Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022.

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Paper Spray Ionization Ion Mobility Mass Spectrometry of Sebum Classifies Biomarker Classes for the Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and identification of robust biomarkers to complement clinical diagnosis will accelerate treatment options. Here, we demonstrate the use of direct infusion of sebum from skin swabs using paper spray ionization coupled with ion mobility mass spectrometry (PS-IM-MS) to determine the regulation of molecular classes of lipids in sebum that are diagnostic of PD. A PS-IM-MS method for sebum samples that takes 3 min per swab was developed and optimized. The method was applied to skin swabs collected from 150 people and elucidates ∼4200 features from each subject, which were independently analyzed. The data included high molecular weight lipids (>600 Da) that differ significantly in the sebum of people with PD. Putative metabolite annotations of several lipid classes, predominantly triglycerides and larger acyl glycerides, were obtained using accurate mass, tandem mass spectrometry, and collision cross section measurements.

Multiomics implicate gut microbiota in altered lipid and energy metabolism in Parkinson's disease.

We aimed to investigate the link between serum metabolites, gut bacterial community composition, and clinical variables in Parkinson's disease (PD) and healthy control subjects (HC). A total of 124 subjects were part of the study (63 PD patients and 61 HC subjects). 139 metabolite features were found to be predictive between the PD and Control groups. No associations were found between metabolite features and within-PD clinical variables. The results suggest alterations in serum metabolite profiles in PD, and the results of correlation analysis between metabolite features and microbiota suggest that several bacterial taxa are associated with altered lipid and energy metabolism in PD.

Metabolomics of sebum reveals lipid dysregulation in Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder, which is characterised by degeneration of distinct neuronal populations, including dopaminergic neurons of the substantia nigra. Here, we use a metabolomics profiling approach to identify changes to lipids in PD observed in sebum, a non-invasively available biofluid. We used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Pathway enrichment analysis shows alterations in lipid metabolism related to the carnitine shuttle, sphingolipid metabolism, arachidonic acid metabolism and fatty acid biosynthesis. This study shows sebum can be used to identify potential biomarkers for PD.

Validating Differential Volatilome Profiles in Parkinson's Disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that does not currently have a robust clinical diagnostic test. Nonmotor symptoms such as skin disorders have long since been associated with the disease, and more recently a characteristic odor emanating from the skin of people with Parkinson's has been identified. Here, dynamic head space (DHS) thermal desorption (TD) gas chromatography-mass spectrometry (GC-MS) is implemented to directly measure the volatile components of sebum on swabs sampled from people with Parkinson's-both drug naïve and those on PD medications (n = 100) and control subjects (n = 29). Supervised multivariate analyses of data showed 84.4% correct classification of PD cases using all detected volatile compounds. Variable importance in projection (VIP) scores were generated from these data, which revealed eight features with VIP > 1 and p < 0.05 which all presented a downregulation within the control cohorts. Purified standards based on previously annotated analytes of interest eicosane and octadecanal did not match to patient sample data, although multiple metabolite features are annotated with these compounds all with high spectral matches indicating the presence of a series of similar structured species. DHS-TD-GC-MS analysis of a range of lipid standards has revealed the presence of common hydrocarbon species rather than differentiated intact compounds which are hypothesized to be breakdown products of lipids. This replication study validates that a differential volatile profile between control and PD cohorts can be measured using an analytical method that measures volatile compounds directly from skin swabs.

Metabolic dysregulation in vitamin E and carnitine shuttle energy mechanisms associate with human frailty.

Global ageing poses a substantial economic burden on health and social care costs. Enabling a greater proportion of older people to stay healthy for longer is key to the future sustainability of health, social and economic policy. Frailty and associated decrease in resilience plays a central role in poor health in later life. In this study, we present a population level assessment of the metabolic phenotype associated with frailty. Analysis of serum from 1191 older individuals (aged between 56 and 84 years old) and subsequent longitudinal validation (on 786 subjects) was carried out using liquid and gas chromatography-mass spectrometry metabolomics and stratified across a frailty index designed to quantitatively summarize vulnerability. Through multivariate regression and network modelling and mROC modeling we identified 12 significant metabolites (including three tocotrienols and six carnitines) that differentiate frail and non-frail phenotypes. Our study provides evidence that the dysregulation of carnitine shuttle and vitamin E pathways play a role in the risk of frailty.

