Assessing integrated biomarkers of triplefin fish Forsterygion capito inhabiting contaminated marine water - A multivariate approach.

The presence of multiple chemicals in aquatic ecosystems makes evaluation of their real impact on the biota difficult. Integrated biomarkers are therefore needed to evaluate how these chemicals contribute to environmental degradation. The aims of the present study were to evaluate responses to and effects of marine pollution using a series of biomarkers through multivariate analyses. Transcriptional responses of cyp1a (cytochrome P450), mt (metallothionein), vtg (vitellogenin) and cyp19b (cytochrome P450 aromatase); branchial and hepatic histological alterations; and Fulton condition factors (CF) were evaluated, as well as the metals and polycyclic aromatic hydrocarbons present in Forsterygion capito in Auckland, New Zealand. Sites were selected along a contamination gradient: four highly contaminated sites and four less contaminated. Molecular responses with a higher relative expression of the mt and cyp1a genes were detected at a highly contaminated site (Panmure). Several histological lesion types were found in the livers of fish inhabiting both types of sites, but gill lesions were present primarily at highly contaminated sites. In terms of general health status, the lowest CF values were overwhelmingly found in fish from the same site (Panmure). The multivariate approach revealed that telangiectasia and hyperplasia were associated with the presence of chemicals, and these showed negative associations with the CF values, with fish from three highly contaminated sites being most affected. In conclusion, the multivariate approach helped to integrate these biological markers in this blennioid fish, thus providing a more holistic view of the complex chemical mixtures involved. Future studies should implement these analyses.

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.

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.