Identification of bacterial species by untargeted NMR spectroscopy of the exo-metabolome

Synovial fluid NMR-based metabolomics in septic and aseptic revision total knee arthroplasty: Implications on diagnosis and treatment

Periprosthetic joint infection (PJI) is one of the most challenging complications following total knee arthroplasty. Despite its importance, there is a paucity of reports in the literature regarding its pathogenesis. Recently, cellular metabolic reprogramming has been shown to play an important role in the progression and outcome of infectious diseases. Therefore, the aim of this study was to evaluate the metabolites composition of the synovial fluid from patients with PJI or aseptic failure of total knee arthroplasties. The synovial fluids from 21 patients scheduled for revision total knee arthroplasty (11 with the diagnosis of PJI and 10 with aseptic failures) were analyzed using 1D 1H NMR spectroscopy. Univariate and multivariate statistical analyzes were used to identify metabolites that were differentially abundant between those groups. A total of 28 metabolites were identified and five of them found to be differentially abundant between infected and non-infected synovial fluids. Lactate, acetate and 3-hydroxybutyrate were found to be in a higher concentration, and glucose and creatine were found reduced in the synovial fluid from PJI patients. Synovial fluid from patients with PJI exhibit a distinct metabolic profile, possibly reflecting metabolic adaptation that occurs in the infected periprosthetic microenvironment. Further research and studies are warranted to gain a broader insight into the metabolic pathways engaged by both pathogen and immune cells in the context of a PJI.

Differentiation of different dibenzothiophene (DBT) desulfurizing bacteria via surface-enhanced Raman spectroscopy (SERS)

Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO x in the atmosphere. SO x is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria (Chryseobacterium sp. IS, Gordonia sp. 4N, Mycolicibacterium sp. J2, and Rhodococcus sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.

Inhibition of anaerobic digestion by various ammonia sources resulted in subtle differences in metabolite dynamics

The impact of ammonia on anaerobic digestion performance and microbial dynamics has been extensively studied, but the concurrent effect of anions brought by ammonium salt should not be neglected. This paper studied this effect using metabolomics and a time-course statistical framework. Metabolomics provides novel perspectives to study microbial processes and facilitates a more profound understanding at the metabolic level. The advanced statistical framework enables deciphering the complexity of large metabolomics data sets. More specifically, a series of lab-scale batch reactors were set up with different ammonia sources added. Samples of nine time points over the degradation were analyzed with liquid chromatography-mass spectrometry. A filtering procedure was applied to select the promising metabolomic peaks from 1262 peaks, followed by modeling their intensities across time. The metabolomic peaks with similar time profiles were clustered, evidencing the correlation of different biological processes. Differential analysis was performed to seek the differences in metabolite dynamics caused by different anions. Finally, tandem mass spectrometry and metabolite annotation provided further information on the molecular structure and possible metabolic pathways. For example, the consumption of 5-aminovaleric acid, a short-chain fatty acid obtained from l-lysine degradation, was slowed down by phosphates. Overall, by investigating the effect of anions on anaerobic digestion, our study demonstrated the effectiveness of metabolomics in providing detailed information in a set of samples from different experimental conditions. With the statistical framework, the approach enables capturing subtle differences in metabolite dynamics between samples while accounting for the differences caused by time variations.

Hyperpolarized 1H and 13C NMR Spectroscopy in a Single Experiment for Metabolomics

The application of NMR spectroscopy to complex mixture analysis and, in particular, to metabolomics is limited by the low sensitivity of NMR. We recently showed that dissolution dynamic nuclear polarization (d-DNP) could enhance the sensitivity of 13C NMR for complex metabolite mixtures, leading to the detection of highly sensitive 13C NMR fingerprints of complex samples such as plant extracts or urine. While such experiments provide heteronuclear spectra, which are complementary to conventional NMR, hyperpolarized 1H NMR spectra would also be highly useful, with improved limits of detection and compatibility with the existing metabolomics workflow and databases. In this technical note, we introduce an approach capable of recording both 1H and 13C hyperpolarized spectra of metabolite mixtures in a single experiment and on the same hyperpolarized sample. We investigate the analytical performance of this method in terms of sensitivity and repeatability, and then we show that it can be efficiently applied to a plant extract. Significant sensitivity enhancements in 1H NMR are reported with a repeatability suitable for metabolomics studies without compromising on the performance of hyperpolarized 13C NMR. This approach provides a way to perform both 1H and 13C hyperpolarized NMR metabolomics with unprecedented sensitivity and throughput.

