1
|
Eicher T, Kelly RS, Braisted J, Siddiqui JK, Celedón J, Clish C, Gerszten R, Weiss ST, McGeachie M, Machiraju R, Lasky-Su J, Mathé EA. Consistent Multi-Omic Relationships Uncover Molecular Basis of Pediatric Asthma IgE Regulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.05.24308502. [PMID: 38883716 PMCID: PMC11178010 DOI: 10.1101/2024.06.05.24308502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.
Collapse
Affiliation(s)
- Tara Eicher
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD USA
- Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH USA
| | - Rachel S. Kelly
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA USA
- Harvard Medical School, Boston, MA USA
| | - John Braisted
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD USA
| | - Jalal K. Siddiqui
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH USA
| | - Juan Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA USA
| | | | - Robert Gerszten
- Harvard Medical School, Boston, MA USA
- Broad Institute, Cambridge, MA USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA USA
- Harvard Medical School, Boston, MA USA
| | - Michael McGeachie
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA USA
- Harvard Medical School, Boston, MA USA
| | - Raghu Machiraju
- Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH USA
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH USA
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA USA
- Harvard Medical School, Boston, MA USA
| | - Ewy A. Mathé
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD USA
| |
Collapse
|
2
|
Al-Daffaie FM, Al-Mudhafar SF, Alhomsi A, Tarazi H, Almehdi AM, El-Huneidi W, Abu-Gharbieh E, Bustanji Y, Alqudah MAY, Abuhelwa AY, Guella A, Alzoubi KH, Semreen MH. Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications. Int J Mol Sci 2024; 25:5071. [PMID: 38791108 PMCID: PMC11120916 DOI: 10.3390/ijms25105071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.
Collapse
Affiliation(s)
- Fatima M. Al-Daffaie
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| | - Sara F. Al-Mudhafar
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
| | - Aya Alhomsi
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
| | - Hamadeh Tarazi
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| | - Ahmed M. Almehdi
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| | - Waseem El-Huneidi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Eman Abu-Gharbieh
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
- School of Pharmacy, The University of Jordan, Amman 11942, Jordan
| | - Yasser Bustanji
- Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates;
- School of Pharmacy, The University of Jordan, Amman 11942, Jordan
| | - Mohammad A. Y. Alqudah
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates;
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Ahmad Y. Abuhelwa
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Adnane Guella
- Nephrology Department, University Hospital Sharjah, Sharjah 27272, United Arab Emirates;
| | - Karem H. Alzoubi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Mohammad H. Semreen
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| |
Collapse
|
3
|
Ma L, Dong Y, Li Z, Meng J, Zhao B, Wang Q. Relationship between circulating metabolites and diabetic retinopathy: a two-sample Mendelian randomization analysis. Sci Rep 2024; 14:4964. [PMID: 38424453 PMCID: PMC10904376 DOI: 10.1038/s41598-024-55704-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
Diabetic retinopathy (DR) is the most frequent microvascular complication of diabetes mellitus, however, its underlying biological mechanisms remain poorly understood. We examined single nucleotide polymorphisms linked to 486 blood metabolites through extensive genome-wide association studies conducted on individuals of European ancestry. The FinnGen Biobank database served as a reference to define DR. Two-sample MR analysis was conducted to reveal the association between the levels of genetically predicted circulating metabolites and the susceptibility to DR. To validate the robustness of the obtained findings, sensitivity analyses with weighted median, weighted mode, and MR-Egger were conducted. 1-oleoylglycerophosphoethanolamine (odds ratio [OR] (OR per one standard deviation [SD] increase) = 0.414; 95% confidence interval [CI] 0.292-0.587; P = 7.613E-07, PFDR = 6.849E-06), pyroglutamine (OR per one SD increase = 0.414; 95% confidence interval [CI] 0.292-0.587; P = 8.31E-04, PFDR = 0.007), phenyllactate (PLA) (OR per one SD increase = 0.591; 95% confidence interval [CI] 0.418-0.836; P = 0.003, PFDR = 0.026), metoprolol acid metabolite (OR per one SD increase = 0.978; 95% confidence interval [CI] 0.962-0.993; P = 0.005, PFDR = 0.042), 10-undecenoate (OR per one SD increase = 0.788; 95% confidence interval [CI] 0.667-0.932; P = 0.005, PFDR = 0.049), erythritol (OR per one SD increase = 0.691; 95% confidence interval [CI] 0.513-0.932; P = 0.015, PFDR = 0.034), 1-stearoylglycerophosphoethanolamine (OR per one SD increase = 0.636; 95% confidence interval [CI] 0.431-0.937; P = 0.022, PFDR = 0.099), 1-arachidonoylglycerophosphoethanolamine (OR per one SD increase = 0.636; 95% confidence interval [CI] 0.431-0.937; P = 0.030, PFDR = 0.099) showed a significant causal relationship with DR and could have protective effects. stachydrine (OR per one SD increase = 1.146; 95% confidence interval [CI] 1.066-1.233; P = 2.270E-04, PFDR = 0.002), butyrylcarnitine (OR per one SD increase = 1.117; 95% confidence interval [CI] 1.023-1.219; P = 0.014, PFDR = 0.062), 5-oxoproline (OR per one SD increase = 1.569; 95% confidence interval [CI] 1.056-2.335; P = 0.026, PFDR = 0.082), and kynurenine (OR = 1.623; 95% CI 1.042-2.526; P = 0.041, PFDR = 0.097) were significantly associated with an increased risk of DR. This study identified metabolites have the potential to be considered prospective compounds for investigating the underlying mechanisms of DR and for selecting appropriate drug targets.
Collapse
Affiliation(s)
- Lingli Ma
- Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, 126 Sendai Avenue, Changchun City, Jilin Province, China
| | - Ying Dong
- Department of Radiotherapy, Jilin Cancer Hospital, Changchun, China
| | - Zimeng Li
- Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, 126 Sendai Avenue, Changchun City, Jilin Province, China
| | - Jian Meng
- Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, 126 Sendai Avenue, Changchun City, Jilin Province, China
| | - Bingqi Zhao
- Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, 126 Sendai Avenue, Changchun City, Jilin Province, China
| | - Qing Wang
- Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, 126 Sendai Avenue, Changchun City, Jilin Province, China.
| |
Collapse
|
4
|
Zhang Y, Yu B, Qi Q, Azarbarzin A, Chen H, Shah NA, Ramos AR, Zee PC, Cai J, Daviglus ML, Boerwinkle E, Kaplan R, Liu PY, Redline S, Sofer T. Metabolomic profiles of sleep-disordered breathing are associated with hypertension and diabetes mellitus development. Nat Commun 2024; 15:1845. [PMID: 38418471 PMCID: PMC10902315 DOI: 10.1038/s41467-024-46019-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 02/12/2024] [Indexed: 03/01/2024] Open
Abstract
Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. Using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we apply principal component analysis (PCA) to seven SDB-related measures. We estimate the associations of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint penalized regression analysis. The discovery analysis includes 3299 individuals, with validation in a separate dataset of 1522 individuals. Five metabolite associations with SDB PCs are discovered and replicated. SDB PC1, characterized by frequent respiratory events common in older and male adults, is associated with pregnanolone and progesterone-related sulfated metabolites. SDB PC2, characterized by short respiratory event length and self-reported restless sleep, enriched in young adults, is associated with sphingomyelins. Metabolite risk scores (MRSs), representing metabolite signatures associated with the two SDB PCs, are associated with 6-year incident hypertension and diabetes. These MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.
