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Hwang YH, Lee EY, Lim HT, Joo ST. Multi-Omics Approaches to Improve Meat Quality and Taste Characteristics. Food Sci Anim Resour 2023; 43:1067-1086. [PMID: 37969318 PMCID: PMC10636221 DOI: 10.5851/kosfa.2023.e63] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/19/2023] [Accepted: 09/27/2023] [Indexed: 11/17/2023] Open
Abstract
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.
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Affiliation(s)
- Young-Hwa Hwang
- Institute of Agriculture & Life
Science, Gyeongsang National University, Jinju 52828,
Korea
| | - Eun-Yeong Lee
- Division of Applied Life Science (BK21
Four), Gyeongsang National University, Jinju 52828,
Korea
| | - Hyen-Tae Lim
- Institute of Agriculture & Life
Science, Gyeongsang National University, Jinju 52828,
Korea
- Division of Animal Science, Gyeongsang
National University, Jinju 52828, Korea
| | - Seon-Tea Joo
- Institute of Agriculture & Life
Science, Gyeongsang National University, Jinju 52828,
Korea
- Division of Applied Life Science (BK21
Four), Gyeongsang National University, Jinju 52828,
Korea
- Division of Animal Science, Gyeongsang
National University, Jinju 52828, Korea
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2
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Karabay A, Bolatov A, Varol HA, Chan MY. A Central Asian Food Dataset for Personalized Dietary Interventions. Nutrients 2023; 15:nu15071728. [PMID: 37049566 PMCID: PMC10096622 DOI: 10.3390/nu15071728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/03/2023] Open
Abstract
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.
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Affiliation(s)
- Aknur Karabay
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan
| | - Arman Bolatov
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan
| | - Huseyin Atakan Varol
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan
| | - Mei-Yen Chan
- School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan
- Correspondence:
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3
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Steg A, Oczkowicz M, Smołucha G. Omics as a Tool to Help Determine the Effectiveness of Supplements. Nutrients 2022; 14:nu14245305. [PMID: 36558464 PMCID: PMC9784029 DOI: 10.3390/nu14245305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/07/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
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.
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4
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Nutrigenomics: An inimitable interaction amid genomics, nutrition and health. INNOV FOOD SCI EMERG 2022. [DOI: 10.1016/j.ifset.2022.103196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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5
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Jia Z, Zhang B, Sharma A, Kim NS, Purohit SM, Green MM, Roche MR, Holliday E, Chen H. Revelation of the sciences of traditional foods. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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6
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The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges. Animals (Basel) 2022; 12:ani12172194. [PMID: 36077913 PMCID: PMC9454634 DOI: 10.3390/ani12172194] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
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.
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7
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
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.
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8
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Pandita D, Pandita A. Omics Technology for the Promotion of Nutraceuticals and Functional Foods. Front Physiol 2022; 13:817247. [PMID: 35634143 PMCID: PMC9136416 DOI: 10.3389/fphys.2022.817247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/23/2022] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Deepu Pandita
- Government Department of School Education, Jammu, India
- *Correspondence: Deepu Pandita,
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9
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Mora I, Arola L, Caimari A, Escoté X, Puiggròs F. Structured Long-Chain Omega-3 Fatty Acids for Improvement of Cognitive Function during Aging. Int J Mol Sci 2022; 23:3472. [PMID: 35408832 PMCID: PMC8998232 DOI: 10.3390/ijms23073472] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Ignasi Mora
- Brudy Technology S.L., 08006 Barcelona, Spain
| | - Lluís Arola
- Nutrigenomics Research Group, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Antoni Caimari
- Eurecat, Centre Tecnològic de Catalunya, Biotechnology Area, 43204 Reus, Spain
| | - Xavier Escoté
- Eurecat, Centre Tecnològic de Catalunya, Nutrition and Health Unit, 43204 Reus, Spain
| | - Francesc Puiggròs
- Eurecat, Centre Tecnològic de Catalunya, Biotechnology Area, 43204 Reus, Spain
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10
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Jensch A, Lopes MB, Vinga S, Radde N. ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data. Stat Methods Med Res 2022; 31:947-958. [PMID: 35072570 PMCID: PMC9014683 DOI: 10.1177/09622802211072456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Antje Jensch
- Institute for Systems Theory and Automatic Control, 9149University of Stuttgart, Germany
| | - Marta B Lopes
- Center for Mathematics and Applications (CMA), NOVA School of Science and Technology, Caparica, Portugal.,NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), NOVA School of Science and Technology, Caparica, Portugal
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, 72971Universidade de Lisboa, Portugal.,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Nicole Radde
- Institute for Systems Theory and Automatic Control, 9149University of Stuttgart, Germany
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11
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Shashikadze B, Flenkenthaler F, Stöckl JB, Valla L, Renner S, Kemter E, Wolf E, Fröhlich T. Developmental Effects of (Pre-)Gestational Diabetes on Offspring: Systematic Screening Using Omics Approaches. Genes (Basel) 2021; 12:1991. [PMID: 34946940 PMCID: PMC8701487 DOI: 10.3390/genes12121991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 12/27/2022] Open
Abstract
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.
