Integrative Biological Analysis of the APOE*3-Leiden Transgenic Mouse

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Metabolomics comparison of metabolites and functional pathways in the gills of Chlamys farreri under cadmium exposure

The biological processes of Chlamys farreri (C. farreri), an economically important shellfish, are affected when exposed to Cd²⁺. In this study, changes to biological processes and metabolite levels in C. farreri were examined when exposed to Cd²⁺. Ultra-performance liquid chromatography-tandem TOF mass spectrometry (UPLC-TOF/MS)-based untargeted metabolomics was used to examine changes in the metabolism of C. farreri gill tissue exposed to 0.050 mg/L Cd²⁺ for 96 h in a natural environment. Sixty-eight metabolites with significant differences were screened by multivariate statistical analysis. Eleven enriched functional pathways displayed significant changes in inactivity. Differential metabolites, mainly C00157 and C00350, have a significant impact on functional pathways and can be used as potential major biomarkers. Lipid phosphorylation, disruption of signal transduction, and autophagy activation were observed to change in C. farreri when exposed to Cd. The metabolome information supplements research on C. farreri exposure to heavy metals and provides a platform for further multi-omics analysis.

Lipid Biomarkers in Acute Myocardial Infarction Before and After Percutaneous Coronary Intervention by Lipidomics Analysis

Background

Reperfusion injury is one of the leading causes of myocardial cell death and heart failure. This study was performed to identify new candidate lipid biomarkers for the purpose of optimizing the diagnosis of myocardial ischemia reperfusion (I/R) injury, assessing the severity of myocardial I/R injury and trying to find the novel mechanism related to lipids.

Material/Methods

Forty patients who were diagnosed with ST-segment elevation myocardial infarction (STEMI) were randomly selected for this study. Serum samples from all the patients with STEMI were collected at 3 time periods: after STEMI diagnosis but prior to reperfusion (T₀); and then at 2 hours (T₂) and 24 hours (T₂₄) after the end of the percutaneous coronary intervention procedure. Plasma lipidomics profiling analysis was performed to identify the lipid metabolic signatures of myocardial I/R injury using lipidomics.

Results

Sixteen types of potential lipid biomarkers at different time periods (T₀, T₂, T₂₄) were identified by using lipidomics technology. The T₀ time periods exhibited 16 differentially metabolized lipid peaks in the patients after STEMI diagnosis but prior to reperfusion. With the increase of reperfusion times, the contents of these 16 lipid biomarkers decreased gradually, but there was a 1.5- to 2-fold increase of those 16 lipid biomarkers contents at T₂ compared with T₂₄.

Conclusions

Lipidomics analysis demonstrated differential change before and after reperfusion, suggesting a potential role of some of these lipids as biomarkers for optimizing the diagnosis of myocardial I/R, as well as for therapeutic targets against myocardial I/R injury.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Metabolomic analyses for atherosclerosis, diabetes, and obesity

Insulin resistance associated with type 2 diabetes mellitus (T2DM), obesity, and atherosclerosis is a global health problem. A portfolio of abnormalities of metabolic and vascular homeostasis accompanies T2DM and obesity, which are believed to conspire to lead to accelerated atherosclerosis and premature death. The complexity of metabolic changes in the diseases presents challenges for a full understanding of the molecular pathways contributing to the development of these diseases. The recent advent of new technologies in this area termed “Metabolomics” may aid in comprehensive metabolic analysis of these diseases. Therefore, metabolomics has been extensively applied to the metabolites of T2DM, obesity, and atherosclerosis not only for the assessment of disease development and prognosis, but also for the biomarker discovery of disease diagnosis. Herein, we summarize the recent applications of metabolomics technology and the generated datasets in the metabolic profiling of these diseases, in particular, the applications of these technologies to these diseases at the cellular, animal models, and human disease levels. In addition, we also extensively discuss the mechanisms linking the metabolic profiling in insulin resistance, T2DM, obesity, and atherosclerosis, with a particular emphasis on potential roles of increased production of reactive oxygen species (ROS) and mitochondria dysfunctions.

