Aerial warfare: An inducible production of volatile bioactive metabolites in a novel species of Scytinostroma sp.

Antimicrobial volatile organic compounds (VOCs) may provide fungi an advantage over other competing microorganisms. As these defensive metabolites are often produced in response to microbial competitors, they are easily overlooked in axenic cultures. We used media supplemented with spent medium from Candida albicans to induce the expression of a broad-spectrum antimicrobial response in a previously uncharacterised white-rot fungus, Scytinostroma sp. Crude extractions of Scytinostroma sp. metabolites were found to be cytotoxic to fibroblast cells and antimicrobial to filamentous fungi, yeasts and Gram-positive bacteria. Volatile antimicrobial activity was observed for Scytinostroma sp. cultures and metabolite extracts using antimicrobial assays in bi-compartmentalised plates. Culture headspace analysis using solid-phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) revealed a pronounced shift in Scytinostroma sp. VOCs when cultured on media supplemented with C. albicans spent medium. We observed a significant increase in the levels of 45 identified VOCs, including 7 metabolites with reported antimicrobial activity. Using preparative HPLC combined with GC-MS, we determined that isovelleral is likely to be the main broad-spectrum antimicrobial metabolite produced by Scytinostroma sp. Isovelleral is a sesquiterpene dialdehyde with both antibiotic and antifeedant properties, previously detected in fruit bodies of other Basidiomycetes.

Metabolomics and a Breath Sensor Identify Acetone as a Biomarker for Heart Failure.

BACKGROUND: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). METHODS: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. RESULTS: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). CONCLUSION: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.

Multiomics, virtual reality and artificial intelligence in heart failure.

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Lay abstract Multiomics is the integration of multiple sources of health information, for example, genomic, metabolite, etc. This delivers more insight than targeted single investigations and provides an ability to perceive subtle individual differences between people. In this study we applied multiomics to patients with heart failure (HF) using DNA sequencing, metabolomics and machine learning applied to ECG echocardiography. We demonstrated significant differences between subsets of patients with HF using these methods. We also showed that machine learning has significant diagnostic potential in identifying HF patients more efficiently than manual or conventional techniques.

Mass Spectrometry-Based Metabolomics to Investigate the Effect of Mechanical Shaking on Sauvignon Blanc Berry Metabolism.

Previous commercial studies carried out in New Zealand showed that mechanical shaking significantly reduced the incidence of Botrytis cinerea infection in wine grapes. However, the reasons behind this reduction are not well understood. Here, we employed a metabolomics approach to gain insights into the biochemical changes that occur in grape berries due to mechanical shaking. Berry samples were analyzed using three different analytical approaches including gas chromatography and mass spectrometry (MS), liquid chromatography and MS, and imaging mass spectrometry (IMS). Combined data provided a comprehensive overview of metabolic changes in grape berry, indicating the initiation of different stress mitigation strategies to overcome the effect of mechanical shaking. Berry primary metabolism was distinctly altered in the green berries in response to mechanical shaking, while secondary metabolism significantly changed in berries collected after veraison. Pathway analysis showed upregulation of metabolites related to nitrogen and lipid metabolism in the berries from shaken vines when compared with controls. From IMS data, we observed an accumulation of different groups of metabolites including phenolic compounds and amino and fatty acids in the areas near to the skin of berries from shaken vines. This observation suggests that mechanical shaking caused an accumulation of these metabolites, which may be associated with the formation of a protective barrier, leading to the reduction in B. cinerea infection in berries from mechanically shaken vines.

Gas chromatography-mass spectrometry (GC/MS) reveals urine metabolites associated to light and heavy infections by Schistosoma mansoni in mice.

High-throughput profiling of metabolites has been used to identify metabolic changes in murine models as a response to the infection by the parasitic trematode Schistosoma. These investigations have contributed to our understanding on the pathogenesis of this tropical neglected disease, with a potential of helping diagnosis. Here, our study aimed to investigate the application of gas chromatography-mass spectrometry (GC/MS) on the profiling of urine metabolites from mice carrying infections by Schistosoma mansoni. Two larval infection doses created distinctive infection intensities in mice, whereby the heavily infected animals were found to release 25 times more eggs in faeces than lightly infected animals. Over 200 urine metabolites were identified from these animals by GC/MS, following two complementary derivatisation methods. A list of 14 individual metabolites with altered relative abundances between groups were identified. Most of the altered metabolites showed a trend of increased abundances in response to infection intensity, indicating host-specific metabolic alterations as a result of the disease. Hippurate, a metabolite which concentration is intimately modulated by the gut microbiota, was found to be highly correlated to infection intensity. Our study showed that urine metabolic profiling by GC/MS can distinguish non-infected animals from those carrying light and heavy infections by S. mansoni, revealing metabolites associated to the infection and providing insights on the pathogenesis of schistosomiasis.

Trace biomarkers associated with spontaneous preterm birth from the maternal serum metabolome of asymptomatic nulliparous women - parallel case-control studies from the SCOPE cohort.

Prediction of spontaneous preterm birth (sPTB) in asymptomatic women remains a great challenge; accurate and reproducible screening tools are still not available in clinical practice. We aimed to investigate whether the maternal serum metabolome together with clinical factors could be used to identify asymptomatic women at risk of sPTB. We conducted two case-control studies using gas chromatography-mass spectrometry to analyse maternal serum samples collected at 15- and 20-weeks' gestation from 164 nulliparous women from Cork, and 157 from Auckland. Smoking and vaginal bleeding before 15 weeks were the only significant clinical predictors of sPTB for Auckland and Cork subsets, respectively. Decane, undecane, and dodecane were significantly associated with sPTB (FDR < 0.05) in the Cork subset. An odds ratio of 1.9 was associated with a one standard deviation increase in log (undecane) in a multiple logistic regression which also included vaginal bleeding as a predictor. In summary, elevated serum levels of the alkanes decane, undecane, and dodecane were associated with sPTB in asymptomatic nulliparous women from Cork, but not in the Auckland cohort. The association is not strong enough to be a useful clinical predictor, but suggests that further investigation of the association between oxidative stress processes and sPTB risk is warranted.

Itaconic acid inhibits growth of a pathogenic marine Vibrio strain: A metabolomics approach.

The antimicrobial role of itaconic acid (ITA) has been recently discovered in mammalian cells. In our previous studies, we discovered that marine molluscs biosynthesise substantial quantities of ITA when exposed to marine pathogens, but its antimicrobial function to Vibrio bacteria is currently unknown. Thus, in this study, we used an untargeted gas chromatography-mass spectrometry (GC-MS) platform to identify metabolic changes of Vibrio sp. DO1 (V. corallyliticus/neptunius-like isolate) caused by ITA exposure. Vibrio sp. DO1 was cultured in Luria-Bertani broth supplemented with 3 mM sodium acetate and with different concentrations of ITA (0, 3 and 6 mM) for 24 h. The results showed that ITA completely inhibited Vibrio sp. growth at 6 mM and partially inhibited the bacterial growth at 3 mM. A principal component analysis (PCA) revealed a clear separation between metabolite profiles of Vibrio sp. DO1 in the 3 mM ITA treatment and the control, which were different in 25 metabolites. Among the altered metabolites, the accumulation of glyoxylic acid and other metabolites in glyoxylate cycle (cis-aconitic acid, isocitric acid and fumaric acid) together with the increase of isocitrate lyase (ICL) activity in the 3 mM ITA treatment compared to the control suggest that ITA inhibited Vibrio sp. growth via disruption of central carbon metabolism.

Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS.

Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laboratory.