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Abebe BK, Guo J, Jilo DD, Wang J, Yu S, Liu H, Cheng G, Zan L. Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review. Mamm Genome 2025:10.1007/s00335-025-10129-9. [PMID: 40343484 DOI: 10.1007/s00335-025-10129-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2025] [Accepted: 04/04/2025] [Indexed: 05/11/2025]
Abstract
The presence of carcinogenic substances in beef poses a significant risk to public health, with far-reaching implications for consumer safety and the meat production industry. Despite advancements in food safety measures, traditional breeding methods have proven inadequate in addressing these risks, revealing a substantial gap in knowledge. This review aims to fill this gap by evaluating the potential of healthy breeding techniques to significantly reduce the levels of carcinogenic compounds in beef. We focus on elucidating the molecular pathways that contribute to the formation of key carcinogens, such as heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs), while exploring the transformative capabilities of advanced genomic technologies. These technologies include genomic selection, CRISPR/Cas9, base editing, prime editing, and artificial intelligence-driven predictive models. Additionally, we examine multi-omics approaches to gain new insights into the genetic and environmental factors influencing carcinogen formation. Our findings suggest that healthy breeding strategies could markedly enhance meat quality, thereby offering a unique opportunity to improve public health outcomes. The integration of these innovative technologies into breeding programs not only provides a pathway to safer beef production but also fosters sustainable livestock management practices. The improvement of these strategies, along with careful consideration of ethical and regulatory challenges, will be crucial for their effective implementation and broader impact.
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Affiliation(s)
- Belete Kuraz Abebe
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
- Department of Animal Science, Werabe University, P.O.Box 46, Werabe, Ethiopia.
| | - Juntao Guo
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Diba Dedacha Jilo
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Shengchen Yu
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
- National Beef Cattle Improvement Center, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
- Department of Animal Science, Werabe University, P.O.Box 46, Werabe, Ethiopia
| | - Haibing Liu
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Gong Cheng
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
- National Beef Cattle Improvement Center, Northwest A&F University, Yangling, 712100, Shaanxi, People's Republic of China.
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Guimarães PAS, Carvalho MGR, Ruiz JC. A computational framework for extracting biological insights from SRA cancer data. Sci Rep 2025; 15:8117. [PMID: 40057525 PMCID: PMC11890766 DOI: 10.1038/s41598-025-91781-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Accepted: 02/24/2025] [Indexed: 05/13/2025] Open
Abstract
The integration of sequenced samples and clinical data from independent yet related studies from public domain databases, such as The Sequence Read Archive (SRA), has the potential to increase sample sizes and enhance the statistical power needed for more precise bioinformatic analysis. Data mining and sample grouping are the starting points in this process and still present several challenges, including the presence of structured and unstructured data, missing deposited data, and varying experimental conditions and techniques applied across the studies. Designed to address the main challenges of data mining and sample grouping for biomarkers research, the proposed methodology employs a computational approach integrating relational database construction, text and data mining, natural language processing, network analysis, search by Pubmed publications, and combining MeSH, TTD and WordNet database to identify groups of samples with the same characteristics. As a result, it identifies and illustrates relationships among sample collections, aiming to discover potential cancer biomarkers. In colorectal cancer (CRC) and acute lymphoblastic leukemia (ALL) case studies, this methodology effectively navigates SRA metadata, retrieving, extracting, and integrating data. It highlights significant connections between samples and patient clinical data, revealing important biological insights. The study grouped 2,737 (CRC) and 3,655 (ALL) samples into potential comparison groups, demonstrating the method's power in identifying relationships and aiding biomarker discovery.
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Affiliation(s)
- Paul Anderson Souza Guimarães
- Grupo Informática de Biossistemas, Bioengenharia e Genômica, Instituto René Rachou, Fiocruz Minas, Av. Augusto de Lima, 1715, Barro Preto, Belo Horizonte, MG, Brazil
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fiocruz, Rio de Janeiro, Brazil
| | - Maria Gabriela Reis Carvalho
- Grupo Informática de Biossistemas, Bioengenharia e Genômica, Instituto René Rachou, Fiocruz Minas, Av. Augusto de Lima, 1715, Barro Preto, Belo Horizonte, MG, Brazil.
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fiocruz, Rio de Janeiro, Brazil.
| | - Jeronimo Conceição Ruiz
- Grupo Informática de Biossistemas, Bioengenharia e Genômica, Instituto René Rachou, Fiocruz Minas, Av. Augusto de Lima, 1715, Barro Preto, Belo Horizonte, MG, Brazil.
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fiocruz, Rio de Janeiro, Brazil.
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Liu Z, You J, Zhao P, Wang X, Sun S, Wang X, Gu S, Xu Q. Metabolomics Profiling and Advanced Methodologies for Wheat Stress Research. Metabolites 2025; 15:123. [PMID: 39997748 PMCID: PMC11857233 DOI: 10.3390/metabo15020123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 02/05/2025] [Accepted: 02/06/2025] [Indexed: 02/26/2025] Open
Abstract
Metabolomics is an omics technology that studies the types, quantities, and changes of endogenous metabolic substances in organisms affected by abiotic and biotic factors. BACKGROUND/OBJECTIVES Based on metabolomics, small molecule metabolites in biological organisms can be qualitatively and quantitatively analysed. This method analysis directly correlates with biological phenotypes, facilitating the interpretation of life conditions. Wheat (Triticum aestivum L.) is one of the major food crops in the world, and its quality and yield play important roles in safeguarding food security. METHODS This review elaborated on the significance of metabolomics research techniques and methods in enhancing wheat resilience against biotic and abiotic stresses. RESULTS Metabolomics plays an important role in identifying the metabolites in wheat that respond to diverse stresses. The integrated examination of metabolomics with other omics disciplines provides new insights and approaches for exploring resistance genes, understanding the genetic basis of wheat metabolism, and revealing the mechanisms involved in stress responses. CONCLUSIONS Emerging metabolomics research techniques to propose innovative avenues of research is important to enhance wheat resistance.
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Affiliation(s)
- Zhen Liu
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Taian 271018, China; (Z.L.); (P.Z.); (S.S.)
| | - Jiahui You
- Shandong Guocangjian Biotechnology Co., Ltd., Taian 271018, China; (J.Y.); (X.W.); (X.W.)
| | - Peiying Zhao
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Taian 271018, China; (Z.L.); (P.Z.); (S.S.)
| | - Xianlin Wang
- Shandong Guocangjian Biotechnology Co., Ltd., Taian 271018, China; (J.Y.); (X.W.); (X.W.)
| | - Shufang Sun
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Taian 271018, China; (Z.L.); (P.Z.); (S.S.)
| | - Xizhen Wang
- Shandong Guocangjian Biotechnology Co., Ltd., Taian 271018, China; (J.Y.); (X.W.); (X.W.)
| | - Shubo Gu
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Taian 271018, China; (Z.L.); (P.Z.); (S.S.)
| | - Qian Xu
- National Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Taian 271018, China; (Z.L.); (P.Z.); (S.S.)
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Liu P, Yuan H, Ning Y, Chakraborty B, Liu N, Peres MA. A modified and weighted Gower distance-based clustering analysis for mixed type data: a simulation and empirical analyses. BMC Med Res Methodol 2024; 24:305. [PMID: 39696017 DOI: 10.1186/s12874-024-02427-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 11/26/2024] [Indexed: 12/20/2024] Open
Abstract
BACKGROUND Traditional clustering techniques are typically restricted to either continuous or categorical variables. However, most real-world clinical data are mixed type. This study aims to introduce a clustering technique specifically designed for datasets containing both continuous and categorical variables to offer better clustering compatibility, adaptability, and interpretability than other mixed type techniques. METHODS This paper proposed a modified Gower distance incorporating feature importance as weights to maintain equal contributions between continuous and categorical features. The algorithm (DAFI) was evaluated using five simulated datasets with varying proportions of important features and real-world datasets from the 2011-2014 National Health and Nutrition Examination Survey (NHANES). Effectiveness was demonstrated through comparisons with 13 clustering techniques. Clustering performance was assessed using the adjusted Rand index (ARI) for accuracy in simulation studies and the silhouette score for cohesion and separation in NHANES. Additionally, multivariable logistic regression estimated the association between periodontitis (PD) and cardiovascular diseases (CVDs), adjusting for clusters in NHANES. RESULTS In simulation studies, the DAFI-Gower algorithm consistently performs better than baseline methods according to the adjusted Rand index in settings investigated, especially on datasets with more redundant features. In NHANES, 3,760 people were analyzed. DAFI-Gower achieves the highest silhouette score (0.79). Four distinct clusters with diverse health profiles were identified. By incorporating feature importance, we found that cluster formations were more strongly influenced by CVD-related factors. The association between periodontitis and cardiovascular diseases, after adjusting for clusters, reveals significant insights (adjusted OR 1.95, 95% CI 1.50 to 2.55, p = 0.012), highlighting severe periodontitis as a potential risk factor for cardiovascular diseases. CONCLUSIONS DAFI performed better than classic clustering baselines on both simulated and real-world datasets. It effectively captures cluster characteristics by considering feature importance, which is crucial in clinical settings where many variables may be similar or irrelevant. We envisage that DAFI offers an effective solution for mixed type clustering.
