Bioinformática y Tecnología alimentaria


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Bioinformática y Tecnología alimentaria

  1. 1. Bioinformatics and data knowledge: the new frontiers for nutrition and foods Frank Desiere,*,y Bruce German,*,x Heribert Watzke,* Andrea Pfeifer* and Sam Saguy{ *Nestle´ Research Center, PO Box 44, 1000 Lausanne 26, Switzerland (tel: +41-21-785-8054; fax: +41-21- 785-8925; e-mail: { The Institute of Biochemistry, Food Science and Nutrition, Faculty of Agriculture, Food and Environmental Quality Sciences, The Hebrew University of Jerusalem, PO Box 12, Rehovot 76100, Israel x Department of Food Science and Technology, University of California, Davis, California, 95616,USA The recent publication of the Human Genome poses the question: how will genome technologies influence food development? Food products will be very different within the decade with considerable new values added as a result of the biological and chemical data that bioinformatics is rapidly converting to usable knowledge. Bioinformatics will provide details of the molecular basis of human health. The immediate benefits of this information will be to extend our understanding of the role of food in the health and well- being of consumers. In the future, bioinformatics will impact foods at a more profound level, defining the physical, structural and biological properties of food commodities leading to new crops, processes and foods with greater quality in all aspects. Bioinformatics will improve the tox- icological assessment of foods making them even safer. Eventually, bioinformatics will extend the already existing trend of personalized choice in the food marketplace to enable consumers to match their food product choices with their own personal health. To build this new knowl- edge and to take full advantage of these tools there is a need for a paradigm shift in assessing, collecting and shar- ing databases, in developing new integrative models of biological structure and function, in standardized experi- mental methods, in data integration and storage, and in analytical and visualization tools. # 2001 Elsevier Science Ltd. All rights reserved. Introduction Bioinformatics and genomics are rapidly expanding fields and in a matter of months have become a crucial technology in Life Science Research. Bioinformatics and knowledge integration have played and will con- tinue to play a enabling role in Food Research inte- grating the massive amounts of data that are generated through new genome-wide experimental procedures with other more traditional techniques. Bioinformatics is defined as: ‘‘Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral, health and nutrition data, including those to acquire, store, organize, archive, analyze, visualize or build bio- logical knowledge from very large and traditionally unre- lated sources’’. It is about to revolutionize biological research and more importantly to apply this research to the human condition. With the availability of the human genome, the completion of the rice genome, the mapping and sequencing of other major crop plants and the publicly available complete genome sequences of ever-growing number of micro-organisms (http://www.ncbi.nlm.nih.- gov/PMGifs/Genomes/org.html), Bioinformatics has, out of necessity, become a key aspect in Life Science Research and Food Research. Bioinformatics is essen- tially a cross-disciplinary activity which includes aspects of computer science, software-engineering and mole- cular and physiological biology. Although database management seems to be the major task, bioinformatics goes much deeper; it provides possible gene-function and cellular role of molecular 0924-2244/02/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0924-2244(01)00089-9 Trends in Food Science & Technology 12 (2002) 215–229 y Corresponding author. Viewpoint
  2. 2. entities, new theoretical frameworks for complex biolo- gical systems and new biological hypotheses for wet-lab research. The combination of genomic data, informa- tion technology and other advanced research tools will give biologists the opportunity to think more broadly— to investigate not only the workings of a single gene, but to study all of the elements of a complex biological sys- tem at the same time. In the future, the starting point for a biological investigation will still be the generation of an hypothesis, but that hypothesis will first be tested theoretically, by modeling and polling existing data- bases. A scientist will begin with a theoretical con- jecture, test it on existing data and only then turning to experiment as a last, not first resort. The same knowledge doctrine is applicable to food science. Food science is a coherent and systematic body of knowledge and understanding of the nature and composition of food biomaterials, and their behavior under the various conditions to which they may be subject. Food technology is the application of food sci- ence to the practical treatment of food materials so as to convert them into food products of the kind, quality and stability, and packaged and distributed, so as to meet the needs of consumers for safe, wholesome, nutritious and attractive foods. ( In this respect, food science integrates the knowledge of several sciences. It includes the knowledge of the chemical composition of food materials, their physical, biological and biochemical properties and behaviors as well as human nutritional requirements and the nutri- tional and trophic factors in food materials; the nature and behavior of enzymes; the microbiology of foods; the interaction of food components with each other, with additives and contaminants, and with packaging mate- rials; the pharmacology and toxicology of food materi- als; and the effects of various manufacturing operations, processes and storage conditions; Thus, food science is an information-based science which integrates knowl- edge from widely disparate sources. The research focus in the food industry is directed by the consumers need for high quality, convenient, tasty, safe and affordable food. The scientific advances in genome research and their biotechnological exploitation alike represent unique opportunities to enhance food performance and to build sound scientific knowledge about its multiple functionalities. In the era before bioinformatics and genomics, biological effects were measurable only according to markers for specific con- ditions (e.g. nutrient deficiencies and impairment of health). Research was therefore targeted solely to con- sumer health problems such as high blood pressure, high cholesterol, lactose intolerance, osteoporosis and diabetes. As our biological knowledge develops in this new era, metabolic conditions consistent with improve- ments in health will be the new markers (Watkins, Hammock, Newman, & German, 2001). This knowl- edge will allow intervention through foods to prevent health problems long before deleterious effects are apparent and the consumer will finally take advantage of the technological breakthrough in these areas which will yield healthy, high quality foods with positive nutritive properties. This is just a part of the promise of how new scientific knowledge of food, gained and made available through bioinformatics will influence the everyday lives of consumers. Information and computer technology Bioinformatics is absolutely dependant on integrated and mature software solutions, which are available through electronic telecommunications to the individual scientist (Table 1). With the massive computing power of modern computer systems we are facing fewer and fewer limitations in storage space and calculation time, the only limiting factor becoming the lack of informa- tion on specific topics. Applications and examples in the food industry Food-grade organisms like bacteria, molds and yeasts are the basis for a variety