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‘Reactive oxygen species’ (ROS) is a generic term that defines a wide variety of oxidant molecules with vastly different properties and biological functions that range from signalling to causing cell damage. Consequently, the description of oxidants needs to be chemically precise to translate research on their biological effects into therapeutic benefit in redox medicine. This Expert Recommendation article pinpoints key issues associated with identifying the physiological roles of oxidants, focusing on H2O2 and O2.–. The generic term ROS should not be used to describe specific molecular agents. We also advocate for greater precision in measurement of H2O2, O2.– and other oxidants, along with more specific identification of their signalling targets. Future work should also consider inter-organellar communication and the interactions of redox-sensitive signalling targets within organs and whole organisms, including the contribution of environmental exposures. To achieve these goals, development of tools that enable site-specific and real-time detection and quantification of individual oxidants in cells and model organisms are needed. We also stress that physiological O2 levels should be maintained in cell culture to better mimic in vivo redox reactions associated with specific cell types. Use of precise definitions and analytical tools will help harmonize research among the many scientific disciplines working on the common goal of understanding redox biology.Reactive oxygen species (ROS) comprise a wide variety of oxidant molecules with vastly different properties and biological functions in physiology and in disease. Approaches to characterize oxidants in the in vivo context and identify their specific cellular targets will be required to understand and control the pathophysiological activities of ROS.

The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

‘Reactive oxygen species’ (ROS) is an umbrella term for an array of derivatives of molecular oxygen that occur as a normal attribute of aerobic life. Elevated formation of the different ROS leads to molecular damage, denoted as ‘oxidative distress’. Here we focus on ROS at physiological levels and their central role in redox signalling via different post-translational modifications, denoted as ‘oxidative eustress’. Two species, hydrogen peroxide (H2O2) and the superoxide anion radical (O2·−), are key redox signalling agents generated under the control of growth factors and cytokines by more than 40 enzymes, prominently including NADPH oxidases and the mitochondrial electron transport chain. At the low physiological levels in the nanomolar range, H2O2 is the major agent signalling through specific protein targets, which engage in metabolic regulation and stress responses to support cellular adaptation to a changing environment and stress. In addition, several other reactive species are involved in redox signalling, for instance nitric oxide, hydrogen sulfide and oxidized lipids. Recent methodological advances permit the assessment of molecular interactions of specific ROS molecules with specific targets in redox signalling pathways. Accordingly, major advances have occurred in understanding the role of these oxidants in physiology and disease, including the nervous, cardiovascular and immune systems, skeletal muscle and metabolic regulation as well as ageing and cancer. In the past, unspecific elimination of ROS by use of low molecular mass antioxidant compounds was not successful in counteracting disease initiation and progression in clinical trials. However, controlling specific ROS-mediated signalling pathways by selective targeting offers a perspective for a future of more refined redox medicine. This includes enzymatic defence systems such as those controlled by the stress-response transcription factors NRF2 and nuclear factor-κB, the role of trace elements such as selenium, the use of redox drugs and the modulation of environmental factors collectively known as the exposome (for example, nutrition, lifestyle and irradiation).Reactive oxygen species (ROS) were originally associated with cellular damage and disease. However, ROS, notably hydrogen peroxide, at low physiological levels also engage in physiological signalling, supporting cellular responses and adaptation to changing environments and stress. Accordingly, controlling specific ROS-mediated signalling pathways offers new perspectives for a more refined redox medicine.

Background Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. Results xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. Conclusions xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Honokiol (HKL) is a natural biphenolic compound derived from the bark of magnolia trees with anti-inflammatory, anti-oxidative, anti-tumour and neuroprotective properties. Here we show that HKL blocks agonist-induced and pressure overload-mediated, cardiac hypertrophic responses, and ameliorates pre-existing cardiac hypertrophy, in mice. Our data suggest that the anti-hypertrophic effects of HKL depend on activation of the deacetylase Sirt3. We demonstrate that HKL is present in mitochondria, enhances Sirt3 expression nearly twofold and suggest that HKL may bind to Sirt3 to further increase its activity. Increased Sirt3 activity is associated with reduced acetylation of mitochondrial Sirt3 substrates, MnSOD and oligomycin-sensitivity conferring protein (OSCP). HKL-treatment increases mitochondrial rate of oxygen consumption and reduces ROS synthesis in wild type, but not in Sirt3-KO cells. Moreover, HKL-treatment blocks cardiac fibroblast proliferation and differentiation to myofibroblasts in a Sirt3-dependent manner. These results suggest that HKL is a pharmacological activator of Sirt3 capable of blocking, and even reversing, the cardiac hypertrophic response. The chemical honokiol is found in the bark of magnolia trees, which are used for traditional medicine in Asian countries. Here, Pillai et al , show honokiol protects the heart from hypertrophic remodelling in mice, and even reverses established cardiac hypertrophy, by activating the deacetylase Sirt3.