Collapse
Affiliation(s)
- Ying Zhang
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Bing Yu
- Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, NY, USA
| | - Ali Azarbarzin
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women's Hospital & Harvard Medical School, Boston, MA, 02115, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Neomi A Shah
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alberto R Ramos
- Sleep Medicine Program, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Phyllis C Zee
- Division of Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL, 60611, USA
| | - Jianwen Cai
- Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Martha L Daviglus
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60612, USA
| | - Eric Boerwinkle
- Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, NY, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Peter Y Liu
- The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women's Hospital & Harvard Medical School, Boston, MA, 02115, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women's Hospital & Harvard Medical School, Boston, MA, 02115, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
| |
Collapse
|
5
|
Casella C, Kiles F, Urquhart C, Michaud DS, Kirwa K, Corlin L. Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis. TOXICS 2023; 11:1014. [PMID: 38133415 PMCID: PMC10748071 DOI: 10.3390/toxics11121014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/18/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023]
Abstract
A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.
Collapse
Affiliation(s)
- Cameron Casella
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Frances Kiles
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Catherine Urquhart
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Kipruto Kirwa
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Laura Corlin
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
| |
Collapse
|
6
|
Iavicoli I, Fontana L, Santocono C, Guarino D, Laudiero M, Calabrese EJ. The challenges of defining hormesis in epidemiological studies: The case of radiation hormesis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166030. [PMID: 37544458 DOI: 10.1016/j.scitotenv.2023.166030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/24/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
In the current radiation protection system, preventive measures and occupational exposure limits for controlling occupational exposure to ionizing radiation are based on the linear no-threshold extrapolation model. However, currently an increasing body of evidence indicates that this paradigm predicts very poorly biological responses in the low-dose exposure region. In addition, several in vitro and in vivo studies demonstrated the presence of hormetic dose response curves correlated to ionizing radiation low exposure. In this regard, it is noteworthy that also the findings of different epidemiological studies, conducted in different categories of occupationally exposed workers (e.g., healthcare, nuclear industrial and aircrew workers), observed lower rates of mortality and/or morbidity from cancer and/or other diseases in exposed workers than in unexposed ones or in the general population, then suggesting the possible occurrence of hormesis. Nevertheless, these results should be considered with caution since the identification of hormetic response in epidemiological studies is rather challenging because of a number of major limitations. In this regard, some of the most remarkable shortcomings found in epidemiological studies performed in workers exposed to ionizing radiation are represented by lack or inadequate definition of exposure doses, use of surrogates of exposure, narrow dose ranges, lack of proper control groups and poor evaluation of confounding factors. Therefore, considering the valuable role and contribution that epidemiological studies might provide to the complex risk assessment and management process, there is a clear and urgent need to overcome the aforementioned limits in order to achieve an adequate, useful and more real-life risk assessment that should also include the key concept of hormesis. Thus, in the present conceptual article we also discuss and provide possible approaches to improve the capacity of epidemiological studies to identify/define the hormetic response and consequently improve the complex process of risk assessment of ionizing radiation at low exposure doses.
Collapse
Affiliation(s)
- Ivo Iavicoli
- Department of Public Health, Section of Occupational Medicine, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy.
| | - Luca Fontana
- Department of Public Health, Section of Occupational Medicine, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy
| | - Carolina Santocono
- Department of Public Health, Section of Occupational Medicine, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy
| | - Davide Guarino
- Department of Public Health, Section of Occupational Medicine, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy
| | - Martina Laudiero
- Department of Public Health, Section of Occupational Medicine, University of Naples Federico II, Via S. Pansini 5, 80131 Naples, Italy
| | - Edward J Calabrese
- Department of Environmental Health Sciences, Morrill I, N344, University of Massachusetts, Amherst, MA 01003, USA
| |
Collapse
|
7
|
Reitz CJ, Kuzmanov U, Gramolini AO. Multi-omic analyses and network biology in cardiovascular disease. Proteomics 2023; 23:e2200289. [PMID: 37691071 DOI: 10.1002/pmic.202200289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.
Collapse
Affiliation(s)
- Cristine J Reitz
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Uros Kuzmanov
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Anthony O Gramolini
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| |
Collapse
|
8
|
Gashu K, Verma PK, Acuña T, Agam N, Bustan A, Fait A. Temperature differences between sites lead to altered phenylpropanoid metabolism in a varietal dependent manner. FRONTIERS IN PLANT SCIENCE 2023; 14:1239852. [PMID: 37929177 PMCID: PMC10620969 DOI: 10.3389/fpls.2023.1239852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/13/2023] [Indexed: 11/07/2023]
Abstract
Elevated temperature has already caused a significant loss of wine growing areas and resulted in inferior fruit quality, particularly in arid and semi-arid regions. The existence of broad genetic diversity in V. vinifera is key in adapting viticulture to climate change; however, a lack of understanding on the variability in berry metabolic response to climate change remains a major challenge to build ad-hoc strategies for quality fruit production. In the present study, we examined the impact of a consistent temperature difference between two vineyards on polyphenol metabolism in the berries of 20 red V. vinifera cultivars across three consecutive seasons (2017-2019). The results emphasize a varietal specific response in the content of several phenylpropanoid metabolites; the interaction factor between the variety and the vineyard location was also found significant. Higher seasonal temperatures were coupled with lower flavonol and anthocyanin contents, but such reductions were not related with the level of expression of phenylpropanoid related genes. Hierarchical clustering analyses of the metabolic data revealed varieties with a location specific response, exceptional among them was Tempranillo, suggesting a greater susceptibility to temperature of this cultivar. In conclusion, our results indicate that the extensive genetic capacity of V. vinifera bears a significant potential to withstand temperature increase associated with climate change.
Collapse
Affiliation(s)
- Kelem Gashu
- The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Be'ersheba, Israel
| | - Pankaj Kumar Verma
- The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Be'ersheba, Israel
| | - Tania Acuña
- The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Be'ersheba, Israel
| | - Nurit Agam
- Wyler Department of Dryland Agriculture, French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Be'ersheba, Israel
| | - Amnon Bustan
- Ramat Negev Desert Agro-Research Center, Ramat Negev Works Ltd., Hazula, Israel
| | - Aaron Fait
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Be'ersheba, Israel
| |
Collapse
|
9
|
Casella C, Kiles F, Urquhart C, Michaud DS, Kirwa K, Corlin L. Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.30.23296386. [PMID: 37873294 PMCID: PMC10592990 DOI: 10.1101/2023.09.30.23296386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.
Collapse
Affiliation(s)
- Cameron Casella
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Frances Kiles
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Catherine Urquhart
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Kipruto Kirwa
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Laura Corlin
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
| |
Collapse
|
10
|
Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
Collapse
Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| |
Collapse
|
11
|
Joshi AD, McCormick N, Yokose C, Yu B, Tin A, Terkeltaub R, Merriman TR, Eliassen AH, Curhan GC, Raffield LM, Choi HK. Prediagnostic Glycoprotein Acetyl Levels and Incident and Recurrent Flare Risk Accounting for Serum Urate Levels: A Population-Based, Prospective Study and Mendelian Randomization Analysis. Arthritis Rheumatol 2023; 75:1648-1657. [PMID: 37043280 PMCID: PMC10524152 DOI: 10.1002/art.42523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/21/2023] [Accepted: 04/06/2023] [Indexed: 04/13/2023]
Abstract
OBJECTIVE To prospectively investigate population-based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate. METHODS We conducted a prediagnostic metabolome-wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006-2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2-sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases. RESULTS Correcting for multiple testing, 88 metabolites were associated with risk of incident gout (N = 1,303 cases) before serum urate adjustment, including glutamine and glycine (inversely), and lipids, branched-chain amino acids, and most prominently, glycoprotein acetyls (GlycA; P = 9.17 × 10-32 ). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22-1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36-11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04-1.17) overall and 1.54 (95% CI 1.21-1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27-2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk. CONCLUSION This prospective, population-based study implicates GlycA, a stable long-term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate.