of biologically based indus- trial food processes (Kuipers, 1999). The fast growing number of complete genomic sequences of organisms relevant to food research (Table 2) promotes the rapid increase in valuable knowledge that can be used in many different areas such as metabolic engineering, improvement of cells as microprocess factories and the development of novel preservation methods.Bioinfor- matics will hasten the development of novel risk assess- ment procedures (Fig. 1). Furthermore, genomic knowledge of bacteria and other microorganisms will revolutionize pre- and probiotic research making it possible to, characterizate the broad range of bacterial properties from growth to stress responses, to multi- species microbial ecology within the human host. Metabolic pathway reconstruction Microbial metabolism has been the basis of a major segment of food processing for centuries. Fermentation of food takes advantage of the ability of desirable microbes to convert substrates (usually carbohydrates) to organic tailor-made compounds contributing to the flavor, structure, texture, stability and safety of the food product. Due to its fundamental importance to such a wide variety of foods from breads to cheeses, wines to sausage, literally over a century of research has focused on understanding microbial metabolism. The potential to build this knowledge into even greater value in foods has been dramatically expanded by the availability of tools to understand and control microbial metabolism using modern genomic and bioinformatic approaches. The production of diacetyl, alanine and ethanol from this sugar metabolism has already been engineered in lactic acid bacteria. With the metabolic reaction network 216 F. Desiere et al. / Trends in Food Science & Technology 12 (2002) 215–229
  3. 3. Table 1. Several bioinformatics resourcesa Bioinformatics companies Company URL Product Area Affymetrix Gene Chip Data Mining Tool Micro-array analysis Applied Biosynthesis BioMerge Server, BioLIMS Genetic analysis system, LIMS Axon Instruments Inc GenePix Pro 3.0 Micro-array analysis Biodiscovery GeneSight GeneSight Micro-array analysis Biomax Informatics BioRS Databases GMBH Pedant-Pro Bioinformatics analysis HarvESTer EST-clustering Compugen Inc. Z3 2D-GE analysis LEADS Expression analysis Gencarta database Prophecy Human genome DB GeneForest DB of expressed genes Clustering Alignment Tools (CAT) EST-clustering Genomica LinkMapper Information management Discovery Manager Hitachi Genetic analysis DNASIS Mol-bio application Systems CHIP Space ChipSpace Expression-analysis DNASpace Bioinformatics analysis IBM DB2 DB-management Incyte Genomics LifeExpress, GEMTools, Bioinformatics tools LifeArray Human genome database LifeSeq Gold Gene-expression microarrays Informax GenoMax Bioinformatics tools Vector NTI Suite Mol-bio tools Integrated Genomics Inc. www.integratedgenomics WITpro, MPW, MicroAceTM Sequencing, genome analysis, metabolic design Lion Bioscience bioSCOUT Bioinformatics tools arraySCOUT Expression analysis genomeSCOUT Genome comparisons SRS DB management ArrayTAG CDNA arrayBase DB of annotated cDNA Molecular Mining Corp. GeneLinker Expression analysis Packard Biochip QuantArray Windows Expression analysis Technologies Celera GeneMatcher Hardware accelerator Paracel Inc CAP4 EST-clustering GeneWise Bioinformatics tools Rosetta Inpharmatics Rosetta Resolver Expression analysis Silicon Genetics Gene Spring Expression analysis, DB Allele Sorter SNP Analysis Silicon Graphics Inc. MineSet Data-mining Spotfire Inc. Data-mining Spotfire Array Expression analysis Commercial bioinformatics web-portals Company Tool URL Ebioinformatics Inc. Bionavigator Over 200 bioinformatics tools, more than 20 databases, access to GCG Integrated Genomics portal, access to an annotated Human Genome sequence, research agents with many bioinformatics tools Incyte IncyteGenomics LifeSeq-ZooSeq-sequence DBs and bioinformatics (Continued on next page) F. Desiere et al. / Trends in Food Science & Technology 12 (2002) 215–229 217
  4. 4. established it becomes possible to determine its under- lying pathway structure by pathway models (Schilling & Palsson, 2000). An important approach to a holistic look at such biological processes uses genomic infor- mation to reconstruct entire metabolic pathways. The integration of the extensive information on metabolic pathways available in the literature and databases (as in KEGG (, EcoCyc (, WIT (http:// with the genomic sequences of bacteria and eventually with stochiometric models will deliver tools to describe cellular processes in detail and to link genotype and phenotype. The match- ing of well annotated genes and their expression level from a new organism with a collection of known meta- bolic pathways from databases is already feasible today. However, the inclusion of kinetic information, which is indispensable to describing the dynamic evolution of these models, remains extremely complex. Beyond that, many of the transcription, regulation and enzymatic control pathways are not well understood. As the knowledge increases in these areas, metabolic recon- struction models will become more important in study- ing the dynamic response of cells to external stimuli. Plants Plant genome research will provide the knowledge to increase the success of genetics and breeding to produce plants of interest for the food industry. Major objectives of plant research are to improve the raw materials of the food supply for higher-quality, better processability, lower cost and safer food. The nutritional health and well-being that plant based foods provide is tradition- ally (DellaPenna, 1999) dominated by their provision of essential vitamins and minerals and only recently has the potential of a number of other health-promoting phytochemicals been recognized to be valuable in the daily diet. Genome sequencing projects are providing novel approaches for identifying plant biosynthetic genes of more specific health importance. Genome research can therefore directly be used to increase the efficiency and effectiveness of breeding for improvement of plants. Biotechnology, accelerated by genomics and bioinformatics, will increase the quality of food, reducing Table 1 (continued) Commercial bioinformatics web-portals Company Tool URL OnLine Research Tools, LifeExpress expression DB Compugen Bioinformatics tools and genome, transcriptome and Proteome DBs, access to PathoGenome Celera Celera Discovery System Access the Celera Human genome sequence, many bioinformatics tools Free bioinformatics resources EMBL CMS Molecular Biology Resource National Centre or Biotechnology Information NCBI European Bioinformatics Institute EBI ExPASy The Institute of Genomic Research TIGR UK Human Genome Mapping Project Resource Centre Weizmann Institute of Science Whitehead Institute MIPS The Sanger Centre GOLD: Genomes OnLine Database Food Research related public bioinformatics sites USDA Biotechnology Information Centre UK Crop Plant Bioinformatics Network (UK CropNet) The USDA-ARS Centre for Bioinformatics and Comparative Genomics a The selection of companies and web-links is not exhaustive and is not an endorsement of the entities mentioned. These resources represent the current status. Due to the dynamic nature of bioinformatics, they may change rapidly. 218 F. Desiere et al. / Trends in Food Science & Technology 12 (2002) 215–229
