Omics and integrated omics for the promotion of food and nutrition science

Multi-Omics Approaches to Improve Meat Quality and Taste Characteristics

With rapid advances in meat science in recent decades, changes in meat quality during the pre-slaughter phase of muscle growth and the post-slaughter process from muscle to meat have been investigated. Commonly used techniques have evolved from early physicochemical indicators such as meat color, tenderness, water holding capacity, flavor, and pH to various omic tools such as genomics, transcriptomics, proteomics, and metabolomics to explore fundamental molecular mechanisms and screen biomarkers related to meat quality and taste characteristics. This review highlights the application of omics and integrated multi-omics in meat quality and taste characteristics studies. It also discusses challenges and future perspectives of multi-omics technology to improve meat quality and taste. Consequently, multi-omics techniques can elucidate the molecular mechanisms responsible for changes of meat quality at transcriptome, proteome, and metabolome levels. In addition, the application of multi-omics technology has great potential for exploring and identifying biomarkers for meat quality and quality control that can make it easier to optimize production processes in the meat industry.

A Central Asian Food Dataset for Personalized Dietary Interventions

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.

Omics as a Tool to Help Determine the Effectiveness of Supplements

There has been considerable interest in dietary supplements in the last two decades. Companies are releasing new specifics at an alarming pace, while dietary supplements are one of the less-studied substances released for public consumption. However, access to state-of-the-art and high-throughput techniques, such as the ones used in omics, make it possible to check the impact of a substance on human transcriptome or proteome and provide answers to whether its use is reasonable and beneficial. In this review, the main domains of omics are briefly introduced. The review focuses on the three most widely used omics techniques: NGS, LC-MS, NMR, and their usefulness in studying dietary supplements. Examples of studies are described for some of the most commonly supplemented substances, such as vitamins: D, E, A, and plant extracts: resveratrol, green tea, ginseng, and curcumin extract. Techniques used in omics have proven to be useful in studying dietary supplements. NGS techniques are helpful in identifying pathways that change upon supplementation and determining polymorphisms or conditions that qualify for the necessity of a given supplementation. LC-MS techniques are used to establish the serum content of supplemented a compound and its effects on metabolites. Both LC-MS and NMR help establish the actual composition of a compound, its primary and secondary metabolites, and its potential toxicity. Moreover, NMR techniques determine what conditions affect the effectiveness of supplementation.

The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges

Simple Summary

In this review we seek to outline the role of new technologies in biomarker discovery, particularly within the veterinary field and with an emphasis on ‘omics’, as well as to examine why many biomarkers-despite much excitement-have not yet made it to clinical practice. Further we emphasise the critical need for close collaboration between clinicians, researchers and funding bodies and the need to set clear goals for biomarker requirements and realistic application in the clinical setting, ensuring that biomarker type, method of detection and clinical utility are compatible, and adequate funding, time and sample size are available for all phases of development.

Abstract

New biomarkers promise to transform veterinary practice through rapid diagnosis of diseases, effective monitoring of animal health and improved welfare and production efficiency. However, the road from biomarker discovery to translation is not always straightforward. This review focuses on molecular biomarkers under development in the veterinary field, introduces the emerging technological approaches transforming this space and the role of ‘omics platforms in novel biomarker discovery. The vast majority of veterinary biomarkers are at preliminary stages of development and not yet ready to be deployed into clinical translation. Hence, we examine the major challenges encountered in the process of biomarker development from discovery, through validation and translation to clinical practice, including the hurdles specific to veterinary practice and to each of the ‘omics platforms–transcriptomics, proteomics, lipidomics and metabolomics. Finally, recommendations are made for the planning and execution of biomarker studies with a view to assisting the success of novel biomarkers in reaching their full potential.

From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?

Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.

