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Stokes T, Cen HH, Kapranov P, Gallagher IJ, Pitsillides AA, Volmar C, Kraus WE, Johnson JD, Phillips SM, Wahlestedt C, Timmons JA. Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200024. [PMID: 37288167 PMCID: PMC10242409 DOI: 10.1002/ggn2.202200024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 06/09/2023]
Abstract
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
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Affiliation(s)
- Tanner Stokes
- Faculty of ScienceMcMaster UniversityHamiltonL8S 4L8Canada
| | - Haoning Howard Cen
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | - Iain J Gallagher
- School of Applied SciencesEdinburgh Napier UniversityEdinburghEH11 4BNUK
| | | | | | | | - James D. Johnson
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | | | - James A. Timmons
- Miller School of MedicineUniversity of MiamiMiamiFL33136USA
- William Harvey Research InstituteQueen Mary University LondonLondonEC1M 6BQUK
- Augur Precision Medicine LTDStirlingFK9 5NFUK
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Manfredi JM, Jacob SI, Boger BL, Norton EM. A one-health approach to identifying and mitigating the impact of endocrine disorders on human and equine athletes. Am J Vet Res 2022; 84:ajvr.22.11.0194. [PMID: 36563063 DOI: 10.2460/ajvr.22.11.0194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Endocrinopathies affect multiple species in ever-increasing percentages of their populations, creating an opportunity to apply one-health approaches to determining creative preventative measures and therapies in athletes. Obesity and alterations in insulin and glucose dynamics are medical concerns that play a role in whole-body health and homeostasis in both horses and humans. The role and impact of endocrine disorders on the musculoskeletal, cardiovascular, and reproductive systems are of particular interest to the athlete. Elucidation of both physiologic and pathophysiologic mechanisms involved in disease processes, starting in utero, is important for development of prevention and treatment strategies for the health and well-being of all species. This review focuses on the unrecognized effects of endocrine disorders associated with the origins of metabolic disease; inflammation at the intersection of endocrine disease and related diseases in the musculoskeletal, cardiovascular, and reproductive systems; novel interventions; and diagnostics that are informed via multiomic and one-health approaches. Readers interested in further details on specific equine performance conditions associated with endocrine disease are invited to read the companion Currents in One Health by Manfredi et al, JAVMA, February 2023.
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Affiliation(s)
- Jane M Manfredi
- Department of Pathobiology and Diagnostic Investigation, College of Veterinary Medicine, Michigan State University, East Lansing, MI
| | - Sarah I Jacob
- Department of Pathobiology and Diagnostic Investigation, College of Veterinary Medicine, Michigan State University, East Lansing, MI
| | - Brooke L Boger
- Comparative Medicine and Integrative Biology, Department of Pathobiology and Diagnostic Investigation, College of Veterinary Medicine, Michigan State University, East Lansing, MI
| | - Elaine M Norton
- Department of Animal and Comparative Biomedical Sciences, College of Agriculture and Life Sciences, The University of Arizona, Tucson, AZ
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Monte AA, Brocker C, Nebert DW, Gonzalez FJ, Thompson DC, Vasiliou V. Improved drug therapy: triangulating phenomics with genomics and metabolomics. Hum Genomics 2014; 8:16. [PMID: 25181945 PMCID: PMC4445687 DOI: 10.1186/s40246-014-0016-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Accepted: 08/05/2014] [Indexed: 12/23/2022] Open
Abstract
Embracing the complexity of biological systems has a greater likelihood to improve prediction of clinical drug response. Here we discuss limitations of a singular focus on genomics, epigenomics, proteomics, transcriptomics, metabolomics, or phenomics-highlighting the strengths and weaknesses of each individual technique. In contrast, 'systems biology' is proposed to allow clinicians and scientists to extract benefits from each technique, while limiting associated weaknesses by supplementing with other techniques when appropriate. Perfect predictive modeling is not possible, whereas modeling of intertwined phenomic responses using genomic stratification with metabolomic modifications may greatly improve predictive values for drug therapy. We thus propose a novel-integrated approach to personalized medicine that begins with phenomic data, is stratified by genomics, and ultimately refined by metabolomic pathway data. Whereas perfect prediction of efficacy and safety of drug therapy is not possible, improvements can be achieved by embracing the complexity of the biological system. Starting with phenomics, the combination of linking metabolomics to identify common biologic pathways and then stratifying by genomic architecture, might increase predictive values. This systems biology approach has the potential, in specific subsets of patients, to avoid drug therapy that will be either ineffective or unsafe.
