Database Resources in Metabolomics: An Overview

Network Pharmacology-driven therapeutic interventions for Interstitial Lung Diseases using Traditional medicines: A Narrative Review

This review explores the progressive domain of network pharmacology and its potential to revolutionize therapeutic approaches for Interstitial Lung Diseases (ILDs), a collective term encompassing Interstitial Pneumonia, Pneumoconiosis, Connective Tissue Disease-related ILDs, and Sarcoidosis. The exploration focuses on the profound legacy of traditional medicines, particularly Ayurveda and Traditional Chinese Medicines (TCM), and their largely unexplored capacity in ILD treatment. These ancient healing systems, characterized by their holistic methodologies and multifaceted treatment modalities, offer a promising foundation for discovering innovative therapeutic strategies. Moreover, the review underscores the amalgamation of artificial intelligence (AI) and machine learning (ML) methodologies with bioinformatics, creating a computational synergy capable of deciphering the intricate biological networks associated with ILDs. Network pharmacology has tailored the hypothesis from the conventional "one target, one drug" towards a "network target, multi-component therapeutics" approach. The fusion of traditional literature and computational technology can unveil novel drugs, targets, and pathways, augmenting effective therapies and diminishing adverse effects related to current medications. In conclusion, this review provides a comprehensive exposition of how Network Pharmacology tools can leverage the insights of Ayurveda and TCM to craft efficacious therapeutic solutions for ILDs. It sets the stage for future investigations in this captivating interdisciplinary domain, validating the use of traditional medicines worldwide.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

massDatabase: utilities for the operation of the public compound and pathway database

SUMMARY

One of the major challenges in liquid chromatography coupled to mass spectrometry data is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed. However, no tool has been developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment. massDatabase is a flexible, simple and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Material.

AVAILABILITY AND IMPLEMENTATION

https://massdatabase.tidymass.org/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Effect of short-chain chlorinated paraffins on metabolic profiling of male SD rats

The toxic effect of high-dose of short-chain chlorinated paraffins (SCCPs) has been extensively studied, however the possible health risks induced by SCCPs at low-dose remain largely unknown. In this study, a comprehensive toxicology analysis of SCCPs was conducted with the exposure levels from the environmental dose to the Lowest Observed Adverse Effect Level (LOAEL) of 100 mg/kg/day. General toxicology analysis revealed inconspicuous toxicity of the environmental dose of SCCPs, high dose SCCP exposure inhibited the growth rate and increased the liver weight of rat. Metabolomics analysis indicated that SCCP-induced toxicity was triggered at environmentally relevant doses. First, inhibition of energy metabolism was observed with the decrease in blood glucose and the dysfunction of TCA cycle, which may have contributed to lower body weight gain in rats exposed to a high dose of SCCPs. Second, the increase of free fatty acids indicated the acceleration of lipid metabolism to compensate for the energy deficiency caused by hypoglycemia. Lipid oxidative metabolism inevitably leads to oxidative stress and stimulates the up-regulation of antioxidant metabolites such as GSH and GSSH. The up-regulation of polyunsaturated fatty acids (PUFAs) and phospholipids composed of arachidonic acid indicates the occurrence of inflammation. Dysfunction of lipid metabolism can be an indicator of SCCP-induced liver injury.

Combining Physiological and Metabolomic Analysis to Unravel the Regulations of Coronatine Alleviating Water Stress in Tobacco (Nicotiana tabacum L.)