Candidate proteins, metabolites and transcripts in the Biomarkers for Spinal Muscular Atrophy (BforSMA) clinical study

Background

Spinal Muscular Atrophy (SMA) is a neurodegenerative motor neuron disorder resulting from a homozygous mutation of the survival of motor neuron 1 (SMN1) gene. The gene product, SMN protein, functions in RNA biosynthesis in all tissues. In humans, a nearly identical gene, SMN2, rescues an otherwise lethal phenotype by producing a small amount of full-length SMN protein. SMN2 copy number inversely correlates with disease severity. Identifying other novel biomarkers could inform clinical trial design and identify novel therapeutic targets.

Objective:

To identify novel candidate biomarkers associated with disease severity in SMA using unbiased proteomic, metabolomic and transcriptomic approaches.

Materials and Methods:

A cross-sectional single evaluation was performed in 108 children with genetically confirmed SMA, aged 2–12 years, manifesting a broad range of disease severity and selected to distinguish factors associated with SMA type and present functional ability independent of age. Blood and urine specimens from these and 22 age-matched healthy controls were interrogated using proteomic, metabolomic and transcriptomic discovery platforms. Analyte associations were evaluated against a primary measure of disease severity, the Modified Hammersmith Functional Motor Scale (MHFMS) and to a number of secondary clinical measures.

Results

A total of 200 candidate biomarkers correlate with MHFMS scores: 97 plasma proteins, 59 plasma metabolites (9 amino acids, 10 free fatty acids, 12 lipids and 28 GC/MS metabolites) and 44 urine metabolites. No transcripts correlated with MHFMS.

Discussion

In this cross-sectional study, “BforSMA” (Biomarkers for SMA), candidate protein and metabolite markers were identified. No transcript biomarker candidates were identified. Additional mining of this rich dataset may yield important insights into relevant SMA-related pathophysiology and biological network associations. Additional prospective studies are needed to confirm these findings, demonstrate sensitivity to change with disease progression, and assess potential impact on clinical trial design.

Trial Registry

Clinicaltrials.gov NCT00756821.

Metabonomics-based omics study and atherosclerosis

Atherosclerosis results from dyslipidemia and systemic inflammation, associated with the strong metabolism and interaction between diet and disease. Strategies based on the global profiling of metabolism would be important to define the mechanisms involved in pathological alterations. Metabonomics is the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. Metabonomics has been used in combination with proteomics and transcriptomics as the part of a systems biology description to understand the genome interaction with the development of atherosclerosis. The present review describes the application of metabonomics to explore the potential role of metabolic disturbances and inflammation in the initiation and development of atherosclerosis. Metabonomics-based omics study offers a new potential for biomarker discovery by disentangling the impacts of diet, environment and lifestyle.

Metabolomics as a tool for cardiac research

Metabolomics represents a paradigm shift in metabolic research, away from approaches that focus on a limited number of enzymatic reactions or single pathways, to approaches that attempt to capture the complexity of metabolic networks. Additionally, the high-throughput nature of metabolomics makes it ideal to perform biomarker screens for diseases or follow drug efficacy. In this Review, we explore the role of metabolomics in gaining mechanistic insight into cardiac disease processes, and in the search for novel biomarkers. High-resolution NMR spectroscopy and mass spectrometry are both highly discriminatory for a range of pathological processes affecting the heart, including cardiac ischemia, myocardial infarction, and heart failure. We also discuss the position of metabolomics in the range of functional-genomic approaches, being complementary to proteomic and transcriptomic studies, and having subdivisions such as lipidomics (the study of intact lipid species). In addition to techniques that monitor changes in the total sizes of pools of metabolites in the heart and biofluids, the role of stable-isotope methods for monitoring fluxes through pathways is examined. The use of these novel functional-genomic tools to study metabolism provides a unique insight into cardiac disease progression.

3-Hydroxykynurenine and clinical symptoms in first-episode neuroleptic-naive patients with schizophrenia

One branch of the tryptophan catabolic cascade is the kynurenine pathway, which produces neurotoxic [3-hydroxykynurenine (3-OHKY), quinolinic acid] and neuroinhibitory (kynurenic acid) compounds. Kynurenic acid acts as a competitive antagonist at the glycine site of N-methyl-d-asparate receptors at high concentrations and as a non-competitive antagonist on the α7-nicotinic acetylcholine receptor at low concentrations. Kynurenine compounds also influence cognitive functions known to be disrupted in schizophrenia. Alterations in tryptophan metabolism are therefore of potential significance for the pathophysiology of this disorder. In this paper, tryptophan metabolites were measured from plasma using high-pressure liquid chromatography coupled with electrochemical coulometric array detection, and relationships were tested between these metabolic signatures and clinical symptoms for 25 first-episode neuroleptic-naive schizophrenia patients. Blood samples were collected and clinical and neurological symptoms were rated at baseline and again at 4 wk following initiation of treatment. Level of 3-OHKY and total clinical symptom scores were correlated when patients were unmedicated and neuroleptic-naive, and this relationship differed significantly from the correlation observed for patients 4 wk after beginning treatment. Baseline psychosis symptoms were predicted only by neurological symptoms. Moreover, baseline 3-OHKY predicted clinical change at 4 wk, with the lowest concentrations of 3-OHKY being associated with the greatest improvement in symptoms. Taken together, our findings suggest a neurotoxic product of tryptophan metabolism, 3-OHKY, predicts severity of clinical symptoms during the early phase of illness and before exposure to antipsychotic drugs. Baseline level of 3-OHKY may also predict the degree of clinical improvement following brief treatment with antipsychotics.

Metabolomics: an integral technique in systems biology

Metabolomics is the unbiased identification and state-specific quantification of all metabolites in a cell, tissue or whole organism, and has developed rapidly into one of the cornerstones of postgenomic techniques for the quantitative analysis of molecular phenotypes. These large-scale analyses of metabolites are intimately bound to advancements in MS technologies and have emerged in parallel with the development of novel mass analyzers and hyphenated techniques, as well as with the combination of different techniques to cope with the physicochemical diversity of a metabolome. This review gives a brief description of the development and applications of these technologies in biochemistry and systems biology, and discusses their significance in the postgenomic era. Especially, the systematic relation between high-throughput metabolomic data and their interpretation with respect to the underlying biochemical regulatory network is discussed.

Recent advances in understanding the role of diet and obesity in the development of colorectal cancer

Colorectal cancer (CRC) is a major cause of premature death in the UK and many developed countries. However, the risk of developing CRC is well recognised to be associated not only with diet but also with obesity and lack of exercise. While epidemiological evidence shows an association with factors such as high red meat intake and low intake of vegetables, fibre and fish, the mechanisms underlying these effects are only now being elucidated. CRC develops over many years and is typically characterised by an accumulation of mutations, which may arise as a consequence of inherited polymorphisms in key genes, but more commonly as a result of spontaneously arising mutations affecting genes controlling cell proliferation, differentiation, apoptosis and DNA repair. Epigenetic changes are observed throughout the progress from normal morphology through formation of adenoma, and the subsequent development of carcinoma. The reasons why this accumulation of loss of homoeostatic controls arises are unclear but chronic inflammation has been proposed to play a central role. Obesity is associated with increased plasma levels of chemokines and adipokines characteristic of chronic systemic inflammation, and dietary factors such as fish oils and phytochemicals have been shown to have anti-inflammatory properties as well as modulating established risk factors such as apoptosis and cell proliferation. There is also some evidence that diet can modify epigenetic changes. This paper briefly reviews the current state of knowledge in relation to CRC development and considers evidence for potential mechanisms by which diet may modify risk.

Altered interactions of tryptophan metabolites in first-episode neuroleptic-naive patients with schizophrenia

Schizophrenia is characterized by complex and dynamically interacting perturbations in multiple neurochemical systems. In the past, evidence for these alterations has been collected piecemeal, limiting our understanding of the interactions among relevant biological systems. Earlier, both hyper- and hyposerotonemia were variously associated with the longitudinal course of schizophrenia, suggesting a disturbance in the central serotonin (5-hydroxytryptamine (5-HT)) function. Using a targeted electrochemistry-based metabolomics platform, we compared metabolic signatures consisting of 13 plasma tryptophan (Trp) metabolites simultaneously between first-episode neuroleptic-naive patients with schizophrenia (FENNS, n=25) and healthy controls (HC, n=30). We also compared these metabolites between FENNS at baseline (BL) and 4 weeks (4w) after antipsychotic treatment. N-acetylserotonin was increased in FENNS-BL compared with HC (P=0.0077, which remained nearly significant after Bonferroni correction). N-acetylserotonin/Trp and melatonin (Mel)/serotonin ratios were higher, and Mel/N-acetylserotonin ratio was lower in FENNS-BL (all P-values<0.0029), but not after treatment, compared with HC volunteers. All three groups had highly significant correlations between Trp and its metabolites, Mel, kynurenine, 3-hydroxykynurenine and tryptamine. However, in the HC, but in neither of the FENNS groups, serotonin was highly correlated with Trp, Mel, kynurenine or tryptamine, and 5-hydroxyindoleacetic acid (5HIAA) was highly correlated with Trp, Mel, kynurenine or 3-hydroxykynurenine. A significant difference between HC and FENNS-BL was further shown only for the Trp-5HIAA correlation. Thus, some metabolite interactions within the Trp pathway seem to be altered in the FENNS-BL patients. Conversion of serotonin to N-acetylserotonin by serotonin N-acetyltransferase may be upregulated in FENNS patients, possibly related to the observed alteration in Trp-5HIAA correlation. Considering N-acetylserotonin as a potent antioxidant, such increases in N-acetylserotonin might be a compensatory response to increased oxidative stress, implicated in the pathogenesis of schizophrenia.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

A systems biology approach to understanding atherosclerosis

Atherosclerosis, a chronic inflammatory disease of the vascular system, presents significant challenges to developing effective molecular diagnostics and novel therapies. A systems biology approach integrating data from large-scale measurements (e.g. transcriptomics, proteomics and genomics) is successfully contributing to deciphering regulatory networks underlying the response of many different cellular systems to perturbations. Such a network analysis strategy using pathway information and data from multiple measurement platforms, tissues and species is a promising approach to elucidate the mechanistic underpinnings of complex diseases. Here, we present our views on the contributions that a systems approach can bring to the study of atherosclerosis, propose ways to tackle the complexity of the disease in a systems manner and review recent systems-level studies of the disease.

Unraveling human complexity and disease with systems biology and personalized medicine

We are all perplexed that current medical practice often appears maladroit in curing our individual illnesses or disease. However, as is often the case, a lack of understanding, tools and technologies are the root cause of such situations. Human individuality is an often-quoted term but, in the context of human biology, it is poorly understood. This is compounded when there is a need to consider the variability of human populations. In the case of the former, it is possible to quantify human complexity as determined by the 35,000 genes of the human genome, the 1-10 million proteins (including antibodies) and the 2000-3000 metabolites of the human metabolome. Human variability is much more difficult to assess, since many of the variables, such as the definition of race, are not even clearly agreed on. In order to accommodate human complexity, variability and its influence on health and disease, it is necessary to undertake a systematic approach. In the past decade, the emergence of analytical platforms and bioinformatics tools has led to the development of systems biology. Such an approach offers enormous potential in defining key pathways and networks involved in optimal human health, as well as disease onset, progression and treatment. The tools and technologies now available in systems biology analyses offer exciting opportunities to exploit the emerging areas of personalized medicine. In this article, we discuss the current status of human complexity, and how systems biology and personalized medicine can impact at the individual and population level.

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

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

Metabolomic strategies to study lipotoxicity in cardiovascular disease

Cardiovascular disease arises from a combination of dyslipidaemia and systemic inflammation in both humans and mouse models of the disease. Given the strong metabolic component and also the strong interaction between diet and disease, one would expect strategies based on the global profiling of metabolism should hold substantial promise in defining the mechanism involved in this collection of pathologies. This review examines how metabolomics is being used both as a research tool to understand mechanisms of pathology and as an approach for biomarker discovery in cardiovascular disease. While the lipid fraction of blood plasma has a profound influence on the development of cardiovascular disease, there is also a growing body of evidence that the aqueous fraction of metabolites also have a role in following the effects of myocardial infarction and monitoring the development of atherosclerosis. Metabolomics has also been used in conjunction with proteomics and transcriptomics as part of a systems biology description of cardiovascular disease and in high-throughput approaches to profile large numbers of patients as part of epidemiology studies to understand how the genome interacts with the development of atherosclerosis.

RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

Systems biology approaches and pathway tools for investigating cardiovascular disease

Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.

Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen

Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.

Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes

Background

The prevalence of overweight is increasing globally and has become a serious health problem. Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development. Novel tools to understand these processes are needed. Metabolic profiling is one such tool that can provide novel insights into the impact of treatments on metabolism.

Methodology

To study the metabolic changes induced by a mild anti-inflammatory drug intervention, plasma metabolic profiling was applied in overweight human volunteers with elevated levels of the inflammatory plasma marker C-reactive protein. Liquid and gas chromatography mass spectrometric methods were used to detect high and low abundant plasma metabolites both in fasted conditions and during an oral glucose tolerance test. This is based on the concept that the resilience of the system can be assessed after perturbing a homeostatic situation.

Conclusions

Metabolic changes were subtle and were only detected using metabolic profiling in combination with an oral glucose tolerance test. The repeated measurements during the oral glucose tolerance test increased statistical power, but the metabolic perturbation also revealed metabolites that respond differentially to the oral glucose tolerance test. Specifically, multiple metabolic intermediates of the glutathione synthesis pathway showed time-dependent suppression in response to the glucose challenge test. The fact that this is an insulin sensitive pathway suggests that inflammatory modulation may alter insulin signaling in overweight men.

Analytical strategies in lipidomics and applications in disease biomarker discovery

Lipidomics is a lipid-targeted metabolomics approach aiming at comprehensive analysis of lipids in biological systems. Recently, lipid profiling, or so-called lipidomics research, has captured increased attention due to the well-recognized roles of lipids in numerous human diseases to which lipid-associated disorders contribute, such as diabetes, obesity, atherosclerosis and Alzheimer's disease. Investigating lipid biochemistry using a lipidomics approach will not only provide insights into the specific roles of lipid molecular species in health and disease, but will also assist in identifying potential biomarkers for establishing preventive or therapeutic approaches for human health. Recent technological advancements in mass spectrometry and rapid improvements in chromatographic techniques have led to the rapid expansion of the lipidomics research field. In this review, emphasis is given to the recent advances in lipidomics technologies and their applications in disease biomarker discovery.

Global metabolic profiling and its role in systems biology to advance personalized medicine in the 21st century

Systems biology attempts to elucidate the complex interaction between genes, proteins and metabolites to provide a mechanistic understanding of cellular function and how this function is affected by disease processes, drug toxicity or drug efficacy effects. Global metabolic profiling is an important component of systems biology that can be applied in both preclinical and clinical settings for drug discovery and development, and to study disease mechanisms. The metabolic profile encodes the phenotype, which is composed of the genotype and environmental factors. The phenotypic profile can be used to make decisions about the best course of treatment for an individual patient. Understanding the combined effects of genetics and environment through a systems biology framework will enable the advancement of personalized medicine.

Systematic -omics analysis of HBV-associated liver diseases

Hepatitis B virus (HBV) infection causes acute and chronic liver diseases and increases the risk of developing hepatocellular carcinoma (HCC). However, the pathogenesis of HBV infection and carcinogenesis of HBV-associated HCC are still elusive. In this review, systematic -omics studies made in the scales of genomics, transcriptomics and proteomics were discussed. The susceptibility to HBV infection and the course of disease progress are greatly different among individuals. Using population- or/and family-based approaches, relevant genes have been mapped or identified to be associated with host immune responses to HBV antigens and susceptibility to HCC. Comprehensive transcriptomic analyses have shown that the HBV-induced hepatocarcinogenesis may involve the whole course from signal transduction, transcription, translation to protein degradation, which differs in some measure from HCV-induced hepatocarcinogenesis, and that exogenous transcription factor HBX and endogenous NF-kappaB are likely two key points of the course. By the means of proteomics, dozens of important dysregulated proteins (including isoforms or fragments) were identified from carcinogenesis mechanism analysis and biomarker validation. Of them, the alteration of heat shock proteins and impairment of methylation cycle were found to be associated with clinical HBV-associated HCC. As a whole, the systematic -omics analysis of HBV-associated liver diseases has offered multi-scale pathological information in the process from HBV infection to HCC onset.

The challenges for molecular nutrition research 3: comparative nutrigenomics research as a basis for entering the systems level

Human nutrition and metabolism may serve as the paradigm for the complex interplay of the genome with its environment. The concept of nutrigenomics now enables science with new tools and comprehensive analytical techniques to investigate this interaction at all levels of the complexity of the organism. Moreover, nutrigenomics seeks to better define the homeostatic control mechanisms, identify the de-regulation in the early phases of diet-related diseases, and attempts to assess to what extent an individual's sensitizing genotype contributes to the overall health or disease state. In a comparative approach nutrigenomics uses biological systems of increasing complexity from yeast to mammalian models to define the general rules of metabolic and genetic mechanisms in adaptations to the nutritional environment. Powerful information technology, bioinformatics and knowledge management tools as well as new mathematical and computational approaches now make it possible to study these molecular mechanisms at the cellular, organ and whole organism level and take it on to modeling the processes in a "systems biology" approach. This review summarizes some of the concepts of a comparative approach to nutrigenomics research, identifies current lacks and proposes a concerted scientific effort to create the basis for nutritional systems biology.

Integration of metabolomic and proteomic phenotypes: analysis of data covariance dissects starch and RFO metabolism from low and high temperature compensation response in Arabidopsis thaliana

Statistical mining and integration of complex molecular data including metabolites, proteins, and transcripts is one of the critical goals of systems biology (Ideker, T., Galitski, T., and Hood, L. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372). A number of studies have demonstrated the parallel analysis of metabolites and large scale transcript expression. Protein analysis has been ignored in these studies, although a clear correlation between transcript and protein levels is shown only in rare cases, necessitating that actual protein levels have to be determined for protein function analysis. Here, we present an approach to investigate the combined covariance structure of metabolite and protein dynamics in a systemic response to abiotic temperature stress in Arabidopsis thaliana wild-type and a corresponding starch-deficient mutant (phosphoglucomutase-deficient). Independent component analysis revealed phenotype classification resolving genotype-dependent response effects to temperature treatment and genotype-independent general temperature compensation mechanisms. An observation is the stress-induced increase of raffinose-family-oligosaccharide levels in the absence of transitory starch storage/mobilization in temperature-treated phosphoglucomutase plants indicating that sucrose synthesis and storage in these mutant plants is sufficient to bypass the typical starch storage/mobilization pathways under abiotic stress. Eventually, sample pattern recognition and correlation network topology analysis allowed for the detection of specific metabolite-protein co-regulation and assignment of a circadian output regulated RNA-binding protein to these processes. The whole concept of high-dimensional profiling data integration from many replicates, subsequent multivariate statistics for dimensionality reduction, and covariance structure analysis is proposed to be a major strategy for revealing central responses of the biological system under study.

Technology insight: metabonomics in gastroenterology-basic principles and potential clinical applications

Metabonomics-the study of metabolic changes in an integrated biologic system-is an emerging field. This discipline joins the other 'omics' (genomics, transcriptomics and proteomics) to give rise to a comprehensive, systems-biology approach to the evaluation of holistic in vivo function. Metabonomics, especially when based on nuclear magnetic resonance spectroscopy, has the potential to identify biomarkers and prognostic factors, enhance clinical diagnosis, and expand hypothesis generation. As a consequence, the use of metabonomics has been extensively explored in the past decade, and applied successfully to the study of human diseases, toxicology, microbes, nutrition, and plant biology. This Review introduces the basic principles of nuclear magnetic resonance spectroscopy and commonly used tools for multivariate data analysis, before considering the applications and future potential of metabonomics in basic and clinical research, with emphasis on applications in the field of gastroenterology.

Biochemical individuality reflected in chromatographic, electrophoretic and mass-spectrometric profiles

This review discusses the current trends in molecular profiling for the emerging systems biology applications. Historically, the methodological developments in separation science were coincident with the availability of new ionization techniques in mass spectrometry. Coupling miniaturized separation techniques with technologically-advanced MS instrumentation and the modern data processing capabilities are at the heart of current platforms for proteomics, glycomics and metabolomics. These are being featured here by the examples from quantitative proteomics, glycan mapping and metabolomic profiling of physiological fluids.

Towards a systems level analysis of health and nutrition

Although theoretical systems analysis has been available for over half a century, the recent advent of omic high-throughput analytical platforms along with the integration of individual tools and technologies has given rise to the field of modern systems biology. Coupled with information technology, bioinformatics, knowledge management and powerful mathematical models, systems biology has opened up new vistas in our understanding of complex biological systems. Currently there are two distinct approaches that include the inductively driven computational systems biology (bottom-up approach) and the deductive data-driven top-down analysis. Such approaches offer enormous potential in the elucidation of disease as well as defining key pathways and networks involved in optimal human health and nutrition. The tools and technologies now available in systems biology analyses offer exciting opportunities to develop the emerging areas of personalized medicine and individual nutritional profiling.

Interactive analysis of systems biology molecular expression data

Background

Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions. Molecular correlations and comparative studies of molecular expression are crucial to establishing interdependent connections in systems biology. The existing software packages provide limited data mining capability. The user must first generate visualization data with a preferred data mining algorithm and then upload the resulting data into the visualization package for graphic visualization of molecular relations.

Results

Presented is a novel interactive visual data mining application, SysNet that provides an interactive environment for the analysis of high data volume molecular expression information of most any type from biological systems. It integrates interactive graphic visualization and statistical data mining into a single package. SysNet interactively presents intermolecular correlation information with circular and heatmap layouts. It is also applicable to comparative analysis of molecular expression data, such as time course data.

Conclusion

The SysNet program has been utilized to analyze elemental profile changes in response to an increasing concentration of iron (Fe) in growth media (an ionomics dataset). This study case demonstrates that the SysNet software is an effective platform for interactive analysis of molecular expression information in systems biology.

Correlation network analysis for data integration and biomarker selection

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectable in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes. In this work we present a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers. The case study we present is the identification of biomarkers of drug-induced hepatic toxicity effects in a rodent model. Biomolecular profiling of both blood plasma and liver tissue from Wistar Hannover rats administered a toxic compound yielded many hundreds of statistically significant molecular changes. We exploited drug-induced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasma molecules as biomarkers of drug-induced hepatic alterations of lipid metabolism and urea cycle processes.

Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics

A multiple ionization mass spectrometry strategy is presented based on the analysis of human serum extracts. Chromatographic separation was interfaced inline with the atmospheric pressure ionization techniques electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) in both positive (+) and negative (-) ionization modes. Furthermore, surface-based matrix-assisted laser desorption/ionization (MALDI) and desorption ionization on silicon (DIOS) mass spectrometry were also integrated with the separation through fraction collection and offline mass spectrometry. Processing of raw data using the XCMS software resulted in time-aligned ion features, which are defined as a unique m/z at a unique retention time. The ion feature lists obtained through LC-MS with ESI and APCI interfaces in both +/- ionization modes were compared, and unique ion tables were generated. Nonredundant, unique ion features, were defined as mass numbers for which no mass numbers corresponding to [M + H](+), [M - H](-), or [M + Na](+) were observed in the other ionization methods at the same retention time. Analysis of the extracted serum using ESI for both (+) and (-) ions resulted in >90% additional unique ions being detected in the (-) ESI mode. Complementing the ESI analysis with APCI resulted in an additional approximately 20% increase in unique ions. Finally, ESI/APCI ionization was combined with fraction collection and offline-MALDI and DIOS mass spectrometry. The parts of the total ion current chromatograms in the LC-MS acquired data corresponding to collected fractions were summed, and m/z lists were compiled and compared to the m/z lists obtained from the DIOS/MALDI spectra. It was observed that, for each fraction, DIOS accounted for approximately 50% of the unique ions detected. These results suggest that true global metabolomics will require multiple ionization technologies to address the inherent metabolite diversity and therefore the complexity in and of metabolomics studies.

Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data

The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.

From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics

Mass spectrometry (MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its utility derives from its wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing mass spectrometry for metabolomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, we will discuss the techniques involved in this exciting area and the current and future applications of this field.

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.

Automated quantitative analysis of complex lipidomes by liquid chromatography/mass spectrometry

Recent advances in mass spectrometry have revolutionized the analysis of lipid compositions of cells and other biomaterials by simplifying the analytical protocol dramatically and by increasing the sensitivity of detection by several orders of magnitude. However, the throughput of the published mass spectrometric methods is severely limited by data analysis, which requires extensive operator involvement. Consequently, we have developed an automated method that allows unattended identification and quantification of lipid molecular species of all the major lipid classes from a two-dimensional chromatographic/mass spectrometric data set. More than 100 polar lipid species could be automatically quantified from different biological samples with good accuracy and reproducibility. The response was linear over approximately 3 orders of magnitude with the equipment used, and approximately 35 samples could be analyzed in a day. This method makes high-throughput lipidomics feasible in biology, biotechnology, and medicine.