Pathogen inhibition and indication by gelatin nonwoven mats with incorporation of polyphenol derivatives

There is a need for non-pharmaceutical intervention methods that can prevent and indicate the risk of airborne disease spread. In this study, we developed a nonwoven mat based on the polyphenol gallic acid, which can inhibit pathogens growth and also indicate pathogen levels in the surrounding environment. Using nuclear magnetic resonance, Fourier-transform infrared spectroscopy, and high-performance liquid chromatography, we characterized this novel gelatin-based nonwoven mat and investigated the mechanism governing its ability to indicate pathogen levels. We demonstrated that the incorporation of gallic acid serves a vital role in indicating the presence of bacteria, causing the nonwoven mat to change in color from white to brown. We have proposed a plausible mechanism for this color change behavior based on a reaction of gallic acid with components excreted by bacteria, including glutamate, valine, and leucine. The concentrations of these components reflect the bacterial counts, enabling a real-time indication of pathogen levels in the surrounding air. In summary, the nonwoven mat presented herein can serve as an excellent antibacterial agent and as an indicator of nearby bacteria for fabricating personal protection equipment like filtration mask.

Deciphering the contribution of microbial biomass to the properties of dissolved and particulate organic matter in anaerobic digestates

The recalcitrant structures either from substrate or microbial biomass contained in digestates after anaerobic digestion (AD) highly influence digestate valorization. To properly assess the microbial biomass contribution to the digested organic matter (OM), a combination of characterization methods and the use of various substrate types in anaerobic continuous reactors was required. The use of totally biodegradable substrates allowed detecting soluble microbial products via fluorescence spectroscopy at emission wavelengths of 420 and 460 nm while the protein-like signature was enhanced by the whey protein. During reactors' operation, a transfer of complex compounds to the dissolved OM from the particulate OM was observed through fluorescence applied on biochemical fractionation. Consequently, the fluorescence complexity index of the dissolved OM increased from 0.59-0.60 to 1.06-1.07, whereas it decreased inversely for the extractable soluble from the particulate OM from 1.16-1.19 to 0.42-0.54. Accordingly, fluorescence regional integration showed differences among reactors based on visual inspection and orthogonal partial latent structures (OPLS) analysis. Similarly, the impact of the substrate type and operation time on the particulate OM was revealed by ¹³C nuclear magnetic resonance using OPLS, providing a good model (R²X = 0.93 and Q² = 0.8) with a clear time-trend. A high signal resonated at ∼30 ppm attributed to CH₂-groups in the aliphatic chain of lipid-like structure besides carbohydrates intensities at 60-110 ppm distinguished the reactor fed with whey protein from the other, which was mostly biomass related. Indeed, this latter displayed a higher presence of peptidoglycan (δH/C: 1.6-2.0/20-25 ppm) derived from microbial biomass by ¹H-¹³C heteronuclear single-quantum coherence (HSQC) nuclear magnetic resonance. Interestingly, the sample distribution obtained by non-metric multidimensional scaling of bacterial communities resembled the attained using ¹³C NMR properties, opening new research perspectives. Overall, this study discloses the microbial biomass contribution to digestates composition to improve the OM transformation mechanism knowledge.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

Bioactive potentiality of secondary metabolites from endophytic bacteria against SARS-COV-2: An in-silico approach