Omics Technology for the Promotion of Nutraceuticals and Functional Foods

The influence of nutrition and environment on human health has been known for ages. Phytonutrients (7,000 flavonoids and phenolic compounds; 600 carotenoids) and pro-health nutrients—nutraceuticals positively add to human health and may prevent disorders such as cancer, diabetes, obesity, cardiovascular diseases, and dementia. Plant-derived bioactive metabolites have acquired an imperative function in human diet and nutrition. Natural phytochemicals affect genome expression (nutrigenomics and transcriptomics) and signaling pathways and act as epigenetic modulators of the epigenome (nutri epigenomics). Transcriptomics, proteomics, epigenomics, miRNomics, and metabolomics are some of the main platforms of complete omics analyses, finding use in functional food and nutraceuticals. Now the recent advancement in the integrated omics approach, which is an amalgamation of multiple omics platforms, is practiced comprehensively to comprehend food functionality in food science.

Structured Long-Chain Omega-3 Fatty Acids for Improvement of Cognitive Function during Aging

Although the human lifespan has increased in the past century owing to advances in medicine and lifestyle, the human healthspan has not kept up the same pace, especially in brain aging. Consequently, the role of preventive health interventions has become a crucial strategy, in particular, the identification of nutritional compounds that could alleviate the deleterious effects of aging. Among nutrients to cope with aging in special cognitive decline, the long-chain omega-3 polyunsaturated fatty acids (ω-3 LCPUFAs) docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), have emerged as very promising ones. Due to their neuroinflammatory resolving effects, an increased status of DHA and EPA in the elderly has been linked to better cognitive function and a lower risk of dementia. However, the results from clinical studies do not show consistent evidence and intake recommendations for old adults are lacking. Recently, supplementation with structured forms of EPA and DHA, which can be derived natural forms or targeted structures, have proven enhanced bioavailability and powerful benefits. This review summarizes present and future perspectives of new structures of ω-3 LCPUFAs and the role of "omic" technologies combined with the use of high-throughput in vivo models to shed light on the relationships and underlying mechanisms between ω-3 LCPUFAs and healthy aging.

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy.

Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods.

We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of 16,600 genes and more than 1,000 samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.

Developmental Effects of (Pre-)Gestational Diabetes on Offspring: Systematic Screening Using Omics Approaches

Worldwide, gestational diabetes affects 2-25% of pregnancies. Due to related disturbances of the maternal metabolism during the periconceptional period and pregnancy, children bear an increased risk for future diseases. It is well known that an aberrant intrauterine environment caused by elevated maternal glucose levels is related to elevated risks for increased birth weights and metabolic disorders in later life, such as obesity or type 2 diabetes. The complexity of disturbances induced by maternal diabetes, with multiple underlying mechanisms, makes early diagnosis or prevention a challenging task. Omics technologies allowing holistic quantification of several classes of molecules from biological fluids, cells, or tissues are powerful tools to systematically investigate the effects of maternal diabetes on the offspring in an unbiased manner. Differentially abundant molecules or distinct molecular profiles may serve as diagnostic biomarkers, which may also support the development of preventive and therapeutic strategies. In this review, we summarize key findings from state-of-the-art Omics studies addressing the impact of maternal diabetes on offspring health.

Omics Technologies to Enhance Plant Based Functional Foods: An Overview

Functional foods are natural products of plants that have health benefits beyond necessary nutrition. Functional foods are abundant in fruits, vegetables, spices, beverages and some are found in cereals, millets, pulses and oilseeds. Efforts to identify functional foods in our diet and their beneficial aspects are limited to few crops. Advances in sequencing and availability of different omics technologies have given opportunity to utilize these tools to enhance the functional components of the foods, thus ensuring the nutritional security. Integrated omics approaches including genomics, transcriptomics, proteomics, metabolomics coupled with artificial intelligence and machine learning approaches can be used to improve the crops. This review provides insights into omics studies that are carried out to find the active components and crop improvement by enhancing the functional compounds in different plants including cereals, millets, pulses, oilseeds, fruits, vegetables, spices, beverages and medicinal plants. There is a need to characterize functional foods that are being used in traditional medicines, as well as utilization of this knowledge to improve the staple foods in order to tackle malnutrition and hunger more effectively.

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.

