The nutrients are able to interact with molecular mechanisms and modulate the physiological functions in the body. The Nutritional Genomics focuses on the interaction between bioactive food components and the genome, which includes Nutrigenetics and Nutrigenomics. The influence of nutrients on f genes expression is called Nutrigenomics, while the heterogeneous response of gene variants to nutrients, dietary components and developing nutraceticals is called Nutrigenetics. Genetic variation is known to affect food tolerances among human subpopulations and may also influence dietary requirements and raising the possibility of individualizing nutritional intake for optimal health and disease prevention on the basis of an individual’s genome. Nutrigenomics provides a genetic understanding for how common dietary components affect the balance between health and disease by altering the expression and/or structure of an individual’s genetic makeup. Nutrigenetics describes that the genetic profile have impact on the response of body to bioactive food components by influencing their absorption, metabolism, and site of action.
In this way, considering different aspects of gene–nutrient interaction and designing appropriate diet for every specific genotype that optimize individual health, diagnosis and nutritional treatment of genome instability, we could prevent and control conversion of healthy phenotype to diseases.
With the completion of human genome sequencing and entering the-Omics area, the new term “Nutritional Genomics” tends to replace the former “nutrient-gene interactions” . It has been demonstrated that numerous genetic polymorphisms can influence protein structure function. The Nutritional genomic area includes two parts: first Nutrigenomics that is the study of interaction between dietary components and the genome, and the regulating changes in proteins and other metabolism; second Nutrigenetics that identify the response to dietary components with regard to genetic differences .
Nutrients are as environmental factors can interact with genetic material. It has been clearly demonstrated that DNA metabolism and repair depend on a wide range of dietary factors that act as cofactors or substrates in metabolic pathway, but much less is known about the impact of cofactors and/or micronutrients deficiency or excess on the fidelity of DNA replication and repair . Although the nutrients can influence the development of a particular phenotype, the response to a specific nutrient that determined by the individual genotype has also to be considered .
Lipidomics
Lipidomics is defined as “the full characterization of lipid molecular species and of their biological roles with respect to expression of proteins involved in lipid metabolism and function, including gene regulation.
Lipidomics has rapidly developed over the past 10 years and can produce enormous amounts of data in a short time. These developments need appropriate computational bioinformatics approaches to overcome the bottleneck of identifying and quantifying lipids from the raw chromatographic and mass spectral data. A comprehensive discussion of bioinformatics approaches to lipidomics can be found in several reported reviews.
As in other “omics” disciplines, data analysis plays a key role in lipidomics. Particularly in the untargeted lipidomics, the amount of data is very large and it is an important challenge to illustrate the data without proper bioinformatics. For MS-based lipidomics, the initial step of data processing is lipid identification. Several software packages and lipid databases have been developed to achieve this purpose. Software for metabolomics can also be used for lipid identification and quantification. The LIPID MAPS has attempted to establish a standard system for lipid nomenclature. If the system were commonly adopted, it would allow far simpler integration of current databases and data sets. The second step of data processing is to normalize the data via a set of internal standards. Once these calculations have been performed, the identified lipids can then be quantified by comparison to appropriate internal standard.
The third step is performing statistical analysis of the complex data sets. Univariate statistical analyses are usually used in lipidomics for comparisons between experimental groups. Principal component analysis (PCA) method is becoming popularity in lipidomics . PCA is an unsupervised analysis that allows any structure in a data set to be observed without any prior knowledge or manipulation, so that trends of the data may be elucidated. The aim of lipidomics is to compare differences between groups and it is important to employ a supervised multivariate analysis to identify these major differences. It is important to emphasize that raw mass spectral data may not reflect changes in concentration; it is essential to perform data normalization and quantification before statistical analysis. Other regression methods including least absolute shrinkage and selection operator and Elastic Net are also ideally suitable for lipidomics. With the development of analytical techniques, new structural information will be obtained and data sets will become larger and more complex. Therefore, any bioinformatics strategies that are put into place will need to be adaptable so that new information can be added to databases and utilized for data interpretation
What is Transcriptomics
The study of the totality of the RNA molecules arising from the expression of genes under specified conditions.
Examination of mRNA expression levels in a given cell population. mRNA content and distribution of a cell closely reflects the activation status of its genes.
It’s the study of transcriptome of organisms. Transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and non-coding RNA produced in one or a population of cells. Transcriptomicsis also referred to as expression profiling, examines the expression level of RNAs in a given cell population.
The transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA transcribed in one cell or a population of cells.
What Is Nutrigenomics and Can It Improve Your Diet?
Experts predict nutrigenomics — eating based on your genetics — will be one of the biggest food trends of 2018.
Diet advice used to go something like this: Follow this one-size-fits-all rule (stay away from sugar, bring on the low-fat everything) to eat healthily. But according to an emerging field of science called nutrigenomics, that way of thinking is about to become as outdated as the cabbage soup diet (yes, that was really a thing).
“Nutrigenomics is the study of how genetics interact with the foods we eat,” s ays Clayton Lewis, CEO and cofounder of Arivale, a company that uses a blood sample to analyze your genes and then pairs you with a nutritionist to explain the best eating plan for your body. “How do they work together to either make us healthier or cause disease?”
As an increasing number of at-home genetics tests will tell you, you’re genetically and biochemically unique from everyone else in your gym. “This means there’s no one-size-fits-all healthy diet,” says Lewis.
Example: While healthy fats such as avocado or olive oil have gotten the scientific stamp of approval, some people are more prone to gain weight on a high-fat diet than others. Your genes can also impact how well you absorb nutrients like vitamin D. Even if you eat tons of D-rich salmon, certain gene variations might mean you still need a supplement.
Getting your genetic blueprint can help you figure out exactly what your body needs to be at its best. “It’s really all about personalization,” says Lewis. Think of old diet advice like a paper map. The information is there, but it’s really hard to tell where youare in the picture. Nutrigenomics is like upgrading to Google Maps — it tells you exactly where you are, so you can get where you want to go.
“To understand nutrition and health, we need to understand how our unique biology works to keep our body in balance,” says Neil Grimmer, CEO and founder of Habit, a start-up using nutrigenomics, metabolic tests, and nutritionists to help you form healthier eating habits.
You’re going to start hearing about this nutrition game changer a lot more — a survey of 740 dietitians by KIND predicted that the personalized nutrition advice gleaned from the field will be one of the top five food trends of 2018. Here’s what you need to know about how nutrigenomics can impact your healthy eating plan.
The Science Behind Nutrigenomics
“While the term ‘nutrigenomics’ became popular about 15 years ago, the idea that we respond differently to food has been around for a long time,” says Grimmer. “In the first century B.C. the Latin writer Lucretius wrote, ‘What is food for one man may be bitter poison to others.’”
The sequencing of the human genome turned that philosophy into something you could use. By analyzing a blood sample (Arivale uses samples collected by a local lab while Habit sends you the tools to take a small sample at home), scientists can spot biomarkers — aka genes — that impact how your body processes certain nutrients.
Take for example the FTO gene, which produces a protein that helps control your desire to wolf down everything in your fridge. “One version, or variant, of this gene,” — called FTO rs9939609, if you want to get scientific — “may predispose you to weight gain,” says Grimmer. “The lab tests for this genetic biomarker and uses that information, plus your waist circumference, to assess your risk of becoming overweight.”
So, while you might be fit AF now thanks to a fast metabolism and devotion to HIIT, your genes can flag any risks for potential waistline expansions in your future.
How to Put It Into Action
Thanks to a crop of new start-ups like Arivale and Habit, an at-home test or simple blood draw can give you a full report to tell you exactly what to put on your plate and what foods might be potentially risky for you.
But the science is still evolving. A 2015 review of nutrigenomics research, published in Applied and Translational Genomics, pointed out that while the evidence is certainly promising, many studies lack definite associations between genes usually examined in nutrigenomics testing and some diet-related diseases. In other words, just because a nutrigenomics report identifies the FTO mutation doesn’t mean you’re definitely going to be overweight.
The future of nutrigenomics holds even more personalization potential. “We need to think not only about genes but also about how the proteins and other metabolites affected by your genes respond to food,” says Grimmer.
This is what’s known as “multi-omic” data — genomics paired with info on “metabolomics” (small molecules) and “proteomics” (proteins), explains Lewis. In plain English, it means zooming in even closer on how your love for avocado will impact your waistline and your risks for certain diseases.
Habit is already steaming ahead with multi-omic data — currently, their at-home kit can assess how your body responds to foods by comparing a fasting blood sample with samples taken after you drink a nutrient-dense shake. “Only recently have advances in molecular biology, data analysis, and nutrition science enabled us to use this data to create recommendations at a more personal level,” Grimmer says. Here’s to upgrading your road map for better health.