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Affiliation(s)
- Bachuki Shashikadze
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377 Munich, Germany; (B.S.); (F.F.); (J.B.S.)
| | - Florian Flenkenthaler
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377 Munich, Germany; (B.S.); (F.F.); (J.B.S.)
| | - Jan B. Stöckl
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377 Munich, Germany; (B.S.); (F.F.); (J.B.S.)
| | - Libera Valla
- Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, 81377 Munich, Germany; (L.V.); (S.R.); (E.K.)
| | - Simone Renner
- Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, 81377 Munich, Germany; (L.V.); (S.R.); (E.K.)
- Center for Innovative Medical Models (CiMM), LMU Munich, 85764 Oberschleißheim, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Elisabeth Kemter
- Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, 81377 Munich, Germany; (L.V.); (S.R.); (E.K.)
- Center for Innovative Medical Models (CiMM), LMU Munich, 85764 Oberschleißheim, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Eckhard Wolf
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377 Munich, Germany; (B.S.); (F.F.); (J.B.S.)
- Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, 81377 Munich, Germany; (L.V.); (S.R.); (E.K.)
- Center for Innovative Medical Models (CiMM), LMU Munich, 85764 Oberschleißheim, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Thomas Fröhlich
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, 81377 Munich, Germany; (B.S.); (F.F.); (J.B.S.)
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12
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Nayak SN, Aravind B, Malavalli SS, Sukanth BS, Poornima R, Bharati P, Hefferon K, Kole C, Puppala N. Omics Technologies to Enhance Plant Based Functional Foods: An Overview. Front Genet 2021; 12:742095. [PMID: 34858472 PMCID: PMC8631721 DOI: 10.3389/fgene.2021.742095] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/13/2021] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Spurthi N. Nayak
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - B. Aravind
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - Sachin S. Malavalli
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - B. S. Sukanth
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - R. Poornima
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - Pushpa Bharati
- Department of Food Science and Nutrition, University of Agricultural Sciences, Dharwad, India
| | - Kathleen Hefferon
- Department of Microbiology, Cornell University, Ithaca, NY, United States
| | - Chittaranjan Kole
- President, International Phytomedomics and Nutriomics Consortium (ipnc.info), Daejeon, South Korea
| | - Naveen Puppala
- New Mexico State University-Agricultural Science Center at Clovis, New Mexico, NM, United States
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13
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Balkir P, Kemahlioglu K, Yucel U. Foodomics: A new approach in food quality and safety. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2020.11.028] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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14
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Statistical and Machine-Learning Analyses in Nutritional Genomics Studies. Nutrients 2020; 12:nu12103140. [PMID: 33066636 PMCID: PMC7602401 DOI: 10.3390/nu12103140] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/08/2020] [Accepted: 10/10/2020] [Indexed: 12/18/2022] Open
Abstract
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.
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15
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Polak AM, Krentowska A, Łebkowska A, Buczyńska A, Adamski M, Adamska-Patruno E, Fiedorczuk J, Krętowski AJ, Kowalska I, Adamska A. The Association of Serum Levels of Leptin and Ghrelin with the Dietary Fat Content in Non-Obese Women with Polycystic Ovary Syndrome. Nutrients 2020; 12:nu12092753. [PMID: 32927680 PMCID: PMC7551083 DOI: 10.3390/nu12092753] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/06/2020] [Accepted: 09/08/2020] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- Aleksandra Maria Polak
- Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, 15-276 Białystok, Poland; (A.M.P.); (A.K.); (A.Ł.); (I.K.)
| | - Anna Krentowska
- Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, 15-276 Białystok, Poland; (A.M.P.); (A.K.); (A.Ł.); (I.K.)
