NutriGenomeDB: a nutrigenomics exploratory and analytical platform

Unraveling the Gut Microbiome-Diet Connection: Exploring the Impact of Digital Precision and Personalized Nutrition on Microbiota Composition and Host Physiology

The human gut microbiome, an intricate ecosystem housing trillions of microorganisms within the gastrointestinal tract, holds significant importance in human health and the development of diseases. Recent advances in technology have allowed for an in-depth exploration of the gut microbiome, shedding light on its composition and functions. Of particular interest is the role of diet in shaping the gut microbiome, influencing its diversity, population size, and metabolic functions. Precision nutrition, a personalized approach based on individual characteristics, has shown promise in directly impacting the composition of the gut microbiome. However, to fully understand the long-term effects of specific diets and food components on the gut microbiome and to identify the variations between individuals, longitudinal studies are crucial. Additionally, precise methods for collecting dietary data, alongside the application of machine learning techniques, hold immense potential in comprehending the gut microbiome's response to diet and providing tailored lifestyle recommendations. In this study, we investigated the complex mechanisms that govern the diverse impacts of nutrients and specific foods on the equilibrium and functioning of the individual gut microbiome of seven volunteers (four females and three males) with an average age of 40.9 ± 10.3 years, aiming at identifying potential therapeutic targets, thus making valuable contributions to the field of personalized nutrition. These findings have the potential to revolutionize the development of highly effective strategies that are tailored to individual requirements for the management and treatment of various diseases.

Indian food habit & food ingredients may have a role in lowering the severity & high death rate from COVID-19 in Indians: findings from the first nutrigenomic analysis

Background & objectives

During the COVID-19 pandemic, the death rate was reportedly 5-8 fold lower in India which is densely populated as compared to less populated western countries. The aim of this study was to investigate whether dietary habits were associated with the variations in COVID-19 severity and deaths between western and Indian population at the nutrigenomics level.

Methods

In this study nutrigenomics approach was applied. Blood transcriptome of severe COVID-19 patients from three western countries (showing high fatality) and two datasets from Indian patients were used. Gene set enrichment analyses were performed for pathways, metabolites, nutrients, etc., and compared for western and Indian samples to identify the food- and nutrient-related factors, which may be associated with COVID-19 severity. Data on the daily consumption of twelve key food components across four countries were collected and a correlation between nutrigenomics analyses and per capita daily dietary intake was investigated.

Results

Distinct dietary habits of Indians were observed, which may be associated with low death rate from COVID-19. Increased consumption of red meat, dairy products and processed foods by western populations may increase the severity and death rate by activating cytokine storm-related pathways, intussusceptive angiogenesis, hypercapnia and enhancing blood glucose levels due to high contents of sphingolipids, palmitic acid and byproducts such as CO2 and lipopolysaccharide (LPS). Palmitic acid also induces ACE2 expression and increases the infection rate. Coffee and alcohol that are highly consumed in western countries may increase the severity and death rates from COVID-19 by deregulating blood iron, zinc and triglyceride levels. The components of Indian diets maintain high iron and zinc concentrations in blood and rich fibre in their foods may prevent CO2 and LPS-mediated COVID-19 severity. Regular consumption of tea by Indians maintains high high-density lipoprotein (HDL) and low triglyceride in blood as catechins in tea act as natural atorvastatin. Importantly, regular consumption of turmeric in daily food by Indians maintains strong immunity and curcumin in turmeric may prevent pathways and mechanisms associated with SARS-CoV-2 infection and COVID-19 severity and lowered the death rate.

Interpretation & conclusions

Our results suggest that Indian food components suppress cytokine storm and various other severity related pathways of COVID-19 and may have a role in lowering severity and death rates from COVID-19 in India as compared to western populations. However, large multi-centered case-control studies are required to support our current findings.

Reducing Virus Infection Risk in Space Environments through Nutrient Supplementation

Space exploration has brought many challenges to human physiology. In order to evaluate and reduce possible pathological reactions triggered by space environments, we conducted bioinformatics analyses on the methylation data of the Mars 520 mission and human transcriptome data in the experiment simulating gravity changes. The results suggest that gene expression levels and DNA methylation levels were changed under the conditions of isolation and gravity changes, and multiple viral infection-related pathways were found in the enrichment analysis results of changed genes including Epstein Barr virus (EBV) infection, Hepatitis B virus (HBV) infection, Herpes simplex virus (HSV) infection and Kaposi’s sarcoma-associated herpesvirus (KHSV) infection. In this study, we found that Epigallocatechin-3-gallate (EGCG) and vitamin D are helpful in reducing viral infection risk. In addition, the causal associations between nutrients and viral infections were calculated using Two sample Mendelian Randomization (2SMR) method, the results indicated that vitamin D can reduce EBV infection and HBV infection risk. In summary, our study suggests that space environments increase the risk of human viral infection, which may be reduced by supplementing EGCG and vitamin D. These results can be used to formulate medical plans for astronauts, which have practical application value for future space exploration.

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.

Nutrigenetics and nutrigenomics-A personalized approach to nutrition

The prevalence of non-communicable diseases has been on an upward trajectory for some time and this puts an enormous burden on the healthcare expenditure. Lifestyle modifications including dietary interventions hold an immense promise to manage and prevent these diseases. Recent advances in genomic research provide evidence that focussing these efforts on individual variations in abilities to metabolize nutrients (nutrigenetics) and exploring the role of dietary compounds on gene expression (nutrigenomics and nutri-epigenomics) can lead to more meaningful personalized dietary strategies to promote optimal health. This chapter aims to provide examples on these gene-diet interactions at multiple levels to support the need of embedding targeted dietary interventions as a way forward to prevent, avoid and manage diseases.