Discovery of Volatile Biomarkers of Parkinson's Disease from Sebum.

Parkinson's disease (PD) is a progressive, neurodegenerative disease that presents with significant motor symptoms, for which there is no diagnostic chemical test. We have serendipitously identified a hyperosmic individual, a "Super Smeller" who can detect PD by odor alone, and our early pilot studies have indicated that the odor was present in the sebum from the skin of PD subjects. Here, we have employed an unbiased approach to investigate the volatile metabolites of sebum samples obtained noninvasively from the upper back of 64 participants in total (21 controls and 43 PD subjects). Our results, validated by an independent cohort (n=31), identified a distinct volatiles-associated signature of PD, including altered levels of perillic aldehyde and eicosane, the smell of which was then described as being highly similar to the scent of PD by our "Super Smeller".

Mitochondrial aconitase is a key regulator of energy production for growth and protein expression in Chinese hamster ovary cells.

INTRODUCTION: Mammalian cells like Chinese hamster ovary (CHO) cells are routinely used for production of recombinant therapeutic proteins. Cells require a continuous supply of energy and nutrients to sustain high cell densities whilst expressing high titres of recombinant proteins. Cultured mammalian cells are primarily dependent on glucose and glutamine metabolism for energy production. OBJECTIVES: The TCA cycle is the main source of energy production and its continuous flow is essential for cell survival. Modulated regulation of TCA cycle can affect ATP production and influence CHO cell productivity. METHODS: To determine the key metabolic reactions of the cycle associated with cell growth in CHO cells, we transiently silenced each gene of the TCA cycle using RNAi. RESULTS: Silencing of at least four TCA cycle genes was detrimental to CHO cell growth. With an exception of mitochondrial aconitase (or Aco2), all other genes were associated with ATP production reactions of the TCA cycle and their resulting substrates can be supplied by other anaplerotic and cataplerotic reactions. This study is the first of its kind to have established key role of aconitase gene in CHO cells. We further investigated the temporal effects of aconitase silencing on energy production, CHO cell metabolism, oxidative stress and recombinant protein production. CONCLUSION: Transient silencing of mitochondrial aconitase inhibited cell growth, reduced ATP production, increased production of reactive oxygen species and reduced cell specific productivity of a recombinant CHO cell line by at least twofold.

Metabolomics for the masses: The future of metabolomics in a personalized world.

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Two Glycerol-3-Phosphate Dehydrogenases from Chlamydomonas Have Distinct Roles in Lipid Metabolism.

The metabolism of glycerol-3-phosphate (G3P) is important for environmental stress responses by eukaryotic microalgae. G3P is an essential precursor for glycerolipid synthesis and the accumulation of triacylglycerol (TAG) in response to nutrient starvation. G3P dehydrogenase (GPDH) mediates G3P synthesis, but the roles of specific GPDH isoforms are currently poorly understood. Of the five GPDH enzymes in the model alga Chlamydomonas reinhardtii, GPD2 and GPD3 were shown to be induced by nutrient starvation and/or salt stress. Heterologous expression of GPD2, a putative chloroplastic GPDH, and GPD3, a putative cytosolic GPDH, in a yeast gpd1Δ mutant demonstrated the functionality of both enzymes. C. reinhardtii knockdown mutants for GPD2 and GPD3 showed no difference in growth but displayed significant reduction in TAG concentration compared with the wild type in response to phosphorus or nitrogen starvation. Overexpression of GPD2 and GPD3 in C. reinhardtii gave distinct phenotypes. GPD2 overexpression lines showed only subtle metabolic phenotypes and no significant alteration in growth. In contrast, GPD3 overexpression lines displayed significantly inhibited growth and chlorophyll concentration, reduced glycerol concentration, and changes to lipid composition compared with the wild type, including increased abundance of phosphatidic acids but reduced abundance of diglycerides, triglycerides, and phosphatidylglycerol lipids. This may indicate a block in the downstream glycerolipid metabolism pathway in GPD3 overexpression lines. Thus, lipid engineering by GPDH modification may depend on the activities of other downstream enzyme steps. These results also suggest that GPD2 and GPD3 GPDH isoforms are important for nutrient starvation-induced TAG accumulation but have distinct metabolic functions.

