacterial species. Multivariate statistical analysis methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS-DA) of the SERS spectra enabled differentiation of NTM mycobacteria from one another with 100% accuracy. These methods are also sensitive enough to differentiate clinically-isolated MTB strains that differed only by the presence or absence of a single extracytoplasmic sigma factor with 83-100% sensitivity and 80-100% specificity. The current work is the first report on discrimination of mycobacteria strains based on the SERS spectra of the constituent mycolic acids in lipophilic extracts. These results suggest that SERS can be used as an accurate and sensitive method for species and strain discrimination in mycobacteria.

Identifying the subfamilies of voltage-gated potassium channels using feature selection technique

Voltage-gated K⁺ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs’ subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems.

Rapid diagnosis of TB using GC-MS and chemometrics

The search continues for a rapid diagnostic test for TB that has high sensitivity and specificity and is useable in sophisticated environments and in deprived regions with poor infrastructure. We discuss here the modern bioanalytical techniques that can be used to discover biomarkers of infection with Mycobacterium tuberculosis, focusing on techniques using GC. We will also discuss the use of GC-MS to identify volatile organic compounds in the headspace of bacterial culture or in samples of breath, serum or urine. Biomarkers discovered in the 'clean' environment of culture may differ from those in patients. A number of biomarkers have been found in patients, with little consistency in the various studies to date. Reproducibility is difficult; the impressive results found initially with a few patients are rarely repeatable when a larger sample series is tested. Mycobacterial lipids offer promise for distinguishing M. tuberculosis from nontuberculous mycobacteria directly in sputum.

Multivariate statistical models of metabolomic data reveals different metabolite distribution patterns in isonitrosoacetophenone-elicited Nicotiana tabacum and Sorghum bicolor cells

Isonitrosoacetophenone (INAP, 2-keto-2-phenyl-acetaldoxime) is a novel inducer of plant defense. Oxime functional groups are rare in natural products, but can serve as substrates depending on existing secondary pathways. Changes in the metabolomes of sorghum and tobacco cells treated with INAP were investigated and chemometric tools and multivariate statistical analysis were used to investigate the changes in metabolite distribution patterns resulting from INAP elicitation. Liquid chromatography combined with mass spectrometry (UHPLC-MS) supplied unique chemical fingerprints that were generated in response to specific metabolomic events. Principal component analysis (PCA) together with hierarchical cluster analysis (HCA) and Metabolic Trees were used for data visualization. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and shared and unique structure (SUS) plots were exploited in parallel to reveal the changes in the metabolomes. PCA indicated that the cells responded differentially to INAP through changes in the metabolite profiles. Furthermore, HCA and Metabolic Trees showed that INAP induced metabolic perturbations in both cell lines and that homeostasis was re-established over time. OPLS-DA-based shared and unique structure (SUS) plots confirmed the results and revealed differences in the metabolites distribution patterns between tobacco and sorghum cells. Chemometric analyses of metabolomic data offers insight into changes in metabolism in response to chemical elicitation. Although similar, the response in sorghum cells was found to be more consistent and well-coordinated when compared to tobacco cells, indicative of the differences in secondary metabolism between cyanogenic and non-cyanogenic plants for oxime metabolism.

The use of functional genomics in conjunction with metabolomics for Mycobacterium tuberculosis research

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a fatal infectious disease, resulting in 1.4 million deaths globally per annum. Over the past three decades, genomic studies have been conducted in an attempt to elucidate the functionality of the genome of the pathogen. However, many aspects of this complex genome remain largely unexplored, as approaches like genomics, proteomics, and transcriptomics have failed to characterize them successfully. In turn, metabolomics, which is relatively new to the “omics” revolution, has shown great potential for investigating biological systems or their modifications. Furthermore, when these data are interpreted in combination with previously acquired genomics, proteomics and transcriptomics data, using what is termed a systems biology approach, a more holistic understanding of these systems can be achieved. In this review we discuss how metabolomics has contributed so far to characterizing TB, with emphasis on the resulting improved elucidation of M. tuberculosis in terms of (1) metabolism, (2) growth and replication, (3) pathogenicity, and (4) drug resistance, from the perspective of systems biology.

miRNA signatures in sera of patients with active pulmonary tuberculosis

Several studies showed that assessing levels of specific circulating microRNAs (miRNAs) is a non-invasive, rapid, and accurate method for diagnosing diseases or detecting alterations in physiological conditions. We aimed to identify a serum miRNA signature to be used for the diagnosis of tuberculosis (TB). To account for variations due to the genetic makeup, we enrolled adults from two study settings in Europe and Africa. The following categories of subjects were considered: healthy (H), active pulmonary TB (PTB), active pulmonary TB, HIV co-infected (PTB/HIV), latent TB infection (LTBI), other pulmonary infections (OPI), and active extra-pulmonary TB (EPTB). Sera from 10 subjects of the same category were pooled and, after total RNA extraction, screened for miRNA levels by TaqMan low-density arrays. After identification of “relevant miRNAs”, we refined the serum miRNA signature discriminating between H and PTB on individual subjects. Signatures were analyzed for their diagnostic performances using a multivariate logistic model and a Relevance Vector Machine (RVM) model. A leave-one-out-cross-validation (LOOCV) approach was adopted for assessing how both models could perform in practice. The analysis on pooled specimens identified selected miRNAs as discriminatory for the categories analyzed. On individual serum samples, we showed that 15 miRNAs serve as signature for H and PTB categories with a diagnostic accuracy of 82% (CI 70.2–90.0), and 77% (CI 64.2–85.9) in a RVM and a logistic classification model, respectively. Considering the different ethnicity, by selecting the specific signature for the European group (10 miRNAs) the diagnostic accuracy increased up to 83% (CI 68.1–92.1), and 81% (65.0–90.3), respectively. The African-specific signature (12 miRNAs) increased the diagnostic accuracy up to 95% (CI 76.4–99.1), and 100% (83.9–100.0), respectively. Serum miRNA signatures represent an interesting source of biomarkers for TB disease with the potential to discriminate between PTB and LTBI, but also among the other categories.