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Affiliation(s)
- Pinyan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Han Yuan
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| | - Marco Aurélio Peres
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore, Singapore
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Martins CF, Matzapetakis M, Ribeiro DM, Kuleš J, Horvatić A, Guillemin N, Eckersall PD, Freire JPB, Almeida AM, Prates JAM. Metabolomics and proteomics insights into hepatic responses of weaned piglets to dietary Spirulina inclusion and lysozyme supplementation. BMC Vet Res 2024; 20:505. [PMID: 39506864 PMCID: PMC11539757 DOI: 10.1186/s12917-024-04339-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Accepted: 10/18/2024] [Indexed: 11/08/2024] Open
Abstract
BACKGROUND Studying the effect of dietary Spirulina and lysozyme supplementation on the metabolome and proteome of liver tissue contributes to understanding potential hepatic adaptations of piglets to these novel diets. This study aimed to understand the influence of including 10% Spirulina on the metabolome and proteome of piglet liver tissue. Three groups of 10 post-weaned piglets, housed in pairs, were fed for 28 days with one of three experimental diets: a cereal and soybean meal-based diet (Control), a base diet with 10% Spirulina (SP), and an SP diet supplemented with 0.01% lysozyme (SP + L). At the end of the trial, animals were sacrificed and liver tissue was collected. Metabolomics analysis (n = 10) was performed using NMR data analysed with PCA and PLS-DA. Proteomics analysis (n = 5) was conducted using a filter aided sample preparation (FASP) protocol and Tandem Mass Tag (TMT)-based quantitative approach with an Orbitrap mass spectrometer. RESULTS Growth performance showed an average daily gain reduction of 9.5% and a feed conversion ratio increase of 10.6% in groups fed Spirulina compared to the control group. Metabolomic analysis revealed no significant differences among the groups and identified 60 metabolites in the liver tissue. Proteomics analysis identified 2,560 proteins, with 132, 11, and 52 differentially expressed in the Control vs. SP, Control vs. SP + L and SP vs. SP + L comparisons, respectively. This study demonstrated that Spirulina enhances liver energy conversion efficiency, detoxification and cellular secretion. It improves hepatic metabolic efficiency through alterations in fatty acid oxidation (e.g., upregulation of enzymes like fatty acid synthase and increased acetyl-CoA levels), carbohydrate catabolism (e.g., increased glucose and glucose-6-phosphate), pyruvate metabolism (e.g., higher levels of pyruvate and phosphoenolpyruvate carboxykinase), and cellular defence mechanisms (e.g., upregulation of glutathione and metallothionein). Lysozyme supplementation mitigates some adverse effects of Spirulina, bringing physiological responses closer to control levels. This includes fewer differentially expressed proteins and improved dry matter, organic matter and energy digestibility. Lysozyme also enhances coenzyme availability, skeletal myofibril assembly, actin-mediated cell contraction, tissue regeneration and development through mesenchymal migration and nucleic acid synthesis pathways. CONCLUSIONS While Spirulina inclusion had some adverse effects on growth performance, it also enhanced hepatic metabolic efficiency by improving fatty acid oxidation, carbohydrate catabolism and cellular defence mechanisms. The addition of lysozyme further improved these benefits by reducing some of the negative impacts on growth and enhancing nutrient digestibility, tissue regeneration, and overall metabolic balance. Together, Spirulina and lysozyme demonstrate potential as functional dietary components, but further optimization is needed to fully realize their benefits without compromising growth performance.
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Affiliation(s)
- Cátia Falcão Martins
- Centro de Investigação Interdisciplinar Em Sanidade Animal, Faculdade de Medicina Veterinária, Universidade de Lisboa, Av. da Universidade Técnica, Lisbon, 1300-477, Portugal
- Associate Laboratory for Animal and Veterinary Sciences, Av. da Universidade Técnica, Lisbon, 1300-477, Portugal
- Linking Landscape, Environment, Agriculture and Food, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisbon, 1349-017, Portugal
| | - Manolis Matzapetakis
- Instituto de Tecnologia Química E Biológica, Universidade Nova de Lisboa, Av. da República, Oeiras, 2780-157, Portugal
| | - David M Ribeiro
- Linking Landscape, Environment, Agriculture and Food, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisbon, 1349-017, Portugal
| | - Josipa Kuleš
- Laboratory of Proteomics, Faculty of Veterinary Medicine, Internal Diseases Clinic, University of Zagreb, Heinzelova 55, Zagreb, 10000, Croatia
- Department of Chemistry and Biochemistry, Faculty of Veterinary Medicine, University of Zagreb, Heinzelova 55, Zagreb, 10000, Croatia
| | - Anita Horvatić
- Laboratory of Proteomics, Faculty of Veterinary Medicine, Internal Diseases Clinic, University of Zagreb, Heinzelova 55, Zagreb, 10000, Croatia
- Department of Chemistry and Biochemistry, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, Zagreb, 10000, Croatia
| | - Nicholas Guillemin
- Laboratory of Proteomics, Faculty of Veterinary Medicine, Internal Diseases Clinic, University of Zagreb, Heinzelova 55, Zagreb, 10000, Croatia
| | - Peter David Eckersall
- Laboratory of Proteomics, Faculty of Veterinary Medicine, Internal Diseases Clinic, University of Zagreb, Heinzelova 55, Zagreb, 10000, Croatia
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, UK
| | - João P B Freire
- Linking Landscape, Environment, Agriculture and Food, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisbon, 1349-017, Portugal
| | - André M Almeida
- Linking Landscape, Environment, Agriculture and Food, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisbon, 1349-017, Portugal.
| | - José A M Prates
- Centro de Investigação Interdisciplinar Em Sanidade Animal, Faculdade de Medicina Veterinária, Universidade de Lisboa, Av. da Universidade Técnica, Lisbon, 1300-477, Portugal.
- Associate Laboratory for Animal and Veterinary Sciences, Av. da Universidade Técnica, Lisbon, 1300-477, Portugal.
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Vitorino R. Transforming Clinical Research: The Power of High-Throughput Omics Integration. Proteomes 2024; 12:25. [PMID: 39311198 PMCID: PMC11417901 DOI: 10.3390/proteomes12030025] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/31/2024] [Accepted: 09/02/2024] [Indexed: 09/26/2024] Open
Abstract
High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.
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Affiliation(s)
- Rui Vitorino
- iBiMED, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
- Department of Surgery and Physiology, Cardiovascular R&D Centre—UnIC@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Sharma V, Khokhar M, Panigrahi P, Gadwal A, Setia P, Purohit P. Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy. Clin Chim Acta 2024; 561:119842. [PMID: 38969086 DOI: 10.1016/j.cca.2024.119842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 06/25/2024] [Accepted: 06/29/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies. METHODS We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN. RESULTS Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care. CONCLUSION Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.
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Affiliation(s)
- V Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - M Khokhar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Panigrahi
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - A Gadwal
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Setia
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Purohit
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India.
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Mokhtari M, Khoshbakht S, Ziyaei K, Akbari ME, Moravveji SS. New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics. Brief Bioinform 2024; 25:bbae074. [PMID: 38446742 PMCID: PMC10939336 DOI: 10.1093/bib/bbae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/14/2023] [Accepted: 02/07/2021] [Indexed: 03/08/2024] Open
Abstract
Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.
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Affiliation(s)
- Majid Mokhtari
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Samane Khoshbakht
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
- Duke Molecular Physiology Institute, Duke University School of Medicine-Cardiology, Durham, NC, 27701, USA
| | - Kobra Ziyaei
- Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran
| | | | - Sayyed Sajjad Moravveji
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
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Gupta NS, Kumar P. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Comput Biol Med 2023; 162:107051. [PMID: 37271113 DOI: 10.1016/j.compbiomed.2023.107051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
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Affiliation(s)
- Nancy Sanjay Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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10
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Bahadoor A, Robinson KA, Loewen MC, Demissie ZA. Clonostachys rosea 'omics profiling: identification of putative metabolite-gene associations mediating its in vitro antagonism against Fusarium graminearum. BMC Genomics 2023; 24:352. [PMID: 37365507 DOI: 10.1186/s12864-023-09463-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/17/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Clonostachys rosea is an established biocontrol agent. Selected strains have either mycoparasitic activity against known pathogens (e.g. Fusarium species) and/or plant growth promoting activity on various crops. Here we report outcomes from a comparative 'omics analysis leveraging a temporal variation in the in vitro antagonistic activities of C. rosea strains ACM941 and 88-710, toward understanding the molecular mechanisms underpinning mycoparasitism. RESULTS Transcriptomic data highlighted specialized metabolism and membrane transport related genes as being significantly upregulated in ACM941 compared to 88-710 at a time point when the ACM941 strain had higher in vitro antagonistic activity than 88-710. In addition, high molecular weight specialized metabolites were differentially secreted by ACM941, with accumulation patterns of some metabolites matching the growth inhibition differences displayed by the exometabolites of the two strains. In an attempt to identify statistically relevant relationships between upregulated genes and differentially secreted metabolites, transcript and metabolomic abundance data were associated using IntLIM (Integration through Linear Modeling). Of several testable candidate associations, a putative C. rosea epidithiodiketopiperazine (ETP) gene cluster was identified as a prime candidate based on both co-regulation analysis and transcriptomic-metabolomic data association. CONCLUSIONS Although remaining to be validated functionally, these results suggest that a data integration approach may be useful for identification of potential biomarkers underlying functional divergence in C. rosea strains.
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Affiliation(s)
- Adilah Bahadoor
- Metrology Research Center, National Research Council Canada, 1200 Montreal Rd, Ottawa, ON, K1A 0R6, Canada
| | - Kelly A Robinson
- Aquatic and Crop Resource Development, National Research Council of Canada, Ottawa, ON, Canada
| | - Michele C Loewen
- Aquatic and Crop Resource Development, National Research Council of Canada, Ottawa, ON, Canada.
| | - Zerihun A Demissie
- Aquatic and Crop Resource Development, National Research Council of Canada, Ottawa, ON, Canada.
