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Hicks C, Dhiman A, Barrymore C, Goswami T. Traumatic Brain Injury Biomarkers, Simulations and Kinetics. Bioengineering (Basel) 2022; 9:612. [PMID: 36354523 PMCID: PMC9687153 DOI: 10.3390/bioengineering9110612] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/02/2022] [Accepted: 10/20/2022] [Indexed: 10/21/2023] Open
Abstract
This paper reviews the predictive capabilities of blood-based biomarkers to quantify traumatic brain injury (TBI). Biomarkers for concussive conditions also known as mild, to moderate and severe TBI identified along with post-traumatic stress disorder (PTSD) and chronic traumatic encephalopathy (CTE) that occur due to repeated blows to the head during one's lifetime. Since the pathways of these biomarkers into the blood are not fully understood whether there is disruption in the blood-brain barrier (BBB) and the time it takes after injury for the expression of the biomarkers to be able to predict the injury effectively, there is a need to understand the protein biomarker structure and other physical properties. The injury events in terms of brain and mechanics are a result of external force with or without the shrapnel, in the wake of a wave result in local tissue damage. Thus, these mechanisms express specific biomarkers kinetics of which reaches half-life within a few hours after injury to few days. Therefore, there is a need to determine the concentration levels that follow injury. Even though current diagnostics linking biomarkers with TBI severity are not fully developed, there is a need to quantify protein structures and their viability after injury. This research was conducted to fully understand the structures of 12 biomarkers by performing molecular dynamics simulations involving atomic movement and energies of forming hydrogen bonds. Molecular dynamics software, NAMD and VMD were used to determine and compare the approximate thermodynamic stabilities of the biomarkers and their bonding energies. Five biomarkers used clinically were S100B, GFAP, UCHL1, NF-L and tau, the kinetics obtained from literature show that the concentration values abruptly change with time after injury. For a given protein length, associated number of hydrogen bonds and bond energy describe a lower bound region where proteins self-dissolve and do not have long enough half-life to be detected in the fluids. However, above this lower bound, involving higher number of bonds and energy, we hypothesize that biomarkers will be viable to disrupt the BBB and stay longer to be modeled for kinetics for diagnosis and therefore may help in the discoveries of new biomarkers.
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Affiliation(s)
- Celeste Hicks
- Biomedical, Industrial and Human Factors Engineering, Wright State University, 3640 Col. Glen Hwy, Dayton, OH 45435, USA
| | - Akshima Dhiman
- Boonshoft School of Medicine, Wright State University, 3640 Col. Glen Hwy, Dayton, OH 45435, USA
| | - Chauntel Barrymore
- Boonshoft School of Medicine, Wright State University, 3640 Col. Glen Hwy, Dayton, OH 45435, USA
| | - Tarun Goswami
- Biomedical, Industrial and Human Factors Engineering, Wright State University, 3640 Col. Glen Hwy, Dayton, OH 45435, USA
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Schranner D, Schönfelder M, Römisch‐Margl W, Scherr J, Schlegel J, Zelger O, Riermeier A, Kaps S, Prehn C, Adamski J, Söhnlein Q, Stöcker F, Kreuzpointner F, Halle M, Kastenmüller G, Wackerhage H. Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes. Physiol Rep 2021; 9:e14885. [PMID: 34152092 PMCID: PMC8215680 DOI: 10.14814/phy2.14885] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/01/2021] [Accepted: 05/04/2021] [Indexed: 12/17/2022] Open
Abstract
Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.
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Affiliation(s)
- Daniela Schranner
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Martin Schönfelder
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | | | - Johannes Scherr
- University Center for Prevention and Sports MedicineUniversity Hospital BalgristUniversität ZürichZurichSwitzerland
| | - Jürgen Schlegel
- Department of NeuropathologyInstitute of PathologyTechnische Universität MünchenMunichGermany
| | - Otto Zelger
- Department of Prevention and Sports MedicineTechnische Universität MünchenMunichGermany
| | - Annett Riermeier
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Stephanie Kaps
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology and MetabolismHelmholtz Zentrum MünchenNeuherbergGermany
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and MetabolismHelmholtz Zentrum MünchenNeuherbergGermany
- German Center for Diabetes ResearchNeuherbergGermany
- Chair of Experimental GeneticsTechnische Universität MünchenFreising‐WeihenstephanGermany
- Department of BiochemistryYong Loo Lin School of MedicineNational University of SingaporeSingapore
| | - Quirin Söhnlein
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Fabian Stöcker
- Teaching and Educational LabDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Florian Kreuzpointner
- Prevention CenterDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Martin Halle
- Department of Prevention and Sports MedicineTechnische Universität MünchenMunichGermany
| | - Gabi Kastenmüller
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- German Center for Diabetes ResearchNeuherbergGermany