Collapse
Affiliation(s)
- Amit D. Joshi
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston MA USA
| | - Natalie McCormick
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA USA
- Department of Medicine, Harvard Medical School, Boston MA USA
- Arthritis Research Canada, Vancouver BC Canada
| | - Chio Yokose
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA USA
- Department of Medicine, Harvard Medical School, Boston MA USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston TX USA
| | - Adrienne Tin
- Department of Medicine, University of Mississippi Medical Center, Jackson MS USA
| | - Robert Terkeltaub
- San Diego VA Healthcare Service and University of California San Diego, La Jolla, CA
| | - Tony R. Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham AL USA
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - A. Heather Eliassen
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston MA USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston MA USA
| | - Gary C. Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston MA USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill NC USA
| | - Hyon K. Choi
- Clinical Epidemiology Program, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston MA USA
- The Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston MA USA
- Department of Medicine, Harvard Medical School, Boston MA USA
- Arthritis Research Canada, Vancouver BC Canada
| |
Collapse
|
12
|
Joshi AD, Rahnavard A, Kachroo P, Mendez KM, Lawrence W, Julián-Serrano S, Hua X, Fuller H, Sinnott-Armstrong N, Tabung FK, Shutta KH, Raffield LM, Darst BF. An epidemiological introduction to human metabolomic investigations. Trends Endocrinol Metab 2023; 34:505-525. [PMID: 37468430 PMCID: PMC10527234 DOI: 10.1016/j.tem.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.
Collapse
Affiliation(s)
- Amit D Joshi
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wayne Lawrence
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sachelly Julián-Serrano
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA
| | - Xinwei Hua
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nasa Sinnott-Armstrong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fred K Tabung
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| |
Collapse
|
13
|
Ancel P, Martin JC, Doukbi E, Houssays M, Gascon P, Righini M, Matonti F, Svilar L, Valmori M, Tardivel C, Venteclef N, Julla JB, Gautier JF, Resseguier N, Dutour A, Gaborit B. Untargeted Multiomics Approach Coupling Lipidomics and Metabolomics Profiling Reveals New Insights in Diabetic Retinopathy. Int J Mol Sci 2023; 24:12053. [PMID: 37569425 PMCID: PMC10418671 DOI: 10.3390/ijms241512053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM) which is the main cause of vision loss in the working-age population. Currently known risk factors such as age, disease duration, and hemoglobin A1c lack sufficient efficiency to distinguish patients with early stages of DR. A total of 194 plasma samples were collected from patients with type 2 DM and DR (moderate to proliferative (PDR) or control (no or mild DR) matched for age, gender, diabetes duration, HbA1c, and hypertension. Untargeted lipidomic and metabolomic approaches were performed. Partial-least square methods were used to analyze the datasets. Levels of 69 metabolites and 85 lipid species were found to be significantly different in the plasma of DR patients versus controls. Metabolite set enrichment analysis indicated that pathways such as metabolism of branched-chain amino acids (methylglutaryl carnitine p = 0.004), the kynurenine pathway (tryptophan p < 0.001), and microbiota metabolism (p-Cresol sulfate p = 0.004) were among the most enriched deregulated pathways in the DR group. Moreover, Glucose-6-phosphate (p = 0.001) and N-methyl-glutamate (p < 0.001) were upregulated in DR. Subgroup analyses identified a specific signature associated with PDR, macular oedema, and DR associated with chronic kidney disease. Phosphatidylcholines (PCs) were dysregulated, with an increase of alkyl-PCs (PC O-42:5 p < 0.001) in DR, while non-ether PCs (PC 14:0-16:1, p < 0.001; PC 18:2-14:0, p < 0.001) were decreased in the DR group. Through an unbiased multiomics approach, we identified metabolites and lipid species that interestingly discriminate patients with or without DR. These features could be a research basis to identify new potential plasma biomarkers to promote 3P medicine.
Collapse
Affiliation(s)
- Patricia Ancel
- Aix-Marseille University, INSERM, INRAE, C2VN, 13005 Marseille, France; (P.A.); (E.D.)
| | - Jean Charles Martin
- Aix-Marseille University, INSERM, INRAE, C2VN, BIOMET Aix-Marseille Technology Platform, 13005 Marseille, France; (J.C.M.); (M.V.); (C.T.)
| | - Elisa Doukbi
- Aix-Marseille University, INSERM, INRAE, C2VN, 13005 Marseille, France; (P.A.); (E.D.)
| | - Marie Houssays
- Medical Evaluation Department, Assistance-Publique Hôpitaux de Marseille, CIC-CPCET, 13005 Marseille, France
| | - Pierre Gascon
- Department of Ophthalmology, Assistance-Publique Hôpitaux de Marseille, 13005 Marseille, France; (P.G.); (M.R.); (F.M.)
- Centre Monticelli Paradis, 433 bis rue Paradis, 13008 Marseille, France
- Groupe Almaviva Santé, Clinique Juge, 116 rue Jean Mermoz, 13008 Marseille, France
| | - Maud Righini
- Department of Ophthalmology, Assistance-Publique Hôpitaux de Marseille, 13005 Marseille, France; (P.G.); (M.R.); (F.M.)
| | - Frédéric Matonti
- Department of Ophthalmology, Assistance-Publique Hôpitaux de Marseille, 13005 Marseille, France; (P.G.); (M.R.); (F.M.)
| | - Ljubica Svilar
- CRIBIOM Aix-Marseille Technology Platform, 13005 Marseille, France;
| | - Marie Valmori
- Aix-Marseille University, INSERM, INRAE, C2VN, BIOMET Aix-Marseille Technology Platform, 13005 Marseille, France; (J.C.M.); (M.V.); (C.T.)
| | - Catherine Tardivel
- Aix-Marseille University, INSERM, INRAE, C2VN, BIOMET Aix-Marseille Technology Platform, 13005 Marseille, France; (J.C.M.); (M.V.); (C.T.)
| | - Nicolas Venteclef
- IMMEDIAB Laboratory, Institut Necker Enfants Malades (INEM), INSERM U1151, CNRS UMR 8253, Université Paris Cité, 75015 Paris, France;
| | - Jean Baptiste Julla
- IMMEDIAB Laboratory, Diabetology and Endocrinology Department, Institut Necker Enfants Malades (INEM), INSERM U1151, CNRS UMR 8253, Université Paris Cité, Lariboisière Hospital, Féderation de Diabétologie, APHP, 75015 Paris, France; (J.B.J.); (J.F.G.)
| | - Jean François Gautier
- IMMEDIAB Laboratory, Diabetology and Endocrinology Department, Institut Necker Enfants Malades (INEM), INSERM U1151, CNRS UMR 8253, Université Paris Cité, Lariboisière Hospital, Féderation de Diabétologie, APHP, 75015 Paris, France; (J.B.J.); (J.F.G.)
| | - Noémie Resseguier
- Aix-Marseille University, Support Unit for Clinical Research and Economic Evaluation, Assistance Publique-Hôpitaux de Marseille, EA 3279 CEReSS-Health Service Research and Quality of Life Center, 13005 Marseille, France;
| | - Anne Dutour
- Aix-Marseille University, INSERM, INRAE, C2VN, Endocrinology, Metabolic Diseases and Nutrition Department, AP-HM, 13005 Marseille, France;
| | - Bénédicte Gaborit
- Aix-Marseille University, INSERM, INRAE, C2VN, Endocrinology, Metabolic Diseases and Nutrition Department, AP-HM, 13005 Marseille, France;
| |
Collapse
|
14
|
Baum L, Johns M, Poikela M, Möller R, Ananthasubramaniam B, Prasser F. Data integration and analysis for circadian medicine. Acta Physiol (Oxf) 2023; 237:e13951. [PMID: 36790321 DOI: 10.1111/apha.13951] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/04/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.