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Flores JE, Claborne DM, Weller ZD, Webb-Robertson BJM, Waters KM, Bramer LM. Missing data in multi-omics integration: Recent advances through artificial intelligence. Front Artif Intell 2023; 6:1098308. [PMID: 36844425 PMCID: PMC9949722 DOI: 10.3389/frai.2023.1098308] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
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Affiliation(s)
- Javier E. Flores
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Daniel M. Claborne
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Zachary D. Weller
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Katrina M. Waters
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
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12
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Principe S, Vijverberg SJH, Abdel-Aziz MI, Scichilone N, Maitland-van der Zee AH. Precision Medicine in Asthma Therapy. Handb Exp Pharmacol 2023; 280:85-106. [PMID: 35852633 DOI: 10.1007/164_2022_598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Asthma is a complex, heterogeneous disease that necessitates a proper patient evaluation to decide the correct treatment and optimize disease control. The recent introduction of new target therapies for the most severe form of the disease has heralded a new era of treatment options, intending to treat and control specific molecular pathways in asthma pathophysiology. Precision medicine, using omics sciences, investigates biological and molecular mechanisms to find novel biomarkers that can be used to guide treatment selection and predict response. The identification of reliable biomarkers indicative of the pathological mechanisms in asthma is essential to unravel new potential treatment targets. In this chapter, we provide a general description of the currently available -omics techniques, focusing on their implications in asthma therapy.
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Affiliation(s)
- Stefania Principe
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
- Dipartimento Universitario di Promozione della Salute, Materno Infantile, Medicina Interna e Specialistica di Eccellenza "G. D'Alessandro" (PROMISE) c/o Pneumologia, AOUP "Policlinico Paolo Giaccone", University of Palermo, Palermo, Italy.
| | - Susanne J H Vijverberg
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Mahmoud I Abdel-Aziz
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt
| | - Nicola Scichilone
- Dipartimento Universitario di Promozione della Salute, Materno Infantile, Medicina Interna e Specialistica di Eccellenza "G. D'Alessandro" (PROMISE) c/o Pneumologia, AOUP "Policlinico Paolo Giaccone", University of Palermo, Palermo, Italy
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13
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Abdelzaher H, Tawfik SM, Nour A, Abdelkader S, Elbalkiny ST, Abdelkader M, Abbas WA, Abdelnaser A. Climate change, human health, and the exposome: Utilizing OMIC technologies to navigate an era of uncertainty. Front Public Health 2022; 10:973000. [PMID: 36211706 PMCID: PMC9533016 DOI: 10.3389/fpubh.2022.973000] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/17/2022] [Indexed: 01/25/2023] Open
Abstract
Climate change is an anthropogenic phenomenon that is alarming scientists and non-scientists alike. The emission of greenhouse gases is causing the temperature of the earth to rise and this increase is accompanied by a multitude of climate change-induced environmental exposures with potential health impacts. Tracking human exposure has been a major research interest of scientists worldwide. This has led to the development of exposome studies that examine internal and external individual exposures over their lifetime and correlate them to health. The monitoring of health has also benefited from significant technological advances in the field of "omics" technologies that analyze physiological changes on the nucleic acid, protein, and metabolism levels, among others. In this review, we discuss various climate change-induced environmental exposures and their potential health implications. We also highlight the potential integration of the technological advancements in the fields of exposome tracking, climate monitoring, and omics technologies shedding light on important questions that need to be answered.
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Affiliation(s)
| | | | | | | | | | | | | | - Anwar Abdelnaser
- Institute of Global Health and Human Ecology, The American University in Cairo, New Cairo, Egypt
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14
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Chuah J, Kruger U, Wang G, Yan P, Hahn J. Framework for Testing Robustness of Machine Learning-Based Classifiers. J Pers Med 2022; 12:1314. [PMID: 36013263 PMCID: PMC9409965 DOI: 10.3390/jpm12081314] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/05/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023] Open
Abstract
There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers' performance and changes in the classifiers' parameter values using factor analysis and Monte Carlo simulations. Specifically, this work evaluates (1) the importance of a classifier's input features and (2) the variability of a classifier's output and model parameter values in response to data perturbations. Additionally, it was found that one can estimate a priori how much replacement noise a classifier can tolerate while still meeting accuracy goals. To illustrate the evaluation framework, six different AI/ML-based biomarkers are developed using commonly used techniques (linear discriminant analysis, support vector machines, random forest, partial-least squares discriminant analysis, logistic regression, and multilayer perceptron) for a metabolomics dataset involving 24 measured metabolites taken from 159 study participants. The framework was able to correctly predict which of the classifiers should be less robust than others without recomputing the classifiers itself, and this prediction was then validated in a detailed analysis.
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Affiliation(s)
- Joshua Chuah
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Uwe Kruger
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Pingkun Yan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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15
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Guo Y, Ren C, Huang W, Yang W, Bao Y. Oncogenic ACSM1 in prostate cancer is through metabolic and extracellular matrix-receptor interaction signaling pathways. Am J Cancer Res 2022; 12:1824-1842. [PMID: 35530294 PMCID: PMC9077067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023] Open
Abstract
Acyl-coenzyme A synthetase medium chain family member 1 (ACSM1) is a medium chain Acyl-CoA Synthetase family member and plays an important role in fatty acid metabolism. The oncogenic roles of ACSM1 are largely unknown. Using comprehensive approaches, we analyzed gene expression profiles and genomic datasets and identified that the expression of ACSM1 was specifically increased in prostate cancer in comparison to the adjacent non-tumor tissues. The increased expression of ACSM1 was associated with increased risks of poor prognosis and shorter survival time. Moreover, genomic copy number alterations of ACSM1, including deletion, amplification, and amino acid changes were frequently observed in prostate cancers, although these mutations did not correlate with gene expression levels. However, ACSM1 gene amplifications were significantly corrected with increased risks of prostate cancer metastasis, and ACSM1 genetic alterations were significantly associated with worse disease-free. And progress-free survival. Gene function stratification and gene set enrichment analysis revealed that the oncogenic roles of ACSM1 in prostate cancer were mainly through metabolic pathways and extracellular matrix (ECM)-receptor interaction signaling pathways, but not associated with microenvironmental immunological signaling pathways, and that ACSM1 expression was not associated with immune cell infiltration in the cancer microenvironment or prostate cancer immune subtypes. In conclusion, the present work has demonstrated that ACSM1 can be specifically and significantly elevated in prostate cancer. ACSM1 gene expression and genomic amplification exhibit important clinical significance through metabolic and ECM-receptor interaction signaling pathways. Thus, ACSM1 may be a novel oncogene and serve as a biomarker for prostate cancer screening and prognosis prediction, and/or a therapeutic target.
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Affiliation(s)
- Yongchen Guo
- Department of Immunology, Mudanjiang Medical UniversityMudanjiang 157011, China
| | - Chunna Ren
- The Second Affiliated Hospital of Mudanjiang Medical UniversityMudanjiang 157011, China
| | - Wentao Huang
- Hongqi Hospital Affiliated to Mudanjiang Medical UniversityMudanjiang 157011, China
| | - Wancai Yang
- Department of Pathology, University of Illinois at ChicagoIL 60612, USA
| | - Yonghua Bao
- Department of Pathology, Mudanjiang Medical UniversityMudanjiang 157011, China
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16
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Current research on simultaneous oxidation of aliphatic and aromatic hydrocarbons by bacteria of genus Pseudomonas. Folia Microbiol (Praha) 2022; 67:591-604. [PMID: 35318574 DOI: 10.1007/s12223-022-00966-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 03/15/2022] [Indexed: 11/04/2022]
Abstract
One of the most frequently used methods for elimination of oil pollution is the use of biological preparations based on oil-degrading microorganisms. Such microorganisms often relate to bacteria of the genus Pseudomonas. Pseudomonads are ubiquitous microorganisms that often have the ability to oxidize various pollutants, including oil hydrocarbons. To date, individual biochemical pathways of hydrocarbon degradation and the organization of the corresponding genes have been studied in detail. Almost all studies of this kind have been performed on degraders of individual hydrocarbons belonging to a single particular class. Microorganisms capable of simultaneous degradation of aliphatic and aromatic hydrocarbons are very poorly studied. Most of the works on such objects have been devoted only to phenotype characteristic and some to genetic studies. To identify the patterns of interaction of several metabolic systems depending on the growth conditions, the most promising are such approaches as transcriptomics and proteomics, which make it possible to obtain a comprehensive assessment of changes in the expression of hundreds of genes and proteins at the same time. This review summarizes the existing data on bacteria of the genus Pseudomonas capable of the simultaneous oxidation of hydrocarbons of different classes (alkanes, monoaromatics, and polyaromatics) and presents the most important results obtained in the studies on the biodegradation of hydrocarbons by representatives of this genus using methods of transcriptomic and proteomic analyses.
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17
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Benevenuto RF, Venter HJ, Zanatta CB, Nodari RO, Agapito-Tenfen SZ. Alterations in genetically modified crops assessed by omics studies: Systematic review and meta-analysis. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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18
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Kang M, Ko E, Mersha TB. A roadmap for multi-omics data integration using deep learning. Brief Bioinform 2022; 23:bbab454. [PMID: 34791014 PMCID: PMC8769688 DOI: 10.1093/bib/bbab454] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 12/18/2022] Open
Abstract
High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.