Computational Approaches for Cancer-Fighting: From Gene Expression to Functional Foods

It is today widely accepted that a healthy diet is very useful to prevent the risk for cancer or its deleterious effects. Nutrigenomics studies are therefore taking place with the aim to test the effects of nutrients at molecular level and contribute to the search for anti-cancer treatments. These efforts are expanding the precious source of information necessary for the selection of natural compounds useful for the design of novel drugs or functional foods. Here we present a computational study to select new candidate compounds that could play a role in cancer prevention and care. Starting from a dataset of genes that are co-expressed in programmed cell death experiments, we investigated on nutrigenomics treatments inducing apoptosis, and searched for compounds that determine the same expression pattern. Subsequently, we selected cancer types where the genes showed an opposite expression pattern and we confirmed that the apoptotic/nutrigenomics expression trend had a significant positive survival in cancer-affected patients. Furthermore, we considered the functional interactors of the genes as defined by public protein-protein interaction data, and inferred on their involvement in cancers and/or in programmed cell death. We identified 7 genes and, from available nutrigenomics experiments, 6 compounds effective on their expression. These 6 compounds were exploited to identify, by ligand-based virtual screening, additional molecules with similar structure. We checked for ADME criteria and selected 23 natural compounds representing suitable candidates for further testing their efficacy in apoptosis induction. Due to their presence in natural resources, novel drugs and/or the design of functional foods are conceivable from the presented results.

The landscape of nutri-informatics: a review of current resources and challenges for integrative nutrition research

Informatics has become an essential component of research in the past few decades, capitalizing on the efficiency and power of computation to improve the knowledge gained from increasing quantities and types of data. While other fields of research such as genomics are well represented in informatics resources, nutrition remains underrepresented. Nutrition is one of the most integral components of human life, and it impacts individuals far beyond just nutrient provisions. For example, nutrition plays a role in cultural practices, interpersonal relationships and body image. Despite this, integrated computational investigations have been limited due to challenges within nutrition informatics (nutri-informatics) and nutrition data. The purpose of this review is to describe the landscape of nutri-informatics resources available for use in computational nutrition research and clinical utilization. In particular, we will focus on the application of biomedical ontologies and their potential to improve the standardization and interoperability of nutrition terminologies and relationships between nutrition and other biomedical disciplines such as disease and phenomics. Additionally, we will highlight challenges currently faced by the nutri-informatics community including experimental design, data aggregation and the roles scientific journals and primary nutrition researchers play in facilitating data reuse and successful computational research. Finally, we will conclude with a call to action to create and follow community standards regarding standardization of language, documentation specifications and requirements for data reuse. With the continued movement toward community standards of this kind, the entire nutrition research community can transition toward greater usage of Findability, Accessibility, Interoperability and Reusability principles and in turn more transparent science.

LC-N2G: a local consistency approach for nutrigenomics data analysis

BACKGROUND

Nutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of the most effective and efficient methods to explore their relationship is the nutritional geometry framework which fits a response surface for the gene expression over two prespecified nutrition variables. However, when the number of nutrients involved is large, it is challenging to find combinations of informative nutrients with respect to a certain gene and to test whether the relationship is stronger than chance. Methods for identifying informative combinations are essential to understanding the relationship between nutrients and genes.

RESULTS

We introduce Local Consistency Nutrition to Graphics (LC-N2G), a novel approach for ranking and identifying combinations of nutrients with gene expression. In LC-N2G, we first propose a model-free quantity called Local Consistency statistic to measure whether there is non-random relationship between combinations of nutrients and gene expression measurements based on (1) the similarity between samples in the nutrient space and (2) their difference in gene expression. Then combinations with small LC are selected and a permutation test is performed to evaluate their significance. Finally, the response surfaces are generated for the subset of significant relationships. Evaluation on simulated data and real data shows the LC-N2G can accurately find combinations that are correlated with gene expression.

CONCLUSION

The LC-N2G is practically powerful for identifying the informative nutrition variables correlated with gene expression. Therefore, LC-N2G is important in the area of nutrigenomics for understanding the relationship between nutrition and gene expression information.

Genes and Eating Preferences, Their Roles in Personalized Nutrition

At present, personalized diets, which take into account consumer genetic characteristics, are growing popular. Nutrigenetics studies the effect of gene variations on metabolism and nutrigenomics, which branches off further and investigates how nutrients and food compounds affect genes. This work deals with the mutations affecting the assimilation of metabolites, contributing to nutrigenetic studies. We searched for the genes responsible for eating preferences which allow for the tailoring of personalized diets. Presently, genetic nutrition is growing in demand, as it contributes to the prevention and/or rehabilitation of non-communicable diseases, both monogenic and polygenic. In this work, we showed single-nucleotide polymorphisms in genes-missense mutations that change the functions of coded proteins, resulting in a particular eating preferences or a disease. We studied the genes influencing food preferences-particularly those responsible for fats and carbohydrates absorption, food intolerance, metabolism of vitamins, taste sensations, oxidation of xenobiotics, eating preferences and food addiction. As a result, 34 genes were identified that affect eating preferences. Significant shortcomings were found in the methods/programs for developing personalized diets that are used today, and the weaknesses were revealed in the development of nutrigenetics (inconsistency of data on SNP genes, ignoring population genetics data, difficult information to understand consumer, etc.). Taking into account all the shortcomings, an approximate model was proposed in the review for selecting an appropriate personalized diet. In the future, it is planned to develop the proposed model for the compilation of individual diets.