Proposed reporting requirements for the description of NMR-based metabolomics experiments

The amount of data generated by NMR-based metabolomic experiments is increasing rapidly. Furthermore, diverse techniques increase the need for informative and comprehensive meta-data. These factors present a challenge in the dissemination, interpretation, reviewing and comparison of experimental results using this technology. Thus, there is a strong case for unification and standardisation of the data representation for both academia and industry. Here, a systems analysis of an NMR-based metabolomics experiment is presented in order to reveal the reporting requirements. An in-depth analysis of the NMR component of a metabolomics experiment has been produced, and a first round of data standard development completed. This has focussed on both one- and two-dimensional ¹H NMR experiments, but is also applicable to higher dimensions and other nuclei. We also report the modelling of this schema using Unified Modelling Language (UML), and have extended this to a proof-of-concept implementation of the standard as an XML schema.     

A proposed unified framework for biological invasions

There has been a dramatic growth in research on biological invasions over the past 20 years, but a mature understanding of the field has been hampered because invasion biologists concerned with different taxa and different environments have largely adopted different model frameworks for the invasion process, resulting in a confusing range of concepts, terms and definitions. In this review, we propose a unified framework for biological invasions that reconciles and integrates the key features of the most commonly used invasion frameworks into a single conceptual model that can be applied to all human-mediated invasions. The unified framework combines previous stage-based and barrier models, and provides a terminology and categorisation for populations at different points in the invasion process.     

A proposed framework for developing indicators of ecosystem health

Considerations involved in developing a suite of indicators to monitor regional environmental health, similar in conception to management use of leading economic indicators, are described. Linkages between human activities and well being and the state of the environment are considered essential to the evaluation of general environmental health. Biogeochemical and socioeconomic indicators are mutually affected by environmental degradation and examples of both categories of indicators are described. Desirable properties in indicators of environmental health vary with their specific management use. Different indicators are called for when collecting data to assess the adequacy of the environment, monitor trends over time, provide early warning of environmental degradation, or diagnose the cause of an existing problem. Tradeoffs between desirable characteristics, costs, and quality of information are inevitable when choosing indicators for management use. Decisions about what information to collect for which purpose can be made more rationally when available indicators are characterized and matched to management goals.     

A symbolic framework for the description of tree architecture models

The system of tree architecture proposed by Hallé and Oldeman consists of 23 models named after botanists playing leading roles in elucidating tree architecture. This system gives no indication why other models do not occur. A symbolism is presented here which can serve as a shorthand in recording tree architectures without assumptions about models. and immediately interpretable. Using this symbolism to represent the models proposed by Hallé and Oldeman permits creation of general rules of tree architecture. some of which raise interesting theoretical questions. Two further tree models that might well be expected to exist and several which would not be expected. are described.     

Properties of bacterial endophytes and their proposed role in plant growth

Bacterial endophytes live inside plants for at least part of their life cycle. Studies of the interaction of endophytes with their host plants and their function within their hosts are important to address the ecological relevance of endophytes. The modulation of ethylene levels in plants by bacterially produced 1-aminocyclopropane-1-carboxylate deaminase is a key trait that enables interference with the physiology of the host plant. Endophytes with this capacity might profit from association with the plant, because colonization is enhanced. In turn, host plants benefit by stress reduction and increased root growth. This mechanism leads to the concept of 'competent' endophytes, defined as endophytes that are equipped with genes important for maintenance of plant-endophyte associations. The ecological role of these endophytes and their relevance for plant growth are discussed here.     

Statistical strategies for avoiding false discoveries in metabolomics and related experiments

Many metabolomics, and other high-content or high-throughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case’ and ‘control’ samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are well-known examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and cross-validation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in particular that classical p-values such as “p < 0.05”, that are often used in biomedicine, are far too optimistic when multiple tests are done simultaneously (as in metabolomics). Ultimately it is desirable that all data and metadata are available electronically, as this allows the entire community to assess conclusions drawn from them. These analyses apply to all high-dimensional ‘omics’ datasets.     

Toward supportive data collection tools for plant metabolomics

Over recent years, a number of initiatives have proposed standard reporting guidelines for functional genomics experiments. Associated with these are data models that may be used as the basis of the design of software tools that store and transmit experiment data in standard formats. Central to the success of such data handling tools is their usability. Successful data handling tools are expected to yield benefits in time saving and in quality assurance. Here, we describe the collection of datasets that conform to the recently proposed data model for plant metabolomics known as ArMet (architecture for metabolomics) and illustrate a number of approaches to robust data collection that have been developed in collaboration between software engineers and biologists. These examples also serve to validate ArMet from the data collection perspective by demonstrating that a range of software tools, supporting data recording and data upload to central databases, can be built using the data model as the basis of their design.     

Climatic characteristics of heat waves and their simulation in plant experiments

Extreme events such as heat waves are emerging as a key facet of climate change, but to date, experiments on the impacts on plants are scarce. Experimental simulation of heat waves requires knowledge of regional heat wave characteristics, as plant responses depend heavily on meteorological conditions. We analysed nine Western European meteorological datasets, and found that heat waves occurring during the growing season in this region encompass more sunshine (+ 69%), lower precipitation (?78%) and a larger vapour pressure deficit (VPD) (+ 111%) compared with normal conditions. Possible consequences for plant responses are discussed, with emphasis on the likely seasonal variation of heat wave impacts. We explain why infrared heating (which typically increases VPD) is an appropriate technique for heat wave simulation. Finally, we advocate experiments to take into account the smaller nighttime compared with daytime temperature increases observed during heat waves, and the precipitation deficits before and during heat waves.     