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Affiliation(s)
- Mingon Kang
- Department of Computer Science at the University of Nevada, Las Vegas, NV, USA
| | - Euiseong Ko
- Department of Computer Science at the University of Nevada, Las Vegas, NV, USA
| | - Tesfaye B Mersha
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
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19
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Forero-Rodríguez LJ, Josephs-Spaulding J, Flor S, Pinzón A, Kaleta C. Parkinson's Disease and the Metal-Microbiome-Gut-Brain Axis: A Systems Toxicology Approach. Antioxidants (Basel) 2021; 11:71. [PMID: 35052575 PMCID: PMC8773335 DOI: 10.3390/antiox11010071] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/02/2021] [Accepted: 12/17/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's Disease (PD) is a neurodegenerative disease, leading to motor and non-motor complications. Autonomic alterations, including gastrointestinal symptoms, precede motor defects and act as early warning signs. Chronic exposure to dietary, environmental heavy metals impacts the gastrointestinal system and host-associated microbiome, eventually affecting the central nervous system. The correlation between dysbiosis and PD suggests a functional and bidirectional communication between the gut and the brain. The bioaccumulation of metals promotes stress mechanisms by increasing reactive oxygen species, likely altering the bidirectional gut-brain link. To better understand the differing molecular mechanisms underlying PD, integrative modeling approaches are necessary to connect multifactorial perturbations in this heterogeneous disorder. By exploring the effects of gut microbiota modulation on dietary heavy metal exposure in relation to PD onset, the modification of the host-associated microbiome to mitigate neurological stress may be a future treatment option against neurodegeneration through bioremediation. The progressive movement towards a systems toxicology framework for precision medicine can uncover molecular mechanisms underlying PD onset such as metal regulation and microbial community interactions by developing predictive models to better understand PD etiology to identify options for novel treatments and beyond. Several methodologies recently addressed the complexity of this interaction from different perspectives; however, to date, a comprehensive review of these approaches is still lacking. Therefore, our main aim through this manuscript is to fill this gap in the scientific literature by reviewing recently published papers to address the surrounding questions regarding the underlying molecular mechanisms between metals, microbiota, and the gut-brain-axis, as well as the regulation of this system to prevent neurodegeneration.
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Affiliation(s)
- Lady Johanna Forero-Rodríguez
- Research Group Bioinformatics and Systems Biology, Instituto de Genetica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (L.J.F.-R.); (A.P.)
- Research Group Medical Systems Biology, Christian-Albrechts-Universität Kiel, Brunswiker Straße 10, 24105 Kiel, Germany; (S.F.); (C.K.)
| | - Jonathan Josephs-Spaulding
- Research Group Medical Systems Biology, Christian-Albrechts-Universität Kiel, Brunswiker Straße 10, 24105 Kiel, Germany; (S.F.); (C.K.)
| | - Stefano Flor
- Research Group Medical Systems Biology, Christian-Albrechts-Universität Kiel, Brunswiker Straße 10, 24105 Kiel, Germany; (S.F.); (C.K.)
| | - Andrés Pinzón
- Research Group Bioinformatics and Systems Biology, Instituto de Genetica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (L.J.F.-R.); (A.P.)
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Christian-Albrechts-Universität Kiel, Brunswiker Straße 10, 24105 Kiel, Germany; (S.F.); (C.K.)
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20
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Green CL, Mitchell SE, Derous D, García-Flores LA, Wang Y, Chen L, Han JDJ, Promislow DEL, Lusseau D, Douglas A, Speakman JR. The Effects of Graded Levels of Calorie Restriction: XVI. Metabolomic Changes in the Cerebellum Indicate Activation of Hypothalamocerebellar Connections Driven by Hunger Responses. J Gerontol A Biol Sci Med Sci 2021; 76:601-610. [PMID: 33053185 DOI: 10.1093/gerona/glaa261] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Indexed: 12/19/2022] Open
Abstract
Calorie restriction (CR) remains the most robust intervention to extend life span and improve healthspan. Though the cerebellum is more commonly associated with motor control, it has strong links with the hypothalamus and is thought to be associated with nutritional regulation and adiposity. Using a global mass spectrometry-based metabolomics approach, we identified 756 metabolites that were significantly differentially expressed in the cerebellar region of the brain of C57BL/6J mice, fed graded levels of CR (10, 20, 30, and 40 CR) compared to mice fed ad libitum for 12 hours a day. Pathway enrichment indicated changes in the pathways of adenosine and guanine (which are precursors of DNA production), aromatic amino acids (tyrosine, phenylalanine, and tryptophan) and the sulfur-containing amino acid methionine. We also saw increases in the tricarboxylic acid cycle (TCA) cycle, electron donor, and dopamine and histamine pathways. In particular, changes in l-histidine and homocarnosine correlated positively with the level of CR and food anticipatory activity and negatively with insulin and body temperature. Several metabolic and pathway changes acted against changes seen in age-associated neurodegenerative disorders, including increases in the TCA cycle and reduced l-proline. Carnitine metabolites contributed to discrimination between CR groups, which corroborates previous work in the liver and plasma. These results indicate the conservation of certain aspects of metabolism across tissues with CR. Moreover, this is the first study to indicate CR alters the cerebellar metabolome, and does so in a graded fashion, after only a short period of restriction.
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Affiliation(s)
- Cara L Green
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Sharon E Mitchell
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Davina Derous
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Libia A García-Flores
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Yingchun Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, China
| | - Jing-Dong J Han
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China
| | - Daniel E L Promislow
- Department of Pathology and Department of Biology, University of Washington at Seattle
| | - David Lusseau
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Alex Douglas
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - John R Speakman
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK.,State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
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21
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Wang Z, He Y. Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research. Brief Funct Genomics 2021; 20:235-248. [PMID: 34159360 PMCID: PMC8287950 DOI: 10.1093/bfgp/elab029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention. SHORT ABSTRACT Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.
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Affiliation(s)
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA
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22
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Ni T, Xu S, Wei Y, Li T, Jin G, Deng WW, Ning J. Understanding the promotion of withering treatment on quality of postharvest tea leaves using UHPLC-orbitrap-MS metabolomics integrated with TMT-Based proteomics. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111614] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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23
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A Statistical Workflow to Evaluate the Modulation of Wine Metabolome and Its Contribution to the Sensory Attributes. FERMENTATION 2021. [DOI: 10.3390/fermentation7020072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
A data-processing and statistical analysis workflow was proposed to evaluate the metabolic changes and its contribution to the sensory characteristics of different wines. This workflow was applied to rosé wines from different fermentation strategies. The metabolome was acquired by means of two high-throughput techniques: gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) for volatile and non-volatile metabolites, respectively, in an untargeted approach, while the sensory evaluation of the wines was performed by a trained panel. Wine volatile and non-volatile metabolites modulation was independently evaluated by means of partial least squares discriminant analysis (PLS-DA), obtaining potential markers of the fermentation strategies. Then, the complete metabolome was integrated by means of sparse generalised canonical correlation analysis discriminant analysis (sGCC-DA). This integrative approach revealed a high link between the volatile and non-volatile data, and additional potential metabolite markers of the fermentation strategies were found. Subsequently, the evaluation of the contribution of metabolome to the sensory characteristics of wines was carried out. First, the all-relevant metabolites affected by the different fermentation processes were selected using PLS-DA and random forest (RF). Each set of volatile and non-volatile metabolites selected was then related to the sensory attributes of the wines by means of partial least squares regression (PLSR). Finally, the relationships among the three datasets were complementary evaluated using regularised generalised canonical correlation analysis (RGCCA), revealing new correlations among metabolites and sensory data.
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24
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Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, Byrum SD. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics 2021; 17:170-185. [PMID: 33347526 PMCID: PMC8058243 DOI: 10.1039/d0mo00041h] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.
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Affiliation(s)
- Stefan Graw
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Kevin Chappell
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Charity L Washam
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
| | - Allen Gies
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Jordan Bird
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Michael S Robeson
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
| | - Stephanie D Byrum
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
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25
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Darmayanti S, Lesmana R, Meiliana A, Abdulah R. Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients. Curr Diabetes Rev 2021; 17:e123120189796. [PMID: 33393899 DOI: 10.2174/1573399817666210101105253] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/19/2020] [Accepted: 10/23/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND There is a continuous rise in the prevalence of type 2 diabetes mellitus (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is diabetes nephropathy (DN). METHODS A literature search was made in view of highlighting the novel applications of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients. RESULTS The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice. DISCUSSION DN is also the leading cause of end-stage renal disease and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN. CONCLUSION The combination of genomics, proteomics, and metabolomics assays as suitable biological systems can provide new and deeper insights into the pathogenesis of diabetes, as well as discover prospects for developing suitable and targeted interventions.
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Affiliation(s)
- Siska Darmayanti
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Jatinangor, Indonesia
| | - Ronny Lesmana
- Department of Biomedical Sciences, Faculty of Medicine, Universitas Padjadjaran, Jatinangor, Indonesia
| | - Anna Meiliana
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Jatinangor, Indonesia
| | - Rizky Abdulah
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Jatinangor, Indonesia
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26
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Low Entropy Sub-Networks Prevent the Integration of Metabolomic and Transcriptomic Data. ENTROPY 2020; 22:e22111238. [PMID: 33287006 PMCID: PMC7712986 DOI: 10.3390/e22111238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/23/2020] [Accepted: 10/27/2020] [Indexed: 02/08/2023]
Abstract
The constantly and rapidly increasing amount of the biological data gained from many different high-throughput experiments opens up new possibilities for data- and model-driven inference. Yet, alongside, emerges a problem of risks related to data integration techniques. The latter are not so widely taken account of. Especially, the approaches based on the flux balance analysis (FBA) are sensitive to the structure of a metabolic network for which the low-entropy clusters can prevent the inference from the activity of the metabolic reactions. In the following article, we set forth problems that may arise during the integration of metabolomic data with gene expression datasets. We analyze common pitfalls, provide their possible solutions, and exemplify them by a case study of the renal cell carcinoma (RCC). Using the proposed approach we provide a metabolic description of the known morphological RCC subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy. This discovery suits and extends the already known poor-prognosis characteristics of RCC. Finally, the goal of this work is also to point out the problem that arises from the integration of high-throughput data with the inherently nonuniform, manually curated low-throughput data. In such cases, the over-represented information may potentially overshadow the non-trivial discoveries.