Quantitative Online Liquid Chromatography-Surface-Enhanced Raman Scattering (LC-SERS) of Methotrexate and its Major Metabolites.

The application of Raman spectroscopy as a detection method coupled with liquid chromatography (LC) has recently attracted considerable interest, although this has currently been limited to isocratic elution. The combination of LC with rapidly advancing Raman techniques, such as surface-enhanced Raman scattering (SERS), allows for rapid separation, identification and quantification, leading to quantitative discrimination of closely eluting analytes. This study has demonstrated the utility of SERS in conjunction with reversed-phase liquid chromatography (RP-LC), for the detection and quantification of the therapeutically relevant drug molecule methotrexate (MTX) and its metabolites 7-hydroxy methotrexate (7-OH MTX) and 2,4-diamino-N(10)-methylpteroic acid (DAMPA) in pure solutions and mixtures, including spikes into human urine from a healthy individual and patients under medication. While the RP-LC analysis developed employed gradient elution, where the chemical constituents of the mobile phase were modified stepwise during analysis, this did not overtly interfere with the SERS signals. In addition, the practicability and clinical utility of this approach has also been demonstrated using authentic patients' urine samples. Here, the identification of MTX, 7-OH MTX and DAMPA are based on their unique SERS spectra, providing limits of detection of 2.36, 1.84, and 3.26 µM respectively. Although these analytes are amenable to LC and LC-MS detection an additional major benefit of the SERS approach is its applicability toward the detection of analytes that do not show UV absorption or are not ionised for mass spectrometry (MS)-based detection. The results of this study clearly demonstrate the potential application of online LC-SERS analysis for real-time high-throughput detection of drugs and their related metabolites in human biofluids.

Rapid, Accurate, and Quantitative Detection of Propranolol in Multiple Human Biofluids via Surface-Enhanced Raman Scattering.

There has been an increasing demand for rapid and sensitive techniques for the identification and quantification of pharmaceutical compounds in human biofluids during the past few decades, and surface-enhanced Raman scattering (SERS) is one of a number of physicochemical techniques with the potential to meet these demands. In this study we have developed a SERS-based analytical approach for the assessment of human biofluids in combination with chemometrics. This novel approach has enabled the detection and quantification of the ß-blocker propranolol spiked into human serum, plasma, and urine at physiologically relevant concentrations. A range of multivariate statistical analysis techniques, including principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least-squares regression (PLSR) were employed to investigate the relationship between the full SERS spectral data and the level of propranolol. The SERS spectra when combined with PCA and PC-DFA demonstrated clear differentiation of neat biofluids and biofluids spiked with varying concentrations of propranolol ranging from 0 to 120 µM, and clear trends in ordination scores space could be correlated with the level of propranolol. Since PCA and PC-DFA are categorical classifiers, PLSR modeling was subsequently used to provide accurate propranolol quantification within all biofluids with high prediction accuracy (expressed as root-mean-square error of predictions) of 0.58, 9.68, and 1.69 for serum, plasma, and urine respectively, and these models also had excellent linearity for the training and test sets between 0 and 120 µM. The limit of detection as calculated from the area under the naphthalene ring vibration from propranolol was 133.1 ng/mL (0.45 µM), 156.8 ng/mL (0.53 µM), and 168.6 ng/mL (0.57 µM) for serum, plasma, and urine, respectively. This result shows a consistent signal irrespective of biofluid, and all are well within the expected physiological level of this drug during therapy. The results of this study demonstrate the potential of SERS application as a diagnostic screening method, following further validation and optimization to improve detection of pharmaceutical compounds and quantification in human biofluids, which may open up new exciting opportunities for future use in various biomedical and forensic applications.