Five endophytic bacterial isolates were studied to identify morphologically and biochemically, according to established protocols and further confirmed by 16S rDNA Sanger sequencing, as Priestia megaterium, Staphylococcus caprae, Neobacillus drentensis, Micrococcus yunnanensis, and Sphingomonas paucimobiliz, which were then tested for phytohormone, ammonia, and hydrolytic enzyme production. Antioxidant compounds total phenolic content (TPC), and total flavonoid content (TFC) were assessed by using bacterial crude extracts obtained from 24-hour shake-flask culture. Phylogenetic tree analysis of those identified isolates shared sequence similarities with the members of Bacillus, Micrococcus, Staphylococcus, and Pseudomonas species, and after GenBank submission, accession numbers for the nucleotide sequences were found to be MW494406, MW494408, MW494401, MW494402, and MZ021340, respectively. In silico analysis was performed to identify their bioactive genes and compounds in the context of bioactive secondary metabolite production with medicinal value, where nine significant bioactive compounds according to six different types of bioactive secondary metabolites were identified, and their structures, gene associations, and protein-protein networks were analyzed by different computational tools and servers, which were reported earlier with their antimicrobial, anti-infective, antioxidant, and anti-cancer capabilities. These compounds were then docked to the 3-chymotrypsin-like protease (3CLpro) of the novel SARS-COV-2. Docking scores were then compared with 3CLpro reference inhibitor (lopinavir), and docked compounds were further subjected to ADMET and drug-likeness analyses. Ligand-protein interactions showed that two compounds (microansamycin and aureusimine) interacted favorably with coronavirus 3CLpro. Besides, in silico analysis, we also performed NMR for metabolite detection whereas three metabolites (microansamycin, aureusimine, and stenothricin) were confirmed from the 1H NMR profiles. As a consequence, the metabolites found from NMR data aligned with our in-silico analysis that carries a significant outcome of this research. Finally, Endophytic bacteria collected from medicinal plants can provide new leading bioactive compounds against target proteins of SARS-COV-2, which could be an effective approach to accelerate drug innovation and development.

Raman Spectroscopy-A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings

Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful-quick, non-invasive, and non-destructive -tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.

Cadaverine Is a Switch in the Lysine Degradation Pathway in Pseudomonas aeruginosa Biofilm Identified by Untargeted Metabolomics

There is a critical need to accurately diagnose, prevent, and treat biofilms in humans. The biofilm forming P. aeruginosa bacteria can cause acute and chronic infections, which are difficult to treat due to their ability to evade host defenses along with an inherent antibiotic-tolerance. Using an untargeted NMR-based metabolomics approach, we identified statistically significant differences in 52 metabolites between P. aeruginosa grown in the planktonic and lawn biofilm states. Among them, the metabolites of the cadaverine branch of the lysine degradation pathway were systematically decreased in biofilm. Exogenous supplementation of cadaverine caused significantly increased planktonic growth, decreased biofilm accumulation by 49% and led to altered biofilm morphology, converting to a pellicle biofilm at the air-liquid interface. Our findings show how metabolic pathway differences directly affect the growth mode in P. aeruginosa and could support interventional strategies to control biofilm formation.

Comparison of bacteria disintegration methods and their influence on data analysis in metabolomics

Metabolomic experiments usually contain many different steps, each of which can strongly influence the obtained results. In this work, metabolic analyses of six bacterial strains were performed in light of three different bacterial cell disintegration methods. Three strains were gram-negative (Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae), and three were gram-positive (Corynebacterium glutamicum, Bacillus cereus, and Enterococcus faecalis). For extraction, the methanol–water extraction method (1:1) was chosen. To compare the efficiency of different cell disintegration methods, sonication, sand mill, and tissue lyser were used. For bacterial extract metabolite analysis, ¹H NMR together with univariate and multivariate analyses were applied. The obtained results showed that metabolite concentrations are strongly dependent on the cell lysing methodology used and are different for various bacterial strains. The results clearly show that one of the disruption methods gives the highest concentration for most identified compounds (e. g. sand mill for E. faecalis and tissue lyser for B. cereus). This study indicated that the comparison of samples prepared by different procedures can lead to false or imprecise results, leaving an imprint of the disintegration method. Furthermore, the presented results showed that NMR might be a useful bacterial strain identification and differentiation method. In addition to disintegration method comparison, the metabolic profiles of each elaborated strain were analyzed, and each exhibited its metabolic profile. Some metabolites were identified by the ¹H NMR method in only one strain. The results of multivariate data analyses (PCA) show that regardless of the disintegration method used, the strain group can be identified. Presented results can be significant for all types of microbial studies containing the metabolomic targeted and non-targeted analysis.