  5. 5. all aspects of the cost including the impact of food crop production on the environment. Cocoa (Theobroma cacao; Fig. 2) as an example is the raw material for all chocolate containing foods and drinks. The breeding and selection of higher quality beans with superior flavor characteristics has been diffi- cult in the past, since the trees must be maintained at least 3–5 years before the cacao bean can be harvested and analyzed. With the establishment of DNA finger- printing technologies for screening plant collections, RFLP markers for the detection of genotypic relation- ships between breeds or species and the determination of more than 300 molecular markers, breeding pro- grams have been greatly enhanced. The future avail- ability of EST sequences and genome comparisons to other sequenced plants, which rely heavily on bioinfor- matic tools, will result in a further acceleration with the possibility to select for desired traits in an early stage of plant development based on the genotype and the phe- notype (Pridmore et al., 2000). Implication of genomics/bioinformatics for health and nutrition Genomics, enabled by bioinformatics will contribute to an improved understanding of the molecular mech- anisms underlying the relationships between food and health, from basic nutrient actions to the interactions between food microorganisms and the human intestinal system, including the gut and immunocompetent cells, and the mechanisms underlying the interactions of the microbial community in the intestinal tract (German, Schiffrin, Reniero, Mollet, Pfeifer, & Neeser, 1999). With the recent explosion of genome data, including genomics, transcriptomics, proteomics, metabolomics and structural genomics, bioinformatics is addressing the task of developing computational methods to deal with the massive flows of data emerging from modern experimental approaches in relating genotype to pheno- type (Lee & Lee, 2000). The approaches include func- tional and comparative genomics and high-throughput technologies such as genome sequencing and DNA microarrays. The knowledge developed from this new science will expand nutrition in three dimensions, mechanism, human variation and time: the genetic mechanisms underlying health, the basis of individual variations in metabolism and the time scales during which diet influences metabolism. The scientific knowledge of both the genetic variation amongst humans and the response of individual genes to ingested molecules (drugs, foods and toxins) is growing Table 2. Genome projects of organisms interesting for the food industrya Organism Genome size (Mbp)b Organism Genome size (Mbp) Spoilage/pathogens Food-grade Bacillus anthracis 4.5/progr. Aspergillus nidulans 29/progr. Bacillus stearothermophilus 10/progr. Bacillus subtilis 4.20/published Candida albicans 15/progr. Lactobacillus acidophilus 1.9/progr. Campylobacter jejuni 1.641/published Lactobacillus sp. 2/progr. Clostridium acetobutylicum 4.1/progr. Lactococcus lactis 2.365/published Enterococcus faecalis 3/progr. Saccharomyces cerevisiae 12.069/published Escherichia coli O157:H7 4.1/published Streptococcus thermophilus 2/progr. Helicobacter pylori 1.667/published Listeria innocua 3.2/progr. Listeria monocytogenes 2.9/completed Mycobacterium bovis 4.4/progr. Others: Mycobacterium leprae 3.2/published Mycobacterium tuberculosis 4.411/published Arabidopsis thaliana (thale cress) 115.428/published Pseudomonas aeruginosa 6.264/published Bos taurus (Cattle) Mapping Pseudomonas putida 6.1/progr. Canis familiaris (Dog) Mapping Salmonella enteritidis 4.5/progr. Felis catus (Cat) Mapping Salmonella paratyphi A 4.6/progr. Glycine max (Soybean) Mapping Salmonella typhi 4.5/progr. Homo sapiens (Human) 3200/published Salmonella typhimurium 4.5/progr. Mus musculus (Mouse) Progr. Shewanella putrefaciens 4.5/progr. Oryza sativa (Rice) 450/finished Shigella flexneri 4.7/progr. Phaseolus vulgaris (Bean) Progr. Staphylococcus aureus 2.8/published Rattus norvegicus (Rat) Progr. Staphylococcus epidermidis 2.4/progr. Solanum tuberosum (Potato) EST-sequencing Streptococcus mutans 2.2/progr. Triticum aestivum (Wheat) Mapping Streptococcus pneumoniae 2/completed Zea mays (Maize) Mapping Streptococcus pyogenes 1.8/published Thermus thermophilus 1.8/progr. Vibrio cholerae 4/published a This table represents the current status. Due to the dynamic nature of bioinformatics it may change rapidly. b MBP, number of mega base pairs; progr., project in progress. F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229 219
  6. 6. exponentially as a result of the arrival of the human genome and the tools of functional genomics (DNA arrays, etc.). This explosion of information is only being converted into usable knowledge because of the arrival of the massive computing power and the bioinformatic tools needed to apply them to large data sets being generated by nutrition-related research. This knowledge will not only drive a new generation of foods with additional values but change dramatically the ability of foods to influence individual quality of life. This knowledge promises also to drive a new value system for agriculture itself. Genetic responsiveness or gene expression The ability of nutrients to directly control the expres- sion of particular genes is at the heart of a new generation of nutritional science allowing researchers to apply genomic information to technologies that can quantify the amount of actively transcribing genes in any cell at any time (e.g. gene expression arrays). With this tech- nology in place, scientists of every biological discipline are discovering the interaction between organisms and their environment with an intimacy never thought pos- sible. Nutrition is at its heart, a multidisciplinary field focusing on integrative metabolism of animals and humans. Nutritionists have strived for the last century to deduce the mechanistic basis of the apparent strong relationship between diet and health through under- standing the interaction of nutrients with metabolic pathways. Needless to say, this was a daunting task with the traditional tools of reductionism biochemistry. Most nutrients affect a wide range of biochemical pathways. The net result is that nutrients exert multiple effects: pleiotropic dysfunctions in their relative absence, i.e. deficiencies, and pleiotropic benefits in their return to appropriate, optimal intakes. Reductionism biochemical Fig. 1. Electron micrograph of Streptococcus thermophilus (oval chains) and Lactobacillus johnsonii (rod-like chains) cells used for starters cultures in food fermentations. Fig. 2. Example of a Cacao plant (Theobroma cacao L.) in natural form as fruits, as beans and finally as ground powder. Cacao trees must be maintained approx. 3–5 years before harvesting the cacao. Selection of specific traits based on genotype in the early development of the plant is therefore highly desirable. Fig. 4. Food production is based on biological raw materials which are refined into food ingredients. A unifiying approach is proposed on the basis of common basic and material properties of the comprising molecules in both domains. Moreover, the vast store of knowledge currently being produced by the biomical sciences (genomics, proteomics, metabolomics) will improve the knowledge on ingredient characteristics and behaviours. Fig. 3. The perceived food qualities are driven by flavors and tex- ture. Both are composite events whose disparate elements show specific interactions. While the elements can be controlled sepa- rately, only understanding the underlying neuro-physiological processes will lead to optimizing the flavor and texture impact of foods. 220 F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229