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Affiliation(s)
- Andrew A Monte
- University of Colorado Department of Emergency Medicine, Leprino Building, 7th Floor Campus Box B-215, 12401 E. 17th Avenue, Aurora, CO, 80045, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, 80045, USA.
- Rocky Mountain Poison & Drug Center, Denver, CO, 80204, USA.
| | - Chad Brocker
- Laboratory of Metabolism, Center for Cancer Research, National Institute of Cancer, Bethesda, MD, 20892, USA.
| | - Daniel W Nebert
- Division of Human Genetics, Department of Pediatrics and Molecular Developmental Biology, University of Cincinnati Medical Center, Cincinnati, OH, 45220, USA.
- Department of Environmental Health and Center for Environmental Genetics, University of Cincinnati Medical Center, Cincinnati, OH, 45220, USA.
| | - Frank J Gonzalez
- Laboratory of Metabolism, Center for Cancer Research, National Institute of Cancer, Bethesda, MD, 20892, USA.
| | - David C Thompson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, 80045, USA.
| | - Vasilis Vasiliou
- Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, 80045, USA.
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Kuehnbaum NL, Gillen JB, Gibala MJ, Britz-McKibbin P. Personalized metabolomics for predicting glucose tolerance changes in sedentary women after high-intensity interval training. Sci Rep 2014; 4:6166. [PMID: 25164777 PMCID: PMC4147371 DOI: 10.1038/srep06166] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 08/04/2014] [Indexed: 12/16/2022] Open
Abstract
High-intensity interval training (HIIT) offers a practical approach for enhancing cardiorespiratory fitness, however its role in improving glucose regulation among sedentary yet normoglycemic women remains unclear. Herein, multi-segment injection capillary electrophoresis-mass spectrometry is used as a high-throughput platform in metabolomics to assess dynamic responses of overweight/obese women (BMI > 25, n = 11) to standardized oral glucose tolerance tests (OGTTs) performed before and after a 6-week HIIT intervention. Various statistical methods were used to classify plasma metabolic signatures associated with post-prandial glucose and/or training status when using a repeated measures/cross-over study design. Branched-chain/aromatic amino acids and other intermediates of urea cycle and carnitine metabolism decreased over time in plasma after oral glucose loading. Adaptive exercise-induced changes to plasma thiol redox and orthinine status were measured for trained subjects while at rest in a fasting state. A multi-linear regression model was developed to predict changes in glucose tolerance based on a panel of plasma metabolites measured for naïve subjects in their untrained state. Since treatment outcomes to physical activity are variable between-subjects, prognostic markers offer a novel approach to screen for potential negative responders while designing lifestyle modifications that maximize the salutary benefits of exercise for diabetes prevention on an individual level.
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Affiliation(s)
- Naomi L Kuehnbaum
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada
| | - Jenna B Gillen
- Department of Kinesiology, McMaster University, Hamilton, Canada
| | - Martin J Gibala
- Department of Kinesiology, McMaster University, Hamilton, Canada
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Tzoulaki I, Ebbels TMD, Valdes A, Elliott P, Ioannidis JPA. Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies. Am J Epidemiol 2014; 180:129-39. [PMID: 24966222 DOI: 10.1093/aje/kwu143] [Citation(s) in RCA: 133] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Metabolomics is the field of "-omics" research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field.
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