Online tools for bioinformatics analyses in nutrition sciences

The Need for Precision Medicine to be Applied to Diabetes

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

From Food for Survival to Food for Personalized Optimal Health: A Historical Perspective of How Food and Nutrition Gave Rise to Nutrigenomics

Human nutrition has progressed impressively from the hunter-gatherer mode to that of promising personalized nutrition for health optimization through advanced and sophisticated omics technologies. The contemporary major diseases, while having strong genetic components, do not conform to Mendelian genetics; hence, their expression/manifestation is not controlled by a single gene. Noncommunicable diseases such as obesity, cancer, type 2 diabetes mellitus, and cardiovascular disease are attributed to a series of chronic anomalies closely related to dietary, among other, environmental factors, and consistent deregulation of one or more groups of genes (polygenic). Collectively, these diseases constitute the main cause of death globally and pose tremendous financial burden on healthcare systems. Dietary interventions offer significant possibilities for cost-effective strategies to reduce risk of a series of metabolic diseases and/or improve the outcome of prognosis. In recent decades, the ability of particular nutrients to influence certain cellular functions as well as the regulation of several metabolic pathways via genomic interplay has been demonstrated. Nutrients can influence cellular responses and hence exert an effect on health parameters and outcomes. Several nutrients have been documented to extend their regulatory capacity at various levels including gene expression profile signatures' modulation. In addition, specific nutrients can modulate expression/activation of genes that encode regulatory hormones, which in turn are signaling agents strongly affecting metabolism and subsequently risk levels for certain metabolic diseases. The field of nutrigenomics attempts to revolutionize modern thinking on diet, food, and health; whether it will deliver is still an open matter of debate Key teaching points: A brief, yet comprehensive account on how food and nutrition evolved to give rise to nutrigenomics. Discusses potential of nutrigenomics for public health contribution in noncommunicable diseases. Debates credibility of nutrigenomics' commercial products versus the bio-hype in the field. Presents experts' and stakeholders' opinions for future directions of nutrigenomics.

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

Single nucleotide differences (SNDs) continue to contaminate the dbSNP database with consequences for human genomics and health

It has been established that up to 8.3% of the biallelic coding SNPs present in dbSNP are actually artefactual polymorphism-like errors, previously termed single nucleotide differences, or SNDs. In this study, a previous analysis of SNPs in dbSNP was extended and updated to examine how the incidence of SNDs has changed over an intervening five year period. The incidence of SNDs was found to be lower than in the previous analysis at 2.2% of all biallelic SNPs. There was only a modest reduction in the percentage of SNDs in the original set of biallelic coding SNPs tested. This suggests that the overall reduction in the incidence of SNDs over the intervening 5-year period is related to an improvement in SNP detection methods and more rigorous curation, rather than efforts to ameliorate the presence of SNDs. We note that SNDs contaminating the dbSNP may lead to erroneous conclusions on human conditions.

Targeted plasma metabolome response to variations in dietary glycemic load in a randomized, controlled, crossover feeding trial in healthy adults

Low versus high glycemic load (GL) diet patterns are inversely associated with obesity and chronic diseases such as cancer and cardiovascular disease. These associations persist beyond the protection afforded by increased fiber alone, representing an important gap in our understanding of the metabolic effects of GL. We conducted a randomized, controlled, crossover feeding trial of two 28-day diet periods of high and low GL. Using LC-MS, targeted metabolomics analysis of 155 metabolites was performed on plasma samples from 19 healthy adults aged 18-45 years. Fourteen metabolites differed significantly between diets (P < 0.05), with kynurenate remaining significant after Bonferroni correction (P < 4 × 10(-4)). Metabolites with the largest difference in abundance were kynurenate and trimethylamine-N-oxide (TMAO), both significantly higher after consumption of the low GL diet. Partial least squares-discriminant analysis showed clear separation between the two diets; however no specific pathway was identified in pathway analyses. We found significant differences in 14 plasma metabolites suggesting a differing metabolic response to low and high GL diets. Kynurenate is associated with reduced inflammation, and may be one mechanism through which protective effects of a low GL diet are manifested and warrants further evaluation. This trial was registered at clinicaltrials.gov as NCT00622661.

Nutrigenomics of body weight regulation: a rationale for careful dissection of individual contributors

Body weight stability may imply active regulation towards a certain physiological condition, a body weight setpoint. This interpretation is ill at odds with the world-wide increase in overweight and obesity. Until now, a body weight setpoint has remained elusive and the setpoint theory did not provide practical clues for body weight reduction interventions. For this an alternative theoretical model is necessary, which is available as the settling point model. The settling point model postulates that there is little active regulation towards a predefined body weight, but that body weight settles based on the resultant of a number of contributors, represented by the individual's genetic predisposition, in interaction with environmental and socioeconomic factors, such as diet and lifestyle. This review refines the settling point model and argues that by taking body weight regulation from a settling point perspective, the road will be opened to careful dissection of the various contributors to establishment of body weight and its regulation. This is both necessary and useful. Nutrigenomic technologies may help to delineate contributors to body weight settling. Understanding how and to which extent the different contributors influence body weight will allow the design of weight loss and weight maintenance interventions, which hopefully are more successful than those that are currently available.

Nutri-informatics: a new kid on the block?

From an epistemological point of view, nutritional physiology has been developed, like other factual sciences such as physics, from a purely descriptive to a mechanismic-explanatory scientific discipline. Nowadays, nutritional physiology has entered the molecular stage. Based on this micro-reductionism, molecular targets (e.g., transcription factors) of energy intake, certain nutrients (e.g., zinc) and selected plant bioactives (e.g., flavonoids) have been identified. Although these results are impressive, molecular approaches in nutritional physiology are limited by nature since the molecular targets of nutrients seem to have no ontic priority to understand the nutritional phenotype of an organism. Here we define, to the best of our knowledge, for the first time Nutri-informatics as a new bioinformatics discipline integrating large-scale data sets from nutritional studies into a stringent nutritional systems biology context. We suggest that Nutri-informatics, as an emerging field, may bridge the gap between nutritional biochemistry, nutritional physiology and metabolism to understand the interactions between an organism and its environment.