Collapse
Affiliation(s)
- Lena Baum
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Marco Johns
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Maija Poikela
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf Möller
- Institute of Information Systems, University of Lübeck, Lübeck, Germany
| | | | - Fabian Prasser
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| |
Collapse
|
15
|
Kim S, Hollinger H, Radke EG. 'Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map. ENVIRONMENT INTERNATIONAL 2022; 164:107243. [PMID: 35551006 DOI: 10.1016/j.envint.2022.107243] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 03/25/2022] [Accepted: 04/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Systematic evidence maps are increasingly used to develop chemical risk assessments. These maps can provide an overview of available studies and relevant study information to be used for various research objectives and applications. Environmental epidemiological studies that examine the impact of chemical exposures on various 'omic profiles in human populations provide relevant mechanistic information and can be used for benchmark dose modeling to derive potential human health reference values. OBJECTIVES To create a systematic evidence map of environmental epidemiological studies examining environmental contaminant exposures with 'omics in order to characterize the extent of available studies for future research needs. METHODS Systematic review methods were used to search and screen the literature and included the use of machine learning methods to facilitate screening studies. The Populations, Exposures, Comparators and Outcomes (PECO) criteria were developed to identify and screen relevant studies. Studies that met the PECO criteria after full-text review were summarized with information such as study population, study design, sample size, exposure measurement, and 'omics analysis. RESULTS Over 10,000 studies were identified from scientific databases. Screening processes were used to identify 84 studies considered PECO-relevant after full-text review. Various contaminants (e.g. phthalate, benzene, arsenic, etc.) were investigated in epidemiological studies that used one or more of the four 'omics of interest: epigenomics, transcriptomics, proteomics, and metabolomics . The epidemiological study designs that were used to explore single or integrated 'omic research questions with contaminant exposures were cohort studies, controlled trials, cross-sectional, and case-control studies. An interactive web-based systematic evidence map was created to display more study-related information. CONCLUSIONS This systematic evidence map is a novel tool to visually characterize the available environmental epidemiological studies investigating contaminants and biological effects using 'omics technology and serves as a resource for investigators and allows for a range of applications in chemical research and risk assessment needs.
Collapse
Affiliation(s)
- Stephanie Kim
- Superfund and Emergency Management Division, Region 2, U.S. Environmental Protection Agency, NY, USA.
| | - Hillary Hollinger
- Office of Pollution Prevention and Toxics, U.S. Environmental Protection Agency, NC, USA.
| | - Elizabeth G Radke
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, D.C, USA.
| |
Collapse
|
16
|
Marie B, Gallet A. Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria. CHEMOSPHERE 2022; 296:134035. [PMID: 35183584 DOI: 10.1016/j.chemosphere.2022.134035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/03/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, and/or metabolomics). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairments. In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by 1H NMR the bio-indicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris' area (France). In the present study, we further investigate on the same samples the high potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome investigation allows a clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations which seem to be influenced by the production of noxious molecules by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches appears to constitute an innovative bio-indicative tool for the assessment of ecological stress, such as toxigenic cyanobacterial blooms, and aim at being further follow up.
Collapse
Affiliation(s)
- Benjamin Marie
- UMR 7245, CNRS/MNHN, Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon - CP 39, 75231, Paris Cedex 05, France.
| | - Alison Gallet
- UMR 7245, CNRS/MNHN, Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon - CP 39, 75231, Paris Cedex 05, France
| |
Collapse
|
17
|
Resurreccion EP, Fong KW. The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer. Metabolites 2022; 12:metabo12060488. [PMID: 35736421 PMCID: PMC9230859 DOI: 10.3390/metabo12060488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 02/06/2023] Open
Abstract
Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.
Collapse
Affiliation(s)
- Eleazer P. Resurreccion
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
| | - Ka-wing Fong
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Correspondence: ; Tel.: +1-859-562-3455
| |
Collapse
|
18
|
Multi-omics approach in tea polyphenol research regarding tea plant growth, development and tea processing: current technologies and perspectives. FOOD SCIENCE AND HUMAN WELLNESS 2022. [DOI: 10.1016/j.fshw.2021.12.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
19
|
Yu CT, Chao BN, Barajas R, Haznadar M, Maruvada P, Nicastro HL, Ross SA, Verma M, Rogers S, Zanetti KA. An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data. Metabolomics 2022; 18:29. [PMID: 35488937 PMCID: PMC9056487 DOI: 10.1007/s11306-022-01878-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/28/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment. AIM OF REVIEW To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data. KEY SCIENTIFIC CONCEPTS OF REVIEW We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.
Collapse
Affiliation(s)
- Catherine T Yu
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Brittany N Chao
- Office of Workforce Planning and Development, National Cancer Institute, Rockville, MD, USA
| | - Rolando Barajas
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Majda Haznadar
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Rockville, MD, USA
| | - Padma Maruvada
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Holly L Nicastro
- Office of Nutrition Research, National Institutes of Health, Bethesda, MD, USA
| | - Sharon A Ross
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
| | - Mukesh Verma
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Scott Rogers
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
| | - Krista A Zanetti
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
| |
Collapse
|
20
|
Metelskaya VA, Gavrilova NE, Zhatkina MV, Yarovaya EB, Drapkina OM. A Novel Integrated Biomarker for Evaluation of Risk and Severity of Coronary Atherosclerosis, and Its Validation. J Pers Med 2022; 12:jpm12020206. [PMID: 35207694 PMCID: PMC8877383 DOI: 10.3390/jpm12020206] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/28/2022] [Accepted: 01/31/2022] [Indexed: 12/10/2022] Open
Abstract
Objective: To assess the feasibility of a combination of biochemical and imaging parameters for estimation of risk and severity of coronary atherosclerosis (CA), and to verify the created integrated biomarker (i-BIO) on independent cohort. Methods: Two cohorts of patients admitted to the hospital for coronary angiography and ultrasound carotid dopplerography were enrolled into the study (n = 205 and n = 216, respectively). The extent of CA was assessed by Gensini Score (GS). Results: According to GS, participants were distributed as follows: atherosclerosis-free (GS = 0), CA of any stage (GS > 0), subclinical CA (GS < 35), severe CA (GS ≥ 35). Based on the analysis of mathematical models, including biochemical and imaging parameters, we selected and combined the most significant variables as i-BIO. The ability of i-BIO to detect the presence and severity of CA was estimated using ROC-analysis with cut-off points determination. Risk of any CA (GS > 0) at i-BIO > 4 was 7.3 times higher than in those with i-BIO ≤ 4; risk of severe CA (GS ≥ 35) at i-BIO ≥ 9 was 3.1 times higher than at i-BIO < 9. Results on the tested cohort confirmed these findings. Conclusions: The i-BIO > 4 detected CA (GS > 0) with sensitivity of 87.9%, i-BIO ≥ 9 excluded patients without severe CA (GS < 35), specificity 79.8%. Validation of i-BIO confirmed the feasibility of i-BIO > 4 to separate patients with any CA with sensitivity 76.2%, and of i-BIO ≥ 9 to exclude atherosclerosis-free subjects with specificity of 84.0%.
Collapse
Affiliation(s)
- Victoria A. Metelskaya
- National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia; (E.B.Y.); (O.M.D.)
- Correspondence:
| | | | | | - Elena B. Yarovaya
- National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia; (E.B.Y.); (O.M.D.)