Validation of biomarkers for distinguishing Mycobacterium tuberculosis from non-tuberculous mycobacteria using gas chromatography-mass spectrometry and chemometrics

Tuberculosis (TB) remains a major international health problem. Rapid differentiation of Mycobacterium tuberculosis complex (MTB) from non-tuberculous mycobacteria (NTM) is critical for decisions regarding patient management and choice of therapeutic regimen. Recently we developed a 20-compound model to distinguish between MTB and NTM. It is based on thermally assisted hydrolysis and methylation gas chromatography-mass spectrometry and partial least square discriminant analysis. Here we report the validation of this model with two independent sample sets, one consisting of 39 MTB and 17 NTM isolates from the Netherlands, the other comprising 103 isolates (91 MTB and 12 NTM) from Stellenbosch, Cape Town, South Africa. All the MTB strains in the 56 Dutch samples were correctly identified and the model had a sensitivity of 100% and a specificity of 94%. For the South African samples the model had a sensitivity of 88% and specificity of 100%. Based on our model, we have developed a new decision-tree that allows the differentiation of MTB from NTM with 100% accuracy. Encouraged by these findings we will proceed with the development of a simple, rapid, affordable, high-throughput test to identify MTB directly in sputum.

Cell metabolomics

Abstract Metabolomics technologies enable the examination and identification of endogenous biochemical reaction products, revealing information on the precise metabolic pathways and processes within a living cell. Metabolism is either directly or indirectly involved with every aspect of cell function, and metabolomics is thus believed to be a reflection of the phenotype of any cell. Metabolomics analysis of cells has many potential applications and advantages compared to currently used methods in the postgenomics era. Cell metabolomics is an emerging field that addresses fundamental biological questions and allows one to observe metabolic phenomena in cells. Cell metabolomics consists of four sequential steps: (a) sample preparation and extraction, (b) metabolic profiles of low-weight metabolites based on MS or NMR spectroscopy techniques, (c) pattern recognition approaches and bioinformatics data analysis, (d) metabolites identification resulting in putative biomarkers and molecular targets. The biomarkers are eventually placed in metabolic networks to provide insight on the cellular biochemical phenomena. This article analyzes the recent developments in use of metabolomics to characterize and interpret the cellular metabolome in a wide range of pathophysiological and clinical contexts, and the putative roles of the endogenous small molecule metabolites in this new frontier of postgenomics biology and systems medicine.

New sputum metabolite markers implicating adaptations of the host to Mycobacterium tuberculosis, and vice versa

In this study, a metabolomics research approach was used to identify new tuberculosis (TB) markers from sputum, in an attempt to better characterise the disease as well as the metabolic response of the host to Mycobacterium tuberculosis infection. After GCxGC-TOFMS analyses, various multivariate and univariate statistical methods were implemented to identify those compounds best describing the variation between the TB-positive and TB-negative patient groups. The interpretation of these new metabolite markers led to a number of new hypotheses, including: 1) support of the previously proposed citramalate cycle in M. tuberculosis; 2) the interaction of this cycle with an up-regulated glyoxylate cycle during pulmonary M. tuberculosis infection; 3) the increased utilisation of fatty acids and glutamate as alternative carbon sources by M. tuberculosis during pulmonary infection; 4) an alternative mechanism by which the host produces hydrogen peroxide via glucose oxidation, in order to eliminate the bacterial infection; 5) inhibition of the ETC due to pronounced oxidative stress during an active TB disease state, resulting in increased concentrations of various neurotransmitters and other metabolites previously associated with an inborn error of metabolism (MADD/GA type II); and 6) elevated concentrations of neurotransmitters associated with a number of previously described symptoms of TB.

A comparison of four sputum pre-extraction preparation methods for identifying and characterising Mycobacterium tuberculosis using GCxGC-TOFMS metabolomics

In many pulmonary diseases, sputum is a valuable sample material for use in disease characterisation and diagnostics. However, due to its high viscosity and uneven consistency (lumpiness), it is difficult to obtain reproducible/repeatable results during compound extraction and analysis. We subsequently investigated and compared four sputum pre-extraction preparation methods using: 1) Sputolysin; 2) a combination of N-acetyl-l-cysteine and sodium hydroxide (NALC-NaOH); 3) NaOH alone, and 4) a simple ethanol homogenisation method, prior to sputum extraction and metabolomics analyses. The simple ethanol homogenisation approach proved to be the comparatively superior sputum pre-extraction preparation method, considering its repeatability, the number of characteristic compounds extracted, its ability to extract those compounds best differentiating the sample groups (Mycobacterium tuberculosis-spiked and clinically confirmed TB-positive patient samples from each of the controls respectively), and its detection limit. This developed methodology subsequently allows for accurate GC based analyses of sputum, and hence, could contribute significantly to the better characterisation or diagnostics of not only tuberculosis, but also potentially other pulmonary diseases, including, interstitial lung disease, cystic fibrosis, lung cancer, pneumonia and any other bacterial induced pulmonary diseases producing sputum.

Metabolomics in food science

Metabolomics, the newest member of the omics techniques, has become an important tool in agriculture, pharmacy, and environmental sciences. Advances in compound extraction, separation, detection, identification, and data analysis have allowed metabolomics applications in food sciences including food processing, quality, and safety. This chapter discusses recent advances and applications of metabolomics in food science.