George G. Harrigan

Bio: George G. Harrigan is an academic researcher from Monsanto. The author has contributed to research in topics: Population & Growing season. The author has an hindex of 22, co-authored 46 publications receiving 1473 citations. Previous affiliations of George G. Harrigan include The Coca-Cola Company.

Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis

Abstract: Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis offers guidelines to currently available technology and bioinformatics and database strategies now being developed. Evidence is presented that metabolic profiling is a valuable addition to genomics and proteomics strategies devoted to drug discovery and development, and that metabolic profiling offers numerous advantages.

Impact of genetics and environment on nutritional and metabolite components of maize grain

Abstract: The Organization for Economic Co-operation and Development (OECD) recommends the measurement of specific plant components for compositional assessments of new biotechnology-derived crops. These components include proximates, nutrients, antinutrients, and certain crop-specific secondary metabolites. A considerable literature on the natural variability of these components in conventional and biotechnology-derived crops now exists. Yet the OECD consensus also suggests measurements of any metabolites that may be directly associated with a newly introduced trait. Therefore, steps have been initiated to assess natural variation in metabolites not typically included in the OECD consensus but which might reasonably be expected to be affected by new traits addressing, for example, nutritional enhancement or improved stress tolerance. The compositional study reported here extended across a diverse genetic range of maize hybrids derived from 48 inbreds crossed against two different testers. These were grown at three different, but geographically similar, locations in the United States. In addition to OECD analytes such as proximates, total amino acids and free fatty acids, the levels of free amino acids, sugars, organic acids, and selected stress metabolites in harvested grain were assessed. The major free amino acids identified were asparagine, aspartate, glutamate, and proline. The major sugars were sucrose, glucose, and fructose. The most predominant organic acid was citric acid, with only minor amounts of other organic acids detected. The impact of genetic background and location was assessed for all components. Overall, natural variation in free amino acids, sugars, and organic acids appeared to be markedly higher than that observed for the OECD analytes.

Natural variation in crop composition and the impact of transgenesis

Abstract: volume 28 number 5 mAY 2010 nature biotechnology These, and other studies (e.g., refs. 3–5), have also suggested a high degree of natural variability inherent to crop biochemical and metabolite composition. It is therefore reasonable to ask if changes in composition associated with modern transgenic breeding practices are different in scope from those attributable to natural genotypic and environmentally mediated variation. We reasoned that a systematic analysis encompassing published compositional data generated under OECD guidelines on several GM products grown in a range of geographies, under different regional agronomic practices and over multiple seasons would provide an effective overview of the relative impacts of transgenesis-derived agronomic traits with natural variation on crop composition. GM corn and GM soybean now represent 30.0% and 53%, respectively, of global production6. Our analysis therefore evaluated compositional data reported on grain and seed harvested from different GM corn and GM soybean products as these now represent a significant percentage of global production of these crops as well as provide an abundance of compositional data from diverse climates and growing regions. The high-quality compositional data generated according to principles outlined in the Organization for Economic Cooperation and Development (OECD; Paris) consensus documents2 are available. On a product-byproduct basis, compositional equivalence of GM crops and their conventional comparators has been demonstrated in potato, cotton, soybean, corn, rice, wheat and alfalfa (for a list of references describing compositional and omics comparisons of GM and non-GM comparators, see Supplementary References). In addition to the compositional studies conducted within regulatory programs, biochemical studies on GM crops have been extensively pursued by public and private research sectors. Although there are complexities in the interpretation of modern profiling technologies, and no standardized framework for comparisons, the lack of variation between GM crops and their conventional comparators at the transcriptomic, proteomic and metabolomic level has been independently corroborated. These profiling evaluations extend to a wide range of plants including wheat, potato, soybean, rice, tomato, tobacco, Arabidopsis and Gerbera (see Supplementary References). To the Editor: Compositional equivalence of crops improved through biotech-derived transgenic, or genetically modified (GM), traits and their conventional (non-GM) comparators is an important criterion in breeding as well as a key aspect of risk assessments of commercial candidates. We present here an analysis evaluated from compositional data on GM corn and GM soybean varieties grown across a range of geographies and growing seasons with the aim of not only assessing the relative impact of transgene insertion on compositional variation in comparison with the effect of environmental factors but also reviewing the implications of these results on the safety assessment process. Specifically, our analysis includes evaluation of seven GM crop varieties from a total of nine countries and eleven growing seasons. On the basis of our data, we conclude that compositional differences between GM varieties and their conventional comparators were encompassed within the natural variability of the conventional crop and that the composition of GM and conventional crops cannot be disaggregated. Plant breeding programs expect to either maintain compositional quality during enhancement of other agronomic traits or improve crop compositional quality through intended changes in the levels of key nutrients or antinutrients. Over the past two decades, one of the most successful approaches to enhancing agronomic traits in crops is the insertion of trait-encoding genes using the techniques of modern biotech. Compositional equivalence between GM crops and conventional (non-GM) comparators is an important breeding goal but is also often considered to provide an “equal or increased assurance of the safety of foods derived from genetically modified plants”1. Comparative compositional studies are therefore included as a significant component of risk assessments of new GM crops. As a consequence, a large body of Natural variation in crop composition and the impact of transgenesis

