Global metabolic changes induced by plant-derived pyrrolizidine alkaloids following a human poisoning outbreak and in a mouse model

Several hundred cases of Hirmi Valley Liver Disease (HVLD), an often fatal liver injury, occurred from 2001 to 2011 in a cluster of rural villages in Tigray, Ethiopia. HVLD is principally caused by contamination of the food supply with plant derived pyrrolizidine alkaloids (PAs), with high exposure to the pesticide DDT among villagers increasing their susceptibility. In an untargeted global approach we aimed to identify metabolic changes induced by PA exposure through 1H NMR spectroscopic based metabolic profiling. We analysed spectra acquired from urine collected from HVLD cases and controls and a murine model of PA exposure and PA/DDT co-exposure, using multivariate partial least squares discriminant analysis. In the human models we identified changes in urinary concentrations of tyrosine, pyruvate, bile acids, N-acetylglycoproteins, N-methylnicotinamide and formate, hippurate, p-cresol sulphate, p-hydroxybenzoate and 3-(3-hydroxyphenyl) propionic acid. Tyrosine and p-cresol sulphate were associated with both exposure and disease. Similar changes to tyrosine, one-carbon intermediates and microbial associated metabolites were observed in the mouse model, with tyrosine correlated with the extent of liver damage. These results provide mechanistic insight and implicate the gut microflora in the human response to challenge with toxins. Pathways identified here may be useful in translational research and as “exposome” signals

Ion-pairing chromatography and amine derivatization provide complementary approaches for the targeted LC-MS analysis of the polar metabolome.

Liquid chromatography coupled to mass spectrometry is a key metabolomics/metabonomics technology. Reversed-phase liquid chromatography (RPLC) is very widely used as a separation step, but typically has poor retention of highly polar metabolites. Here, we evaluated the combination of two alternative methods for improving retention of polar metabolites based on 6-aminoquinoloyl-N-hydroxysuccinidimyl carbamate derivatization for amine groups, and ion-pairing chromatography (IPC) using tributylamine as an ion-pairing agent to retain acids. We compared both of these methods to RPLC and also to each other, for targeted analysis using a triple-quadrupole mass spectrometer, applied to a library of ca. 500 polar metabolites. IPC and derivatization were complementary in terms of their coverage: combined, they improved the proportion of metabolites with good retention to 91%, compared to just 39% for RPLC alone. The combined method was assessed by analyzing a set of liver extracts from aged male and female mice that had been treated with the polyphenol compound ampelopsin. Not only were a number of significantly changed metabolites detected, but also it could be shown that there was a clear interaction between ampelopsin treatment and sex, in that the direction of metabolite change was opposite for males and females

Indisulam targets RNA splicing and metabolism to serve as a therapeutic strategy for high-risk neuroblastoma

Neuroblastoma is the most common paediatric solid tumour and prognosis remains poor for high-risk cases despite the use of multimodal treatment. Analysis of public drug sensitivity data showed neuroblastoma lines to be sensitive to indisulam, a molecular glue that selectively targets RNA splicing factor RBM39 for proteosomal degradation via DCAF15-E3-ubiquitin ligase. In neuroblastoma models, indisulam induces rapid loss of RBM39, accumulation of splicing errors and growth inhibition in a DCAF15-dependent manner. Integrative analysis of RNAseq and proteomics data highlight a distinct disruption to cell cycle and metabolism. Metabolic profiling demonstrates metabolome perturbations and mitochondrial dysfunction resulting from indisulam. Complete tumour regression without relapse was observed in both xenograft and the Th-MYCN transgenic model of neuroblastoma after indisulam treatment, with RBM39 loss, RNA splicing and metabolic changes confirmed in vivo. Our data show that dual-targeting of metabolism and RNA splicing with anticancer indisulam is a promising therapeutic approach for high-risk neuroblastoma

Simplivariate Models: Ideas and First Examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli

Application of holistic liquid chromatography-high resolution mass spectrometry based urinary metabolomics for prostate cancer detection and biomarker discovery

Human exhibit wide variations in their metabolic profiles because of differences in genetic factors, diet and lifestyle. Therefore in order to detect metabolic differences between individuals robust analytical methods are required. A protocol was produced based on the use of Liquid Chromatography- High Resolution Mass Spectrometry (LC-HRMS) in combination with orthogonal Hydrophilic Interaction (HILIC) and Reversed Phase (RP) liquid chromatography methods for the analysis of the urinary metabolome, which was then evaluated as a diagnostic tool for prostate cancer (a common but highly heterogeneous condition). The LC-HRMS method was found to be robust and exhibited excellent repeatability for retention times (0.9. In addition, using the receiver operator characteristics (ROC) test, the area under curve (AUC) for the combination of the four best characterised biomarker compounds was 0.896. The four biomarker compounds were also found to differ significantly (

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem

Divergent Effects of Human Cytomegalovirus and Herpes Simplex Virus-1 on Cellular Metabolism

Viruses rely on the metabolic network of the host cell to provide energy and macromolecular precursors to fuel viral replication. Here we used mass spectrometry to examine the impact of two related herpesviruses, human cytomegalovirus (HCMV) and herpes simplex virus type-1 (HSV-1), on the metabolism of fibroblast and epithelial host cells. Each virus triggered strong metabolic changes that were conserved across different host cell types. The metabolic effects of the two viruses were, however, largely distinct. HCMV but not HSV-1 increased glycolytic flux. HCMV profoundly increased TCA compound levels and flow of two carbon units required for TCA cycle turning and fatty acid synthesis. HSV-1 increased anapleurotic influx to the TCA cycle through pyruvate carboxylase, feeding pyrimidine biosynthesis. Thus, these two related herpesviruses drive diverse host cells to execute distinct, virus-specific metabolic programs. Current drugs target nucleotide metabolism for treatment of both viruses. Although our results confirm that this is a robust target for HSV-1, therapeutic interventions at other points in metabolism might prove more effective for treatment of HCMV

Able-Bodied Wild Chimpanzees Imitate a Motor Procedure Used by a Disabled Individual to Overcome Handicap

Chimpanzee culture has generated intense recent interest, fueled by the technical complexity of chimpanzee tool-using traditions; yet it is seriously doubted whether chimpanzees are able to learn motor procedures by imitation under natural conditions. Here we take advantage of an unusual chimpanzee population as a ‘natural experiment’ to identify evidence for imitative learning of this kind in wild chimpanzees. The Sonso chimpanzee community has suffered from high levels of snare injury and now has several manually disabled members. Adult male Tinka, with near-total paralysis of both hands, compensates inability to scratch his back manually by employing a distinctive technique of holding a growing liana taut while making side-to-side body movements against it. We found that seven able-bodied young chimpanzees also used this ‘liana-scratch’ technique, although they had no need to. The distribution of the liana-scratch technique was statistically associated with individuals' range overlap with Tinka and the extent of time they spent in parties with him, confirming that the technique is acquired by social learning. The motivation for able-bodied chimpanzees copying his variant is unknown, but the fact that they do is evidence that the imitative learning of motor procedures from others is a natural trait of wild chimpanzees

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results