Drought is a major abiotic stress that restricts plants growth, development, and yield. Coronatine (COR), a mimic of JA-Ile, functions in plant tolerance to multiple stresses. In our study, we examined the effects of COR in tobacco under polyethylene glycol (PEG) stress. COR treatment improved plant growth under stress as measured by fresh weight (FW) and dry weight (DW). The enzyme activity assay indicated that, under osmotic stress conditions, the activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and glutathione reductase (GR) were enhanced by COR treatment. Histochemical analyses via nitrotetrazolium blue chloride (NBT) and 3,3'-diaminobenzidine (DAB) staining showed that COR reduced reactive oxygen species (ROS) accumulation during osmotic stress. Metabolite profiles revealed that COR triggered significant metabolic changes in tobacco leaves under osmotic stress, and many essential metabolites, such as sugar and sugar derivatives, organic acids, and nitrogen-containing compounds, which might play active roles in osmotic-stressed tobacco plants, were markedly accumulated in the COR-treated tobacco. The work presented here provides a comprehensive understanding of the COR-mediated physiological, biochemical, and metabolic adjustments that minimize the adverse impact of osmotic stress on tobacco.

1H-NMR Metabolomics Analysis of the Effects of Sulfated Polysaccharides from Masson Pine Pollen in RAW264.7 Macrophage Cells

Many polysaccharides have been shown to be bioactive, with the addition of sulfate often enhancing or altering this bioactivity. In previous studies, masson pine pollen polysaccharides, to include a sulfate derivative, have been shown to promote macrophage proliferation similarly to LPS. However, the exact metabolic mechanisms promoting this proliferation remain unclear. In this study, RAW264.7 macrophage cells were treated with a purified masson pine pollen polysaccharide (PPM60-D), a sulfate derivative (SPPM60-D), or LPS. Proliferation levels at a variety of concentrations were examined using MTT assay, with optimal concentration used when performing metabolomic analysis via ¹H nuclear magnetic resonance (¹H-NMR). This process resulted in the identification of thirty-five intracellular metabolites. Subsequent multivariate statistical analysis showed that both LPS and SPPM60-D promote RAW264.7 proliferation by promoting aerobic respiration processes and reducing processes associated with glycolysis. While some insight was gained regarding the mechanistic differences between SPPM60-D and LPS, the specific mechanisms governing the effect of SPPM60 on RAW264.7 cells will require further elucidation. These findings show that both LPS and SPPM60-D effectively promote RAW264.7 proliferation and may have beneficial uses in maintaining cellular vitality or inhibiting cancer.

A Blockchain-Based Notarization Service for Biomedical Knowledge Retrieval

Biomedical research and clinical decision depend increasingly on scientific evidence realized by a number of authoritative databases, mostly public and continually enriched via peer scientific contributions. Given the dynamic nature of biomedical evidence data and their usage in the sensitive domain of biomedical science, it is important to ensure retrieved data integrity and non-repudiation. In this work, we present a blockchain-based notarization service that uses smart digital contracts to seal a biomedical database query and the respective results. The goal is to ensure that retrieved data cannot be modified after retrieval and that the database cannot validly deny that the particular data has been provided as a result of a specific query. Biomedical evidence data versioning is also supported. The feasibility of the proposed notarization approach is demonstrated using a real blockchain infrastructure and is tested on two different biomedical evidence databases: a publicly available medical risk factor reference repository and on the PubMed database of biomedical literature references and abstracts.

Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition

High-quality mass spectral libraries have become crucial in mass spectrometry-based metabolomics. Here, we investigate a workflow to generate accurate mass discrete and composite spectral libraries for metabolite identification and for SWATH mass spectrometry data processing. Discrete collision energy (5-100 eV) accurate mass spectra were collected for 532 metabolites from the human metabolome database (HMDB) by flow injection analysis and compiled into composite spectra over a large collision energy range (e.g., 10-70 eV). Full scan response factors were also calculated. Software tools based on accurate mass and predictive fragmentation were specially developed and found to be essential for construction and quality control of the spectral library. First, elemental compositions constrained by the elemental composition of the precursor ion were calculated for all fragments. Secondly, all possible fragments were generated from the compound structure and were filtered based on their elemental compositions. From the discrete spectra, it was possible to analyze the specific fragment form at each collision energy and it was found that a relatively large collision energy range (10-70 eV) gives informative MS/MS spectra for library searches. From the composite spectra, it was possible to characterize specific neutral losses as radical losses using in silico fragmentation. Radical losses (generating radical cations) were found to be more prominent than expected. From 532 metabolites, 489 provided a signal in positive mode [M+H]⁺ and 483 in negative mode [M-H]^-. MS/MS spectra were obtained for 399 compounds in positive mode and for 462 in negative mode; 329 metabolites generated suitable spectra in both modes. Using the spectral library, LC retention time, response factors to analyze data-independent LC-SWATH-MS data allowed the identification of 39 (positive mode) and 72 (negative mode) metabolites in a plasma pool sample (total 92 metabolites) where 81 previously were reported in HMDB to be found in plasma. Graphical abstract Library generation workflow for LC-SWATH MS, using collision energy spread, accurate mass, and fragment annotation.