  7. 7. approaches describe very well the effects of a single nutrient’s interaction with a single target; however, they fail to adequately explore the multiplicity of metabolic effects on the entire organism. The perspective of mod- ern genomics is ostensibly the reverse (expansionist) approach, to measure everything. Genomic-based investigations do not avoid pleiotropic behavior of exo- genous nutrients; quite the contrary, they reveal it. The goal of differential gene expression array experiments are to describe the full spectrum of transcriptional responses to any variable, including nutrients. Such global experimental designs are only possible due to the advent of bioinformatic tools to adequately manage and analyze the sheer volume of data that are produced. With the arrival of broadly parallel assessment tools including gene expression arrays and metabolomics, single biomarkers of disease risk will no longer be con- sidered useful (Watkins et al., 2001). Since it will be as straightforward to measure the expression of 30,000 genes as the expression of one gene, knowledge from expression profiling will impact health assessment. It is equally certain that the days of building dossiers of effi- cacy and safety based on a single metabolic endpoint, e.g. cholesterol, are limited. Such comprehensive knowledge of the effects of discrete food and nutritional variables to overall metabolism will add new under- standing to their health value. Genetic variability With the genome of one ‘individual’ human com- pleted, the effective technologies to establish variations from that single genome, are being implemented. The Single Nucleotide Polymorphisms (SNP) Consortium ( is mapping the polymorphic regions of the genome that control individual pheno- typic differences among the population (Sachida- nandam et al., 2001). While these variations are being viewed initially as the key to the discovery of genetic diseases, they are also the keys to individual variation in diet and health. Sequence variation in particular genes even as slight as single nucleotides can influence the quantitative need for and physiological response to various nutrients. Knowing that genes influence nutri- tion, of course is not new. An understanding of this variation is inherent in population recommendations for essential nutrients (Young Scrimshaw, 1979). How- ever, allowing for the variation in human genetics by incorporating a large margin for error in quantitative recommendations is not the same as designing diets for specific individuals according to their genetic profiles (Eckhardt, 2001; Nichols, 2000). An example of poly- morphisms that influence nutrition and disease is phe- nylketonuria, in which the inability to metabolize phenylalanine renders this nutrient toxic (Lindee, 2000). The occurrence of lactose intolerance is due to poly- morphisms both in the structure of the lactase gene which produce dysfunctional enzyme and in regulatory regions of the genome that prevent perfectly functional lactase enzyme from being produced in adults (Harvey et al., 1998). With genomics will come the knowledge of the integrative nature of multiple genes in predicting health. The potential opportunity of bioinformatics to deliver that knowledge to the individual consumer will eventually lead to individualized dietary choices in the hands of the consumer. This bold future is arriving because of bioinformatic tools capable of managing the volume of data implied by quantitatively assessing indi- vidual metabolism and intervening in an that indivi- dual’s metabolism using foods to improve their health. Genomic and bioinformatic tools will improve human clinical research. Historically, many nutrition trials failed to find statistically significant effects of various nutrients and food choices not because there was no benefit, but because the magnitude of the benefit was small relative to the overall variability in a sample of humans chosen at random from the population. Humans do not respond homogeneously to even the most straightforward nutritional variables. A great value of genotyping individuals in clinical trials is to begin to assign the variation of the population to spe- cific genetic differences. Clinical and epidemiological trials are now being analyzed using SNP data as inde- pendent input variables (Takeoka et al., 2001). Most clinical trials are already cataloguing the SNPs of genes whose variation in function have shown to be important to the endpoint measures of these trials, for example cancer, autoimmunity and heart disease (Marth et al., 2001). Such ‘data-mining’ approaches have been suc- cessful not only in identifying the causes of statistical variation among trial participants but in identifying the potential biochemical mechanisms responsible for the variation in response. This approach is already proving so powerful that scientific agencies are recognizing that traditional avenues of scientific publishing aren’t ade- quate and the processes of scientific discovery of genetic polymorphism and health are accelerated by the avail- ability of SNP data sets and bioinformatic packages on the internet (Clifford, Edmonson, Hu, Nguyen, Scherpbier, Buetow, 2000). Genetic polymorphism and nutrient requirements Polymorphisms in the various genes encoding enzymes, transporters and regulatory proteins affect the absolute quantities of essential nutrients that are neces- sary to achieve sufficiency, including vitamins, minerals, etc. (Bailey Gregory, 1999). Thus, the variation in the population’s nutrient status is not simply the result of variations in food intakes but also the result of inherent variation amongst individuals within the population in their genetically defined abilities to absorb, metabolize and utilize these nutrients. Recommended daily allow- ances of each nutrient are determined to meet the needs F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229 221
  8. 8. of a statistically representative fraction of the popula- tion; however, the range of responses to both micro- and macronutrients in the general population is large. Very recent research using genomic tools is highlighting just how specifically individual food choices, genetics and nutrition are linked. Polymorphism in a recently identified sweet receptor protein has been proposed to be the basis for the varying intakes of caloric-rich foods, i.e. the famous sweet tooth (Davenport, 2001). As genomics begins to reveal the basis for food pre- ference and the respective roles of genetics and envir- onment, nutritional superior foods could be made more organoleptically attractive to precisely the subset of the population for whom they are most appropriate. How- ever, an important step is still missing. At this point, while the technologies to describe the effects of diet on various individuals experimentally are widely used for example in clinical trials, the technologies are not yet part of routine consumer assessment. Therefore, con- sumers cannot take advantage of nutritional knowledge about themselves, because they do not have it. This lack of knowledge transfer is clearly the largest single factor constraining a more widespread improvement in nutri- tional health in the consumer population. Genetic variation and the response to variations in overall diet Genetic differences affect the basic metabolism of macronutrients and in particular fat and carbohydrate in humans. For example, polymorphisms in the apo- protein genes (apoE, apoAIV) or lipoprotein catalysts (lipoprotein lipase) have been shown to directly affect the clearance of dietary lipids. Hence polymorphisms in lipid metabolic genes dictate the response of these indi- viduals to dietary fat (Hockey et al., 2001; Pimstone et al., 1996). Polymorphism in the genes encoding for the apoE protein influence the functionality of this protein in clearing liver-derived lipoproteins (VLDL and LDL) from blood (Weintraub, Eisenberg, Breslow, 1986). Health outcomes beyond heart disease including Alz- heimer’s disease have been shown to be correlated to apoE phenotypes. Once again, diet plays a differential role in the development of these diseases according to genotype through the role of diet in influencing the quantitative flux of hepatic lipoprotein metabolism (Corella et al., 2001). Many consumers are concerned about the widespread application of genomic testing in the population because they see little value to themselves. However, there is great value in acquiring knowledge about individual variation in diet-responsive