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27
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Wu JX, Pascovici D, Wu Y, Walker AK, Mirzaei M. Workflow for Rapidly Extracting Biological Insights from Complex, Multicondition Proteomics Experiments with WGCNA and PloGO2. J Proteome Res 2020; 19:2898-2906. [PMID: 32407095 DOI: 10.1021/acs.jproteome.0c00198] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
We describe a useful workflow for characterizing proteomics experiments incorporating many conditions and abundance data using the popular weighted gene correlation network analysis (WGCNA) approach and functional annotation with the PloGO2 R package, the latter of which we have extended and made available to Bioconductor. The approach can use quantitative data from labeled or label-free experiments and was developed to handle multiple files stemming from data partition or multiple pairwise comparisons. The WGCNA approach can similarly produce a potentially large number of clusters of interest, which can also be functionally characterized using PloGO2. Enrichment analysis will identify clusters or subsets of proteins of interest, and the WGCNA network topology scores will produce a ranking of proteins within these clusters or subsets. This can naturally lead to prioritized proteins to be considered for further analysis or as candidates of interest for validation in the context of complex experiments. We demonstrate the use of the package on two published data sets using two different biological systems (plant and human plasma) and proteomics platforms (sequential window acquisition of all theoretical fragment-ion spectra (SWATH) and tandem mass tag (TMT)): an analysis of the effect of drought on rice over time generated using TMT and a pediatric plasma sample data set generated using SWATH. In both, the automated workflow recapitulates key insights or observations of the published papers and provides additional suggestions for further investigation. These findings indicate that the data set analysis using WGCNA combined with the updated PloGO2 package is a powerful method to gain biological insights from complex multifaceted proteomics experiments.
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Affiliation(s)
- Jemma X Wu
- Australian Proteome Analysis Facility, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Dana Pascovici
- Australian Proteome Analysis Facility, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Yunqi Wu
- Australian Proteome Analysis Facility, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Adam K Walker
- Neurodegeneration Pathobiology Laboratory, Queensland Brain Institute, The University of Queensland, St. Lucia, Queensland 4072, Australia.,Centre for Motor Neuron Disease Research, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney,New South Wales 2109, Australia
| | - Mehdi Mirzaei
- Australian Proteome Analysis Facility, Macquarie University, Sydney, New South Wales 2109, Australia.,Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia.,Department of Clinical Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
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28
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Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, Aizat WM. Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology. FRONTIERS IN PLANT SCIENCE 2020; 11:944. [PMID: 32754171 PMCID: PMC7371031 DOI: 10.3389/fpls.2020.00944] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/10/2020] [Indexed: 05/03/2023]
Abstract
Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.
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Affiliation(s)
- Ili Nadhirah Jamil
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Juwairiah Remali
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Kamalrul Azlan Azizan
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Masanori Arita
- Bioinformation & DDBJ Center, National Institute of Genetics (NIG), Mishima, Japan
- Metabolome Informatics Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Hoe-Han Goh
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Wan Mohd Aizat
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
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29
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Jenkins K, Mateeva T, Szabó I, Melnik A, Picotti P, Csikász-Nagy A, Rosta E. Combining data integration and molecular dynamics for target identification in α-Synuclein-aggregating neurodegenerative diseases: Structural insights on Synaptojanin-1 (Synj1). Comput Struct Biotechnol J 2020; 18:1032-1042. [PMID: 32419904 PMCID: PMC7215115 DOI: 10.1016/j.csbj.2020.04.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/15/2020] [Accepted: 04/18/2020] [Indexed: 12/19/2022] Open
Abstract
Parkinson’s disease (PD), Alzheimer’s disease (AD) and Amyotrophic lateral sclerosis (ALS) are neurodegenerative diseases hallmarked by the formation of toxic protein aggregates. However, targeting these aggregates therapeutically have thus far shown no success. The treatment of AD has remained particularly problematic since no new drugs have been approved in the last 15 years. Therefore, novel therapeutic targets need to be identified and explored. Here, through the integration of genomic and proteomic data, a set of proteins with strong links to α-synuclein-aggregating neurodegenerative diseases was identified. We propose 17 protein targets that are likely implicated in neurodegeneration and could serve as potential targets. The human phosphatidylinositol 5-phosphatase synaptojanin-1, which has already been independently confirmed to be implicated in Parkinson’s and Alzheimer’s disease, was among those identified. Despite its involvement in PD and AD, structural aspects are currently missing at the molecular level. We present the first atomistic model of the 5-phosphatase domain of synaptojanin-1 and its binding to its substrate phosphatidylinositol 4,5-bisphosphate (PIP2). We determine structural information on the active site including membrane-embedded molecular dynamics simulations. Deficiency of charge within the active site of the protein is observed, which suggests that a second divalent cation is required to complete dephosphorylation of the substrate. The findings in this work shed light on the protein’s binding to phosphatidylinositol 4,5-bisphosphate (PIP2) and give additional insight for future targeting of the protein active site, which might be of interest in neurodegenerative diseases where synaptojanin-1 is overexpressed.
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Affiliation(s)
- Kirsten Jenkins
- Randall Division of Cell and Molecular Biophysics, Institute for Mathematical and Molecular Biomedicine, King's College London, London SE1 1UL, UK
| | - Teodora Mateeva
- Department of Chemistry, King's College London, London SE1 1DB, UK
| | - István Szabó
- Department of Chemistry, King's College London, London SE1 1DB, UK
| | - Andre Melnik
- Institute of Biochemistry, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Paola Picotti
- Institute of Biochemistry, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Attila Csikász-Nagy
- Randall Division of Cell and Molecular Biophysics, Institute for Mathematical and Molecular Biomedicine, King's College London, London SE1 1UL, UK.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary
| | - Edina Rosta
- Department of Chemistry, King's College London, London SE1 1DB, UK
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30
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Hu X, Go YM, Jones DP. Omics Integration for Mitochondria Systems Biology. Antioxid Redox Signal 2020; 32:853-872. [PMID: 31891667 PMCID: PMC7074923 DOI: 10.1089/ars.2019.8006] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 12/30/2019] [Indexed: 12/13/2022]
Abstract
Significance: Elucidation of the central importance of mitophagy in homeostasis of cells and organisms emphasizes that mitochondrial functions extend far beyond short-term needs for energy production. In mitochondria systems biology, the mitochondrial genome, proteome, and metabolome operate as a functional network in coordination of cell activities. Organization occurs through subnetworks that are interconnected by membrane potential, transport activities, allosteric and cooperative interactions, redox signaling mechanisms, rheostatic control by post-translational modifications, and metal ion homeostasis. These subnetworks enable use of varied energy precursors, defense against environmental stressors, and macromolecular rewiring to titrate energy production, biosynthesis, and detoxification according to cell-specific needs. Rewiring mechanisms, termed mitochondrial reprogramming, enhance fitness to respond to metabolic resources and challenges from the environment. Maladaptive responses can cause cell death. Maladaptive rewiring can cause disease. In cancer, adaptive rewiring can interfere with effective treatment. Recent Advances: Many recent advances have been facilitated by the development of new omics tools, which create opportunities to use data-driven analysis of omics data to address these complex adaptive and maladaptive mechanisms of mitochondrial reprogramming in human disease. Critical Issues: Application of omics integration to model systems reveals a critical role for metal ion homeostasis broadly impacting mitochondrial reprogramming. Importantly, data show that trans-omics associations are more robust and biologically relevant than single omics associations. Future Directions: Application of omics integration to mitophagy research creates new opportunities to link the complex, interactive functions of mitochondrial form and function in mitochondria systems biology.
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Affiliation(s)
- Xin Hu
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, Georgia
| | - Young-Mi Go
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, Georgia
| | - Dean P. Jones
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, Georgia
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31
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Green CL, Mitchell SE, Derous D, Wang Y, Chen L, Han JDJ, Promislow DEL, Lusseau D, Douglas A, Speakman JR. The Effects of Graded Levels of Calorie Restriction: XIV. Global Metabolomics Screen Reveals Brown Adipose Tissue Changes in Amino Acids, Catecholamines, and Antioxidants After Short-Term Restriction in C57BL/6 Mice. J Gerontol A Biol Sci Med Sci 2020; 75:218-229. [PMID: 31220223 PMCID: PMC7530471 DOI: 10.1093/gerona/glz023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Indexed: 12/15/2022] Open
Abstract
Animals undergoing calorie restriction (CR) often lower their body temperature to conserve energy. Brown adipose tissue (BAT) is stimulated through norepinephrine when rapid heat production is needed, as it is highly metabolically active due to the uncoupling of the electron transport chain from ATP synthesis. To better understand how BAT metabolism changes with CR, we used metabolomics to identify 883 metabolites that were significantly differentially expressed in the BAT of C57BL/6 mice, fed graded CR (10%, 20%, 30%, and 40% CR relative to their individual baseline intake), compared with mice fed ad libitum (AL) for 12 hours a day. Pathway analysis revealed that graded CR had an impact on the TCA cycle and fatty acid degradation. In addition, an increase in nucleic acids and catecholamine pathways was seen with graded CR in the BAT metabolome. We saw increases in antioxidants with CR, suggesting a beneficial effect of mitochondrial uncoupling. Importantly, the instigator of BAT activation, norepinephrine, was increased with CR, whereas its precursors l-tyrosine and dopamine were decreased, indicating a shift of metabolites through the activation pathway. Several of these key changes were correlated with food anticipatory activity and body temperature, indicating BAT activation may be driven by responses to hunger.