Metabolomics Comparison of Drug-Resistant and Drug-Susceptible Pseudomonas aeruginosa Strain (Intra- and Extracellular Analysis)

Pseudomonas aeruginosa is a common human pathogen belonging to the ESKAPE group. The multidrug resistance of bacteria is a considerable problem in treating patients and may lead to increased morbidity and mortality rate. The natural resistance in these organisms is caused by the production of specific enzymes and biofilm formation, while acquired resistance is multifactorial. Precise recognition of potential antibiotic resistance on different molecular levels is essential. Metabolomics tools may aid in the observation of the flux of low molecular weight compounds in biochemical pathways yielding additional information about drug-resistant bacteria. In this study, the metabolisms of two P. aeruginosa strains were compared-antibiotic susceptible vs. resistant. Analysis was performed on both intra- and extracellular metabolites. The ¹H NMR method was used together with multivariate and univariate data analysis, additionally analysis of the metabolic pathways with the FELLA package was performed. The results revealed the differences in P. aeruginosa metabolism of drug-resistant and drug-susceptible strains and provided direct molecular information about P. aeruginosa response for different types of antibiotics. The most significant differences were found in the turnover of amino acids. This study can be a valuable source of information to complement research on drug resistance in P. aeruginosa.

Array-Based Biosensors for Bacteria Detection: From the Perspective of Recognition

Array-based biosensors have shown as effective and powerful tools to distinguish intricate mixtures with infinitesimal differences among analytes such as nucleic acids, proteins, microorganisms, and other biomolecules. In array-based bacterial sensing, the recognition of bacteria is the initial step that can crucially influence the analytical performance of a biosensor array. Bacteria recognition as well as the signal readout and mathematical analysis are indispensable to ensure the discrimination ability of array-based biosensors. Strategies for bacteria recognition mainly include the specific interaction between biomolecules and the corresponding receptors on bacteria, the noncovalent interaction between materials and bacteria, and the specific targeting of bacterial metabolites. In this review, recent advances in array-based bacteria sensors are discussed from the perspective of bacteria recognition relying on the characteristics of different bacteria. Principles of bacteria recognition and signal readout for bacteria detection are highlighted as well as the discussion on future trends in array-based biosensors.

Molecular Identification of Endophytic Bacteria in Leucojum aestivum In Vitro Culture, NMR-Based Metabolomics Study and LC-MS Analysis Leading to Potential Amaryllidaceae Alkaloid Production

In this study, endophytic bacteria belonging to the Bacillus genus were isolated from in vitro bulblets of Leucojum aestivum and their ability to produce Amaryllidaceae alkaloids was studied. Proton Nuclear Magnetic Resonance (¹H NMR)-based metabolomics combined with multivariate data analysis was chosen to compare the metabolism of this plant (in vivo bulbs, in vitro bulblets) with those of the endophytic bacteria community. Primary metabolites were quantified by quantitative ¹H NMR (qNMR) method. The results showed that tyrosine, one precursor of the Amaryllidaceae alkaloid biosynthesis pathway, was higher in endophytic extract compared to plant extract. In total, 22 compounds were identified including five molecules common to plant and endophyte extracts (tyrosine, isoleucine, valine, fatty acids and tyramine). In addition, endophytic extracts were analyzed using Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography-Mass Spectrometry (GC-MS) for the identification of compounds in very low concentrations. Five Amaryllidaceae alkaloids were detected in the extracts of endophytic bacteria. Lycorine, previously detected by ¹H NMR, was confirmed with LC-MS analysis. Tazettine, pseudolycorine, acetylpseudolycorine, 1,2-dihydro-chlidanthine were also identified by LC-MS using the positive ionization mode or by GC-MS. In addition, 11 primary metabolites were identified in the endophytic extracts such as tyramine, which was obtained by decarboxylation of tyrosine. Thus, Bacillus sp. isolated from L. aestivum bulblets synthesized some primary and specialized metabolites in common with the L.aestivum plant. These endophytic bacteria are an interesting new approach for producing the Amaryllidaceae alkaloid such as lycorine.

Hyperpolarized NMR Metabolomics at Natural 13C Abundance

Metabolomics plays a pivotal role in systems biology, and NMR is a central tool with high precision and exceptional resolution of chemical information. Most NMR metabolomic studies are based on 1H 1D spectroscopy, severely limited by peak overlap. 13C NMR benefits from a larger signal dispersion but is barely used in metabolomics due to ca. 6000-fold lower sensitivity. We introduce a new approach, based on hyperpolarized 13C NMR at natural abundance, that circumvents this limitation. A new untargeted NMR-based metabolomic workflow based on dissolution dynamic nuclear polarization (d-DNP) for the first time enabled hyperpolarized natural abundance 13C metabolomics. Statistical analysis of resulting hyperpolarized 13C data distinguishes two groups of plant (tomato) extracts and highlights biomarkers, in full agreement with previous results on the same biological model. We also optimize parameters of the semiautomated d-DNP system suitable for high-throughput studies.

