Molecular signatures of angiogenesis inhibitors: a single-embryo untargeted metabolomics approach in zebrafish.

Angiogenesis is a key process in embryonic development, a disruption of this process can lead to severe developmental defects, such as limb malformations. The identification of molecular perturbations representative of antiangiogenesis in zebrafish embryo (ZFE) may guide the assessment of developmental toxicity from an endpoint- to a mechanism-based approach, thereby improving the extrapolation of findings to humans. Thus, the aim of the study was to discover molecular changes characteristic of antiangiogenesis and developmental toxicity. We exposed ZFEs to two antiangiogenic drugs (SU4312, sorafenib) and two developmental toxicants (methotrexate, rotenone) with putative antiangiogenic action. Molecular changes were measured by performing untargeted metabolomics in single embryos. The metabolome response was accompanied by the occurrence of morphological alterations. Two distinct metabolic effect patterns were observed. The first pattern comprised common effects of two specific angiogenesis inhibitors and the known teratogen methotrexate, strongly suggesting a shared mode of action of antiangiogenesis and developmental toxicity. The second pattern involved joint effects of methotrexate and rotenone, likely related to disturbances in energy metabolism. The metabolites of the first pattern, such as phosphatidylserines, pterines, retinol, or coenzyme Q precursors, represented potential links to antiangiogenesis and related developmental toxicity. The metabolic effect pattern can contribute to biomarker identification for a mechanism-based toxicological testing.

The short-term toxicity and metabolome of dicyclopentadiene.

Dicyclopentadiene (DCPD) was investigated in a 14-day oral rat toxicity study based on the OECD 407 guideline in combination with plasma metabolomics. Wistar rats received the compound daily via gavage at dose levels of 0, 50 and 150 mg/kg bw. The high dose induced transient clinical signs of toxicity and in males only reduced body weight gain. High dose liver changes were characterized by altered clinical chemistry parameters in both sexes and pathological changes in females. In high dose males an accumulation of alpha-2 u-globulin in the kidney was noted. Comparing the DCPD metabolome with previously established specific metabolome patterns in the MetaMap® Tox data base suggested that the high dose would result in liver enzyme induction leading to increased breakdown of thyroid hormones for males and females. An indication for liver toxicity in males was also noted. Metabolomics also suggested an effect on the functionality of the adrenals in high dose males, which together with published data, is suggestive of a stress related effect in this organ. The results of the present 14-day combined toxicity and metabolome investigations were qualitatively in line with literature data from subchronic oral studies in rats with DCPD. Importantly no other types of organ toxicity, or hormone dysregulation beyond the ones associated with liver enzyme induction and stress were indicated, again in line with results of published 90-day studies. It is therefore suggested that short term "smart" studies, combining classical toxicity with 'omics technologies, could be a 2 R (refine and reduce) new approach method allowing for the reduction of in vivo toxicity testing.

Toward implementing virtual control groups in nonclinical safety studies.

Historical data from control groups in animal toxicity studies is currently mainly used for comparative purposes to assess validity and robustness of study results. Due to the highly controlled environment in which the studies are performed and the homogeneity of the animal collectives it has been proposed to use the historical data for building so-called virtual control groups, which could replace partly or entirely the concurrent control. This would constitute a substantial contribution to the reduction of animal use in safety studies. Before the concept can be implemented, the prerequisites regarding data collection, curation and statistical evaluation together with a validation strategy need to be identified to avoid any impairment of the study outcome and subsequent consequences for human risk assessment. To further assess and develop the concept of virtual control groups the transatlantic think tank for toxicology (t4) sponsored a workshop with stakeholders from the pharmaceutical and chemical industry, academia, FDA, pharmaceutical, contract research organizations (CROs), and non-governmental organizations in Washington, which took place in March 2023. This report summarizes the current efforts of a European initiative to share, collect and curate animal control data in a centralized database and the first approaches to identify optimal matching criteria between virtual controls and the treatment arms of a study as well as first reflections about strategies for a qualification procedure and potential pitfalls of the concept.

Animal safety studies are usually performed with three groups of animals where increasing amounts of the test chemical are given to the animals and one control group where the animals do not receive the test chemical. The design of such studies, the characteristics of the animals, and the measured parameters are often very similar from study to study. Therefore, it has been suggested that measurement data from the control groups could be reused from study to study to lower the total number of animals per study. This could reduce animal use by up to 25% for such standardized studies. A workshop was held to discuss the pros and cons of such a concept and what would have to be done to implement it without threatening the reliability of the study outcome or the resulting human risk assessment.

Application of high throughput in vitro metabolomics for hepatotoxicity mode of action characterization and mechanistic-anchored point of departure derivation: a case study with nitrofurantoin.

Omics techniques have been increasingly recognized as promising tools for Next Generation Risk Assessment. Targeted metabolomics offer the advantage of providing readily interpretable mechanistic information about perturbed biological pathways. In this study, a high-throughput LC-MS/MS-based broad targeted metabolomics system was applied to study nitrofurantoin metabolic dynamics over time and concentration and to provide a mechanistic-anchored approach for point of departure (PoD) derivation. Upon nitrofurantoin exposure at five concentrations (7.5 µM, 15 µM, 20 µM, 30 µM and 120 µM) and four time points (3, 6, 24 and 48 h), the intracellular metabolome of HepG2 cells was evaluated. In total, 256 uniquely identified metabolites were measured, annotated, and allocated in 13 different metabolite classes. Principal component analysis (PCA) and univariate statistical analysis showed clear metabolome-based time and concentration effects. Mechanistic information evidenced the differential activation of cellular pathways indicative of early adaptive and hepatotoxic response. At low concentrations, effects were seen mainly in the energy and lipid metabolism, in the mid concentration range, the activation of the antioxidant cellular response was evidenced by increased levels of glutathione (GSH) and metabolites from the de novo GSH synthesis pathway. At the highest concentrations, the depletion of GSH, together with alternations reflective of mitochondrial impairments, were indicative of a hepatotoxic response. Finally, a metabolomics-based PoD was derived by multivariate PCA using the whole set of measured metabolites. This approach allows using the entire dataset and derive PoD that can be mechanistically anchored to established key events. Our results show the suitability of high throughput targeted metabolomics to investigate mechanisms of hepatoxicity and derive point of departures that can be linked to existing adverse outcome pathways and contribute to the development of new ones.

A high-throughput metabolomics in vitro platform for the characterization of hepatotoxicity.

Cell-based metabolomics provides multiparametric physiologically relevant readouts that can be highly advantageous for improved, biologically based decision making in early stages of compound development. Here, we present the development of a 96-well plate LC-MS/MS-based targeted metabolomics screening platform for the classification of liver toxicity modes of action (MoAs) in HepG2 cells. Different parameters of the workflow (cell seeding density, passage number, cytotoxicity testing, sample preparation, metabolite extraction, analytical method, and data processing) were optimized and standardized to increase the efficiency of the testing platform. The applicability of the system was tested with seven substances known to be representative of three different liver toxicity MoAs (peroxisome proliferation, liver enzyme induction, and liver enzyme inhibition). Five concentrations per substance, aimed at covering the complete dose-response curve, were analyzed and 221 uniquely identified metabolites were measured, annotated, and allocated in 12 different metabolite classes such as amino acids, carbohydrates, energy metabolism, nucleobases, vitamins and cofactors, and diverse lipid classes. Multivariate and univariate analyses showed a dose response of the metabolic effects, a clear differentiation between liver toxicity MoAs and resulted in the identification of metabolite patterns specific for each MoA. Key metabolites indicative of both general and mechanistic specific hepatotoxicity were identified. The method presented here offers a multiparametric, mechanistic-based, and cost-effective hepatotoxicity screening that provides MoA classification and sheds light into the pathways involved in the toxicological mechanism. This assay can be implemented as a reliable compound screening platform for improved safety assessment in early compound development pipelines.

A metabolomics approach to reveal the mechanism of developmental toxicity in zebrafish embryos exposed to 6-propyl-2-thiouracil.

A crucial component of a substance registration and regulation is the evaluation of human prenatal developmental toxicity. Current toxicological tests are based on mammalian models, but these are costly, time consuming and may pose ethical concerns. The zebrafish embryo has evolved as a promising alternative model to study developmental toxicity. However, the implementation of the zebrafish embryotoxicity test is challenged by lacking information on the relevance of observed morphological alterations in fish for human developmental toxicity. Elucidating the mechanism of toxicity could help to overcome this limitation. Through LC-MS/MS and GC-MS metabolomics, we investigated whether changes to the endogenous metabolites can indicate pathways associated with developmental toxicity. To this aim, zebrafish embryos were exposed to different concentrations of 6-propyl-2-thiouracil (PTU), a compound known to induce developmental toxicity. The reproducibility and the concentration-dependence of the metabolome response and its association with morphological alterations were studied. Major morphological findings were reduced eye size, and other craniofacial anomalies; major metabolic changes included increased tyrosine, pipecolic acid and lysophosphatidylcholine levels, decreased methionine levels, and disturbance of the 'Phenylalanine, tyrosine and tryptophan biosynthesis' pathway. This pathway, and the changes in tyrosine and pipecolic acid levels could be linked to the mode of action of PTU, i.e., inhibition of thyroid peroxidase (TPO). The other findings suggested neurodevelopmental impairments. This proof-of-concept study demonstrated that metabolite changes in zebrafish embryos are robust and provide mechanistic information associated with the mode of action of PTU.

Connecting Gut Microbial Diversity with Plasma Metabolome and Fecal Bile Acid Changes Induced by the Antibiotics Tobramycin and Colistin Sulfate.

The diversity of microbial species in the gut has a strong influence on health and development of the host. Further, there are indications that the variation in expression of gut bacterial metabolic enzymes is less diverse than the taxonomic profile, underlying the importance of microbiome functionality, particularly from a toxicological perspective. To address these relationships, the gut bacterial composition of Wistar rats was altered by a 28 day oral treatment with the antibiotics tobramycin or colistin sulfate. On the basis of 16S marker gene sequencing data, tobramycin was found to cause a strong reduction in the diversity and relative abundance of the microbiome, whereas colistin sulfate had only a marginal impact. Associated plasma and fecal metabolomes were characterized by targeted mass spectrometry-based profiling. The fecal metabolome of tobramycin-treated animals had a high number of significant alterations in metabolite levels compared to controls, particularly in amino acids, lipids, bile acids (BAs), carbohydrates, and energy metabolites. The accumulation of primary BAs and significant reduction of secondary BAs in the feces indicated that the microbial alterations induced by tobramycin inhibit bacterial deconjugation reactions. The plasma metabolome showed less, but still many alterations in the same metabolite groups, including reductions in indole derivatives and hippuric acid, and furthermore, despite marginal effects of colistin sulfate treatment, there were nonetheless systemic alterations also in BAs. Aside from these treatment-based differences, we also uncovered interindividual differences particularly centering on the loss of Verrucomicrobiaceae in the microbiome, but with no apparent associated metabolite alterations. Finally, by comparing the data set from this study with metabolome alterations in the MetaMapTox database, key metabolite alterations were identified as plasma biomarkers indicative of altered gut microbiomes resulting from a wide activity spectrum of antibiotics.

Gut Microbiota as Well as Metabolomes of Wistar Rats Recover within Two Weeks after Doripenem Antibiotic Treatment.

An understanding of the changes in gut microbiome composition and its associated metabolic functions is important to assess the potential implications thereof on host health. Thus, to elucidate the connection between the gut microbiome and the fecal and plasma metabolomes, two poorly bioavailable carbapenem antibiotics (doripenem and meropenem), were administered in a 28-day oral study to male and female Wistar rats. Additionally, the recovery of the gut microbiome and metabolomes in doripenem-exposed rats were studied one and two weeks after antibiotic treatment (i.e., doripenem-recovery groups). The 16S bacterial community analysis revealed an altered microbial population in all antibiotic treatments and a recovery of bacterial diversity in the doripenem-recovery groups. A similar pattern was observed in the fecal metabolomes of treated animals. In the recovery group, particularly after one week, an over-compensation was observed in fecal metabolites, as they were significantly changed in the opposite direction compared to previously changed metabolites upon 28 days of antibiotic exposure. Key plasma metabolites known to be diagnostic of antibiotic-induced microbial shifts, including indole derivatives, hippuric acid, and bile acids were also affected by the two carbapenems. Moreover, a unique increase in the levels of indole-3-acetic acid in plasma following meropenem treatment was observed. As was observed for the fecal metabolome, an overcompensation of plasma metabolites was observed in the recovery group. The data from this study provides insights into the connectivity of the microbiome and fecal and plasma metabolomes and demonstrates restoration post-antibiotic treatment not only for the microbiome but also for the metabolomes. The importance of overcompensation reactions for health needs further studies.

Investigating the gut microbiome and metabolome following treatment with artificial sweeteners acesulfame potassium and saccharin in young adult Wistar rats.

To elucidate if artificial sweeteners modify fecal bacterial composition and the fecal and plasma metabolomes, Wistar rats from both sexes were treated for 28 days with acesulfame potassium (40 and 120 mg/kg body weight) and saccharin (20 and 100 mg/kg body weight). Targeted MS-based metabolome profiling (plasma and feces) and fecal 16S gene sequencing were conducted. Both sweeteners exhibited only minor effects on the fecal metabolome and microbiota. Saccharin treatment significantly altered amino acids, lipids, energy metabolism and specifically, bile acids in the plasma metabolome. Additionally, sex-specific differences were observed for conjugated primary and secondary bile acids. Acesulfame potassium treated male rats showed larger alterations in glycine conjugated primary and secondary bile-acids than females. Other changes in the plasma metabolome were more profound for saccharin than acesulfame potassium, for both sexes. Changes in conjugated bile-acids in plasma, which are often associated with microbiome changes, and the absence of similarly large changes in microbiota suggest an adaptative change of the latter, rather than toxicity. Further studies with a high resolution 16S sequencing data and/or metagenomics approach, with particular emphasis on bile acids, will be required to explore the mechanisms driving this metabolic outcome of saccharin in Wistar rats.

ReactPRED: a tool to predict and analyze biochemical reactions.

MOTIVATION: Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. RESULTS: ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. AVAILABILITY AND IMPLEMENTATION: ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: anirban.b@samsung.com or ty76.kim@samsung.comSupplementary information: Supplementary data are available at Bioinformatics online.

RxnSim: a tool to compare biochemical reactions.

UNLABELLED: : Quantitative assessment of chemical reaction similarity aids database searches, classification of reactions and identification of candidate enzymes. Most methods evaluate reaction similarity based on chemical transformation patterns. We describe a tool, RxnSim, which computes reaction similarity based on the molecular signatures of participating molecules. The tool is able to compare reactions based on similarities of substrates and products in addition to their transformation. It allows masking of user-defined chemical moieties for weighted similarity computations. AVAILABILITY AND IMPLEMENTATION: RxnSim is implemented in R and is freely available from the Comprehensive R Archive Network, CRAN (http://cran.r-project.org/web/packages/RxnSim/). CONTACT: anirban.b@samsung.com or ty76.kim@samsung.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks.

Unraveling the structure of complex biological networks and relating it to their functional role is an important task in systems biology. Here we attempt to characterize the functional organization of the large-scale metabolic networks of three microorganisms. We apply flux balance analysis to study the optimal growth states of these organisms in different environments. By investigating the differential usage of reactions across flux patterns for different environments, we observe a striking bimodal distribution in the activity of reactions. Motivated by this, we propose a simple algorithm to decompose the metabolic network into three subnetworks. It turns out that our reaction classifier, which is blind to the biochemical role of pathways, leads to three functionally relevant subnetworks that correspond to input, output, and intermediate parts of the metabolic network with distinct structural characteristics. Our decomposition method unveils a functional bow-tie organization of metabolic networks that is different from the bow-tie structure determined by graph-theoretic methods that do not incorporate functionality.

The origin of large molecules in primordial autocatalytic reaction networks.

Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the 'food set', being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the catalytic network, can focus the resources of the system into a sparse set of molecules. ACSs can produce a bistability in the population dynamics and, in particular, steady states wherein the ACS molecules dominate the population. However to reach these steady states from initial conditions that contain only the food set typically requires very large catalytic strengths, growing exponentially with the size of the catalyst molecule. We present a solution to this problem by studying 'nested ACSs', a structure in which a small ACS is connected to a larger one and reinforces it. We show that when the network contains a cascade of nested ACSs with the catalytic strengths of molecules increasing gradually with their size (e.g., as a power law), a sparse subset of molecules including some very large molecules can come to dominate the system.

Low degree metabolites explain essential reactions and enhance modularity in biological networks.

BACKGROUND: Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints. RESULTS: In the metabolic networks of Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus, we identified metabolites with a low degree of connectivity, particularly those that are produced and/or consumed in just a single reaction. Using flux balance analysis (FBA) we also determined reactions essential for growth in these metabolic networks. We find that most reactions identified as essential in these networks turn out to be those involving the production or consumption of low degree metabolites. Applying graph theoretic methods to these metabolic networks, we identified connected clusters of these low degree metabolites. The genes involved in several operons in E. coli are correctly predicted as those of enzymes catalyzing the reactions of these clusters. Furthermore, we find that larger sized clusters are over-represented in the real network and are analogous to a 'network motif. Using FBA for the above mentioned three organisms we independently identified clusters of reactions whose fluxes are perfectly correlated. We find that the composition of the latter 'functional clusters' is also largely explained in terms of clusters of low degree metabolites in each of these organisms. CONCLUSION: Our findings mean that most metabolic reactions that are essential can be tagged by one or more low degree metabolites. Those reactions are essential because they are the only ways of producing or consuming their respective tagged metabolites. Furthermore, reactions whose fluxes are strongly correlated can be thought of as 'glued together' by these low degree metabolites. The methods developed here could be used in predicting essential reactions and metabolic modules in other organisms from the list of metabolic reactions.