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Affiliation(s)
- Cara L Green
- School of Biological Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK
| | - Sharon E Mitchell
- School of Biological Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK
| | - Davina Derous
- School of Biological Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK
| | - Yingchun Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, China
| | - Jing-Dong J Han
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China
| | - Daniel E L Promislow
- Department of Pathology and Department of Biology, University of Washington at Seattle
| | - David Lusseau
- School of Biological Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK
| | - Alex Douglas
- School of Biological Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK
| | - John R Speakman
- School of Biological Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, China
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32
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Valerio A, Casadei L, Giuliani A, Valerio M. Fecal Metabolomics as a Novel Noninvasive Method for Short-Term Stress Monitoring in Beef Cattle. J Proteome Res 2020; 19:845-853. [PMID: 31873020 DOI: 10.1021/acs.jproteome.9b00655] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Traditional measures of short-term stress response such as fecal glucocorticoid metabolites (FGM) are widely used in controlled settings to quantify the intensity of stimulation to which cattle are exposed. However, FGMs present several methodological and interpretation pitfalls when applied on animals in free-ranging conditions. In this study, we proposed an NMR-based fecal metabolomics strategy for noninvasive stress detection in beef cattle. Using a longitudinal sample collection, we monitored the changes in the fecal metabolome and FGM concentrations before and after an acute stressful event. Our results showed that while the fecal metabolome changed as a function of stress (p < 0.001), the mean concentrations of FGM did not change (Levene's test: F-ratio: 0.87, p-value: 0.44). Furthermore, we showed that the interanimal variability observed in the stress response was correlated with the individual fecal microbiota. This result was in line with recent findings, indicating the gut microbiome as a crucial mediator of stress response. We conclude that NMR-based fecal metabolomics proved to be a reliable methodology to assess stress response and that its future applicability to studies for stress monitoring in range animals may be more appropriate than FGM analysis.
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Affiliation(s)
- Azzurra Valerio
- School of the Environment , Washington State University , Pullman , Washington 99164 , United States
| | - Luca Casadei
- School of the Environment , Washington State University , Pullman , Washington 99164 , United States
| | - Alessandro Giuliani
- Department of Environment and Health , National Institute of Health , Rome 00161 , Italy
| | - Mariacristina Valerio
- School of the Environment , Washington State University , Pullman , Washington 99164 , United States
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33
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Omics Potential in Herbicide-Resistant Weed Management. PLANTS 2019; 8:plants8120607. [PMID: 31847327 PMCID: PMC6963460 DOI: 10.3390/plants8120607] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/11/2019] [Accepted: 12/12/2019] [Indexed: 12/20/2022]
Abstract
The rapid development of omics technologies has drastically altered the way biologists conduct research. Basic plant biology and genomics have incorporated these technologies, while some challenges remain for use in applied biology. Weed science, on the whole, is still learning how to integrate omics technologies into the discipline; however, omics techniques are more frequently being implemented in new and creative ways to address basic questions in weed biology as well as the more practical questions of improving weed management. This has been especially true in the subdiscipline of herbicide resistance where important questions are the evolution and genetic basis of herbicide resistance. This review examines the advantages, challenges, potential solutions, and outlook for omics technologies in the discipline of weed science, with examples of how omics technologies will impact herbicide resistance studies and ultimately improve management of herbicide-resistant populations.
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34
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Nyunt T, Britton M, Wanichthanarak K, Budamagunta M, Voss JC, Wilson DW, Rutledge JC, Aung HH. Mitochondrial oxidative stress-induced transcript variants of ATF3 mediate lipotoxic brain microvascular injury. Free Radic Biol Med 2019; 143:25-46. [PMID: 31356870 PMCID: PMC6848793 DOI: 10.1016/j.freeradbiomed.2019.07.024] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 06/30/2019] [Accepted: 07/23/2019] [Indexed: 12/06/2022]
Abstract
Elevation of blood triglycerides, primarily triglyceride-rich lipoproteins (TGRL), is an independent risk factor for cardiovascular disease and vascular dementia (VaD). Accumulating evidence indicates that both atherosclerosis and VaD are linked to vascular inflammation. However, the role of TGRL in vascular inflammation, which increases risk for VaD, remains largely unknown and its underlying mechanisms are still unclear. We strived to determine the effects of postprandial TGRL exposure on brain microvascular endothelial cells, the potential risk factor of vascular inflammation, resulting in VaD. We showed in Aung et al., J Lipid Res., 2016 that postprandial TGRL lipolysis products (TL) activate mitochondrial reactive oxygen species (ROS) and increase the expression of the stress-responsive protein, activating transcription factor 3 (ATF3), which injures human brain microvascular endothelial cells (HBMECs) in vitro. In this study, we deployed high-throughput sequencing (HTS)-based RNA sequencing methods and mito stress and glycolytic rate assays with an Agilent Seahorse XF analyzer and profiled the differential expression of transcripts, constructed signaling pathways, and measured mitochondrial respiration, ATP production, proton leak, and glycolysis of HBMECs treated with TL. Conclusions: TL potentiate ROS by mitochondria which activate mitochondrial oxidative stress, decrease ATP production, increase mitochondrial proton leak and glycolysis rate, and mitochondria DNA damage. Additionally, CPT1A1 siRNA knockdown suppresses oxidative stress and prevents mitochondrial dysfunction and vascular inflammation in TL treated HBMECs. TL activates ATF3-MAPKinase, TNF, and NRF2 signaling pathways. Furthermore, the NRF2 signaling pathway which is upstream of the ATF3-MAPKinase signaling pathway, is also regulated by the mitochondrial oxidative stress. We are the first to report differential inflammatory characteristics of transcript variants 4 (ATF3-T4) and 5 (ATF3-T5) of the stress responsive gene ATF3 in HBMECs induced by postprandial TL. Specifically, our data indicates that ATF3-T4 predominantly regulates the TL-induced brain microvascular inflammation and TNF signaling. Both siRNAs of ATF3-T4 and ATF3-T5 suppress cells apoptosis and lipotoxic brain microvascular endothelial cells. These novel signaling pathways triggered by oxidative stress-responsive transcript variants, ATF3-T4 and ATF3-T5, in the brain microvascular inflammation induced by TGRL lipolysis products may contribute to pathophysiological processes of vascular dementia.
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Affiliation(s)
- Tun Nyunt
- Division of Cardiovascular Medicine, Department of Internal Medicine, School of Medicine, University of California, Davis, Davis, CA, 95616, USA
| | - Monica Britton
- Genome Center and Bioinformatics Core Facility, University of California, Davis, CA, 95616, USA
| | - Kwanjeera Wanichthanarak
- West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, CA, USA; Department of Biochemistry and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Madhu Budamagunta
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, 95616, USA
| | - John C Voss
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, 95616, USA
| | - Dennis W Wilson
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, 95616, USA
| | - John C Rutledge
- Division of Cardiovascular Medicine, Department of Internal Medicine, School of Medicine, University of California, Davis, Davis, CA, 95616, USA
| | - Hnin H Aung
- Division of Cardiovascular Medicine, Department of Internal Medicine, School of Medicine, University of California, Davis, Davis, CA, 95616, USA.
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35
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Green CL, Soltow QA, Mitchell SE, Derous D, Wang Y, Chen L, Han JDJ, Promislow DEL, Lusseau D, Douglas A, Jones DP, Speakman JR. The Effects of Graded Levels of Calorie Restriction: XIII. Global Metabolomics Screen Reveals Graded Changes in Circulating Amino Acids, Vitamins, and Bile Acids in the Plasma of C57BL/6 Mice. J Gerontol A Biol Sci Med Sci 2019; 74:16-26. [PMID: 29718123 PMCID: PMC6298180 DOI: 10.1093/gerona/gly058] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Indexed: 12/15/2022] Open
Abstract
Calorie restriction (CR) remains the most robust intervention to extend life span and improve health span. Using a global mass spectrometry–based metabolomics approach, we identified metabolites that were significantly differentially expressed in the plasma of C57BL/6 mice, fed graded levels of calorie restriction (10% CR, 20% CR, 30% CR, and 40% CR) compared with mice fed ad libitum for 12 hours a day. The differential expression of metabolites increased with the severity of CR. Pathway analysis revealed that graded CR had an impact on vitamin E and vitamin B levels, branched chain amino acids, aromatic amino acids, and fatty acid pathways. The majority of amino acids correlated positively with fat-free mass and visceral fat mass, indicating a strong relationship with body composition and vitamin E metabolites correlated with stomach and colon size, which may allude to the beneficial effects of investing in gastrointestinal organs with CR. In addition, metabolites that showed a graded effect, such as the sphinganines, carnitines, and bile acids, match our previous study on liver, which suggests not only that CR remodels the metabolome in a way that promotes energy efficiency, but also that some changes are conserved across tissues.