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.     

Sperm abnormalities in the PL/J mouse strain: A description and proposed mechanism for malformation

A high frequency (42%) of sperm from the inbred homozygous mouse strain PL/J are abnormal. Head shape abnormalities occur in 15% of the total sperm; and 27% of the sperm are headless, with the mitochondria condensed into a mass at the caudal end of the midpiece region. The sperm without heads exhibit relatively normal motility. Electron microscopy of the testes indicates that some of the abnormal sperm in PL/J males result from a failure of the paired centrioles to attain a normal position on the nucleus opposite the acrosome prior to implantation, or to attach at all. The centrioles that are not attached to the nuclear envelope can differentiate to form the principal piece and midpiece region. The frequency of headless variants in heterozygous F₁ indicates that the trait is mainly recessive. The offspring from the backcross of the F₁ to homozygous PL/J parents did not give a clear-cut segregation pattern. The frequency of abnormal sperm in the F₁ and the backcross is higher when the female parent is a PL/J.     

A statistical framework for the design of microarray experiments and effective detection of differential gene expression

MOTIVATION: Microarray experiments generate a high data volume. However, often due to financial or experimental considerations, e.g. lack of sample, there is little or no replication of the experiments or hybridizations. These factors combined with the intrinsic variability associated with the measurement of gene expression can result in an unsatisfactory detection rate of differential gene expression (DGE). Our motivation was to provide an easy to use measure of the success rate of DGE detection that could find routine use in the design of microarray experiments or in post-experiment assessment. RESULTS: In this study, we address the problem of both random errors and systematic biases in microarray experimentation. We propose a mathematical model for the measured data in microarray experiments and on the basis of this model present a t-based statistical procedure to determine DGE. We have derived a formula to determine the success rate of DGE detection that takes into account the number of microarrays, the number of genes, the magnitude of DGE, and the variance from biological and technical sources. The formula and look-up tables based on the formula, can be used to assist in the design of microarray experiments. We also propose an ad hoc method for estimating the fraction of non-differentially expressed genes within a set of genes being tested. This will help to increase the power of DGE detection. AVAILABILITY: The functions to calculate the success rate of DGE detection have been implemented as a Java application, which is accessible at http://www.le.ac.uk/mrctox/microarray_lab/Microarray_Softwares/Microarray_Softwares.htm     

A proposed framework for determining the environmental impact of replacing agricultural grassland with Miscanthus in Ireland

Energy crops offer an opportunity to substantially increase bioenergy resources which can replace rapidly depleting fossil fuel reserves and mitigate the effect of climate change. Energy crops are typically established within traditional agricultural systems such as tillage land or grassland. Associated land use conversion has environmental implications. The aim of this paper is to propose a framework to examine how such environmental implications can be assessed, based on (a) a Strategic Environmental Assessment (SEA) approach which considers potential impacts at different stages of a plan across a wide range of environmental receptors and (b) a literature review. The example we used was that of Miscanthus replacing grassland farming. This scenario is particularly relevant to Ireland, where over 90% of the agricultural land is permanent pasture, but is also applicable to grassland conversion throughout Europe and the United States. Two consecutive phases of land‐use change were identified for assessment, each with a distinct set of environmental impacts. The first was a transition phase, lasting from initial livestock clearance and grassland ploughing until the Miscanthus crop became established (2–3 years). The second phase was the mature crop phase, lasting up to 25 years. Miscanthus cultivation was more likely to impact negatively on the environment during the transition phase than the mature phase, primarily due to abrupt disturbance and the time required for a new equilibrium to establish. However, a literature review of the impact on the environmental receptors revealed that replacing Irish agricultural grassland with Miscanthus had the potential to improve biodiversity, water, air and soil quality, and climatic factors once the crop became established and reached maturity. In order to confirm these findings an appropriate monitoring programme involving objectives and indicators associated with each environmental receptor would need to be developed.     

A general framework for analyzing data from two short time-series microarray experiments

We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence Criterion (HSIC)-based framework adapting it to the time-series scenario by utilizing tensor analysis for data transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are found to be both biologically meaningful and consistent with published studies.     

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results

Informatics standards and controlled vocabularies are essentialfor allowing information technology to help exchange, manage,interpret and compare large data collections. In a rapidly evolvingfield, the challenge is to work out how best to describe, butnot prescribe, the use of these technologies and methods. AMetabolomics Standards Workshop was held by the US NationalInstitutes of Health (NIH) to bring together multiple ongoingstandards efforts in metabolomics with the NIH research community.The goals were to discuss metabolomics workflows (methods, technologiesand data treatments) and the needs, challenges and potentialapproaches to developing a Metabolomics Standards Initiativethat will help facilitate this rapidly growing field which hasbeen a focus of the NIH roadmap effort. This report highlightsspecific aspects of what was presented and discussed at the1st and 2nd August 2005 Metabolomics Standards Workshop.      

A simple device for measuring a vertical jump: description and results

A simple and cheap device has been designed which makes it possible to quantify a vertical jump. The parameters which can be measured or calculated with this device include: height of the jump, duration of thrust, maximal velocity and thus the corresponding maximal power output. The device was tested on 22 young soccer players for whom the height of the jump (0.47 m, SEM 0.015) and maximal power output (34.9 W. kg-1, SEM 1.04) were considered. The device is proposed for assessing training methods and sports aptitude.