Chemical Profiling Provides Insights into the Metabolic Machinery of Hydrocarbon-Degrading Deep-Sea Microbes

Marine microbes are known to degrade hydrocarbons; however, microbes inhabiting deep-sea sediments remain largely unexplored. Previous studies into the classical pathways of marine microbial metabolism reveal diverse chemistries; however, metabolic profiling of marine microbes cultured with hydrocarbons is limited. In this study, taxonomic (amplicon sequencing) profiles of two environmental deep-sea sediments (>1,200 m deep) were obtained, along with taxonomic and metabolomic (mass spectrometry-based metabolomics) profiles of microbes harbored in deep-sea sediments cultured with hydrocarbons as the sole energy source. Samples were collected from the Gulf of México (GM) and cultured for 28 days using simple (toluene, benzene, hexadecane, and naphthalene) and complex (petroleum API 40) hydrocarbon mixtures as the sole energy sources. The sediment samples harbored diverse microbial communities predominantly classified into Woeseiaceae and Kiloniellaceae families, whereas Pseudomonadaceae and Enterobacteriaceae families prevailed after sediments were cultured with hydrocarbons. Chemical profiling of microbial metabolomes revealed diverse chemical groups belonging primarily to the lipids and lipid-like molecules superclass, as well as the organoheterocyclic compound superclass (ClassyFire annotation). Metabolomic data and prediction of functional profiles indicated an increase in aromatic and alkane degradation in samples cultured with hydrocarbons. Previously unreported metabolites, identified as intermediates in the degradation of hydrocarbons, were annotated as hydroxylated polyunsaturated fatty acids and carboxylated benzene derivatives. In summary, this study used mass spectrometry-based metabolomics coupled to chemoinformatics to demonstrate how microbes from deep-sea sediments could be cultured in the presence of hydrocarbons. This study also highlights how this experimental approach can be used to increase the understanding of hydrocarbon degradation by deep-sea sediment microbes.IMPORTANCE High-throughput technologies and emerging informatics tools have significantly advanced knowledge of hydrocarbon metabolism by marine microbes. However, research into microbes inhabiting deep-sea sediments (>1,000 m) is limited compared to those found in shallow waters. In this study, a nontargeted and nonclassical approach was used to examine the diversity of bacterial taxa and the metabolic profiles of hydrocarbon-degrading deep-sea microbes. In conclusion, this study used metabolomics and chemoinformatics to demonstrate that microbes from deep-sea sediment origin thrive in the presence of toxic and difficult-to-metabolize hydrocarbons. Notably, this study provides evidence of previously unreported metabolites and the global chemical repertoire associated with the metabolism of hydrocarbons by deep-sea microbes.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.

Bacterial dominance is due to effective utilisation of secondary metabolites produced by competitors

Interactions between bacteria govern the progression of respiratory infections; however, the mechanisms underpinning these interactions are still unclear. Understanding how a bacterial species comes to dominate infectious communities associated with respiratory infections has direct relevance to treatment. In this study, Burkholderia, Pseudomonas, and Staphylococcus species were isolated from the sputum of an individual with Cystic Fibrosis and assembled in a fully factorial design to create simple microcosms. Measurements of growth and habitat modification were recorded over time, the later using proton Nuclear Magnetic Resonance spectra. The results showed interactions between the bacteria became increasingly neutral over time. Concurrently, the bacteria significantly altered their ability to modify the environment, with Pseudomonas able to utilise secondary metabolites produced by the other two isolates, whereas the reverse was not observed. This study indicates the importance of including data about the habitat modification of a community, to better elucidate the mechanisms of bacterial interactions.