genes if it can lead to suc- cessful intervention. For example, genotype predicts a difference in post-prandial lipid metabolism of dietary fat (Hockey et al., 2001). The most exciting aspect of this discovery is the realization that this knowledge is not just academic, but leads to an immediate individual recommendation how to alter the intakes of dietary fat for those affected. Thus, the information of how an individual responds to foods provides that individual with the means to change their diet to improve their health. With each new discovery of genetic polymorph- isms linked to health, the complexity of the science increases. Fortunately, modern bioinformatics tools are inherently integrative adding each new discovery into a rapidly expanding coherent picture of diet and health of individual consumers. Food quality Food is one of life’s great delights. Modern science and technology have provided unparalleled value to consumers in the breadth of individual choices in deli- cious, safe and nutritious foods. This great value has been driven by scientific knowledge at all levels of the agricultural food chain from genetic improvements in production agriculture to food process engineering to precision in the analysis of consumer sensation. With its power to build detailed molecular knowledge of biolo- gical organisms, modern bioinformatic technologies are assembling the means to re-invent the food supply. In no other aspect of life do humans interface with other biological organisms to the same extent as in the con- sumption of food. Thus, the most tangible, daily value that genomics will eventually produce for humans is a dramatic increase in the quality of their lives through the quality of their foods. Bioinformatics will help understand the basis of different food flavors, and tex- tures and even further why we find them delicious, and hence how to enhance that experience. Bioinformatics will not only define in molecular detail which foods are safe, but develop foods that make consumers themselves safer. Bioinformatics will not only improve the processes of forming foods, but design foods that form themselves. The understanding of the biomolecular basis of flavor perception has been a major success of the last 5 years of scientific investigation in the molecular biology of sensation (Fig. 3). Success in identifying, in molecular and genetic details, the taste and flavor receptors has been remark- able in the past months. These include: Bitter: A family of 50 G protein-coupled receptors (GPCRs) identified in human taste cells (Chandrashekar et al., 2000); Salt: The epithelial ion channel, ENaC is responsible for over 80% of salt taste transduc- tion (Nagel, Szellas, Riordan, Friedrich, Har- tung, 2001); Sour: An ion channel, identical to degenerin-1, is proposed to be the receptor (Ugawa et al., 1998); Umami: A ‘splice variant’ of brain glutamate receptor, mGluR4 identified in rat taste cells (Matsunami, Montmayeur, Buck, 2000); and 222 F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229
  9. 9. Sweet: The putative identity of the sweetness receptor identified as a G protein coupled recep- tor Tas1r3 (Max et al., 2001). The discovery of these taste receptors is being trans- lated rapidly into a variety of research programs designed to discover the next generation of taste modi- fiers for foods. The sugar substitutes demonstrated the potential for replacing the traditional sweet molecules (simple sugars) with non-caloric, non-cariagenic and non-glycemic alternatives in a variety of food products. Now, with the balance of taste receptors known, it will be possible to develop flavor systems that either produce or enhance positive or mask negative tastes. Much of this work will be possible using combinatorial chemistry approaches that use bioinformatic tools to screen thou- sands of molecules and combinations at a time. Such molecular simulations once took weeks and very large super-computer installations. New developments in computing power, computational algorithms and soft- ware and the available databases of known structures and successful simulations has brought molecular mod- eling into mainstream food chemistry. Such simulations will make it possible to develop not only more intense tasting compounds as food additives, but understand the basis of taste persistence, antagonism and com- plementation. Flavor systems will become more com- plex, more attractive and more individualized to consumers. Olfaction: a family of 1000 GPCRs, about 300 identified Not far behind the taste receptors the much more abundant odor receptors are being identified as well. The full olfactory complement of genes has been pub- lished (Glusman, Yanai, Rubin, Lancet, 2001). The number of odor receptors exceeds the number of taste receptors by a factor of 100. In spite of this expansion in size and complexity, bioinformatics will have little diffi- culty in translating the principles of ligand–receptor interaction developed with taste into similar applica- tions to odor sensations. With such capabilities, sophis- ticated flavor systems will be designed from the perspective not simply of what is available in natural commodities and foods, but with final flavor perception as the goal. Ultimately, it will be possible to design fla- vor systems that optimize flavor perception in highly nutritious foods that are currently organoleptically undesirable in spite of their superior health value. Making the next connection, i.e. understanding the basis for healthy and unhealthy food choices, is already proceeding. Recently, the connection between gratification and the brain was verified in rats (Cardinal et al., 2001). Similar developments in our understanding of the brain could lead the way to furnish tailor made specific orga- nolopetic attributes as well as nutrition needs. Bioinformatics and food processing The most immediate application of bioinformatics to food processing will be in optimizing the quantitative compositional parameters of traditional unit operations. Food commodities are processed largely to achieve sto- rage stability and safety with considerable excess of energy applied to ensure a large margin for error. This margin of error is necessary due to our inexact knowl- edge of the composition and structural complexity of biological materials, the natural variability of living organisms as food process input streams and the response of these materials to processing parameters. With the considerable knowledge of biological organ- isms from bacteria and viruses to plants and animals that is emerging from bioinformatics, food process design will become optimized with narrower margins of all cost-important inputs, especially energy. The great future for food processing however is not in simply processing for greater safety, but in merging biological knowledge of living organisms with the bio- material knowledge necessary to convert them to foods. Traditional food processing relies on the aggressive input of energy to restructure the biomaterials of living organisms into simpler macrostructure forms of stable, relatively uniform foods. In most cases the inherent biological properties of the living systems are lost to the final food product in the need to eliminate potentially hazardous properties of some of the constituent mole- cules (protease inhibitors, etc.). The arrival of the knowledge base of modern bioinformatics, however, is providing a detailed description of the inherent com- plexity of biological macromolecules within living cells together with the structural properties of these mole- cules that provide much of their functions. Such knowledge is the cornerstone of functional genomics and proteomics. The arrival of such knowledge, how- ever, provides an unprecedented opportunity to trans- late this knowledge into an equally accurate assessment of the biomaterial properties of each of the molecules in a complex mixture. It will soon be possible to use the inherent structural properties of natural food commod- ities to self-assemble new foods with a minimum of external energy retaining a maximum of biological and nutritional value. The biological structure–function relationships discovered through bioinformatics of liv- ing systems will be able to be mapped into the struc- ture–function relationships of the next generation of foods with delightful results (Fig. 4). All foodstuffs are ostensibly modified tissues. Thus, the natural biomaterial properties of the molecules that make up living organisms underlie the basic biomaterial properties of foods. In most traditional food process- ing, however, little advantage is taken of the unique F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229 223