Classification of Bacillus and Brevibacillus species using rapid analysis of lipids by mass spectrometry.

Bacillus are aerobic spore-forming bacteria that are known to lead to specific diseases, such as anthrax and food poisoning. This study focuses on the characterization of these bacteria by the detection of lipids extracted from 33 well-characterized strains from the Bacillus and Brevibacillus genera, with the aim to discriminate between the different species. For the purpose of analysing the lipids extracted from these bacterial samples, two rapid physicochemical techniques were used: matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF-MS) and liquid chromatography in conjunction with mass spectrometry (LC-MS). The findings of this investigation confirmed that MALDI-TOF-MS could be used to identify different bacterial lipids and, in combination with appropriate chemometrics, allowed for the discrimination between these different bacterial species, which was supported by LC-MS. The average correct classification rates for the seven species of bacteria were 62.23 and 77.03 % based on MALDI-TOF-MS and LC-MS data, respectively. The Procrustes distance for the two datasets was 0.0699, indicating that the results from the two techniques were very similar. In addition, we also compared these bacterial lipid MALDI-TOF-MS profiles to protein profiles also collected by MALDI-TOF-MS on the same bacteria (Procrustes distance, 0.1006). The level of discrimination between lipids and proteins was equivalent, and this further indicated the potential of MALDI-TOF-MS analysis as a rapid, robust and reliable method for the classification of bacteria based on different bacterial chemical components. Graphical abstract MALDI-MS has been successfully developed for the characterization of bacteria at the subspecies level using lipids and benchmarked against HPLC.

Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry.

INTRODUCTION: An exceptionally interesting stress response of Pseudomonas putida strains to toxic substances is the induction of efflux pumps that remove toxic chemical substances from the bacterial cell out to the external environment. To exploit these microorganisms to their full potential a deeper understanding of the interactions between the bacteria and organic solvents is required. Thus, this study focuses on investigation of metabolic changes in P. putida upon exposure to toluene. OBJECTIVE: Investigate observable metabolic alterations during interactions of three strains of P. putida (DOT-T1E, and its mutants DOT-T1E-PS28 and DOT-T1E-18) with the aromatic hydrocarbon toluene. METHODS: The growth profiles were measured by taking optical density (OD) measurement at 660 nm (OD660) at various time points during incubation. For fingerprinting analysis, Fourier-transform infrared (FT-IR) spectroscopy was used to investigate any phenotypic changes resulting from exposure to toluene. Metabolic profiling analysis was performed using gas chromatography-mass spectrometry (GC-MS). Principal component-discriminant function analysis (PC-DFA) was applied to the FT-IR data while multiblock principal component analysis (MB-PCA) and N-way analysis of variance (N-way ANOVA) were applied to the GC-MS data. RESULTS: The growth profiles demonstrated the effect of toluene on bacterial cultures and the results suggest that the mutant P. putida DOT-T1E-18 was more sensitive (significantly affected) to toluene compared to the other two strains. PC-DFA on FT-IR data demonstrated the differentiation between different conditions of toluene on bacterial cells, which indicated phenotypic changes associated with the presence of the solvent within the cell. Fifteen metabolites associated with this phenotypic change, in P. putida due to exposure to solvent, were from central metabolic pathways. Investigation of MB-PCA loading plots and N-way ANOVA for condition | strain × time blocking (dosage of toluene) suggested ornithine as the most significant compound that increased upon solvent exposure. CONCLUSION: The combination of metabolic fingerprinting and profiling with suitable multivariate analysis revealed some interesting leads for understanding the mechanism of Pseudomonas strains response to organic solvent exposure.

Metabolomics Analysis Reveals the Participation of Efflux Pumps and Ornithine in the Response of Pseudomonas putida DOT-T1E Cells to Challenge with Propranolol.