Short overview on metabolomic approach and redox changes in psychiatric disorders

Schizophrenia, depression and posttraumatic stress disorder (PTSD) are severe mental disorders and complicated diagnostic entities, due to their phenotypic, biological and genetic heterogeneity, unknown etiology, and poorly understood alterations in biological pathways and biological mechanisms. Disturbed homeostasis between overproduction of oxidant species, overcoming redox regulation and a lack of cellular antioxidant defenses, resulting in free radical-mediated pathology and subsequent neurotoxicity contributes to development of depression, schizophrenia and PTSD, their heterogeneous clinical presentation and resistance to treatment. Metabolomics is a discipline that combines different strategies with the aim to extract, detect, identify and quantify all metabolites that are present in a biological sample and might provide mechanistic insights into the etiology of various psychiatric disorders. Therefore, oxidative stress research combined with metabolomics might offer a novel approach in dissecting psychiatric disorders, since these data-driven but not necessarily hypothesis-driven methods might identify new targets, molecules and pathways responsible for development of schizophrenia, depression or PTSD. Findings from the oxidative research in psychiatry together with metabolomics data might facilitate development of specific and validated prognostic, therapeutic and clinical biomarkers. These methods might reveal bio-signatures of individual patients, leading to individualized treatment approach. In reviewing findings related to oxidative stress and metabolomics in selected psychiatric disorders, we have highlighted how these novel approaches might make a unique contribution to deeper understanding of psychopathological alterations underlying schizophrenia, depression and PTSD.

New frontiers in metabolomics: from measurement to insight

Metabolomics is the newest addition to the "omics" disciplines and has shown rapid growth in its application to human health research because of fundamental advancements in measurement and analysis techniques. Metabolomics has unique and proven advantages in systems biology and biomarker discovery. The next generation of analysis techniques promises even richer and more complete analysis capabilities that will enable earlier clinical diagnosis, drug refinement, and personalized medicine. A review of current advancements in methodologies and statistical analysis that are enhancing and improving the performance of metabolomics is presented along with highlights of some recent successful applications.

Biomonitoring in the Era of the Exposome

BACKGROUND

The term "exposome" was coined in 2005 to underscore the importance of the environment to human health and to bring research efforts in line with those on the human genome. The ability to characterize environmental exposures through biomonitoring is key to exposome research efforts.

OBJECTIVES

Our objectives were to describe why traditional and nontraditional (exposomic) biomonitoring are both critical in studies aiming to capture the exposome and to make recommendations on how to transition exposure research toward exposomic approaches. We describe the biomonitoring needs of exposome research and approaches and recommendations that will help fill the gaps in the current science.

DISCUSSION

Traditional and exposomic biomonitoring approaches have key advantages and disadvantages for assessing exposure. Exposomic approaches differ from traditional biomonitoring methods in that they can include all exposures of potential health significance, whether from endogenous or exogenous sources. Issues of sample availability and quality, identification of unknown analytes, capture of nonpersistent chemicals, integration of methods, and statistical assessment of increasingly complex data sets remain challenges that must continue to be addressed.

CONCLUSIONS

To understand the complexity of exposures faced throughout the lifespan, both traditional and nontraditional biomonitoring methods should be used. Through hybrid approaches and the integration of emerging techniques, biomonitoring strategies can be maximized in research to define the exposome.

Introduction to metabolomics and its applications in ophthalmology

Metabolomics is the study of endogenous and exogenous metabolites in biological systems, which aims to provide comparative semi-quantitative information about all metabolites in the system. Metabolomics is an emerging and potentially powerful tool in ophthalmology research. It is therefore important for health professionals and researchers involved in the speciality to understand the basic principles of metabolomics experiments. This article provides an overview of the experimental workflow and examples of its use in ophthalmology research from the study of disease metabolism and pathogenesis to identification of biomarkers.

SpeckTackle: JavaScript charts for spectroscopy

BACKGROUND

Spectra visualisation from methods such as mass spectroscopy, infrared spectroscopy or nuclear magnetic resonance is an essential part of every web-facing spectral resource. The development of an intuitive and versatile visualisation tool is a time- and resource-intensive task, however, most databases use their own embedded viewers and new databases continue to develop their own viewers.

RESULTS

We present SpeckTackle, a custom-tailored JavaScript charting library for spectroscopy in life sciences. SpeckTackle is cross-browser compatible and easy to integrate into existing resources, as we demonstrate for the MetaboLights database. Its default chart types cover common visualisation tasks following the de facto 'look and feel' standards for spectra visualisation.

CONCLUSIONS

SpeckTackle is released under GNU LGPL to encourage uptake and reuse within the community. The latest version of the library including examples and documentation on how to use and extend the library with additional chart types is available online in its public repository.

Making a big thing of a small cell--recent advances in single cell analysis

Single cell analysis is an emerging field requiring a high level interdisciplinary collaboration to provide detailed insights into the complex organisation, function and heterogeneity of life. This review is addressed to life science researchers as well as researchers developing novel technologies. It covers all aspects of the characterisation of single cells (with a special focus on mammalian cells) from morphology to genetics and different omics-techniques to physiological, mechanical and electrical methods. In recent years, tremendous advances have been achieved in all fields of single cell analysis: (1) improved spatial and temporal resolution of imaging techniques to enable the tracking of single molecule dynamics within single cells; (2) increased throughput to reveal unexpected heterogeneity between different individual cells raising the question what characterizes a cell type and what is just natural biological variation; and (3) emerging multimodal approaches trying to bring together information from complementary techniques paving the way for a deeper understanding of the complexity of biological processes. This review also covers the first successful translations of single cell analysis methods to diagnostic applications in the field of tumour research (especially circulating tumour cells), regenerative medicine, drug discovery and immunology.

A new paradigm for known metabolite identification in metabonomics/metabolomics: metabolite identification efficiency

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.

UHPLC-LTQ-Orbitrap MS combined with spike-in method for plasma metabonomics analysis of acute myocardial ischemia rats and pretreatment effect of Danqi Tongmai tablet

Undoubtedly, metabonomics can reveal the comprehensive efficacies of traditional Chinese medicine (TCM) formulae and its complex mechanism at the molecular biological level. In this study, an attempt was made to address the pretreatment effect of a TCM formula. In this case, as a critical point, we should first know how to really reflect the various endogenous metabolites in a disease status before a TCM formula is employed in a therapeutic procedure. Here, we explored an approach that combined high resolution LTQ-Orbitrap mass spectrometry with a spike-in method to characterize endogenous metabolites in acute myocardial ischemia (AMI) rats. As a result, 19 potential biomarkers in rat plasma were identified and 10 related disturbed pathways were perturbed in the early stages of AMI development. Subsequently, the metabonomics method was applied to investigate the pretreatment effect of the TCM formula named the Danqi Tongmai tablet (DQTM). The results revealed that the DQTM pretreatment could reduce the AMI injury and partially regulate the perturbed TCA cycle and amino and nucleotide metabolism, which were presumable related to energy metabolism and myocardial cells apoptosis/necrosis. In conclusion, UHPLC-LTQ-Orbitrap MS combined with a spike-in method were successfully applied to the metabonomics analysis of DQTM, which demonstrated that not only a comprehensive metabolic profile in the early stages of AMI development was achieved, but also that the underlying holistic efficacies were assessed and it was helpful to understand the possible mechanism of pretreatment with DQTM.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Quality control for building libraries from electrospray ionization tandem mass spectra

Electrospray ionization (ESI) tandem mass spectrometry coupled with liquid chromatography is a routine technique for identifying and quantifying compounds in complex mixtures. The identification step can be aided by matching acquired tandem mass spectra (MS(2)) against reference library spectra as is routine for electron ionization (EI) spectra from gas chromatography/mass spectrometry (GC/MS). However, unlike the latter spectra, ESI MS(2) spectra are likely to originate from various precursor ions for a given target molecule and may be acquired at varying energies and resolutions and have characteristic noise signatures, requiring processing methods very different from EI to obtain complete and high quality reference spectra for individual analytes. This paper presents procedures developed for creating a tandem mass spectral library that addresses these factors. Library building begins by acquiring MS(2) spectra for all major MS(1) peaks in an infusion run, followed by assigning MS(2) spectra to clusters and creating a consensus spectrum for each. Intensity-based constraints for cluster membership were developed, as well as peak testing to recognize and eliminate suspect peaks and reduce noise. Consensus spectra were then examined by a human evaluator using a number of criteria, including a fraction of annotated peaks and consistency of spectra for a given ion at different energies. These methods have been developed and used to build a library from >9000 compounds, yielding 230,000 spectra.

Metabolic characterization of hepatocellular carcinoma using nontargeted tissue metabolomics

Hepatocellular carcinoma has a poor prognosis due to its rapid development and early metastasis. In this report, we characterized the metabolic features of hepatocellular carcinoma using a nontargeted metabolic profiling strategy based on liquid chromatography-mass spectrometry. Fifty pairs of liver cancer samples and matched normal tissues were collected from patients having hepatocellular carcinoma, including tumor tissues, adjacent noncancerous tissues, and distal noncancerous tissues, and 105 metabolites were filtered and identified from the tissue metabolome. The principal metabolic alternations in HCC tumors included elevated glycolysis, gluconeogenesis, and β-oxidation with reduced tricarboxylic acid cycle and Δ-12 desaturase. Furthermore, increased levels of glutathione and other antioxidative molecules, together with decreased levels of inflammatory-related polyunsaturated fatty acids and phospholipase A2, were observed. Differential metabolite levels in tissues were tested in 298 serum specimens from patients with chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Betaine and propionylcarnitine were confirmed to confer good diagnostic potential to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis. External validation of cirrhosis and hepatocellular carcinoma serum specimens further showed that this combination biomarker is useful for diagnosis of hepatocellular carcinoma with a supplementary role to α-fetoprotein.

Biomarkers of food intake and metabolite differences between plasma and red blood cell matrices; a human metabolomic profile approach

Untargeted metabolomic analyses of plasma and red blood cells (RBCs) can provide complementary information on biomarkers of food consumption. To assess blood collection differences in biomarkers, fasting blood was drawn from 10 healthy individuals using sodium citrate and lithium heparin as anticoagulants. Plasma and RBCs were separated into aqueous and lipid fractions to be analyzed using 1D and 2D (1)H NMR spectroscopy. Fatty acids were analyzed using gas chromatography-mass spectrometry (GC-MS). Polyphenols were extracted from plasma and RBCs by micro-elution solid-phase extraction and analyzed by ultra performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). (1)H NMR demonstrated higher aqueous metabolites such as glucose in plasma compared to RBCs, while RBCs contained higher ADP-ATP, creatine and acetone than plasma. Lipoproteins and their subclasses were higher in plasma than in RBCs. Percentages of saturated fatty acids (SFA) 16 : 0, 17 : 0, 20 : 0, 24 : 0 and polyunsaturated fatty acids (PUFA) 22 : 6 n-3 (docosahexaenoic acid) and 20 : 4 n-6 (arachidonic acid) were higher in RBCs than in plasma (p < 0.05), while SFA 14 : 0, monounsaturated fatty acids (MUFA) 14 : 1 n-5, 16 : 1 n-7, 17 : 1 n-7 and 18 : 1 n-9 and PUFA 18 : 3 n-3, 18 : 2 n-6, 18 : 3 n-6 and 20 : 3 n-6 were higher in plasma than in RBCs (p < 0.05). Polyphenols differed in plasma from those of RBCs. Biomarker concentrations were lower in sodium citrate compared to lithium heparin plasma. In conclusion, metabolomic profiles generated by NMR spectroscopy, GC-MS and UPLC-MS/MS analyses of RBCs versus plasma show complementary information on several specific molecular biomarkers that could be applied in nutritional assessment.

Computational tools for the secondary analysis of metabolomics experiments

Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.

Progress toward single cell metabolomics

The metabolome refers to the entire set of small molecules, or metabolites, within a biological sample. These molecules are involved in many fundamental intracellular functions and reflect the cell's physiological condition. The ability to detect and identify metabolites and determine and monitor their amounts at the single cell level enables an exciting range of studies of biological variation and functional heterogeneity between cells, even within a presumably homogenous cell population. Significant progress has been made in the development and application of bioanalytical tools for single cell metabolomics based on mass spectrometry, microfluidics, and capillary separations. Remarkable improvements in the sensitivity, specificity, and throughput of these approaches enable investigation of multiple metabolites simultaneously in a range of individual cell samples.

Online tools for bioinformatics analyses in nutrition sciences

Recent advances in "omics" research have resulted in the creation of large datasets that were generated by consortiums and centers, small datasets that were generated by individual investigators, and bioinformatics tools for mining these datasets. It is important for nutrition laboratories to take full advantage of the analysis tools to interrogate datasets for information relevant to genomics, epigenomics, transcriptomics, proteomics, and metabolomics. This review provides guidance regarding bioinformatics resources that are currently available in the public domain, with the intent to provide a starting point for investigators who want to take advantage of the opportunities provided by the bioinformatics field.

Nutritional metabolomics: progress in addressing complexity in diet and health

Nutritional metabolomics is rapidly maturing to use small-molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment, and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health, and complex pathobiology serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and nontargeted metabolic profiling, along with bioinformatics, pathway mapping, and computational modeling, are now used for nutrition research on diet, metabolism, microbiome, and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics, and health phenotyping, to support the integrated models required for personalized diet and nutrition forecasting.

Patient-controlled encrypted genomic data: an approach to advance clinical genomics

Background

The revolution in DNA sequencing technologies over the past decade has made it feasible to sequence an individual’s whole genome at a relatively low cost. The potential value of the information generated by genomic technologies for medicine and society is enormous. However, in order for exome sequencing, and eventually whole genome sequencing, to be implemented clinically, a number of major challenges need to be overcome. For instance, obtaining meaningful informed-consent, managing incidental findings and the great volume of data generated (including multiple findings with uncertain clinical significance), re-interpreting the genomic data and providing additional counselling to patients as genetic knowledge evolves are issues that need to be addressed. It appears that medical genetics is shifting from the present “phenotype-first” medical model to a “data-first” model which leads to multiple complexities.

Discussion

This manuscript discusses the different challenges associated with integrating genomic technologies into clinical practice and describes a “phenotype-first” approach, namely, “Individualized Mutation-weighed Phenotype Search”, and its benefits. The proposed approach allows for a more efficient prioritization of the genes to be tested in a clinical lab based on both the patient’s phenotype and his/her entire genomic data. It simplifies “informed-consent” for clinical use of genomic technologies and helps to protect the patient’s autonomy and privacy. Overall, this approach could potentially render widespread use of genomic technologies, in the immediate future, practical, ethical and clinically useful.

Summary

The “Individualized Mutation-weighed Phenotype Search” approach allows for an incremental integration of genomic technologies into clinical practice. It ensures that we do not over-medicalize genomic data but, rather, continue our current medical model which is based on serving the patient’s concerns. Service should not be solely driven by technology but rather by the medical needs and the extent to which a technology can be safely and effectively utilized.

LC-MS-based metabolomics

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants.

Understanding and classifying metabolite space and metabolite-likeness

While the entirety of ‘Chemical Space’ is huge (and assumed to contain between 10⁶³ and 10²⁰⁰ ‘small molecules’), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as ‘naturally occurring’ products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a ‘metabolite-likeness’ score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more ‘synthetic’ and the other being more ‘metabolite-like’. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

IQMNMR: Open source software using time-domain NMR data for automated identification and quantification of metabolites in batches

BACKGROUND

One of the most promising aspects of metabolomics is metabolic modeling and simulation. Central to such applications is automated high-throughput identification and quantification of metabolites. NMR spectroscopy is a reproducible, nondestructive, and nonselective method that has served as the foundation of metabolomics studies. However, the automated high-throughput identification and quantification of metabolites in NMR spectroscopy is limited by severe spectral overlap. Although numerous software programs have been developed for resolving overlapping resonances, as well as for identifying and quantifying metabolites, most of these programs are frequency-domain methods, considerably influenced by phase shifts and baseline distortions, and effective only in small-scale studies. Almost all these programs require multiple spectra for each application, and do not automatically identify and quantify metabolites in batches.

RESULTS

We created IQMNMR, an R package that integrates a relaxation algorithm, digital filter, and similarity search algorithm. It differs from existing software in that it is a time-domain method; it uses not only frequency to resolve overlapping resonances but also relaxation time constants; it requires only one NMR spectrum per application; is uninfluenced by phase shifts and baseline distortions; and most important, yields a batch of quantified metabolites.

CONCLUSIONS

IQMNMR provides a solution that can automatically identify and quantify metabolites by one-dimensional proton NMR spectroscopy. Its time-domain nature, stability against phase shifts and baseline distortions, requirement for only one NMR spectrum, and capability to output a batch of quantified metabolites are of considerable significance to metabolic modeling and simulation.IQMNMR is available at http://cran.r-project.org/web/packages/IQMNMR/.

Bioinformatics for mass spectrometry-based metabolomics

The broad view of the state of biological systems cannot be complete without the added value of integrating proteomic and genomic data with metabolite measurement. By definition, metabolomics aims at quantifying not less than the totality of small molecules present in a biofluid, tissue, organism, or any material beyond living systems. To cope with the complexity of the task, mass spectrometry (MS) is the most promising analytical environment to fulfill increasing appetite for more accurate and larger view of the metabolome while providing sufficient data generation throughput. Bioinformatics and associated disciplines naturally play a central role in bridging the gap between fast evolving technology and domain experts. Here, we describe the strategies to translate crude MS information into features characteristics of metabolites, and resources available to guide scientists along the metabolomics pipeline. A particular emphasis is put on pragmatic solutions to interpret the outcome of metabolomics experiments at the level of signal processing, statistical treatment, and biochemical understanding.

Deciphering squamous cell carcinoma using multidimensional genomic approaches

Squamous cell carcinomas (SqCCs) arise in a wide range of tissues including skin, lung, and oral mucosa. Although all SqCCs are epithelial in origin and share common nomenclature, these cancers differ greatly with respect to incidence, prognosis, and treatment. Current knowledge of genetic similarities and differences between SqCCs is insufficient to describe the biology of these cancers, which arise from diverse tissue origins. In this paper we provide a general overview of whole genome approaches for gene and pathway discovery and highlight the advancement of integrative genomics as a state-of-the-art technology in the study of SqCC genetics.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

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.