Impact of genetics and environment on the metabolite composition of maize grain.

Abstract: This study sought to assess genetic and environmental impacts on the metabolite composition of maize grain. Gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) measured 119 identified metabolites including free amino acids, free fatty acids, sugars, organic acids, and other small molecules in a range of hybrids derived from 48 inbred lines crossed against two different tester lines (from the C103 and Iodent heterotic groups) and grown at three locations in Iowa. It was reasoned that expanded metabolite coverage would contribute to a comprehensive evaluation of the grain metabolome, its degree of variability, and, in principle, its relationship to other compositional and agronomic features. The metabolic profiling results established that the small molecule metabolite pool is highly dependent on genotypic variation and that levels of certain metabolite classes may have an inverse genotypic relationship to each other. Different metabolic phenotypes were clearly associated with the two...

Metabolome analyses : strategies for systems biology

Abstract: Product Description Metabolome analysis is now recognized as a crucial component of functional genomic and systems biology investigations. Innovative approaches to the study of metabolic regulation in microbial, plant and animal systems are increasingly facilitating the emergence of systems approaches in biology. This book highlights analytical and bioinformatics strategies now available for investigating metabolic networks in microbial, plant and animal systems. The contributing authors are world leaders in this field and they present an unambiguous case for pursuing metabolome analysis as a means to attain a systems level understanding of complex biological systems. From the Back Cover Metabolome Analyses is intended as a follow-up to Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis (Kluwer, 2003). That text offered guidelines to currently available technology, bioinformatics and databases. Evidence was presented showing metabolic profiling as a valuable addition to genomics and proteomics strategies devoted to drug discovery and development. This book focuses on how metabolic profiling is being more comprehensively integrated with the other "omics" technologies. It provides more practical applications of such "panomics" or "Systems Biology" approaches. The expanding use of mass spectrometry as a measurement technology in metabolic profiling is addressed through demonstrated applications. The integration of metabolic profiling and proteomics is probably most developed for plant-based studies, which was not addressed in Volume 1. Other areas related to metabolic profiling continue to show significant development. These include database strategies and an increased acceptance by the pharmaceutical industry of metabolic profiling. Also covered is the use of in silico metabolic networks. Again the focus is primarily on the pharmaceutical industry but the importance of metabolic profiling to studies on human nutrition (a burgeoning area) is discussed. The primary audience for Metabolome Analyses consists of academics (professors, post-doctoral researchers) involved in metabolic analyses, genomics transcriptomics, proteomics, bioinformatics; and corresponding researchers in the pharmaceutical and biotechnology industries (including Big Pharma, mid ?capital and small venture-capital based enterprises) and in research institutes.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Mass spectrometry-based metabolomics.

Abstract: This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.

Gas chromatography mass spectrometry–based metabolite profiling in plants

Abstract: The concept of metabolite profiling has been around for decades, but technical innovations are now enabling it to be carried out on a large scale with respect to the number of both metabolites measured and experiments carried out. Here we provide a detailed protocol for gas chromatography mass spectrometry (GC-MS)-based metabolite profiling that offers a good balance of sensitivity and reliability, being considerably more sensitive than NMR and more robust than liquid chromatography-linked mass spectrometry. We summarize all steps from collecting plant material and sample handling to derivatization procedures, instrumentation settings and evaluating the resultant chromatograms. We also define the contribution of GC-MS-based metabolite profiling to the fields of diagnostics, gene annotation and systems biology. Using the protocol described here facilitates routine determination of the relative levels of 300-500 analytes of polar and nonpolar extracts in approximately 400 experimental samples per week per machine.

Metabolomics: Current analytical platforms and methodologies

Abstract: During the previous decade, a new array of analytical methodologies and technologies were introduced related to the analysis of microbial, plant and animal metabolomes (complete collections of all low molecular weight compounds in a cell). The scientific field of metabolomics was born. In this review, we discuss advances in methodologies and technologies, and outline applications.