Efflux pumps are critically important membrane components that play a crucial role in strain tolerance in Pseudomonas putida to antibiotics and aromatic hydrocarbons that result in these toxicants being expelled from the bacteria. Here, the effect of propranolol on P. putida was examined by sudden addition of 0.2, 0.4 and 0.6 mg mL-1 of this ß-blocker to several strains of P. putida, including the wild type DOT-T1E and the efflux pump knockout mutants DOT-T1E-PS28 and DOT-T1E-18. Bacterial viability measurements reveal that the efflux pump TtgABC plays a more important role than the TtgGHI pump in strain tolerance to propranolol. Mid-infrared (MIR) spectroscopy was then used as a rapid, high-throughput screening tool to investigate any phenotypic changes resulting from exposure to varying levels of propranolol. Multivariate statistical analysis of these MIR data revealed gradient trends in resultant ordination scores plots, which were related to the concentration of propranolol. MIR illustrated phenotypic changes associated with the presence of this drug within the cell that could be assigned to significant changes that occurred within the bacterial protein components. To complement this phenotypic fingerprinting approach metabolic profiling was performed using gas chromatography mass spectrometry (GC-MS) to identify metabolites of interest during the growth of bacteria following toxic perturbation with the same concentration levels of propranolol. Metabolic profiling revealed that ornithine, which was only produced by P. putida cells in the presence of propranolol, presents itself as a major metabolic feature that has important functions in propranolol stress tolerance mechanisms within this highly significant and environmentally relevant species of bacteria.

Electronic cigarette exposure triggers neutrophil inflammatory responses.

BACKGROUND: The use of electronic cigarettes (e-cigs) is increasing and there is widespread perception that e-cigs are safe. E-cigs contain harmful chemicals; more research is needed to evaluate the safety of e-cig use. Our aim was to investigate the effects of e-cigs on the inflammatory response of human neutrophils. METHODS: Neutrophils were exposed to e-cig vapour extract (ECVE) and the expression of CD11b and CD66b was measured by flow cytometry and MMP-9 and CXCL8 by ELISA. We also measured the activity of neutrophil elastase (NE) and MMP-9, along with the activation of inflammatory signalling pathways. Finally we analysed the biochemical composition of ECVE by ultra-high performance liquid chromatography mass spectrometry. RESULTS: ECVE caused an increase in the expression of CD11b and CD66b, and increased the release of MMP-9 and CXCL8. Furthermore, there was an increase in NE and MMP-9 activity and an increase in p38 MAPK activation. We also identified several harmful chemicals in ECVE, including known carcinogens. CONCLUSIONS: ECVE causes a pro-inflammatory response from human neutrophils. This raises concerns over the safety of e-cig use.

Fractional Factorial Design of MALDI-TOF-MS Sample Preparations for the Optimized Detection of Phospholipids and Acylglycerols.

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has successfully been used for the analysis of high molecular weight compounds, such as proteins and nucleic acids. By contrast, analysis of low molecular weight compounds with this technique has been less successful due to interference from matrix peaks which have a similar mass to the target analyte(s). Recently, a variety of modified matrices and matrix additives have been used to overcome these limitations. An increased interest in lipid analysis arose from the feasibility of correlating these components with many diseases, e.g. atherosclerosis and metabolic dysfunctions. Lipids have a wide range of chemical properties making their analysis difficult with traditional methods. MALDI-TOF-MS shows excellent potential for sensitive and rapid analysis of lipids, and therefore this study focuses on computational-analytical optimization of the analysis of five lipids (4 phospholipids and 1 acylglycerol) in complex mixtures using MALDI-TOF-MS with fractional factorial design (FFD) and Pareto optimality. Five different experimental factors were investigated using FFD which reduced the number of experiments performed by identifying 720 key experiments from a total of 8064 possible analyses. Factors investigated included the following: matrices, matrix preparations, matrix additives, additive concentrations, and deposition methods. This led to a significant reduction in time and cost of sample analysis with near optimal conditions. We discovered that the key factors used to produce high quality spectra were the matrix and use of appropriate matrix additives.

Metabolic Fingerprinting of Pseudomonas putida DOT-T1E Strains: Understanding the Influence of Divalent Cations in Adaptation Mechanisms Following Exposure to Toluene.

