What can metabolomics learn from genomics and proteomics?

Analysis of Key Differential Metabolites in Intervertebral Disc Degeneration Based on Untargeted Metabolomics

Background

Intervertebral disc degeneration disease (IVDD) is a prevalent orthopedic condition that causes chronic lower back pain, imposing a substantial economic burden on patients and society. Despite its high incidence, the pathophysiological mechanisms of IVDD remain incompletely understood.

Objective

This study aimed to identify metabolomic alterations in IVDD patients and explore the key metabolic pathways and metabolites involved in its pathogenesis.

Methods

Serum samples from 20 IVDD patients and 20 healthy controls were analyzed using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). The identified metabolites were mapped to metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results

Significant alterations were observed in metabolites such as 2-methyl-1,3-cyclohexadiene, stearoyl sphingomyelin, methylcysteine, L-methionine, and cis, cis-muconic acid. These metabolites were involved in pathways including glycine, serine, and threonine metabolism, cyanoamino acid metabolism, and the citrate cycle (TCA cycle).

Conclusion

The identified metabolic alterations provide insights into the pathogenesis of IVDD and suggest potential therapeutic targets for future investigation.

Altered serum metabolome associated with vascular calcification developed from CKD and the critical pathways

Introduction

Vascular calcification (VC) is more likely to be detected in the chronic kidney disease (CKD) population. The mechanism of VC development from CKD is different from that for simple VC and has always been a major research area. The aim of this study was to detect alterations in the metabolome during development of VC in CKD and to identify the critical metabolic pathways and metabolites involved in its pathogenesis.

Methods

Rats in the model group were given an adenine gavage combined with a high-phosphorus diet to imitate VC in CKD. The aorta calcium content was measured and used to divide the model group into a VC group and non-vascular calcification group (non-VC group). The control group was fed a normal rat diet and given a saline gavage. Ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) was used to determine the altered serum metabolome in the control, VC, and non-VC groups. The identified metabolites were mapped into the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/) for pathway and network analyses.

Result

There were 14 metabolites that changed significantly in the VC group, with three metabolic pathways playing critical roles in the pathogenesis of VC in CKD: steroid hormone biosynthesis; valine, leucine and isoleucine biosynthesis; and pantothenate and CoA biosynthesis.

Conclusion

Our results indicated changes in the expression of steroid sulfatase and estrogen sulfotransferase, and down-regulation of the in situ synthesis of estrogens in the VC group. In conclusion, the serum metabolome alters significantly during the pathogenesis of VC in CKD. The key pathways, metabolites, and enzymes we identified are worth further study and may become a promising therapeutic target for the treatment of VC in CKD.

Proteomic insights into floral biology

The flower is the most important biological structure for ensuring angiosperms reproductive success. Not only does the flower contain critical reproductive organs, but the wide variation in morphology, color, and scent has evolved to entice specialized pollinators, and arguably mankind in many cases, to ensure the successful propagation of its species. Recent proteomic approaches have identified protein candidates related to these flower traits, which has shed light on a number of previously unknown mechanisms underlying these traits. This review article provides a comprehensive overview of the latest advances in proteomic research in floral biology according to the order of flower structure, from corolla to male and female reproductive organs. It summarizes mainstream proteomic methods for plant research and recent improvements on two dimensional gel electrophoresis and gel-free workflows for both peptide level and protein level analysis. The recent advances in sequencing technologies provide a new paradigm for the ever-increasing genome and transcriptome information on many organisms. It is now possible to integrate genomic and transcriptomic data with proteomic results for large-scale protein characterization, so that a global understanding of the complex molecular networks in flower biology can be readily achieved. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Review of current and "omics" methods for assessing the toxicity (genotoxicity, teratogenicity and nephrotoxicity) of herbal medicines and mushrooms

ETHNOPHARMACOLOGICAL RELEVANCE

The increasing use of traditional herbal medicines around the world requires more scientific evidence for their putative harmlessness. To this end, a plethora of methods exist, more or less satisfying. In this post-genome era, recent reviews are however scarce, not only on the use of new "omics" methods (transcriptomics, proteomics, metabonomics) for genotoxicity, teratogenicity, and nephrotoxicity assessment, but also on conventional ones.