  10. 10. properties of specific molecules and instead, all bio- molecules of a particular class, e.g. proteins, are exposed to substantial physical, thermal and mechanical energy to make these properties uniform in order to restructure the material into more stable, and/or more bioavailable food systems. Such processing eliminates the subtle differences within most of the classes of the major bio- molecules that are inherent to and the basis of complex structure–function relationships of living organisms. Processing replaces biological complexity with the statistical average properties of the broad classes of biomaterials, i.e. proteins, carbohydrates, lipids. The processing of commodities to eliminate the com- plexity of their biological structures are not necessary to the quality of foods, in fact the opposite. There are vivid examples in which highly specific biological properties of the original living organism are a key to the proces- sing strategy and ultimately the organoleptic attractive- ness of final food products. The renneting of bovine milk to induce the natural aggregation of milk caseins leading to the gelation events of cheese manufacture is such a process. The final product takes advantage of the unique self-assembly properties of milk casein micelles that are colloidally stabilized in milk by kappa caseins but destabilized when enzymatically cleaved of their solubilizing glycomacropeptide. Another example is leavened bread in which a combination of both compo- site processing and biological restructuring is the basis of breads’ structures, textures and nutrition. In this case, wheat seeds are ground to disassemble the major- ity of their biological structures through mechanical energy, but then the biological processes of yeast fer- mentation achieve simultaneously the enzymatic elim- ination of phytic acid during dough incubation and the biochemical production of carbon dioxide gas as lea- vening within a mechanically reworked protein gel structure. In each of these cases, bread and cheese, tak- ing advantage of the biological properties of the living organisms, led to substantial value both organolepti- cally and in greater safety and nutritional value. Fur- thermore, the inherent variation in biological organisms that plagues the standardization of simpler food pro- cessing objectives is not a disadvantage to these two food staples, but rather a wonderful benefit leading to literally hundreds of distinctly flavored and textured cheeses and varieties of breads. Thus, cheeses and breads provide proof of what is possible when the bio- logical processes of catalysis, self-assembly and restruc- turation is retained as the basis of food processing. Heretofore, empirical trial and error was the major route to discovery of biodriven food processing. How- ever, the biological knowledge that is emerging with functional genomics, proteomics and metabolomics is providing precisely the knowledge necessary to read- dress food processing using bimolecular activities rather than simply composite biomaterial properties. The entire protein–protein interaction map of yeast, i.e. all possible interactions between the 6000 proteins of yeast, has been completed (Ito, Chiba, Ozawa, Yoshida, Hat- tori, Sazaki, 2001). In the future, the structure func- tion properties of living organisms that are emerging so rapidly with bioinformatics will increasingly dictate the design of new foods and new food processes. Once such tools are in hand, process design engineers can then work in a coordinated fashion with plant bioengineers to produce crops that are not simply enriched in a single valuable component, but instead redesigned with a renewed purpose to increase the myriad values of foods in providing quality of life. Flavor analysis The complex flavor profiles of many delightful com- modities (e.g. fruits, baked goods) are not due to single compounds but rather are the result of the presence and interactions of literally dozens of different molecules. This knowledge will provide the link and the compiler integrating processing, quality and nutrition paving the road for new product development based on insight knowledge of actual consumers’ preferences and needs. The impact of genomics on the quality assurance of foods Food safety is becoming more and more a major area of concern for consumers and the food industry has developed a coherent research programme to ensure food safety with well-established classical methodolo- gies but also new state-of-the-art research tools. The goal here is to ensure that the inactivation or inhibition of undesired microbes is possible using the minimum treatment of foods necessary, to increase the under- standing on the ecology of food-born microbial popu- lations, to find-out how these populations respond to environmental factors like stress and last but not least the toxicological evaluation of foods and food com- pounds. The genomics era delivers many new tools like pro- teomics and DNA-array technology to tackle the abovementioned problems. These new technologies are now a vital part of the scientific strategic plan to serve the diet and health theme and to provide safe food to the consumer. Toxicogenomics, for example, is an emerging field which utilizes DNA arrays (tox-chips) to test the tox- icological effects of a specific compound. These DNA arrays probe human or animal genetic material printed on miniature devices to profile gene expression in cells exposed to test compounds rather than using animal pathology to define illness (Lovett, 2000). The advan- tages of this test goes beyond the speed and the ease of use which is typical for DNA expression analysis; it also reduces massively animal testing. Another challenge here is the massive amounts of data which are produced 224 F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229
  11. 11. via these high-density DNA arrays and the analysis and the interpretation of the results is a real challenge. Once this task has been tackled, the integration of tox-chip data must be integrated into the knowledge basis of the research institution to draw a maximum of benefit for the acceleration of the development pipeline. Data integration The explosion of data, ever increasing developments in information technology, abundant availability of powerful computers and the ability to connect them worldwide, affects enormous changes in knowledge management. However, in order to gain full access to these emerging powerful tools, it is paramount to resolve the enormous challenge of unifying complex and dissimilar data, each describing a large spectrum of applications, each of which could be extremely far apart. The need to combine observations from numer- ous sources and domains, into a unified, seamlessly searchable database and turning it into knowledge is only the beginning of this uphill battle that will impact every facet of food and nutrition science. Advances in data collection, storage and distribution technologies have far outpaced techniques to assist the analysis and