| | - Agnieszka Łebkowska
- Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, 15-276 Białystok, Poland; (A.M.P.); (A.K.); (A.Ł.); (I.K.)
| | - Angelika Buczyńska
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Białystok, 15-276 Białystok, Poland; (A.B.); (A.J.K.)
| | - Marcin Adamski
- Faculty of Computer Science, Bialystok University of Technology, 15-351 Białystok, Poland;
| | - Edyta Adamska-Patruno
- Clinical Research Centre, Medical University of Bialystok, 15-276 Białystok, Poland; (E.A.-P.); (J.F.)
| | - Joanna Fiedorczuk
- Clinical Research Centre, Medical University of Bialystok, 15-276 Białystok, Poland; (E.A.-P.); (J.F.)
| | - Adam Jacek Krętowski
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Białystok, 15-276 Białystok, Poland; (A.B.); (A.J.K.)
- Clinical Research Centre, Medical University of Bialystok, 15-276 Białystok, Poland; (E.A.-P.); (J.F.)
| | - Irina Kowalska
- Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, 15-276 Białystok, Poland; (A.M.P.); (A.K.); (A.Ł.); (I.K.)
| | - Agnieszka Adamska
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Białystok, 15-276 Białystok, Poland; (A.B.); (A.J.K.)
- Correspondence: ; Tel.: +48-85-746-8660; Fax: +48-85-744-7611
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16
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Role of Personalized Nutrition in Chronic-Degenerative Diseases. Nutrients 2019; 11:nu11081707. [PMID: 31344895 PMCID: PMC6723746 DOI: 10.3390/nu11081707] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/15/2019] [Accepted: 07/20/2019] [Indexed: 02/07/2023] Open
Abstract
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.
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17
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Misra BB, Langefeld CD, Olivier M, Cox LA. Integrated Omics: Tools, Advances, and Future Approaches. J Mol Endocrinol 2018; 62:JME-18-0055. [PMID: 30006342 DOI: 10.1530/jme-18-0055] [Citation(s) in RCA: 214] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 07/02/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Biswapriya B Misra
- B Misra, Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, United States
| | - Carl D Langefeld
- C Langefeld, Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, United States
| | - Michael Olivier
- M Olivier, Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, United States
| | - Laura A Cox
- L Cox, Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, United States
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18
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Gligorijević V, Malod-Dognin N, Pržulj N. Integrative methods for analyzing big data in precision medicine. Proteomics 2016; 16:741-58. [PMID: 26677817 DOI: 10.1002/pmic.201500396] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 11/16/2015] [Accepted: 12/09/2015] [Indexed: 12/19/2022]
Abstract
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.
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Affiliation(s)
| | | | - Nataša Pržulj
- Department of Computing, Imperial College London, London, UK
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19
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Tsurumaki M, Kotake M, Iwasaki M, Saito M, Tanaka K, Aw W, Fukuda S, Tomita M. The application of omics technologies in the functional evaluation of inulin and inulin-containing prebiotics dietary supplementation. Nutr Diabetes 2015; 5:e185. [PMID: 26619369 PMCID: PMC4672356 DOI: 10.1038/nutd.2015.35] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 09/23/2015] [Accepted: 10/19/2015] [Indexed: 12/25/2022] Open
Abstract
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.
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Affiliation(s)
- M Tsurumaki
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - M Kotake
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - M Iwasaki
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - M Saito
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - K Tanaka
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - W Aw
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
- Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - S Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
- Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - M Tomita
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
- Graduate School of Media and Governance, Keio University, Fujisawa, Japan
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20
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Li Q, Freeman LM, Rush JE, Huggins GS, Kennedy AD, Labuda JA, Laflamme DP, Hannah SS. Veterinary Medicine and Multi-Omics Research for Future Nutrition Targets: Metabolomics and Transcriptomics of the Common Degenerative Mitral Valve Disease in Dogs. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2015; 19:461-70. [DOI: 10.1089/omi.2015.0057] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Qinghong Li
- Nestlé Purina Research, Saint Louis, Missouri
| | - Lisa M. Freeman
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, Massachusetts
| | - John E. Rush
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, Massachusetts
| | - Gordon S. Huggins
- MCRI Center for Translational Genomics, Molecular Cardiology Research Institute, Tufts Medical Center, and Tufts University School of Medicine, Boston, Massachusetts
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