| | - Henning Wackerhage
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
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Kistner S, Rist MJ, Döring M, Dörr C, Neumann R, Härtel S, Bub A. An NMR-Based Approach to Identify Urinary Metabolites Associated with Acute Physical Exercise and Cardiorespiratory Fitness in Healthy Humans-Results of the KarMeN Study. Metabolites 2020; 10:metabo10050212. [PMID: 32455749 PMCID: PMC7281079 DOI: 10.3390/metabo10050212] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/15/2020] [Accepted: 05/19/2020] [Indexed: 12/19/2022] Open
Abstract
Knowledge on metabolites distinguishing the metabolic response to acute physical exercise between fit and less fit individuals could clarify mechanisms and metabolic pathways contributing to the beneficial adaptations to exercise. By analyzing data from the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study, we characterized the acute effects of a standardized exercise tolerance test on urinary metabolites of 255 healthy women and men. In a second step, we aimed to detect a urinary metabolite pattern associated with the cardiorespiratory fitness (CRF), which was determined by measuring the peak oxygen uptake (VO2peak) during incremental exercise. Spot urine samples were collected pre- and post-exercise and 47 urinary metabolites were identified by nuclear magnetic resonance (NMR) spectroscopy. While the univariate analysis of pre-to-post-exercise differences revealed significant alterations in 37 urinary metabolites, principal component analysis (PCA) did not show a clear separation of the pre- and post-exercise urine samples. Moreover, both bivariate correlation and multiple linear regression analyses revealed only weak relationships between the VO2peak and single urinary metabolites or urinary metabolic pattern, when adjusting for covariates like age, sex, menopausal status, and lean body mass (LBM). Taken as a whole, our results show that several urinary metabolites (e.g., lactate, pyruvate, alanine, and acetate) reflect acute exercise-induced alterations in the human metabolism. However, as neither pre- and post-exercise levels nor the fold changes of urinary metabolites substantially accounted for the variation of the covariate-adjusted VO2peak, our results furthermore indicate that the urinary metabolites identified in this study do not allow to draw conclusions on the individual's physical fitness status. Studies investigating the relationship between the human metabolome and functional variables like the CRF should adjust for confounders like age, sex, menopausal status, and LBM.
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Affiliation(s)
- Sina Kistner
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany; (R.N.); (S.H.); (A.B.)
- Correspondence: ; Tel.: +49-721-608-46981
| | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, 76131 Karlsruhe, Germany; (M.J.R.); (M.D.); (C.D.)
| | - Maik Döring
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, 76131 Karlsruhe, Germany; (M.J.R.); (M.D.); (C.D.)
| | - Claudia Dörr
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, 76131 Karlsruhe, Germany; (M.J.R.); (M.D.); (C.D.)
| | - Rainer Neumann
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany; (R.N.); (S.H.); (A.B.)
| | - Sascha Härtel
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany; (R.N.); (S.H.); (A.B.)
| | - Achim Bub
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany; (R.N.); (S.H.); (A.B.)
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, 76131 Karlsruhe, Germany; (M.J.R.); (M.D.); (C.D.)
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Leskanicova A, Chovancova O, Babincak M, Verboova L, Benetinova Z, Macekova D, Kostolny J, Smajda B, Kiskova T. Sexual Dimorphism in Energy Metabolism of Wistar Rats Using Data Analysis. Molecules 2020; 25:molecules25102353. [PMID: 32443550 PMCID: PMC7287681 DOI: 10.3390/molecules25102353] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/06/2020] [Accepted: 05/11/2020] [Indexed: 12/25/2022] Open
Abstract
The prevalence of some chronic diseases, such as cancer or neurodegenerative disorders, differs between sexes. Animal models provide an important tool to adopt potential therapies from preclinical studies to humans. Laboratory rats are the most popular animals in toxicology, neurobehavioral, or cancer research. Our study aimed to reveal the basic differences in blood metabolome (amino acids, biogenic amines, and acylcarnitines) of the adult male (n = 10) and female (n = 10) Wistar rats. Partial least square-discrimination analysis (PLS-DA) and a variance im portance in projection (VIP) score was used to identify the key sex-specific metabolites. All groups of metabolites, as the main markers of energy metabolism, showed a significant sex-dependent pattern. The most important features calculated in PLS-DA according to VIP score were free carnitine (C0), tyrosine (Tyr), and acylcarnitine C5-OH. While aromatic amino acids, such as Tyr and phenylalanine (Phe), were significantly elevated in the blood plasma of males, tryptophan (Trp) was found in higher levels in the blood plasma of females. Besides, significant sex-related changes in urea cycle were found. Our study provides an important insight into sex-specific differences in energy metabolism in rats and indicates that further studies should consider sex as the main aspect in design and data interpretation.