- Department of Probability Theory, Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, 119234 Moscow, Russia
| | - Oxana M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia; (E.B.Y.); (O.M.D.)
| |
Collapse
|
21
|
Vike NL, Bari S, Stetsiv K, Walter A, Newman S, Kawata K, Bazarian JJ, Martinovich Z, Nauman EA, Talavage TM, Papa L, Slobounov SM, Breiter HC. A preliminary model of football-related neural stress that integrates metabolomics with transcriptomics and virtual reality. iScience 2022; 25:103483. [PMID: 35106455 PMCID: PMC8786649 DOI: 10.1016/j.isci.2021.103483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/23/2021] [Accepted: 11/19/2021] [Indexed: 12/06/2022] Open
Abstract
Research suggests contact sports affect neurological health. This study used permutation-based mediation statistics to integrate measures of metabolomics, neuroinflammatory miRNAs, and virtual reality (VR)-based motor control to investigate multi-scale relationships across a season of collegiate American football. Fourteen significant mediations (six pre-season, eight across-season) were observed where metabolites always mediated the statistical relationship between miRNAs and VR-based motor control (pSobelperm≤ 0.05; total effect > 50%), suggesting a hypothesis that metabolites sit in the statistical pathway between transcriptome and behavior. Three results further supported a model of chronic neuroinflammation, consistent with mitochondrial dysfunction: (1) Mediating metabolites were consistently medium-to-long chain fatty acids, (2) tricarboxylic acid cycle metabolites decreased across-season, and (3) accumulated head acceleration events statistically moderated pre-season metabolite levels to directionally model post-season metabolite levels. These preliminary findings implicate potential mitochondrial dysfunction and highlight probable peripheral blood biomarkers underlying repetitive head impacts in otherwise healthy collegiate football athletes. Permutation-based mediation statistics can be applied to multi-scale biology problems Fatty acids were a critical link between elevated miRNAs and motor control HAEs interacted with pre-season metabolite levels to model post-season levels Together, our observations point to brain-related mitochondrial dysfunction
Collapse
Affiliation(s)
- Nicole L Vike
- Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sumra Bari
- Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Khrystyna Stetsiv
- Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Alexa Walter
- Department of Kinesiology, Pennsylvania State University, University Park, PA 16801, USA
| | - Sharlene Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Keisuke Kawata
- Department of Kinesiology, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA.,Program in Neuroscience, College of Arts and Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Jeffrey J Bazarian
- Department of Emergency Medicine, University of Rochester, Rochester, NY 14627, USA
| | - Zoran Martinovich
- Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Eric A Nauman
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.,School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.,Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Thomas M Talavage
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.,School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.,Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Linda Papa
- Department of Emergency Medicine, Orlando Regional Medical Center, Orlando, FL 32806, USA
| | - Semyon M Slobounov
- Department of Kinesiology, Pennsylvania State University, University Park, PA 16801, USA
| | - Hans C Breiter
- Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.,Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA 02114, USA
| |
Collapse
|
22
|
Rochette L, Rigal E, Dogon G, Malka G, Zeller M, Vergely C, Cottin Y. Mitochondrial-derived peptides: New markers for cardiometabolic dysfunction. Arch Cardiovasc Dis 2022; 115:48-56. [PMID: 34972639 DOI: 10.1016/j.acvd.2021.10.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 02/07/2023]
Abstract
Great attention is being paid to the evaluation of new markers in blood circulation for the estimation of tissue metabolism disturbance. This endogenous disturbance may contribute to the onset and progression of cardiometabolic disease. In addition to their role in energy production and metabolism, mitochondria play a main function in cellular mechanisms, including apoptosis, oxidative stress and calcium homeostasis. Mitochondria produce mitochondrial-derived peptides that mediate the transcriptional stress response by translocating into the nucleus and interacting with deoxyribonucleic acid. This class of peptides includes humanin, mitochondrial open reading frame of the 12S ribosomal ribonucleic acid type c (MOTS-c) and small humanin-like peptides. Mitochondrial-derived peptides are regulators of metabolism, exerting cytoprotective effects through antioxidative stress, anti-inflammatory responses and antiapoptosis; they are emerging biomarkers reflecting mitochondrial function, and the circulating concentration of these proteins can be used to diagnose cardiometabolic dysfunction. The aims of this review are: (1) to describe the emerging role for mitochondrial-derived peptides as biomarkers; and (2) to discuss the therapeutic application of these peptides.
Collapse
Affiliation(s)
- Luc Rochette
- Équipe d'Accueil (EA 7460), physiopathologie et épidémiologie cérébro-cardiovasculaires (PEC2), faculté des sciences de santé, université de Bourgogne-Franche Comté, 21000 Dijon, France.
| | - Eve Rigal
- Équipe d'Accueil (EA 7460), physiopathologie et épidémiologie cérébro-cardiovasculaires (PEC2), faculté des sciences de santé, université de Bourgogne-Franche Comté, 21000 Dijon, France
| | - Geoffrey Dogon
- Équipe d'Accueil (EA 7460), physiopathologie et épidémiologie cérébro-cardiovasculaires (PEC2), faculté des sciences de santé, université de Bourgogne-Franche Comté, 21000 Dijon, France
| | - Gabriel Malka
- Centre interface applications médicales (CIAM), université Mohammed VI Polytechnique, 43150 Ben Guerir, Morocco
| | - Marianne Zeller
- Équipe d'Accueil (EA 7460), physiopathologie et épidémiologie cérébro-cardiovasculaires (PEC2), faculté des sciences de santé, université de Bourgogne-Franche Comté, 21000 Dijon, France
| | - Catherine Vergely
- Équipe d'Accueil (EA 7460), physiopathologie et épidémiologie cérébro-cardiovasculaires (PEC2), faculté des sciences de santé, université de Bourgogne-Franche Comté, 21000 Dijon, France
| | - Yves Cottin
- Cardiology Unit, CHU de Dijon-Bourgogne, 21000 Dijon, France
| |
Collapse
|
23
|
Gander J, Carrard J, Gallart-Ayala H, Borreggine R, Teav T, Infanger D, Colledge F, Streese L, Wagner J, Klenk C, Nève G, Knaier R, Hanssen H, Schmidt-Trucksäss A, Ivanisevic J. Metabolic Impairment in Coronary Artery Disease: Elevated Serum Acylcarnitines Under the Spotlights. Front Cardiovasc Med 2021; 8:792350. [PMID: 34977199 PMCID: PMC8716394 DOI: 10.3389/fcvm.2021.792350] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/09/2021] [Indexed: 12/26/2022] Open
Abstract
Coronary artery disease (CAD) remains the leading cause of death worldwide. Expanding patients' metabolic phenotyping beyond clinical chemistry investigations could lead to earlier recognition of disease onset and better prevention strategies. Additionally, metabolic phenotyping, at the molecular species level, contributes to unravel the roles of metabolites in disease development. In this cross-sectional study, we investigated clinically healthy individuals (n = 116, 65% male, 70.8 ± 8.7 years) and patients with CAD (n = 54, 91% male, 67.0 ± 11.5 years) of the COmPLETE study. We applied a high-coverage quantitative liquid chromatography-mass spectrometry approach to acquire a comprehensive profile of serum acylcarnitines, free carnitine and branched-chain amino acids (BCAAs), as markers of mitochondrial health and energy homeostasis. Multivariable linear regression analyses, adjusted for confounders, were conducted to assess associations between metabolites and CAD phenotype. In total, 20 short-, medium- and long-chain acylcarnitine species, along with L-carnitine, valine and isoleucine were found to be significantly (adjusted p ≤ 0.05) and positively associated with CAD. For 17 acylcarnitine species, associations became stronger as the number of affected coronary arteries increased. This implies that circulating acylcarnitine levels reflect CAD severity and might play a role in future patients' stratification strategies. Altogether, CAD is characterized by elevated serum acylcarnitine and BCAA levels, which indicates mitochondrial imbalance between fatty acid and glucose oxidation.