METHODS

The present work aims (i) to review conventional methods used to assess genotoxicity, teratogenicity and nephrotoxicity of medicinal plants and mushrooms; (ii) to report recent progress in the use of "omics" technologies in this field; (iii) to underline advantages and limitations of promising methods; and lastly (iv) to suggest ways whereby the genotoxicity, teratogenicity, and nephrotoxicity assessment of traditional herbal medicines could be more predictive.

RESULTS

Literature and safety reports show that structural alerts, in silico and classical in vitro and in vivo predictive methods are often used. The current trend to develop "omics" technologies to assess genotoxicity, teratogenicity and nephrotoxicity is promising but most often relies on methods that are still not standardized and validated.

CONCLUSION

Hence, it is critical that toxicologists in industry, regulatory agencies and academic institutions develop a consensus, based on rigorous methods, about the reliability and interpretation of endpoints. It will also be important to regulate the integration of conventional methods for toxicity assessments with new "omics" technologies.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

From metabolic reactions to networks and pathways

Enzymatic reactions form a hypergraph structure and their translation into a graph structure accompanies an information loss. This chapter introduces well-known topological transformations from metabolic reactions to a graph, and discusses their advantages and disadvantages. Also discussed is the legitimacy of defining cofactors or currency metabolites, and suitable application area of each representation.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Databases on food phytochemicals and their health-promoting effects

Considerable information on the chemistry and biological properties of dietary phytochemicals has accumulated over the past three decades. The scattering of the data in tens of thousands publications and the diversity of experimental approaches and reporting formats all make the exploitation of this information very difficult. Some of the data have been collected and stored in electronic databases so that they can be automatically updated and retrieved. These databases will be particularly important in the evaluation of the effects on health of phytochemicals and in facilitating the exploitation of nutrigenomic data. The content of over 50 databases on chemical structures, spectra, metabolic pathways in plants, occurrence and concentrations in foods, metabolism in humans and animals, biological properties, and effects on health or surrogate markers of health is reviewed. Limits of these databases are emphasized, and needs and recommendations for future developments are underscored. More investments in the construction of databases on phytochemicals and their effects on health are clearly needed. They should greatly contribute to the success of future research in this field.

The evolving field of imaging mass spectrometry and its impact on future biological research

Within the past decade, imaging mass spectrometry (IMS) has been increasingly recognized as an indispensable technique for studying biological systems. Its rapid evolution has resulted in an impressive array of instrument variations and sample applications, yet the tools and data are largely confined to specialists. It is therefore important that at this junction the IMS community begin to establish IMS as a permanent fixture in life science research thereby making the technology and/or the data approachable by non-mass spectrometrists, leading to further integration into biological and clinical research. In this perspective article, we provide insight into the evolution and current state of IMS and propose some of the directions that IMS could develop in order to stay on course to become one of the most promising new tools in life science research.

Advances in mass spectrometry applied to pharmaceutical metabolomics

Metabolomics, also referred to in the literature as metabonomics, is a relatively new systems biology tool for drug discovery and development and is increasingly being used to obtain a detailed picture of a drug's effect on the body. Metabolomics is the qualitative assessment and relative or absolute quantitative measurement of the endogenous metabolome, defined as the complement of all native small molecules (metabolites less than 1,500 Da). A metabolomics study frequently involves the comparative analysis of sample sets from a normal state and a perturbed state, where the perturbation can be of any nature, such as genetic knockout, administration of a drug, or change in diet or lifestyle. Advances in mass spectrometry (MS) technologies including direct introduction or in-line chromatographic separation modes, ionization techniques, mass analyzers, and detection methods have provided powerful tools to assess the molecular changes in the metabolome. This review focuses on advances in MS pertaining to the analytical data generation for the main metabolomics methods, namely, fingerprinting, nontargeted, and targeted approaches, as they are applied to pharmaceutical drug discovery and development.