Marginalized populations experience disproportionate rates of preterm birth and early term birth. Exposure to per- and polyfluoroalkyl substances (PFAS) has been reported to reduce length of gestation, but the underlying mechanisms are unknown. In the present study, we characterized the molecular signatures of prenatal PFAS exposure and gestational age at birth outcomes in the newborn dried blood spot metabolome among 267 African American dyads in Atlanta, Georgia between 2016 and 2020. Pregnant people with higher serum perfluorooctanoic acid and perfluorohexane sulfonic acid concentrations had increased odds of an early birth. After false discovery rate correction, the effect of prenatal PFAS exposure on reduced length of gestation was associated with 8 metabolomic pathways and 52 metabolites in newborn dried blood spots, which suggested perturbed tissue neogenesis, neuroendocrine function, and redox homeostasis. These mechanisms explain how prenatal PFAS exposure gives rise to the leading cause of infant death in the United States. Mechanisms of the impact of PFAS (also known as forever chemicals) on adverse birth outcomes remain largely unknown. Here, authors identified tissue neogenesis, neuroendocrine function, and redox homeostasis as imprints of prenatal PFAS exposures and reduced gestational age in the newborn metabolome.

Background Redox signaling, which can be assessed by circulating aminothiols, reflects oxidative stress (OS) status and has been linked to clinical cardiovascular disease and its risk factors. These, in turn, are related to executive function decline. OS may precede the pro-inflammatory state seen in vascular disease. The objective of this study is to investigate the association between aminothiol markers of OS and inflammation in cognitive decline, especially in the executive cognitive domain which is highly susceptible to cardiovascular risk factors and is an important predictor of cognitive disability. Methods The study design is that of a longitudinal cohort study within the setting of a large academic institution with participants being university employees ( n  = 511), mean age 49 years, 68% women, and 23% African-American. These participants were followed for four consecutive years with a yearly cognitive assessment conducted using computerized versions of 15 cognitive tests. Peripheral cystine, glutathione, their disulfide derivatives, and C-reactive protein (CRP) were measured. Results Lower levels of glutathione at baseline was associated with a decline in the executive domain over 4 years (covariate-adjusted relative risk (RR) for glutathione = 1.70 (95% CI = 1.02–2.85), p  = 0.04). Furthermore, a longitudinal decline in glutathione level was associated with a faster decline in the executive domain ( p  = 0.03). None of the other OS markers or CRP were linked to cognitive decline over 4 years. Conclusion Increased OS reflected by decreased glutathione was associated with a decline in executive function in a healthy population. In contrast, inflammation was not linked to cognitive decline. OS may be an earlier biomarker that precedes the inflammatory phase of executive decline with aging.

Progression of Parkinson's disease (PD) is highly variable, indicating that differences between slow and rapid progression forms could provide valuable information for improved early detection and management. Unfortunately, this represents a complex problem due to the heterogeneous nature of humans in regards to demographic characteristics, genetics, diet, environmental exposures and health behaviors. In this pilot study, we employed high resolution mass spectrometry-based metabolic profiling to investigate the metabolic signatures of slow versus rapidly progressing PD present in human serum. Archival serum samples from PD patients obtained within 3 years of disease onset were analyzed via dual chromatography-high resolution mass spectrometry, with data extraction by xMSanalyzer and used to predict rapid or slow motor progression of these patients during follow-up. Statistical analyses, such as false discovery rate analysis and partial least squares discriminant analysis, yielded a list of statistically significant metabolic features and further investigation revealed potential biomarkers. In particular, N8-acetyl spermidine was found to be significantly elevated in the rapid progressors compared to both control subjects and slow progressors. Our exploratory data indicate that a fast motor progression disease phenotype can be distinguished early in disease using high resolution mass spectrometry-based metabolic profiling and that altered polyamine metabolism may be a predictive marker of rapidly progressing PD.

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers and may reveal pathways involved in TB disease pathogenesis and resolution.

To determine if plasma metabolic profiles can detect differences between patients with neovascular age-related macular degeneration (NVAMD) and similarly-aged controls. Metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry (LC-FTMS) was performed on plasma samples from 26 NVAMD patients and 19 controls. Data were collected from mass/charge ratio (m/z) 85 to 850 on a Thermo LTQ-FT mass spectrometer, and metabolic features were extracted using an adaptive processing software package. Both non-transformed and log2 transformed data were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Orthogonal Partial Least Squares-Discriminant Analysis was performed to determine metabolic features that distinguished NVAMD patients from controls. Individual m/z features were matched to the Kyoto Encyclopedia of Genes and Genomes database and the Metlin metabolomics database, and metabolic pathways associated with NVAMD were identified using MetScape. Of the 1680 total m/z features detected by LC-FTMS, 94 unique m/z features were significantly different between NVAMD patients and controls using FDR (q = 0.05). A comparison of these features to those found with log2 transformed data (n = 132, q = 0.2) revealed 40 features in common, reaffirming the involvement of certain metabolites. Such metabolites included di- and tripeptides, covalently modified amino acids, bile acids, and vitamin D-related metabolites. Correlation analysis revealed associations among certain significant features, and pathway analysis demonstrated broader changes in tyrosine metabolism, sulfur amino acid metabolism, and amino acids related to urea metabolism. These data suggest that metabolomic analysis can identify a panel of individual metabolites that differ between NVAMD cases and controls. Pathway analysis can assess the involvement of certain metabolic pathways, such as tyrosine and urea metabolism, and can provide further insight into the pathophysiology of AMD.