Discrimination of Escherichia coli and Shigella spp. by Nuclear Magnetic Resonance Based Metabolomic Characterization of Culture Media

Dysentery is a major health threat that dramatically impacts childhood morbidity and mortality in developing countries. Various pathogenic agents cause dysentery, such as Shigella spp. and Escherichia coli, which are very closely related if not identical species. Sensitive and precise detection and identification of the infectious agent is important to target the best therapeutic strategy, but the differential diagnosis of these two groups remains a challenge using conventional methods. Here, we present a nuclear magnetic resonance (NMR) based multivariate classification model employing bacterial metabolic footprints in postculture growth media with remarkable segregation capability, including the discrimination of lactose negative E. coli and Shigella spp. Our results confirm the potential of metabolomic markers in the field of bacterial identification for the distinction of even very closely related species.

Metabolomic studies of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a common, Gram-negative environmental organism. It can be a significant pathogenic factor of severe infections in humans, especially in cystic fibrosis patients. Due to its natural resistance to antibiotics and the ability to form biofilms, infection with this pathogen can cause severe therapeutic problems. In recent years, metabolomic studies of P. aeruginosa have been performed. Therefore, in this review, we discussed recent achievements in the use of metabolomics methods in bacterial identification, differentiation, the interconnection between genome and metabolome, the influence of external factors on the bacterial metabolome and identification of new metabolites produced by P. aeruginosa. All of these studies may provide valuable information about metabolic pathways leading to an understanding of the adaptations of bacterial strains to a host environment, which can lead to new drug development and/or elaboration of new treatment and diagnostics strategies for Pseudomonas.

Exo-Metabolites of Phaseolus vulgaris-Nodulating Rhizobial Strains

Rhizobia are able to convert dinitrogen into biologically available forms of nitrogen through their symbiotic association with leguminous plants. This results in plant growth promotion, and also in conferring host resistance to different types of stress. These bacteria can interact with other organisms and survive in a wide range of environments, such as soil, rhizosphere, and inside roots. As most of these processes are molecularly mediated, the aim of this research was to identify and quantify the exo-metabolites produced by Rhizobium etli CFN42, Rhizobium leucaenae CFN299, Rhizobium tropici CIAT899, Rhizobium phaseoli Ch24-10, and Sinorhizobium americanum CFNEI156, by nuclear magnetic resonance (NMR). Bacteria were grown in free-living cultures using minimal medium containing sucrose and glutamate. Interestingly, we found that even when these bacteria belong to the same family (Rhizobiaceae) and all form nitrogen-fixing nodules on Phaseolus vulgaris roots, they exhibited different patterns and concentrations of chemical species produced by them.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Identification and Antibiotic-Susceptibility Profiling of Infectious Bacterial Agents: A Review of Current and Future Trends

Antimicrobial resistance is one of the most worrying threats to humankind with extremely high healthcare costs associated. The current technologies used in clinical microbiology to identify the bacterial agent and profile antimicrobial susceptibility are time-consuming and frequently expensive. As a result, physicians prescribe empirical antimicrobial therapies. This scenario is often the cause of therapeutic failures, causing higher mortality rates and healthcare costs, as well as the emergence and spread of antibiotic resistant bacteria. As such, new technologies for rapid identification of the pathogen and antimicrobial susceptibility testing are needed. This review summarizes the current technologies, and the promising emerging and future alternatives for the identification and profiling of antimicrobial resistance bacterial agents, which are expected to revolutionize the field of clinical diagnostics.

Influence of Metabolite Extraction Methods on 1H-NMR-Based Metabolomic Profiling of Enteropathogenic Yersinia

Metabolite extraction is one of the critical steps in microbial metabolome analysis. It affects both the observed metabolite content and biological interpretation of the data. Several methods exist for metabolite extraction of microbes, but the literature is not consistent regarding the sample model, adequacy, and performance of each method. In this study, an optimal extraction protocol for Yersinia intracellular metabolites was investigated. The effect of five extraction protocols consisting of different extraction solvent systems (60% methanol, 100% methanol, acetonitrile/methanol/water (2:2:1), chloroform/methanol/water (2:1:1), and 60% ethanol) on Yersinia metabolic profiles were compared. The number of detected peaks, sample-to-sample variation, and metabolite yield were used as criteria. Extracted metabolites were analyzed by ¹H-NMR and principal component analysis (PCA), as well as partial least squares discriminant analysis (PLS-DA) multivariate statistics. The extraction protocol using 100% methanol as the extraction solvent provided the highest number of detected peaks for both Yersinia species analyzed, yielding more spectral information. Together with the reproducibility and spectrum quality, 100% methanol extraction was suitable for intracellular metabolite extraction from both species. However, depending on the metabolites of interest, other solvents might be more suitable for future studies, as distinct profiles were observed amongst the extraction methods.