Collapse
Affiliation(s)
- Joséphine Gander
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Rébecca Borreggine
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Denis Infanger
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Flora Colledge
- Division of Sports Science, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Lukas Streese
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Jonathan Wagner
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Christopher Klenk
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Gilles Nève
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Raphael Knaier
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Henner Hanssen
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Arno Schmidt-Trucksäss
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
- Arno Schmidt-Trucksäss
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- *Correspondence: Julijana Ivanisevic
| |
Collapse
|
24
|
Goerdten J, Floegel A. Exposure assessment in early life: it is about time for multi-omics approaches. BMC Med 2021; 19:210. [PMID: 34446014 PMCID: PMC8393438 DOI: 10.1186/s12916-021-02088-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 11/25/2022] Open
Affiliation(s)
- Jantje Goerdten
- Unit Molecular Epidemiology, Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany
| | - Anna Floegel
- Unit Molecular Epidemiology, Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany.
| |
Collapse
|
25
|
Gillenwater LA, Helmi S, Stene E, Pratte KA, Zhuang Y, Schuyler RP, Lange L, Castaldi PJ, Hersh CP, Banaei-Kashani F, Bowler RP, Kechris KJ. Multi-omics subtyping pipeline for chronic obstructive pulmonary disease. PLoS One 2021; 16:e0255337. [PMID: 34432807 PMCID: PMC8386883 DOI: 10.1371/journal.pone.0255337] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 07/14/2021] [Indexed: 11/25/2022] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.
Collapse
Affiliation(s)
| | - Shahab Helmi
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, United States of America
| | - Evan Stene
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, United States of America
| | | | - Yonghua Zhuang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Ronald P. Schuyler
- Department of Immunology & Microbiology, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States of America
| | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Peter J. Castaldi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, United States of America
| | | | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| |
Collapse
|
26
|
Gupta N, Ramakrishnan S, Wajid S. Emerging role of metabolomics in protein conformational disorders. Expert Rev Proteomics 2021; 18:395-410. [PMID: 34227444 DOI: 10.1080/14789450.2021.1948330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Introduction: Metabolomics focuses on interactions among different metabolites associated with various cellular functions in cells, tissues, and organs. In recent years, metabolomics has emerged as a powerful tool to identify perturbed metabolites, pathways influenced by the environment, for protein conformational diseases (PCDs) and also offers wide clinical application.Area Covered: This review provides a brief overview of recent advances in metabolomics as applied to identify metabolic variations in PCDs, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, prion disease, and cardiac amyloidosis. The 'PubMed' and 'Google Scholar' database search methods have been used to screen the published reports with key search terms: metabolomics, biomarkers, and protein conformational disorders.Expert opinion: Metabolomics is the large-scale study of metabolites and is deemed to overwhelm other omics. It plays a crucial role in finding variations in diseases due to protein conformational changes. However, many PCDs are yet to be identified. Metabolomics is still an emerging field; there is a need for new high-resolution analytical techniques and more studies need to be carried out to generate new information.
Collapse
Affiliation(s)
- Nimisha Gupta
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, India
| | | | - Saima Wajid
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, India
| |
Collapse
|
27
|
Zancarini A, Westerhuis JA, Smilde AK, Bouwmeester HJ. Integration of omics data to unravel root microbiome recruitment. Curr Opin Biotechnol 2021; 70:255-261. [PMID: 34242993 DOI: 10.1016/j.copbio.2021.06.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/14/2021] [Accepted: 06/17/2021] [Indexed: 12/13/2022]
Abstract
The plant microbiome plays an essential role in supporting plant growth and health, but plant molecular mechanisms underlying its recruitment are still unclear. Multi-omics data integration methods can be used to unravel new signalling relationships. Here, we review the effects of plant genetics and root exudates on root microbiome recruitment, and discuss methodological advances in data integration approaches that can help us to better understand and optimise the crop-microbiome interaction for a more sustainable agriculture.
Collapse
Affiliation(s)
- Anouk Zancarini
- Plant Hormone Biology Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands; Biosystems Data Analysis Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands.
| | - Johan A Westerhuis
- Biosystems Data Analysis Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
| | - Age K Smilde
- Biosystems Data Analysis Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
| | - Harro J Bouwmeester
- Plant Hormone Biology Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
| |
Collapse
|
28
|
Zanetti KA. Building infrastructure at the National Cancer Institute to support metabolomic analyses in epidemiological studies. Metabolomics 2021; 17:46. [PMID: 33942170 DOI: 10.1007/s11306-021-01791-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/01/2021] [Indexed: 11/24/2022]
Abstract
In recent years, metabolomic analyses have been increasingly performed in studies with an epidemiological design. Since 2012, the National Cancer Institute has recognized the importance of supporting these efforts by strategically building resources and infrastructure to move this area forward. This review outlines those efforts, including building infrastructure, leveraging existing resources, establishing the COnsortium of METabolomics Studies (COMETS), and improving rigor and reproducibility.
Collapse
Affiliation(s)
- Krista A Zanetti
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, 20892, USA.
| |
Collapse
|
29
|
Carrard J, Gallart-Ayala H, Infanger D, Teav T, Wagner J, Knaier R, Colledge F, Streese L, Königstein K, Hinrichs T, Hanssen H, Ivanisevic J, Schmidt-Trucksäss A. Metabolic View on Human Healthspan: A Lipidome-Wide Association Study. Metabolites 2021; 11:metabo11050287. [PMID: 33946321 PMCID: PMC8146132 DOI: 10.3390/metabo11050287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 04/23/2021] [Accepted: 04/28/2021] [Indexed: 12/22/2022] Open
Abstract
As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.
Collapse
Affiliation(s)
- Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
| | - Denis Infanger
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
| | - Jonathan Wagner
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Raphael Knaier
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Flora Colledge
- Division of Sports Science, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland;
| | - Lukas Streese
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Karsten Königstein
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Timo Hinrichs
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Henner Hanssen
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
- Correspondence: (J.I.); (A.S.-T.)
| | - Arno Schmidt-Trucksäss
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
- Correspondence: (J.I.); (A.S.-T.)
| |
Collapse
|
30
|
Jendoubi T. Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer. Metabolites 2021; 11:184. [PMID: 33801081 PMCID: PMC8003953 DOI: 10.3390/metabo11030184] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.
Collapse
Affiliation(s)
- Takoua Jendoubi
- Department of Statistical Science, University College London, London WC1E 6BT, UK
| |
Collapse
|
31
|
Nalbantoglu S, Karadag A. Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics. J Pharm Biomed Anal 2021; 199:114031. [PMID: 33857836 DOI: 10.1016/j.jpba.2021.114031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023]
Abstract
Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.
Collapse
Affiliation(s)
- Sinem Nalbantoglu
- TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Molecular, Oncology Laboratory, Gebze, Kocaeli, Turkey.
| | - Abdullah Karadag
- TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Molecular, Oncology Laboratory, Gebze, Kocaeli, Turkey
| |
Collapse
|
32
|
Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
Collapse
Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
| |
Collapse
|
33
|
Gaining a deeper understanding of social determinants of preterm birth by integrating multi-omics data. Pediatr Res 2021; 89:336-343. [PMID: 33188285 PMCID: PMC7898277 DOI: 10.1038/s41390-020-01266-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/13/2020] [Accepted: 10/20/2020] [Indexed: 12/14/2022]
Abstract
In the US, high rates of preterm birth (PTB) and profound Black-White disparities in PTB have persisted for decades. This review focuses on the role of social determinants of health (SDH), with an emphasis on maternal stress, in PTB disparity and biological embedding. It covers: (1) PTB disparity in US Black women and possible contributors; (2) the role of SDH, highlighting maternal stress, in the persistent racial disparity of PTB; (3) epigenetics at the interface between genes and environment; (4) the role of the genome in modifying maternal stress-PTB associations; (5) recent advances in multi-omics studies of PTB; and (6) future perspectives on integrating multi-omics with SDH to elucidate the Black-White disparity in PTB. Available studies have indicated that neither environmental exposures nor genetics alone can adequately explain the Black-White PTB disparity. Preliminary yet promising findings of epigenetic and gene-environment interaction studies underscore the value of integrating SDH with multi-omics in prospective birth cohort studies, especially among high-risk Black women. In an era of rapid advancements in biomedical sciences and technologies and a growing number of prospective birth cohort studies, we have unprecedented opportunities to advance this field and finally address the long history of health disparities in PTB. IMPACT: This review provides an overview of social determinants of health (SDH) with a focus on maternal stress and its role on Black-White disparity in preterm birth (PTB). It summarizes the available literature on the interplay of maternal stress with key biological layers (e.g., individual genome and epigenome in response to environmental stressors) and significant knowledge gaps. It offers perspectives that such knowledge may provide deeper insight into how SDH affects PTB and why some women are more vulnerable than others and underscores the critical need for integrating SDH with multi-omics in prospective birth cohort studies, especially among high-risk Black women.