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Affiliation(s)
- Cara L Green
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Quinlyn A Soltow
- Division of Pulmonary, Allergy and Critical Care Medicine, Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia
| | - Sharon E Mitchell
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Davina Derous
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Yingchun Wang
- State Key laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Chaoyang, Beijing, China
| | - Luonan Chen
- Key laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, China
| | - Jing-Dong J Han
- Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China
| | - Daniel E L Promislow
- Department of Pathology, Seattle.,Department of Biology, University of Washington, Seattle
| | - David Lusseau
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Alex Douglas
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK
| | - Dean P Jones
- Division of Pulmonary, Allergy and Critical Care Medicine, Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia
| | - John R Speakman
- Institute of Biological and Environmental Sciences, University of Aberdeen, UK.,State Key laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Chaoyang, Beijing, China
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36
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Hinshaw SJ, Lee AHY, Gill EE, Hancock REW. MetaBridge: enabling network-based integrative analysis via direct protein interactors of metabolites. Bioinformatics 2019; 34:3225-3227. [PMID: 29688253 DOI: 10.1093/bioinformatics/bty331] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 04/20/2018] [Indexed: 01/23/2023] Open
Abstract
Summary Here, we present MetaBridge, a tool that collates protein interactors (curated metabolite-enzyme interactions) that influence the levels of specific metabolites including both biosynthetic and degradative enzymes. This enables network-based integrative analysis of metabolomics data with other omics data types. MetaBridge is designed to complement a systems-biology approach to analysis, pairing well with network analysis tools such as NetworkAnalyst.ca, but can be used in any bioinformatics workflow. Availability and implementation MetaBridge has been implemented as a web tool at https://www.metabridge.org, and the source code is available at https://github.com/samhinshaw/metabridge_shiny (GNU GPLv3).
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Affiliation(s)
- Samuel J Hinshaw
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Amy H Y Lee
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Erin E Gill
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Robert E W Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
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37
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Chiaiese P, Corrado G, Minutolo M, Barone A, Errico A. Transcriptional Regulation of Ascorbic Acid During Fruit Ripening in Pepper ( Capsicum annuum) Varieties with Low and High Antioxidants Content. PLANTS (BASEL, SWITZERLAND) 2019; 8:E206. [PMID: 31277433 PMCID: PMC6681188 DOI: 10.3390/plants8070206] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/27/2019] [Accepted: 07/01/2019] [Indexed: 12/24/2022]
Abstract
Research on plant antioxidants, such as ascorbic acid (AsA) and polyphenols, is of increasing interest in plant science because of the health benefits and preventive role in chronic diseases of these natural compounds. Pepper (Capiscum annuum L.) is a major dietary source of antioxidants, especially AsA. Although considerable advance has been made, our understanding of AsA biosynthesis and its regulation in higher plants is not yet exhaustive. For instance, while it is accepted that AsA content in cells is regulated at different levels (e.g., transcriptional and post-transcriptional), their relative prominence is not fully understood. In this work, we identified and studied two pepper varieties with low and high levels of AsA to shed light on the transcriptional mechanisms that can account for the observed phenotypes. We quantified AsA and polyphenols in leaves and during fruit maturation, and concurrently, we analyzed the transcription of 14 genes involved in AsA biosynthesis, degradation, and recycling. The differential transcriptional analysis indicated that the higher expression of genes involved in AsA accumulation is a likely explanation for the observed differences in fruits. This was also supported by the identification of gene-metabolite relations, which deserve further investigation. Our results provide new insights into AsA differential accumulation in pepper varieties and highlight the phenotypic diversity in local germplasm, a knowledge that may ultimately contribute to the increased level of health-related phytochemicals.
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Affiliation(s)
- Pasquale Chiaiese
- Dipartimento di Agraria, Università degli Studi di Napoli "Federico II", via Università 100, 80055 Portici (NA), Italy.
| | - Giandomenico Corrado
- Dipartimento di Agraria, Università degli Studi di Napoli "Federico II", via Università 100, 80055 Portici (NA), Italy
| | - Maria Minutolo
- Dipartimento di Agraria, Università degli Studi di Napoli "Federico II", via Università 100, 80055 Portici (NA), Italy
| | - Amalia Barone
- Dipartimento di Agraria, Università degli Studi di Napoli "Federico II", via Università 100, 80055 Portici (NA), Italy
| | - Angela Errico
- Dipartimento di Agraria, Università degli Studi di Napoli "Federico II", via Università 100, 80055 Portici (NA), Italy
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38
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Saia S, Fragasso M, De Vita P, Beleggia R. Metabolomics Provides Valuable Insight for the Study of Durum Wheat: A Review. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:3069-3085. [PMID: 30829031 DOI: 10.1021/acs.jafc.8b07097] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Metabolomics is increasingly being applied in various fields offering a highly informative tool for high-throughput diagnostics. However, in plant sciences, metabolomics is underused, even though plant studies are relatively easy and cheap when compared to those on humans and animals. Despite their importance for human nutrition, cereals, and especially wheat, remain understudied from a metabolomics point of view. The metabolomics of durum wheat has been essentially neglected, although its genetic structure allows the inference of common mechanisms that can be extended to other wheat and cereal species. This review covers the present achievements in durum wheat metabolomics highlighting the connections with the metabolomics of other cereal species (especially bread wheat). We discuss the metabolomics data from various studies and their relationships to other "-omics" sciences, in terms of wheat genetics, abiotic and biotic stresses, beneficial microbes, and the characterization and use of durum wheat as feed, food, and food ingredient.
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Affiliation(s)
- Sergio Saia
- Council for Agricultural Research and Economics (CREA) , Research Centre for Cereal and Industrial Crops (CREA-CI) , S.S. 673 , Km 25,200, 71122 Foggia , Italy
- Council for Agricultural Research and Economics (CREA) , Research Centre for Cereal and Industrial Crops (CREA-CI) , S.S. 11 per Torino , Km 2,5, 13100 Vercelli , Italy
| | - Mariagiovanna Fragasso
- Council for Agricultural Research and Economics (CREA) , Research Centre for Cereal and Industrial Crops (CREA-CI) , S.S. 673 , Km 25,200, 71122 Foggia , Italy
| | - Pasquale De Vita
- Council for Agricultural Research and Economics (CREA) , Research Centre for Cereal and Industrial Crops (CREA-CI) , S.S. 673 , Km 25,200, 71122 Foggia , Italy
| | - Romina Beleggia
- Council for Agricultural Research and Economics (CREA) , Research Centre for Cereal and Industrial Crops (CREA-CI) , S.S. 673 , Km 25,200, 71122 Foggia , Italy
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39
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Manzoni C, Kia DA, Vandrovcova J, Hardy J, Wood NW, Lewis PA, Ferrari R. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 2019; 19:286-302. [PMID: 27881428 PMCID: PMC6018996 DOI: 10.1093/bib/bbw114] [Citation(s) in RCA: 428] [Impact Index Per Article: 71.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Indexed: 02/07/2023] Open
Abstract
Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
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Affiliation(s)
- Claudia Manzoni
- School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.,Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Demis A Kia
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Jana Vandrovcova
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - John Hardy
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Nicholas W Wood
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Patrick A Lewis
- School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.,Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Raffaele Ferrari
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
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40
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Uppal K, Ma C, Go YM, Jones DP, Wren J. xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics 2019; 34:701-702. [PMID: 29069296 DOI: 10.1093/bioinformatics/btx656] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 10/17/2017] [Indexed: 12/13/2022] Open
Abstract
Summary Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms. Availability and implementation https://kuppal.shinyapps.io/xmwas (Online) and https://github.com/kuppal2/xMWAS/ (R). Contact kuppal2@emory.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Karan Uppal
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Chunyu Ma
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Young-Mi Go
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Dean P Jones
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA 30322, USA
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Wu C, Zhou F, Ren J, Li X, Jiang Y, Ma S. A Selective Review of Multi-Level Omics Data Integration Using Variable Selection. High Throughput 2019; 8:E4. [PMID: 30669303 PMCID: PMC6473252 DOI: 10.3390/ht8010004] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 12/24/2018] [Accepted: 01/10/2019] [Indexed: 01/02/2023] Open
Abstract
High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods. We first summarize published reviews on integrating multi-level omics data. Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively. The strength and limitations of the methods are discussed in detail. No existing integration method can dominate the rest. The computation aspects are also investigated. The review concludes with possible limitations and future directions for multi-level omics data integration.
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Affiliation(s)
- Cen Wu
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA.
| | - Fei Zhou
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA.
| | - Jie Ren
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA.
| | - Xiaoxi Li
- Department of Statistics, Kansas State University, Manhattan, KS 66506, USA.
| | - Yu Jiang
- Division of Epidemiology, Biostatistics and Environmental Health, School of Public Health, University of Memphis, Memphis, TN 38152, USA.
| | - Shuangge Ma
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT 06510, USA.
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42
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Ghosh D, Bernstein JA, Khurana Hershey GK, Rothenberg ME, Mersha TB. Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine. Front Immunol 2018; 9:2727. [PMID: 30631320 PMCID: PMC6315155 DOI: 10.3389/fimmu.2018.02727] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 11/05/2018] [Indexed: 12/14/2022] Open
Abstract
Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.