Pseudomonas putida strains can adapt and overcome the activity of toxic organic solvents by the employment of several resistant mechanisms including efflux pumps and modification to lipopolysaccharides (LPS) in their membranes. Divalent cations such as magnesium and calcium play a crucial role in the development of solvent tolerance in bacterial cells. Here, we have used Fourier transform infrared (FT-IR) spectroscopy directly on cells (metabolic fingerprinting) to monitor bacterial response to the absence and presence of toluene, along with the influence of divalent cations present in the growth media. Multivariate analysis of the data using principal component-discriminant function analysis (PC-DFA) showed trends in scores plots, illustrating phenotypic alterations related to the effect of Mg(2+), Ca(2+) and toluene on cultures. Inspection of PC-DFA loadings plots revealed that several IR spectral regions including lipids, proteins and polysaccharides contribute to the separation in PC-DFA space, thereby indicating large phenotypic response to toluene and these cations. Finally, the saturated fatty acid ratio from the FT-IR spectra showed that upon toluene exposure, the saturated fatty acid ratio was reduced, while it increased in the presence of divalent cations. This study clearly demonstrates that the combination of metabolic fingerprinting with appropriate chemometric analysis can result in practicable knowledge on the responses of important environmental bacteria to external stress from pollutants such as highly toxic organic solvents, and indicates that these changes are manifest in the bacterial cell membrane. Finally, we demonstrate that divalent cations improve solvent tolerance in P. putida DOT­T1E strains.

Meat, the metabolites: an integrated metabolite profiling and lipidomics approach for the detection of the adulteration of beef with pork.

Adulteration of high quality food products with sub-standard and cheaper grades is a world-wide problem taxing the global economy. Currently, many traditional tests suffer from poor specificity, highly complex outputs and a lack of high-throughput processing. Metabolomics has been successfully used as an accurate discriminatory technique in a number of applications including microbiology, cancer research and environmental studies and certain types of food fraud. In this study, we have developed metabolomics as a technique to assess the adulteration of meat as an improvement on current methods. Different grades of beef mince and pork mince, purchased from a national retail outlet were combined in a number of percentage ratios and analysed using GC-MS and UHPLC-MS. These techniques were chosen because GC-MS enables investigations of metabolites involved in primary metabolism whilst UHPLC-MS using reversed phase chromatography provides information on lipophilic species. With the application of chemometrics and statistical analyses, a panel of differential metabolites were found for identification of each of the two meat types. Additionally, correlation was observed between metabolite content and percentage of fat declared on meat products' labelling.

Chicken, beams, and Campylobacter: rapid differentiation of foodborne bacteria via vibrational spectroscopy and MALDI-mass spectrometry.

Campylobacter species are one of the main causes of food poisoning worldwide. Despite the availability of established culturing and molecular techniques, due to the fastidious nature of these microorganisms, simultaneous detection and species differentiation still remains challenging. This study focused on the differentiation of eleven Campylobacter strains from six species, using Fourier transform infrared (FT-IR) and Raman spectroscopies, together with matrix-assisted laser desorption ionisation-time of flight-mass spectrometry (MALDI-TOF-MS), as physicochemical approaches for generating biochemical fingerprints. Cluster analysis of data from each of the three analytical approaches provided clear differentiation of each Campylobacter species, which was generally in agreement with a phylogenetic tree based on 16S rRNA gene sequences. Notably, although C. fetus subspecies fetus and venerealis are phylogenetically very closely related, using FT-IR and MALDI-TOF-MS data these subspecies were readily differentiated based on differences in the lipid (2920 and 2851 cm(-1)) and fingerprint regions (1500-500 cm(-1)) of the FT-IR spectra, and the 500-2000 m/z region of the MALDI-TOF-MS data. A finding that was further investigated with targeted lipidomics using liquid chromatography-mass spectrometry (LC-MS). Our results demonstrate that such metabolomics approaches combined with molecular biology techniques may provide critical information and knowledge related to the risk factors, virulence, and understanding of the distribution and transmission routes associated with different strains of foodborne Campylobacter spp.