A metabolomics cell-based approach for anticipating and investigating drug-induced liver injury

In preclinical stages of drug development, anticipating potential adverse drug effects such as toxicity is an important issue for both saving resources and preventing public health risks. Current in vitro cytotoxicity tests are restricted by their predictive potential and their ability to provide mechanistic information. This study aimed to develop a metabolomic mass spectrometry-based approach for the detection and classification of drug-induced hepatotoxicity. To this end, the metabolite profiles of human derived hepatic cells (i.e., HepG2) exposed to different well-known hepatotoxic compounds acting through different mechanisms (i.e., oxidative stress, steatosis, phospholipidosis, and controls) were compared by multivariate data analysis, thus allowing us to decipher both common and mechanism-specific altered biochemical pathways. Briefly, oxidative stress damage markers were found in the three mechanisms, mainly showing altered levels of metabolites associated with glutathione and γ-glutamyl cycle. Phospholipidosis was characterized by a decreased lysophospholipids to phospholipids ratio, suggestive of phospholipid degradation inhibition. Whereas, steatosis led to impaired fatty acids β-oxidation and a subsequent increase in triacylglycerides synthesis. The characteristic metabolomic profiles were used to develop a predictive model aimed not only to discriminate between non-toxic and hepatotoxic drugs, but also to propose potential drug toxicity mechanism(s)

Dynamic metabolic patterns tracking neurodegeneration and gliosis following 26S proteasome dysfunction in mouse forebrain neurons

Metabolite profling is an important tool that may better capture the multiple features of neurodegeneration. With the considerable parallels between mouse and human metabolism, the use of metabolomics in mouse models with neurodegenerative pathology provides mechanistic insight and ready translation into aspects of human disease. Using 400MHz nuclear magnetic resonance spectroscopy we have carried out a temporal region-specifc investigation of the metabolome of neuron-specifc 26S proteasome knockout mice characterised by progressive neurodegeneration and Lewy-like inclusion formation in the forebrain. An early signifcant decrease in N-acetyl aspartate revealed evidence of neuronal dysfunction before cell death that may be associated with changes in brain neuroenergetics, underpinning the use of this metabolite to track neuronal health. Importantly, we show early and extensive activation of astrocytes and microglia in response to targeted neuronal dysfunction in this context, but only late changes in myo-inositol; the best established glial cell marker in magnetic resonance spectroscopy studies, supporting recent evidence that additional early neuroinfammatory markers are needed. Our results extend the limited understanding of metabolite changes associated with gliosis and provide evidence that changes in glutamate homeostasis and lactate may correlate with astrocyte activation and have biomarker potential for tracking neuroinfammation

Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview

The etiopathogenesis of cardiovascular diseases (CVDs) is multifactorial. Adverse blood pressure (BP) is a major independent risk factor for epidemic CVD affecting ~40% of the adult population worldwide and resulting in significant morbidity and mortality. Metabolic phenotyping of biological fluids has proven its application in characterizing low-molecular-weight metabolites providing novel insights into gene-environmental-gut microbiome interaction in relation to a disease state. In this review, we synthesize key results from the INTERnational study of MAcro/micronutrients and blood Pressure (INTERMAP) Study, a cross-sectional epidemiologic study of 4680 men and women aged 40-59 years from Japan, the People's Republic of China, the United Kingdom and the United States. We describe the advancements we have made regarding the following: (1) analytical techniques for high-throughput metabolic phenotyping; (2) statistical analyses for biomarker identification; (3) discovery of unique food-specific biomarkers; and (4) application of metabolome-wide association studies to gain a better understanding into the molecular mechanisms of cross-cultural and regional BP differences

Two multiple-imaged z=4.05 galaxies in the cluster-lens Abell 2390

We present the first results on the identification and study of very distant field galaxies in the core of cluster-lenses, using a selection criterium based on both lens modelling and photometric redshifts. We concentrate on two multiple-imaged sources at z = 4.05 in the cluster Abell 2390. The two objects presented in this paper, namely H3 (cusp are) and H5 (fold are), were identified through lens modelling as multiple images of high-redshift sources at z greater than or similar to 3.5 (Kneib et al. 1999). We confirm the excellent agreement between this identification and both their photometric redshifts and morphologies. Our CFHT/WHT program for a systematic redshift survey of arcs in clusters has allowed to obtain a set of spectra on three different images at z similar to 4: the brightest image of H3, whose redshift was already confirmed by Frye & Broadhurst (1998), and the two brightest images of H5. The later is then confirmed spectroscopically as a multiple image, giving a strong support to the lens model. The main feature in each of these spectra is a strong emission line, identified as Ly a, leading to z. = 4.05 for both H3 and H5. The spectrophotometric properties of these galaxies are studied, in particular the degeneracy in the parameter-space defined by the SFR type, age, metallicity and reddening. H3 and H5 are intrinsically bright and clumpy galaxies (M-B* to M-B* -2 magnitudes), located similar to 100h(50)(-1) kpc apart on the source plane, with mean metallicities compatible with a fraction of solar or even solar values. These results seem to favour a hierarchical merging scenario, when we are seeing a relatively evolved phase in these two z similar to 4 objects, with stars forming locally and efficiently

A workflow for integrated processing of multi-cohort untargeted 1H NMR metabolomics data in large scale metabolic epidemiology

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines which can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from approximately 8000 individuals, in 3 cohorts, profiled by 6 assays in 2 phases using both 1H-NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling’s T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR datasets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling’s T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636 indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multi-faceted study

Validation of metabolomics for toxic mechanism of action screening with the earthworm Lumbricus rubellus

One of the promises of environmental metabolomics, together with other ecotoxicogenomic approaches, is that it can give information on toxic compound mechanism of action (MOA), by providing a specific response profile or fingerprint. This could then be used either for screening in the context of chemical risk assessment, or potentially in contaminated site assessment for determining what compound classes were causing a toxic effect. However for either of these two ends to be achievable, it is first necessary to know if different compounds do indeed elicit specific and distinct metabolic profile responses. Such a comparative study has not yet been carried out for the earthworm Lumbricus rubellus. We exposed L. rubellus to sub-lethal concentrations of three very different toxicants (CdCl2, atrazine, and fluoranthene, representing three compound classes with different expected MOA), by semi-chronic exposures in a laboratory test, and used NMR spectroscopy to obtain metabolic profiles. We were able to use simple multivariate pattern-recognition analyses to distinguish different compounds to some degree. In addition, following the ranking of individual spectral bins according to their mutual information with compound concentrations, it was possible to identify both general and specific metabolite responses to different toxic compounds, and to relate these to concentration levels causing reproductive effects in the worms

PhenoMeNal: processing and analysis of metabolomics data in the cloud.

BACKGROUND: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains