Answering biological questions: querying a systems biology database for nutrigenomics

The requirement of systems biology for connecting different levels of biological research leads directly to a need for integrating vast amounts of diverse information in general and of omics data in particular. The nutritional phenotype database addresses this challenge for nutrigenomics. A particularly urgent objective in coping with the data avalanche is making biologically meaningful information accessible to the researcher. This contribution describes how we intend to meet this objective with the nutritional phenotype database. We outline relevant parts of the system architecture, describe the kinds of data managed by it, and show how the system can support retrieval of biologically meaningful information by means of ontologies, full-text queries, and structured queries. Our contribution points out critical points, describes several technical hurdles. It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies. Finally, three directions for future research are given.     

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

Background

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as ‘unknown compounds’. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

Aim of review

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

Key scientific concepts of review

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

     

Answering six questions in extracting children��s mismatch negativity through combining wavelet decomposition and independent component analysis

This study combines wavelet decomposition and independent component analysis (ICA) to extract mismatch negativity (MMN) from electroencephalography (EEG) recordings. As MMN is a small event-related potential (ERP), a systematic ICA based approach is designed, exploiting MMN’s temporal, frequency and spatial information. Moreover, this study answers which type of EEG recordings is more appropriate for ICA to extract MMN, what kind of the preprocessing is beneficial for ICA decomposition, which algorithm of ICA can be chosen to decompose EEG recordings under the selected type, how to determine the desired independent component extracted by ICA, how to improve the accuracy of the back projection of the selected independent component in the electrode field, and what can be finally obtained with the application of ICA. Results showed that the proposed method extracted MMN with better properties than those estimated by difference wave only using temporal information or ICA only using spatial information. The better properties mean that the deviant with larger magnitude of deviance to repeated stimuli in the oddball paradigm can elicit MMN with larger peak amplitude and shorter latency. As other ERPs also have the similar information exploited here, the proposed method can be used to study other ERPs.     

Integrated sampling procedure for metabolome analysis

Metabolome analysis, the analysis of large sets of intracellular metabolites, has become an important systems analysis method in biotechnological and pharmaceutical research. In metabolic engineering, the integration of metabolome data with fluxome and proteome data into large-scale mathematical models promises to foster rational strategies for strain and cell line improvement. However, the development of reproducible sampling procedures for quantitative analysis of intracellular metabolite concentrations represents a major challenge, accomplishing (i) fast transfer of sample, (ii) efficient quenching of metabolism, (iii) quantitative metabolite extraction, and (iv) optimum sample conditioning for subsequent quantitative analysis. In addressing these requirements, we propose an integrated sampling procedure. Simultaneous quenching and quantitative extraction of intracellular metabolites were realized by short-time exposure of cells to temperatures < or =95 degrees C, where intracellular metabolites are released quantitatively. Based on these findings, we combined principles of heat transfer with knowledge on physiology, for example, turnover rates of energy metabolites, to develop an optimized sampling procedure based on a coiled single tube heat exchanger. As a result, this sampling procedure enables reliable and reproducible measurements through (i) the integration of three unit operations into a one unit operation, (ii) the avoidance of any alteration of the sample due to chemical reagents in quenching and extraction, and (iii) automation. A sampling frequency of 5 s(-)(1) and an overall individual sample processing time faster than 30 s allow observing responses of intracellular metabolite concentrations to extracellular stimuli on a subsecond time scale. Recovery and reliability of the unit operations were analyzed. Impact of sample conditioning on subsequent IC-MS analysis of metabolites was examined as well. The integrated sampling procedure was validated through consistent results from steady-state metabolite analysis of Escherichia coli cultivated in a chemostat at D = 0.1 h(-)(1).     

生物多样性研究及其问题

围绕生物多样性主要有3个理论问题需要进一步深入研究,（1）生物多样性与生态系统稳定性的关系：本世纪70年代以前,生态学家普遍认为,稳定性随生物多样性增加而提高;自70年代初一些理论生态学家向这一普遍看法提出挑战以来,在物种多样性层次出现了观点截然不同的两大阵营;（2）生物多样性和土地生产力的关系：达尔文（1872）的研究表明,生物多样性有利于土地生产力的提高,这一结论已被许多国家运用于指导农业实践;然而,本世纪70年代以来,一些生态学家向达尔文的这一观点提出了异议;（3）生物多样性与景观连通性：本世纪90年代以来,一些景观生态学家认为,景观连通性与生物多样性有正相关关系,但目前为数不多的研究还不能肯定这一结论的正确性。通过总结生物多样性的研究历史,发现,以上的急诊和结论基于27种不同的分析模型;并且这些模型中的大多数在理论上是不完善的,认为,运用理论上合理的模型、有关概念的统一正确定义和全面系统的实验对以上急诊和结论进行更进一步的论证与实证是必要的。     

Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance

Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies.

     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

Double-stranded RNA analysis of strawberry plants affected by June yellows

Using an improved method for extraction of dsRNA from strawberry leaf tissue, small quantities of several dsRNA species with mol. wt greater than 1.0 × 10⁶were detected in strawberry plants free from known strawberry viruses but affected by June yellows (JY). No such dsRNA species were detected in plants of Fragaria vesca or seven strawberry cultivars known to be free from JY. Neither JY symptoms nor these dsRNA species were detected in healthy strawberry and F. vesca plants graft-inoculated with tissue from JY-affected plants. It is not known whether the JY-associated dsRNA species are those of a causal agent of JY or are a consequence of the JY condition. Nevertheless, the detection of such dsRNA species in plants affected by JY may offer a possible objective method for detecting the incipient condition in symptomless strawberry plants. However, the concentrations of dsRNA in JY-affected plants are very low and dsRNA analysis is thought not to be sufficiently reliable for routine testing of plants. The occurrence of anomalous dsRNA species in extracts from some strawberry plants was caused by dsRNA from two-spotted spider mites (Tetranychus urticae) infesting the plants.     

Digitizing the metabolome

The biological mechanisms that direct the generation and accumulationof the vast diversity of metabolites observed in the plant kingdomare not fully understood. An exciting and promising approachto understand these mechanisms is described in the paper byXie et al. (2009). The authors have coupled state of the artmetabolomic analyses with novel bioinformatic techniques toidentify apparent ‘metabolic modules’ in turmeric(Curcuma longa) rhizomes. A metabolic module is defined as agroup of co-regulated metabolites and this approach elegantlyrepresents a basic innovative and practical attempt to understandand predict metabolic pathways using detailed bioinformaticsdata mining following careful and well-documented GC-MS andLC-MS     

Evaluation of cell damage caused by cold sampling and quenching for metabolome analysis

Cell damage during sampling and quenching for metabolome analysis have been investigated at whole sample level using an OD-based method and ATP loss investigation, and at single cell level by means of flow cytometry. Escherichia coli was cultivated in shake flasks and sampled into several cold quenching solutions during exponential growth phase varying quenching solution composition and sampling temperature. For single cell analysis, the samples were incubated with selective propidium iodide dye and analysed via flow cytometry to differentiate between intact and damaged cells. It was found that every combination of quenching solution, temperature, or cooling rate tested influenced the E. coli cell membrane integrity indicating rupture which will not only let the dye in, but also intracellular ATP out of the cells, which is not desired in in vivo metabolome analysis.     

Answering the big questions in neuroscience: DoD's experimental research wing takes on massive, high-risk projects

When the Defense Advanced Research Projects Agency (DARPA) asks research questions, it goes big. This is, after all, the same agency that put together teams of scientists and engineers to find a way to connect the worlds computers and, in doing so, developed the precursor to the Internet. DARPA, the experimental research wing of the U.S. Department of Defense, funds the types of research queries that scientists and engineers dream of tackling. Unlike a traditional granting agency that conservatively metes out its funding and only to projects with a good chance of success, DARPA puts its money on massive, multi-institutional projects that have no guarantees, but have enormous potential. In the 1990s, DARPA began its biological and medical science research to improve the safety, health, and well being of military personnel, according to DARPA program manager and Army Colonel Geoffrey Ling, Ph.D., M.D. More recently, DARPA has entered the realm of neuroscience and neurotechnology. Its focus with these projects is on its prime customer, the U.S. Department of Defense, but Ling acknowledged that technologies developed in its programs "certainly have potential to cascade into civilian uses."     

Quantitative metabolome analysis using liquid chromatography-high-resolution mass spectrometry

In this report, we introduce a liquid chromatography single-mass spectrometry method for metabolome quantification, using the LTQ Orbitrap high-resolution mass spectrometer. Analytes were separated with hydrophilic interaction liquid chromatography. At a working resolution of 30,000 (at m/z 400), the limit of detection varied from 50 fmol to 5 pmol for 25 metabolites tested. In terms of metabolite concentration, the linearity was about 2 to 3 orders of magnitude for most compounds (R² > 0.99). To determine the accuracy of the system in complex sample matrices, the isotope dilution method was evaluated from mixtures of pure compounds and uniformly ¹³C-labeled cell extracts. With the application of this method, quantification was possible within single runs even when the pool sizes of individual metabolites varied from 0.13 to 55.6 μM. As a case study, intracellular concentrations of central metabolites were determined for Methylobacterium extorquens AM1 during growth on two different carbon sources, methanol and succinate. Reproducible results from technical and biological repetitions were obtained that revealed significant variations of intracellular metabolite pool sizes, depending on the carbon source. The LTQ Obitrap offers new perspectives and strategies for metabolome quantification.     

Use of protein-interaction maps to formulate biological questions

Protein-interaction mapping approaches generate functional information for large numbers of genes that are predicted from complete genome sequences. This information, released as databases available on the Internet, is likely to transform the way biologists formulate and then address their questions of interest.     

Quantitative metabolome analysis using capillary electrophoresis mass spectrometry

A new approach for the comprehensive and quantitative analysis of charged metabolites by capillary electrophoresis mass spectrometry (CE-MS) is proposed. Metabolites are first separated by CE based on charge and size and then selectively detected using MS by monitoring over a large range of m/z values. This method enabled the determination of 352 metabolic standards and its utility was demonstrated in the analysis of 1692 metabolites from Bacillus subtilis extracts, revealing significant changes in metabolites during B. subtilis sporulation.