Collapse
|
34
|
Henny J, Nadif R, Got SL, Lemonnier S, Ozguler A, Ruiz F, Beaumont K, Brault D, Sandt E, Goldberg M, Zins M. The CONSTANCES Cohort Biobank: An Open Tool for Research in Epidemiology and Prevention of Diseases. Front Public Health 2020; 8:605133. [PMID: 33363097 PMCID: PMC7758208 DOI: 10.3389/fpubh.2020.605133] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 11/18/2020] [Indexed: 12/12/2022] Open
Abstract
“General-purpose cohorts” in epidemiology and public health are designed to cover a broad scope of determinants and outcomes, in order to answer several research questions, including those not defined at study inception. In this context, the general objective of the CONSTANCES project is to set up a large population-based cohort that will contribute to the development of epidemiological research by hosting ancillary projects on a wide range of scientific domains, and to provide public health information. CONSTANCES was designed as a randomly selected sample of French adults aged 18–69 years at study inception; 202,045 subjects were included over an 8-year period. At inclusion, the selected participants are invited to attend one of the 24 participating Health Prevention Centers (HPCs) for a comprehensive health examination. The follow-up includes a yearly self-administered questionnaire, and a periodic visit to an HPC. Procedures have been developed to use the national healthcare databases to allow identification and validation of diseases over the follow-up. The biological collection (serum, lithium heparinized plasma, EDTA plasma, urine and buffy coat) began gradually in June 2018. At the end of the inclusions, specimens from 83,000 donors will have been collected. Specimens are collected according to a standardized protocol, identical in all recruitment centers. All operations relating to bio-banking have been entrusted by Inserm to the Integrated Biobank of Luxembourg (IBBL). A quality management system has been put in place. Particular attention has been paid to the traceability of all operations. The nature of the biological samples stored has been deliberately limited due to the economic and organizational constraints of the inclusion centers. Some research works may require specific collection conditions, and can be developed on request for a limited number of subjects and in specially trained centers. The biological specimens that are collected will allow for a large spectrum of biomarkers studies and genetic and epigenetic markers through candidate or agnostic approaches. By linking the extensive data on personal, lifestyle, environmental, occupational and social factors with the biomarker data, the CONSTANCES cohort offers the opportunity to study the interplays between these factors using an integrative approach and state-of-the-art methods.
Collapse
Affiliation(s)
- J Henny
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - R Nadif
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Équipe d'Épidémiologie respiratoire intégrative, CESP, Villejuif, France
| | - S Le Got
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - S Lemonnier
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - A Ozguler
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - F Ruiz
- ClinSearch, Malakoff, France
| | - K Beaumont
- Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - D Brault
- Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - E Sandt
- Integrated Biobank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - M Goldberg
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France.,Faculty of Medicine, University of Paris, Paris, France
| | - M Zins
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France.,Faculty of Medicine, University of Paris, Paris, France
| |
Collapse
|
35
|
Krassowski M, Das V, Sahu SK, Misra BB. State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Front Genet 2020; 11:610798. [PMID: 33362867 PMCID: PMC7758509 DOI: 10.3389/fgene.2020.610798] [Citation(s) in RCA: 139] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 11/20/2020] [Indexed: 12/24/2022] Open
Abstract
Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods' limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.
Collapse
Affiliation(s)
- Michal Krassowski
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Vivek Das
- Novo Nordisk Research Center Seattle, Inc, Seattle, WA, United States
| | | | | |
Collapse
|
36
|
Ghantous CM, Kamareddine L, Farhat R, Zouein FA, Mondello S, Kobeissy F, Zeidan A. Advances in Cardiovascular Biomarker Discovery. Biomedicines 2020; 8:biomedicines8120552. [PMID: 33265898 PMCID: PMC7759775 DOI: 10.3390/biomedicines8120552] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/18/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular diseases are the leading causes of mortality worldwide. Among them, hypertension and its pathological complications pose a major risk for the development of other cardiovascular diseases, including heart failure and stroke. Identifying novel and early stage biomarkers of hypertension and other cardiovascular diseases is of paramount importance in predicting and preventing the major morbidity and mortality associated with these diseases. Biomarkers of such diseases or predisposition to their development are identified by changes in a specific indicator’s expression between healthy individuals and patients. These include changes in protein and microRNA (miRNA) levels. Protein profiling using mass spectrometry and miRNA screening utilizing microarray and sequencing have facilitated the discovery of proteins and miRNA as biomarker candidates. In this review, we summarized some of the different, promising early stage protein and miRNA biomarker candidates as well as the currently used biomarkers for hypertension and other cardiovascular diseases. Although a number of promising markers have been identified, it is unlikely that a single biomarker will unambiguously aid in the classification of these diseases. A multi-marker panel-strategy appears useful and promising for classifying and refining risk stratification among patients with cardiovascular disease.
Collapse
Affiliation(s)
- Crystal M. Ghantous
- Department of Nursing and Health Sciences, Faculty of Nursing and Health Sciences, Notre Dame University-Louaize, Keserwan 72, Lebanon;
| | - Layla Kamareddine
- Biomedical Sciences Department, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar;
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha 2713, Qatar
| | - Rima Farhat
- Department of Anatomy, Cell Biology and Physiology, Faculty of Medicine, American University of Beirut, Beirut 1107, Lebanon;
| | - Fouad A. Zouein
- Department of Pharmacology and Toxicology, Faculty of Medicine, American University of Beirut, Beirut 1107, Lebanon;
| | - Stefania Mondello
- Oasi Research Institute-IRCCS, 94018 Troina, Italy;
- Department of Biomedical and Dental Sciences and Morpho-functional Imaging, University of Messina, 98125 Messina, Italy
| | - Firas Kobeissy
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut 1107, Lebanon;
| | - Asad Zeidan
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha 2713, Qatar
- Department of Basic Medical Science, Faculty of Medicine, QU Health, Qatar University, Doha 2713, Qatar
- Correspondence: ; Tel.: +97-431-309-19
| |
Collapse
|
37
|
Vasquez EC, Aires R, Ton AMM, Amorim FG. New Insights on the Beneficial Effects of the Probiotic Kefir on Vascular Dysfunction in Cardiovascular and Neurodegenerative Diseases. Curr Pharm Des 2020; 26:3700-3710. [DOI: 10.2174/1381612826666200304145224] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 02/26/2020] [Indexed: 12/14/2022]
Abstract
The mechanisms responsible for cardiovascular and neurodegenerative diseases have been the focus of
experimental and clinical studies for decades. The relationship between the gut microbiota and the organs and
system tissues represents the research field that has generated the highest number of publications. Homeostasis of
the gut microbiota is important to the host because it promotes maturation of the autoimmune system, harmonic
integrative functions of the brain, and the normal function of organs related to cardiovascular and metabolic systems.