The Association of Serum Levels of Leptin and Ghrelin with the Dietary Fat Content in Non-Obese Women with Polycystic Ovary Syndrome

Women with polycystic ovary syndrome (PCOS) are at an increased risk of developing insulin resistance and abdominal obesity in the state of an improper diet balance. Leptin is a peptide considered to be a satiety hormone that plays an important role in the long-term energy balance, whereas ghrelin is a hormone that controls short-term appetite regulation and is considered a hunger hormone. The aim of the present study was to assess the relationship between serum leptin and ghrelin concentrations and the dietary macronutrient content in PCOS women. We examined 73 subjects: 39 women diagnosed with PCOS by the Rotterdam criteria and 34 healthy controls, matched by the body mass index. The subjects completed a consecutive three-day dietary diary to identify the macronutrient and micronutrient intake. Serum concentrations of leptin and total ghrelin were measured and homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. The studied groups did not differ significantly in terms of the intake of macronutrients (proteins, fats, and carbohydrates) and serum concentrations of ghrelin and leptin (all p > 0.05). In the PCOS group, the serum leptin concentration positively correlated with the intake of total fat (r = 0.36, p = 0.02), total cholesterol (r = −0.36, p = 0.02), saturated fatty acids (r = 0.43, p < 0.01), and monounsaturated fatty acids (MUFA) (r = 0.37, p = 0.02), whereas the serum ghrelin concentration correlated in an inverse manner with the intake of total fat (r = −0.37, p = 0.02), MUFA (r = −0.37, p = 0.02), polyunsaturated fatty acids (r = −0.34, p = 0.03), and long chain polyunsaturated fatty acids (r = −0.38, p = 0.02). In this group, we also found a negative association of HOMA-IR with serum ghrelin levels (r = −0.4, p = 0.03) and a positive relationship with the serum leptin concentration (r = 0.5, p < 0.01) and relationships between HOMA-IR and total dietary fat (r = 0.38, p = 0.03) and MUFA (r = 0.35, p = 0.04) intake. In PCOS women, dietary components such as the total fat and type of dietary fat and HOMA-IR are positively connected to serum leptin concentrations and negatively connected to serum ghrelin concentrations, which may influence the energy balance.

Role of Personalized Nutrition in Chronic-Degenerative Diseases

Human nutrition is a branch of medicine based on foods biochemical interactions with the human body. The phenotypic transition from health to disease status can be attributed to changes in genes and/or protein expression. For this reason, a new discipline has been developed called “-omic science”. In this review, we analyzed the role of “-omics sciences” (nutrigenetics, nutrigenomics, proteomics and metabolomics) in the health status and as possible therapeutic tool in chronic degenerative diseases. In particular, we focused on the role of nutrigenetics and the relationship between eating habits, changes in the DNA sequence and the onset of nutrition-related diseases. Moreover, we examined nutrigenomics and the effect of nutrients on gene expression. We perused the role of proteomics and metabolomics in personalized nutrition. In this scenario, we analyzed also how dysbiosis of gut microbiota can influence the onset and progression of chronic degenerative diseases. Moreover, nutrients influencing and regulating gene activity, both directly and indirectly, paves the way for personalized nutrition that plays a key role in the prevention and treatment of chronic degenerative diseases.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.

The application of omics technologies in the functional evaluation of inulin and inulin-containing prebiotics dietary supplementation

Inulin, a natural renewable polysaccharide resource produced by various plants in nature, has been reported to possess a significant number of diverse pharmaceutical and food applications. Recently, there has been rapid progress in high-throughput technologies and platforms to assay global mRNA, proteins, metabolites and gut microbiota. In this review, we will describe the current status of utilizing omics technologies of elucidating the impact of inulin and inulin-containing prebiotics at the transcriptome, proteome, metabolome and gut microbiome levels. Although many studies in this review have addressed the impact of inulin comprehensively, these omics technologies only enable us to understand physiological information at each different stage of mRNA, protein, metabolite and gut microbe. We believe that a synergistic approach is vital in order to fully illustrate the intricate beauty behind the relatively modest influence of food factors like inulin on host health.