digestion of this information. In the past, most databases were quite small and utilized as typeset tables or simple online documents. Today, far larger and more complex databases are emerging in many fields at a level well beyond the reach of the traditional model of solitary workers or small groups. (Maurer, Firestone, Scriver, 2000). This has led to an all-too- common data glut situation creating a strong need and a valuable opportunity for extracting knowledge from databases collected throughout RD and elsewhere. One of the greatest challenges we are facing is how to turn this rapidly expanding or even exploding data into accessible and actionable knowledge. Moreover, food and nutrition RD is engaged in an assortment of complex studies producing enpoint measures comprised of numeric, sensory and perceptions, structure, biologi- cal, chemical and vision data. This need to manage such disparate inputs is critical as the amount of data dou- bles almost every 20 months (Colbourn Rowe, 2000). Underlying the need to convert data into actionable knowledge, organizations have started an aggressive effort to deploy Knowledge Discovery in Databases (KDD), Knowledge Management (KM), Data Mining (DM) and Intellectual Asset Management (IAM). These areas of common interest to researchers are: pattern recognition, statistics and statistical inference, intelligent databases, knowledge acquisition, data visualization, high performance computing and expert systems, to mention just a few. Although these high technology information management systems are starting to play a fundamental role for the experts who are working on their develop- ment, they are however almost invisible for most users. Data mining refers to a new genre of bioinformatics tools used to sift through the mass of raw data, finding and extracting relevant information and developing relationships among them. As advances in instrumenta- tion and experimental techniques have led to the accu- mulation of massive amounts of information, data mining applications are providing the tools to harvest the fruits of these labors. Maximally useful data mining applications should: Process information from disparate experimental techniques, and technologies, including data that have both temporal (time studies) and spatial (organism, organ, cell type, sub-cellular location) dimensions; Identifying and interpreting outlying, spurious and rare data; Analyze data in an iterative process, re-applying gained knowledge to constantly examine and re- examine data; Utilize novel text-mining and pattern recognition algorithms. In the early years of modern scientific discovery, research findings would appear in a journal and then get buried in the depths of poorly accessible library space. Information existed in various formats (e.g. graphic, hard copy, tape), and was not easily retrievable. Data analyses were generally limited to slide rule and manual manipulation. However, technological advances in computational science and scientific instrumentation have facilitated the exponential growth, not only in data, but also the tools to record and analyze these data. What was the Computer Age as we entered the 1990s has been supplanted by the Information Age. This change was made possible by the advent of the Internet, in particular the World Wide Web. This innovative, truly universal mechanism of information dissemina- tion, in concert with new computation-based analytical tools, has provided practically endless opportunities for scientific discovery. The exponential rate of discovery in the era of mod- ern molecular biology is phenomenal, culminating with the June 2000 announcement that preliminary sequen- cing of the human genome had been completed. This landmark is just a taste of the scientific successes that are to come. As impressive as it is, the determination of the sequence of the approximately 3.2 billion nucleo- tides of the human genome, encoding an estimated 100,000 proteins, represents only the first step down a long road of knowledge discovery and its application to added value to consumers. Another application of bioinformatics that is growing extremely fast is Chemometrics, the chemical discipline that applies mathematics and statistical methods, and F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229 225
  12. 12. uses designs of experiment to understand the effects and interactions of several process parameters, and also to optimize specific outcomes (Otto, 1999). Chemometrics, originally rooted in analytical chemistry, is currently more focused on addressing issues related to molecular conformations and behavior. With the increasing avail- ability of databases (e.g. through WWW), the need for improved techniques that help extracting information and turn it into knowledge has been therefore ever growing (Brazma, Robinson, Cameron, Ashburner, 2000). It should be highlighted that food and nutrition are related topics and are prone to another more crucial problem. Generally, advanced data mining and other sophisticated search tools are no better than the infor- mation provided. As the scientific literature may contain both editorial and/or more fundamental errors (e.g. false methodology, unjustified conclusions, faulty appa- ratus), hence the need for the impartial scrutiny of human editorial judgement is indispensable. One might make a compelling case that the value of the databases is compromised most by their inherent bias: in concept and design towards only benefit and in publication towards only a positive outcome. Databases are most valuable to data mining and bioinformatics searchers when they are balanced. It should be emphasized that if data mining techniques are polling databases that are so inherently unbalanced that no matter what the truth is, the data mining will invariably reflect the inherent bias in the databases that has been the result of con- scious or unconscious editorial influence. Hence, like most other computer applications, the outcome in the short term will be only as good as the quality of the data. Moreover, the more complex the calculation is, the more paramount is the need for adequate checks and balances. The solution is for more balanced data collection. At present, this is not the norm for nutri- tional research. Typical examples, far from being representative, yet demonstrating how knowledge management is utilized, are provided: 1. Food industry—A software package (NetStat) was developed for analyzing reams of data, and is reported to have changed every aspect of the Pillsbury company (i.e. from the way it develops new products to how it capitalizes on consumers’ tastes). The NetStat uniqueness is its ability to share information across all the company’s nine brands including manufacturing lines. The pro- gram is implemented as a Web site shared by researchers across a 70-country conglomerate, and allows engineers and scientists to perform rigorous tests and compare them with data and specifications and consumer information (Crockett, 2000). 2. Pharmaceutical industry—Building of huge combinatorial libraries by automatically synthe- sizing all possible combinations of components is underway. The number of compounds in such a database can now be confidently stated to be in the hundreds of thousands or even millions. The new automated screening technologies can test each of these compounds, giving an indication of whether a compound is going to be effective against a specific biochemical target and a spe- cific disease. 3. Chemical industry—Chemical reaction databases are available and could be used to derive knowl- edge for predicting the course and products of chemical reactions as well as to design organic syntheses. To reach this goal, the essential fea- tures of the chemical reaction have only to be recognized and generalized. This was achieved by classifying a set of reactions by unsupervised learning techniques such as self-organizing maps (Kohonen). In this approach, reactions are char- acterized by physicochemical features directly derived by computations from the constitution of the starting materials or products of a reaction (Gasteiger Sacher, 1999). 