On the other hand, when a gut microbiota dysbiosis occurs, the target organs become vulnerable to the
onset or aggravation of complex chronic conditions, such as cardiovascular (e.g., arterial hypertension) and neurodegenerative
(e.g., dementia) diseases. In the present brief review, we discuss the main mechanisms involved in
those disturbances and the promising beneficial effects that have been revealed using functional food (nutraceuticals),
such as the traditional probiotic Kefir. Here, we highlight the current scientific advances, concerns, and
limitations about the use of this nutraceutical. The focus of our discussion is the endothelial dysfunction that
accompanies hypertension and the neurovascular dysfunction that characterizes ageing-related dementia in patients
suffering from Alzheimer's disease.
Collapse
Affiliation(s)
- Elisardo C. Vasquez
- Pharmaceutical Sciences Graduate Program, Vila Velha University (UVV), Vila Velha, ES, Brazil
| | - Rafaela Aires
- Physiological Sciences Graduate Program, Federal University of Espirito Santo (UFES), Vitoria, ES, Brazil
| | - Alyne M. M. Ton
- Pharmaceutical Sciences Graduate Program, Vila Velha University (UVV), Vila Velha, ES, Brazil
| | - Fernanda G. Amorim
- Pharmaceutical Sciences Graduate Program, Vila Velha University (UVV), Vila Velha, ES, Brazil
| |
Collapse
|
38
|
Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
Collapse
Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
| |
Collapse
|
39
|
An Integrative Approach to Assessing Diet-Cancer Relationships. Metabolites 2020; 10:metabo10040123. [PMID: 32218376 PMCID: PMC7241082 DOI: 10.3390/metabo10040123] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/23/2020] [Accepted: 03/24/2020] [Indexed: 02/06/2023] Open
Abstract
The relationship between diet and cancer is often viewed with skepticism by the public and health professionals, despite a considerable body of evidence and general consistency in recommendations over the past decades. A systems biology approach which integrates 'omics' data including metabolomics, genetics, metagenomics, transcriptomics and proteomics holds promise for developing a better understanding of how diet affects cancer and for improving the assessment of diet through biomarker discovery thereby renewing confidence in diet-cancer links. This review discusses the application of multi-omics approaches to studies of diet and cancer. Considerations and challenges that need to be addressed to facilitate the investigation of diet-cancer relationships with multi-omic approaches are also discussed.
Collapse
|
40
|
Long NP, Nghi TD, Kang YP, Anh NH, Kim HM, Park SK, Kwon SW. Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine. Metabolites 2020; 10:E51. [PMID: 32013105 PMCID: PMC7074059 DOI: 10.3390/metabo10020051] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/17/2020] [Accepted: 01/21/2020] [Indexed: 12/18/2022] Open
Abstract
Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.
Collapse
Affiliation(s)
- Nguyen Phuoc Long
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Tran Diem Nghi
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea; (T.D.N.); (S.K.P.)
| | - Yun Pyo Kang
- Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Sang Ki Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea; (T.D.N.); (S.K.P.)
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| |
Collapse
|
41
|
Agakidou E, Agakidis C, Gika H, Sarafidis K. Emerging Biomarkers for Prediction and Early Diagnosis of Necrotizing Enterocolitis in the Era of Metabolomics and Proteomics. Front Pediatr 2020; 8:602255. [PMID: 33425815 PMCID: PMC7793899 DOI: 10.3389/fped.2020.602255] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Necrotizing Enterocolitis (NEC) is a catastrophic disease affecting predominantly premature infants and is characterized by high mortality and serious long-term consequences. Traditionally, diagnosis of NEC is based on clinical and radiological findings, which, however, are non-specific for NEC, thus confusing differential diagnosis of other conditions such as neonatal sepsis and spontaneous intestinal perforation. In addition, by the time clinical and radiological findings become apparent, NEC has already progressed to an advanced stage. During the last three decades, a lot of research has focused on the discovery of biomarkers, which could accurately predict and make an early diagnosis of NEC. Biomarkers used thus far in clinical practice include acute phase proteins, inflammation mediators, and molecules involved in the immune response. However, none has been proven accurate enough to predict and make an early diagnosis of NEC or discriminate clinical from surgical NEC or other non-NEC gastrointestinal diseases. Complexity of mechanisms involved in NEC pathogenesis, which remains largely poorly elucidated, could partly explain the unsatisfactory diagnostic performance of the existing NEC biomarkers. More recently applied technics can provide important insight into the pathophysiological mechanisms underlying NEC but can also aid the detection of potentially predictive, early diagnostic, and prognostic biomarkers. Progress in omics technology has allowed for the simultaneous measurement of a large number of proteins, metabolic products, lipids, and genes, using serum/plasma, urine, feces, tissues, and other biological specimens. This review is an update of current data on emerging NEC biomarkers detected using proteomics and metabolomics, further discussing limitations and future perspectives in prediction and early diagnosis of NEC.
Collapse
Affiliation(s)
- Eleni Agakidou
- 1st Department of Neonatology, Faculty of Medicine, Ippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Charalampos Agakidis
- 1st Department of Pediatrics, Faculty of Medicine, Ippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Helen Gika
- Laboratory of Forensic Medicine and Toxicology, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,BIOMIC_AUTH, Bioanalysis and Omics Laboratory, Centre for Interdisciplinary Research and Innovation, CIRI-AUTH B1.4, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kosmas Sarafidis
- 1st Department of Neonatology, Faculty of Medicine, Ippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
42
|
Virkud YV, Kelly RS, Wood C, Lasky-Su JA. The nuts and bolts of omics for the clinical allergist. Ann Allergy Asthma Immunol 2019; 123:558-563. [PMID: 31562939 DOI: 10.1016/j.anai.2019.09.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Omics, aka multi-omics, is an emerging area of research that is advancing the use of personalized medicine in clinical practice and is therefore relevant for the practicing allergist. DATA SOURCES We performed a literature search of a selection of scientific findings in omics and allergy, including variants that may be important to allergy outcomes in the genome, transcriptome, metabolome, microbiome, epigenome, and exposome, among others. STUDY SELECTIONS Basic science papers and review articles. RESULTS The use of multi-omic data in clinical practice is changing how clinicians treat their patients whereby more personalized approaches are becoming standard in medical practice and has the potential to transform the treatment of allergies. CONCLUSION Multi-omic data are relevant and will become increasingly important for the clinical allergist.
Collapse
Affiliation(s)
- Yamini V Virkud
- Department of Pediatrics, Massachusetts General Hospital for Children and Harvard Medical School, Boston, Massachusetts; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Rachel S Kelly
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Caleb Wood
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
| |
Collapse
|
43
|
Anh NH, Long NP, Kim SJ, Min JE, Yoon SJ, Kim HM, Yang E, Hwang ES, Park JH, Hong SS, Kwon SW. Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis. Metabolites 2019; 9:E199. [PMID: 31546652 PMCID: PMC6835899 DOI: 10.3390/metabo9100199] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 09/09/2019] [Accepted: 09/17/2019] [Indexed: 02/07/2023] Open
Abstract
Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.
Collapse
Affiliation(s)
- Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | | | - Sun Jo Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Jung Eun Min
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Sang Jun Yoon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Eugine Yang
- College of Pharmacy, Ewha Womans University, Seoul 03760, Korea.
| | - Eun Sook Hwang
- College of Pharmacy, Ewha Womans University, Seoul 03760, Korea.
| | - Jeong Hill Park
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Soon-Sun Hong
- Department of Biomedical Sciences, College of Medicine, Inha University, Incheon 22212, Korea.
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|