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Affiliation(s)
- Debajyoti Ghosh
- Division of Immunology, Allergy & Rheumatology, Department of Internal Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Jonathan A Bernstein
- Division of Immunology, Allergy & Rheumatology, Department of Internal Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Gurjit K Khurana Hershey
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Marc E Rothenberg
- Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Tesfaye B Mersha
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
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Metabolomics and transcriptomics pathway approach reveals outcome-specific perturbations in COPD. Sci Rep 2018; 8:17132. [PMID: 30459441 PMCID: PMC6244246 DOI: 10.1038/s41598-018-35372-w] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 10/25/2018] [Indexed: 12/22/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) comprises multiple phenotypes such as airflow obstruction, emphysema, and frequent episodes of acute worsening of respiratory symptoms, known as exacerbations. The goal of this pilot study was to test the usefulness of unbiased metabolomics and transcriptomics approaches to delineate biological pathways associated with COPD phenotypes and outcomes. Blood was collected from 149 current or former smokers with or without COPD and separated into peripheral blood mononuclear cells (PBMC) and plasma. PBMCs and plasma were analyzed using microarray and liquid chromatography mass spectrometry, respectively. Statistically significant transcripts and compounds were mapped to pathways using IMPaLA. Results showed that glycerophospholipid metabolism was associated with worse airflow obstruction and more COPD exacerbations. Sphingolipid metabolism was associated with worse lung function outcomes and exacerbation severity requiring hospitalizations. The strongest associations between a pathway and a certain COPD outcome were: fat digestion and absorption and T cell receptor signaling with lung function outcomes; antigen processing with exacerbation frequency; arginine and proline metabolism with exacerbation severity; and oxidative phosphorylation with emphysema. Overlaying transcriptomic and metabolomics datasets across pathways enabled outcome and phenotypic differences to be determined. Findings are relevant for identifying molecular targets for animal intervention studies and early intervention markers in human cohorts.
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44
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Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S. Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2018; 22:630-636. [PMID: 30124358 PMCID: PMC6207407 DOI: 10.1089/omi.2018.0097] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
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Affiliation(s)
- Dmitry Grapov
- CDS-Creative Data Solutions LLC, Ballwin, Missouri, www.createdatasol.com
| | - Johannes Fahrmann
- Department of Clinical Cancer Prevention, University of Texas MD Anderson, Houston, Texas
| | - Kwanjeera Wanichthanarak
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Sakda Khoomrung
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Dickinson E, Rusilowicz MJ, Dickinson M, Charlton AJ, Bechtold U, Mullineaux PM, Wilson J. Integrating transcriptomic techniques and k-means clustering in metabolomics to identify markers of abiotic and biotic stress in Medicago truncatula. Metabolomics 2018; 14:126. [PMID: 30830458 PMCID: PMC6153691 DOI: 10.1007/s11306-018-1424-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 09/03/2018] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Nitrogen-fixing legumes are invaluable crops, but are sensitive to physical and biological stresses. Whilst drought and infection from the soil-borne pathogen Fusarium oxysporum have been studied individually, their combined effects have not been widely investigated. OBJECTIVES We aimed to determine the effect of combined stress using methods usually associated with transcriptomics to detect metabolic differences between treatment groups that could not be identified by more traditional means, such as principal component analysis and partial least squares discriminant analysis. METHODS Liquid chromatography-high resolution mass spectrometry data from the root and leaves of model legume Medicago truncatula were analysed using Gaussian Process 2-Sample Test, k-means cluster analysis and temporal clustering by affinity propagation. RESULTS Metabolic differences were detected: we identified known stress markers, including changes in concentration for sucrose and citric acid, and showed that combined stress can exacerbate the effect of drought. Changes in roots were found to be smaller than those in leaves, but differences due to Fusarium infection were identified. The transfer of sucrose from leaves to roots can be seen in the time series using transcriptomic techniques with the metabolomics time series. Other metabolite concentrations that change as a result of treatment include phosphoric acid, malic acid and tetrahydroxychalcone. CONCLUSIONS Probing metabolomic data with transcriptomic tools provides new insights and could help to identify resilient plant varieties, thereby increasing future crop yield and improving food security.
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Affiliation(s)
| | | | | | | | - Ulrike Bechtold
- School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, UK
| | | | - Julie Wilson
- Department of Mathematics, University of York, York, YO1 5DD, UK
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46
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Lower genital tract cytokine profiles in South African women living with HIV: influence of mucosal sampling. Sci Rep 2018; 8:12203. [PMID: 30111808 PMCID: PMC6093917 DOI: 10.1038/s41598-018-30663-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 08/03/2018] [Indexed: 11/25/2022] Open
Abstract
Measurement of cytokines in the lower female genital tract offer insight into risk for HIV infection and reproductive complications. However, few studies have systematically compared mucosal collection methods or whether collection order matters. We compared longitudinal cytokine profiles in matched genital samples collected from women living with HIV using menstrual cup (MC), endocervical swabs (ECS) and swab-enriched cervicovaginal lavage (eCVL). Samples were collected at enrollment [MC:ECS:eCVL], 3-months (ECS:eCVL:MC) and 6-months (eCVL:MC:ECS) and concentrations of 28 cytokines determined by Luminex. Cytokine clustering was assessed using Principle Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLSDA) and factor analysis. Generally, higher cytokine concentrations were detected in MC samples, followed by ECS and eCVL, irrespective of study visit or sampling order. Factor analysis and PCA identified ECS to be inferior for measuring regulatory cytokines and IP-10 than eCVL or MC. Although concentrations differed, the majority of cytokines correlated between methods. Sampling order influenced cytokine concentrations marginally, and cytokines clustered more strongly by method than study visit. Variance in profiles was lowest in MC, suggesting greater consistency of sampling compared to other methods. We conclude that MC sampling offered advantages over other methods for detecting cytokines in women, with order marginally influencing profiles.
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47
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Kaushik AK, DeBerardinis RJ. Applications of metabolomics to study cancer metabolism. Biochim Biophys Acta Rev Cancer 2018; 1870:2-14. [PMID: 29702206 PMCID: PMC6193562 DOI: 10.1016/j.bbcan.2018.04.009] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Accepted: 04/20/2018] [Indexed: 12/13/2022]
Abstract
Reprogrammed metabolism supports tumor growth and provides a potential source of therapeutic targets and disease biomarkers. Mass spectrometry-based metabolomics has emerged as a broadly informative technique for profiling metabolic features associated with specific oncogenotypes, disease progression, therapeutic liabilities and other clinically relevant aspects of tumor biology. In this review, we introduce the applications of metabolomics to study deregulated metabolism and metabolic vulnerabilities in cancer. We provide examples of studies that used metabolomics to discover novel metabolic regulatory mechanisms, including processes that link metabolic alterations with gene expression, protein function, and other aspects of systems biology. Finally, we discuss emerging applications of metabolomics for in vivo isotope tracing and metabolite imaging, both of which hold promise to advance our understanding of the role of metabolic reprogramming in cancer.
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Affiliation(s)
- Akash K Kaushik
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-8502, United States
| | - Ralph J DeBerardinis
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-8502, United States.
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48
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Pirola CJ, Sookoian S. Multiomics biomarkers for the prediction of nonalcoholic fatty liver disease severity. World J Gastroenterol 2018; 24:1601-1615. [PMID: 29686467 PMCID: PMC5910543 DOI: 10.3748/wjg.v24.i15.1601] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 03/13/2018] [Accepted: 03/30/2018] [Indexed: 02/06/2023] Open
Abstract
This review intends to uncover how information from large-scale genetic profiling (whole genome sequencing, and whole exome sequencing) of nonalcoholic fatty liver disease (NAFLD), as well as information from circulating transcriptomics (cell-free miRNAs) and metabolomics, contributes to the understanding of NAFLD pathogenesis. A further aim is to address the question of whether OMICs information is ready to be implemented in the clinics. The available evidence suggests that any new knowledge pertaining to molecular signatures associated with NAFLD and nonalcoholic steatohepatitis should be promptly translated into the clinical setting. Nevertheless, rigorous steps that must include validation and replication are mandatory before utilizing OMICs biomarkers in diagnostics to identify patients at risk of advanced disease, including liver cancer.
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Affiliation(s)
- Carlos J Pirola
- Department of Genetics and Molecular Biology of Complex Diseases. University of Buenos Aires, Institute of Medical Research A Lanari, Buenos Aires, Argentina, National Scientific and Technical Research Council-University of Buenos Aires. Institute of Medical Research (IDIM), CABA 1427, Argentina
| | - Silvia Sookoian
- Clinical and Molecular Hepatology, University of Buenos Aires, Institute of Medical Research A Lanari, Buenos Aires, Argentina, National Scientific and Technical Research Council-University of Buenos Aires. Institute of Medical Research (IDIM), CABA 1427, Argentina
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Siddiqui JK, Baskin E, Liu M, Cantemir-Stone CZ, Zhang B, Bonneville R, McElroy JP, Coombes KR, Mathé EA. IntLIM: integration using linear models of metabolomics and gene expression data. BMC Bioinformatics 2018; 19:81. [PMID: 29506475 PMCID: PMC5838881 DOI: 10.1186/s12859-018-2085-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 02/21/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. RESULTS The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. CONCLUSIONS IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.
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Affiliation(s)
- Jalal K Siddiqui
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Elizabeth Baskin
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Mingrui Liu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Carmen Z Cantemir-Stone
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Bofei Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.,Biomedical Engineering Undegraduate Program, The Ohio State University, Columbus, OH, 43210, USA
| | - Russell Bonneville
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH, USA.,Comprehensive Cancer Center, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Joseph P McElroy
- Center for Biostatistics, The Ohio State University, Columbus, OH, USA
| | - Kevin R Coombes
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Ewy A Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
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Zhang B, Hu S, Baskin E, Patt A, Siddiqui JK, Mathé EA. RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites. Metabolites 2018; 8:E16. [PMID: 29470400 PMCID: PMC5876005 DOI: 10.3390/metabo8010016] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 02/14/2018] [Accepted: 02/16/2018] [Indexed: 01/04/2023] Open
Abstract
The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.
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Affiliation(s)
- Bofei Zhang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Senyang Hu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Elizabeth Baskin
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Andrew Patt
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
- Biomedical Engineering Graduate Program, The Ohio State University, Columbus, OH 43210, USA.
| | - Jalal K Siddiqui
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Ewy A Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
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