4. Information industry—Chemical Abstracts Ser- vice (CAS) has launched its SciFinder 2000, empowering the user with greater visualization tools and the ability to cross-tabulate and display searches graphically. This ‘wizard’ allows a researcher to simultaneously locate information within a multitude of databases and subse- quently explore the relationship between them. The retrieved data may be displayed in a 3D representation that can be further manipulated to zero in on the requisite research. The use of such data mining could revolutionize the way scientists approach their research projects (Massie, 2000). 5. Environmental safety—To reduce the need for animal testing, Unilever has applied data mining techniques (Clementine) to model skin corrosiv- ity of organic acids, bases and phenols. This facilitated uncovering new information from the existing database, and eventually will furnish toxicologists with neural network based packa- ges to help assess and predict corrosivity and other toxicological properties. This approach is much more approachable than current tech- niques (e.g. principal component analysis). It is hoped that it will lead to a movement away from in vivo and in vitro experimentation towards ‘in silico’ analyses, reducing costs, time scales for product development, and minimizing the need for animal testing ( clementine/). 226 F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229
  13. 13. 6. Consumers—Data mining techniques are now being used to extract a surprising amount of information on individual customers and their buying patterns. These data are then used to develop customer loyalty programs, for carefully focused marketing or additional services that fit the customer’s individual preferences, and for identifying possible synergies with other compa- nies who might share the same or similar base of customers. Applications are ranging from direct marketers, books, to credit card companies, which identify trends, potential users, and target marketing strategies. Development needs for data integration Computational biology and electronic technologies will be crucial for the future of Life science research and offer in addition promising opportunities to many industries. Future central issues for the shortening of research driven product development and gaining com- petitive advantage will be the issue of data integration. Companies which started initiatives in this area are now struggling to integrate legacy enterprise resource plan- ning and data warehouse technologies with bioinfor- matics. Compared to this challenge all other issues including electronic commerce fade into insignificance. To be successful, companies are now focusing on spe- cific enabling technologies like Java, message-oriented middleware and XML to encourage web-based colla- boration between research teams and operating units. Clear integration paths and benchmarks are, however, still lacking. The ability to make better, faster and more innovative research decisions is paramount to progress. Emerging technologies and the exploding amount of data high- light the need for new approaches. The availability of a large number of fast PC’s connected together allows parallel processing, overcoming barriers due to speed and computer resources. However, the ability to inte- grate the data and utilize KM is a real challenge, which is compounded by the increased economic pressures and demanding marketplace, global competition, regulation, and consumer demands. Implementing these new meth- odologies could open new avenues improving our ability to quickly and efficiently gain new knowledge and insights from cell structure to consumer perceived sen- sory attributes. Ultimately, one should envision ‘an engine’ able to ‘plug and play’ into various data domains, integrating all the facets of a business increas- ing the likelihood of identifying the next target or new food product for development and quality improvement addressing the consumers’ real and perceived needs. Planning for the future is no longer a luxury; it is a standard operating procedure for the existence and well- being of the enterprise. Future areas required development are: Models—Models that describe a class of reac- tions in an actual food system or food concept ‘in silico’ (Hultzman, 2000). These models should be designed so that they could also be applied for testing the validity of previous data reported. This goal also mandates that terminology be harmonized, to improve accessibility. It could lead to a movement away from in vivo and in vitro experimentation towards ‘in silico’ ana- lyses, reducing costs, time scales for product development, and minimizing the need for ani- mal testing. Standardized protocols—Standard experimental design and replication must be set if data accu- mulated by different groups and various techni- ques should be integrated. Thus, leading to improved reproducibility, reduce variability, fur- nishing truly quantitative data, increase sensitiv- ity and provides means for comparing data obtained from different sets (e.g. Lee, Kuo, Whitmore, Sklar, 2000). Data integration and storage—Linking, inte- grating interoperable large databases with differ- ent heterogeneous structure and data types is far from being a straightforward task when con- sidering the vast differences that do exist between various domains makes this task immense. Simi- larly the ever-growing amount of information needs adequate storage and maintenance. Cata- loging and automated extraction (e.g. Andrade Bork, 2000) are paramount. As the informa- tion complexity and quantity grows, the food practitioners need to define and develop a unified and acceptable approach. This task requires sig- nificant planning where all facets of the food, nutrition, biology and other domains are involved. Predictive tools—Techniques allowing the auto- mated discovery from large and different data sets need to be further developed before they could be fully utilized in the food and nutrition domains. Once implemented, it would open new avenues towards broad interdisciplinary science that involves both conceptual and practical tools for generation, processing, analyzing and propa- gation of information leading ultimately to fun- damental understanding. Data visualization—A large volume of the human brain is devoted to visual data processing (Going Gusterson, 1999). Data visualization methods therefore will play a significant role allowing pattern characteristics and recognition. Paradigm shift—Food and nutrition science should develop a holistic approach, by moving F. Desiere et al. / Trends in Food Science Technology 12 (2002) 215–229 227
  14. 14. away from studying ‘vertically’ the role(s) of few variables to ‘horizontally’ studying simulta- neously many variables and applying advanced modeling and analysis techniques (e.g., Fiehn, Kloska, Altmann, 2001). Conclusions Biomics, comprised of genomics, proteomics and metabolomics, is taking up its position as a lead science for the 21st century. Its influence is already felt through out the biological sciences. Moreover, its influence on nutrition and food science will generate a unified area of research where both nutritional benefit and traditional food values become parts of an extended life science driving towards enhanced quality of life. Impacts of the knowledge obtained through this research on raw materials, ingredients, safety, quality and nutrition can be expected to have a far greater impact on product improvements than today’s functional food research is imagining. 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