Breathomics for Assessing the Effects of Treatment and Withdrawal With Inhaled Beclomethasone/Formoterol in Patients With COPD

Background: Prospective pharmacological studies on breathomics profiles in COPD patients have not been previously reported. We assessed the effects of treatment and withdrawal of an extrafine inhaled corticosteroid (ICS)-long-acting β₂-agonist (LABA) fixed dose combination (FDC) using a multidimensional classification model including breathomics.

Methods: A pilot, proof-of-concept, pharmacological study was undertaken in 14 COPD patients on maintenance treatment with inhaled fluticasone propionate/salmeterol (500/50 μg b.i.d.) for at least 8 weeks (visit 1). Patients received 2-week treatment with inhaled beclomethasone dipropionate/formoterol (100/6 μg b.i.d.) (visit 2), 4-week treatment with formoterol alone (6 μg b.i.d.) (visit 3), and 4-week treatment with beclomethasone/formoterol (100/6 μg b.i.d.) (visit 4). Exhaled breath analysis with two e-noses, based on different technologies, and exhaled breath condensate (EBC) NMR-based metabolomics were performed. Sputum cell counts, sputum supernatant and EBC prostaglandin E₂ (PGE₂) and 15-F_2t-isoprostane, fraction of exhaled nitric oxide, and spirometry were measured.

Results: Compared with formoterol alone, EBC acetate and sputum PGE₂, reflecting airway inflammation, were reduced after 4-week beclomethasone/formoterol. Three independent breathomics techniques showed that extrafine beclomethasone/formoterol short-term treatment was associated with different breathprints compared with regular fluticasone propionate/salmeterol. Either ICS/LABA FDC vs. formoterol alone was associated with increased pre-bronchodilator FEF_25−75% and FEV₁/FVC (P = 0.008–0.029). The multidimensional model distinguished fluticasone propionate/salmeterol vs. beclomethasone/formoterol, fluticasone propionate/salmeterol vs. formoterol, and formoterol vs. beclomethasone/formoterol (accuracy > 70%, P < 0.01).

Conclusions: Breathomics could be used for assessing ICS treatment and withdrawal in COPD patients. Large, controlled, prospective pharmacological trials are required to clarify the biological implications of breathomics changes. EUDRACT number: 2012-001749-42.

Molecular processes underlying synergistic linuron mineralization in a triple-species bacterial consortium biofilm revealed by differential transcriptomics

The proteobacteria Variovorax sp. WDL1, Comamonas testosteroni WDL7, and Hyphomicrobium sulfonivorans WDL6 compose a triple‐species consortium that synergistically degrades and grows on the phenylurea herbicide linuron. To acquire a better insight into the interactions between the consortium members and the underlying molecular mechanisms, we compared the transcriptomes of the key biodegrading strains WDL7 and WDL1 grown as biofilms in either isolation or consortium conditions by differential RNAseq analysis. Differentially expressed pathways and cellular systems were inferred using the network‐based algorithm PheNetic. Coculturing affected mainly metabolism in WDL1. Significantly enhanced expression of hylA encoding linuron hydrolase was observed. Moreover, differential expression of several pathways involved in carbohydrate, amino acid, nitrogen, and sulfur metabolism was observed indicating that WDL1 gains carbon and energy from linuron indirectly by consuming excretion products from WDL7 and/or WDL6. Moreover, in consortium conditions, WDL1 showed a pronounced stress response and overexpression of cell to cell interaction systems such as quorum sensing, contact‐dependent inhibition, and Type VI secretion. Since the latter two systems can mediate interference competition, it prompts the question if synergistic linuron degradation is the result of true adaptive cooperation or rather a facultative interaction between bacteria that coincidentally occupy complementary metabolic niches.

Rapid discrimination of strain-dependent fermentation characteristics among Lactobacillus strains by NMR-based metabolomics of fermented vegetable juice

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods.