Effect of the histone deacetylase inhibitor trichostatin a on the metabolome of cultured primary hepatocytes

A Quantitative HILIC-MS/MS Assay of the Metabolic Response of Huh-7 Cells Exposed to 2,3,7,8-Tetrachlorodibenzo-p-Dioxin

A hydrophilic interaction liquid chromatography (HILIC)–ultra high-pressure liquid chromatography (UHPLC) coupled with tandem mass spectrometry (MS/MS) method was developed and applied to profile metabolite changes in human Huh-7 cells exposed to the potent aryl hydrocarbon receptor (AHR) ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Comparisons of sensitivity (limit of detection as low as 0.01 µM) and reproducibility (84% of compounds had an interday relative standard deviation (RSD) less than 10.0%; 83% of compounds had an intraday RSD less than 15.0%) were assessed for all the metabolites. The exposure of Huh-7 cells to the hepatotoxic carcinogen TCDD at low doses (1 nM and 10 nM for 4 h and 24 h, respectively) was reflected by the disturbance of amino acid metabolism, energy metabolism (glycolysis, TCA cycle), and nucleic acid metabolism. TCDD caused a significant decrease in amino acids such as serine, alanine, and proline while promoting an increase in arginine levels with 24 h treatment. Energy metabolism intermediates such as phosphoenolpyruvate and acetyl–CoA and nucleosides such as UMP, XMP, and CMP were also markedly decreased. These results support the application of HILIC–UHPLC–MS/MS for robust and reliable analysis of the cellular response to environmentally relevant toxicants at lower doses.

Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions

INTRODUCTION

Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols.

OBJECTIVES

This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them.

METHOD

Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling.

RESULT

The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information.

CONCLUSION

LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.

Trichostatin A combined with cytokines induces differentiation of embryonic stem cells into hepatocytes

AIM: To present a novel 3-step procedure to efficiently direct the differentiation of mouse embryonic stem cells (ESCs) into hepatocytes.

METHODS: Mouse ESCs were first induced to differentiate into definitive endoderm cells by three days of activin A treatment. Next, definitive endoderm cells were induced to efficiently differentiate to hepatocytes in the presence of acid fibroblast growth factor (aFGF) and trichostatin A (TSA) in the culture medium for 5 d.

RESULTS: After 10 d of further in vitro maturation, the morphological and phenotypic markers of hepatocytes were characterized using light microscopy, immunofluorescence and RT-PCR. Furthermore, these cells were tested for the functions associated with mature hepatocytes including glycogen storage, indocyanine green uptake and release, and the rate of hepatic differentiation was determined by counting the albumin-positive cells, which showed that the rate of hepatic differentiation was 57.38%.

CONCLUSION: The method presented in this study provides a new resource for hepatocyte transplantation.

Down-regulation of sirtuin 3 is associated with poor prognosis in hepatocellular carcinoma after resection

Background

Sirtuin 3 (Sirt3), one of the seven Sirtuins family members, plays critical roles in the progression of multiple cancer types. However, its role in the prognosis of hepatocellular carcinoma (HCC) has not yet been investigated systematically.

Methods

The correlation of Sirtuins expression with prognosis of HCC was determined by immunohistochemistry (IHC) in a large HCC patient cohort (n = 342). Expression of Sirt3 in tumoral and peritumoral tissues of HCC patients were further determined by western blotting (WB).

Results

IHC and WB studies both showed a decreased expression of Sirt3 in tumoral tissues compared with peritumoral tissues (P = 0.003 for IHC, P = 0.0042 for WB). Decreased expression of Sirt3 in both tumoral and peritumoral tissues was associated with increased recurrence probability and decreased overall survival rate by univariate analyses (intratumoral Sirt3: P = 0.011 for TTR, P = 0.001 for OS; peritumoral Sirt3: P = 0.017 for TTR, P = 0.023 for OS), the prognostic value was strengthened by multivariate analyses (intratumoral Sirt3: P = 0.031 for TTR, P = 0.001 for OS; peritumoral Sirt3: P = 0.047 for TTR, P = 0.031 for OS). Intratumoral Sirt3 also showed a favorable prognostic value in patients with BCLC stage A (TTR, P = 0.011; OS, P < 0.001). In addition, we found that IHC studies of other sirtuin members showed a decreased expression of Sirt2, Sirt4 and Sirt5 and an increased expression of Sirt1, Sirt6 and Sirt7 in intratumoral tissues compared with peritumoral tissues. In contrast to Sirt3, other members did not showed a remarkable correlation with HCC prognosis.

Conclusions

Down-regulation of intratumoral and peritumoral Sirt3 were both associated with poor outcome in HCC, moreover, intratumoral Sirt3 was a favorable prognostic predictor in early stage patients.

A combination of transcriptomics and metabolomics uncovers enhanced bile acid biosynthesis in HepG2 cells expressing CCAAT/enhancer-binding protein β (C/EBPβ), hepatocyte nuclear factor 4α (HNF4α), and constitutive androstane receptor (CAR)

The development of hepatoma-based in vitro models to study hepatocyte physiology is an invaluable tool for both industry and academia. Here, we develop an in vitro model based on the HepG2 cell line that produces chenodeoxycholic acid, the main bile acid in humans, in amounts comparable to human hepatocytes. A combination of adenoviral transfections for CCAAT/enhancer-binding protein β (C/EBPβ), hepatocyte nuclear factor 4α (HNF4α), and constitutive androstane receptor (CAR) decreased intracellular glutamate, succinate, leucine, and valine levels in HepG2 cells, suggestive of a switch to catabolism to increase lipogenic acetyl CoA and increased anaplerosis to replenish the tricarboxylic acid cycle. Transcripts of key genes involved in bile acid synthesis were significantly induced by approximately 160-fold. Consistently, chenodeoxycholic acid production rate was increased by more than 20-fold. Comparison between mRNA and bile acid levels suggest that 12-alpha hydroxylation of 7-alpha-hydroxy-4-cholesten-3-one is the limiting step in cholic acid synthesis in HepG2 cells. These data reveal that introduction of three hepatocyte-related transcription factors enhance anabolic reactions in HepG2 cells and provide a suitable model to study bile acid biosynthesis under pathophysiological conditions.

Metabolic characterization of Leishmania major infection in activated and nonactivated macrophages

Infection with Leishmania spp. can lead to a range of symptoms in the affected individual, depending on underlying immune-metabolic processes. The macrophage activation state hereby plays a key role. Whereas the l-arginine pathway has been described in detail as the main biochemical process responsible for either nitric oxide mediated parasite killing (classical activation) or amplification of parasite replication (alternative activation), we were interested in a wider characterization of metabolic events in vitro. We therefore assessed cell growth medium, parasite extract, and intra- and extracellular metabolome of activated and nonactivated macrophages, in presence and absence of Leishmania major. A metabolic profiling approach was applied combining ¹H NMR spectroscopy with multi- and univariate data treatment. Metabolic changes were observed along both conditional axes, that is, infection state and macrophage activation, whereby significantly higher levels of potential parasite end products were found in parasite exposed samples including succinate, acetate, and alanine, compared to uninfected macrophages. The different macrophage activation states were mainly discriminated by varying glucose consumption. The presented profiling approach allowed us to obtain a metabolic snapshot of the individual biological compartments in the assessed macrophage culture experiments and represents a valuable read out system for further multiple compartment in vitro studies.

Primary hepatocyte cultures as in vitro tools for toxicity testing: quo vadis?

Cultures of primary hepatocytes are versatile tools that can serve many in vitro toxicity testing purposes. However, they cope with dedifferentiation, a process that is already initiated during the hepatocyte isolation procedure and that is manifested as the progressive loss of functionality upon subsequent cultivation. A number of strategies to prevent dedifferentiation have been introduced over the last decades, all which aim at re-establishing the in vivo hepatocyte micro-environment in vitro, but that are of merely limited success. Recent mechanistic insight into the mechanisms that underlie hepatocyte dedifferentiation has opened new avenues for the development of novel approaches that target the actual causes of this deteriorative process and thus for the generation of a long-term hepatic in vitro tool. Such experimental system is urgently needed, especially in the light of the stringent European legislative modifications that are currently encountered by the pharmaceutical, chemical and, particularly, the cosmetic industry.

An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin

Background

In vitro cell systems together with omics methods represent promising alternatives to conventional animal models for toxicity testing. Transcriptomic and proteomic approaches have been widely applied in vitro but relatively few studies have used metabolomics. Therefore, the goal of the present study was to develop an untargeted methodology for performing reproducible metabolomics on in vitro systems. The human liver cell line HepG2, and the well-known hepatotoxic and non-genotoxic carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), were used as the in vitro model system and model toxicant, respectively.

Results

The study focused on the analysis of intracellular metabolites using NMR, LC-MS and GC-MS, with emphasis on the reproducibility and repeatability of the data. State of the art pre-processing and alignment tools and multivariate statistics were used to detect significantly altered levels of metabolites after exposing HepG2 cells to TCDD. Several metabolites identified using databases, literature and LC-nanomate-Orbitrap analysis were affected by the treatment. The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD.

Conclusions

Untargeted profiling of the polar and apolar metabolites of in vitro cultured HepG2 cells is a valid approach to studying the effects of TCDD on the cell metabolome. The approach described in this research demonstrates that highly reproducible experiments and correct normalization of the datasets are essential for obtaining reliable results. The effects of TCDD on HepG2 cells reported herein are in agreement with previous studies and serve to validate the procedures used in the present work.

Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles

Modern nuclear magnetic resonance (NMR) spectroscopy generates complex and information-rich metabolic profiles. These require robust, accurate, and often sophisticated statistical techniques to yield the maximum meaningful knowledge. In this chapter, we describe methods typically used to analyze such data. We begin by describing seven goals of metabolic profile analysis, ranging from production of a data table to multi-omic integration for systems biology. Methods for preprocessing and pretreatment are then presented, including issues such as instrument-level spectral processing, data reduction and deconvolution, normalization, scaling, and transformations of the data. We then discuss methods for exploratory modeling and exemplify three techniques: principal components analysis, hierarchical clustering, and self-organizing maps. Moving to predictive modeling, we focus our discussion on partial least squares regression, orthogonal partial least squares regression, and genetic algorithm approaches. A typical set of in vitro metabolic profiles is used where possible to compare and contrast the methods. The importance of validating statistical models is highlighted, and standard techniques for doing so, such as training/test set and cross-validation are described. Finally, we discuss the contributions of statistical techniques such as statistical total correlation spectroscopy, and other correlation-based methods have made to the process of structural characterization for unknown metabolites.

Nuclear magnetic resonance (NMR)-based metabolomics

Biofluids are by far the most commonly studied sample type in metabolic profiling studies, encompassing blood, urine, cerebrospinal fluid, cell culture media and many others. A number of these fluids can be obtained at a high sampling frequency with minimal invasion, permitting detailed characterisation of dynamic metabolic events. One of the attractive properties of solution-state metabolomics is the ability to generate profiles from these fluids following simple preparation, allowing the analyst to gain a naturalistic, largely unbiased view of their composition that is highly representative of the in vivo situation. Solution-state samples can also be generated from the extraction of tissue or cellular samples that can be tailored to target metabolites with particular properties. Nuclear magnetic resonance (NMR) provides an excellent technique for profiling these fluids and is especially adept at characterising complex solutions. Profiling biofluid samples by NMR requires appropriate preparation and experimental conditions to overcome the demands of varied sample matrices, including those with high protein, lipid or saline content, as well as the presence of water in aqueous samples.

NMR methods for unravelling the spectra of complex mixtures

The main methods for the simplification of the NMR of complex mixtures by selective attenuation/suppression of the signals of certain components are presented. The application of relaxation, diffusion and PSR filters and other techniques to biological samples, pharmaceuticals, foods, living organisms and natural products are illustrated with examples.