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Affiliation(s)
- Andrea Leskanicova
- Institute of Biology and Ecology, Faculty of Sciences, University of Pavol Jozef Šafárik in Košice, Šrobárova 2, 041 80 Košice, Slovakia; (A.L.); (M.B.)
| | - Olga Chovancova
- Department of Informatics, Faculty of Management Sciences and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia; (O.C.); (D.M.); (J.K.)
| | - Marian Babincak
- Institute of Biology and Ecology, Faculty of Sciences, University of Pavol Jozef Šafárik in Košice, Šrobárova 2, 041 80 Košice, Slovakia; (A.L.); (M.B.)
| | - Ludmila Verboova
- Department of Pathology, Faculty of Medicine, University of Pavol Jozef Šafárik in Košice, Rastislavova 43, 040 01 Košice, Slovakia; (L.V.); (Z.B.)
| | - Zuzana Benetinova
- Department of Pathology, Faculty of Medicine, University of Pavol Jozef Šafárik in Košice, Rastislavova 43, 040 01 Košice, Slovakia; (L.V.); (Z.B.)
| | - Denisa Macekova
- Department of Informatics, Faculty of Management Sciences and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia; (O.C.); (D.M.); (J.K.)
| | - Jozef Kostolny
- Department of Informatics, Faculty of Management Sciences and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia; (O.C.); (D.M.); (J.K.)
| | - Benadik Smajda
- Institute of Biology and Ecology, Faculty of Sciences, University of Pavol Jozef Šafárik in Košice, Šrobárova 2, 041 80 Košice, Slovakia; (A.L.); (M.B.)
- Correspondence: (B.S.); (T.K.); Tel.: +421-55-234-1216 (T.K.); Fax: +421-55-622-2124 (T.K.)
| | - Terezia Kiskova
- Institute of Biology and Ecology, Faculty of Sciences, University of Pavol Jozef Šafárik in Košice, Šrobárova 2, 041 80 Košice, Slovakia; (A.L.); (M.B.)
- Correspondence: (B.S.); (T.K.); Tel.: +421-55-234-1216 (T.K.); Fax: +421-55-622-2124 (T.K.)
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Genetic Networks Underlying Natural Variation in Basal and Induced Activity Levels in Drosophila melanogaster. G3-GENES GENOMES GENETICS 2020; 10:1247-1260. [PMID: 32014853 PMCID: PMC7144082 DOI: 10.1534/g3.119.401034] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Exercise is recommended by health professionals across the globe as part of a healthy lifestyle to prevent and/or treat the consequences of obesity. While overall, the health benefits of exercise and an active lifestyle are well understood, very little is known about how genetics impacts an individual's inclination for and response to exercise. To address this knowledge gap, we investigated the genetic architecture underlying natural variation in activity levels in the model system Drosophila melanogaster Activity levels were assayed in the Drosophila Genetics Reference Panel fly strains at baseline and in response to a gentle exercise treatment using the Rotational Exercise Quantification System. We found significant, sex-dependent variation in both activity measures and identified over 100 genes that contribute to basal and induced exercise activity levels. This gene set was enriched for genes with functions in the central nervous system and in neuromuscular junctions and included several candidate genes with known activity phenotypes such as flightlessness or uncoordinated movement. Interestingly, there were also several chromatin proteins among the candidate genes, two of which were validated and shown to impact activity levels. Thus, the study described here reveals the complex genetic architecture controlling basal and exercise-induced activity levels in D. melanogaster and provides a resource for exercise biologists.
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Schranner D, Kastenmüller G, Schönfelder M, Römisch-Margl W, Wackerhage H. Metabolite Concentration Changes in Humans After a Bout of Exercise: a Systematic Review of Exercise Metabolomics Studies. SPORTS MEDICINE-OPEN 2020; 6:11. [PMID: 32040782 PMCID: PMC7010904 DOI: 10.1186/s40798-020-0238-4] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 01/20/2020] [Indexed: 12/27/2022]
Abstract
Background Exercise changes the concentrations of many metabolites, which are small molecules (< 1.5 kDa) metabolized by the reactions of human metabolism. In recent years, especially mass spectrometry-based metabolomics methods have allowed researchers to measure up to hundreds of metabolites in a single sample in a non-biased fashion. To summarize human exercise metabolomics studies to date, we conducted a systematic review that reports the results of experiments that found metabolite concentrations changes after a bout of human endurance or resistance exercise. Methods We carried out a systematic review following PRISMA guidelines and searched for human metabolomics studies that report metabolite concentrations before and within 24 h after endurance or resistance exercise in blood, urine, or sweat. We then displayed metabolites that significantly changed their concentration in at least two experiments. Results Twenty-seven studies and 57 experiments matched our search criteria and were analyzed. Within these studies, 196 metabolites changed their concentration significantly within 24 h after exercise in at least two experiments. Human biofluids contain mainly unphosphorylated metabolites as the phosphorylation of metabolites such as ATP, glycolytic intermediates, or nucleotides traps these metabolites within cells. Lactate, pyruvate, TCA cycle intermediates, fatty acids, acylcarnitines, and ketone bodies all typically increase after exercise, whereas bile acids decrease. In contrast, the concentrations of proteinogenic and non-proteinogenic amino acids change in different directions. Conclusion Across different exercise modes and in different subjects, exercise often consistently changes the average concentrations of metabolites that belong to energy metabolism and other branches of metabolism. This dataset is a useful resource for those that wish to study human exercise metabolism.
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Affiliation(s)
- Daniela Schranner
- Exercise Biology Group, Department of Sport and Health Sciences, Technische Universität München, Munich, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martin Schönfelder
- Exercise Biology Group, Department of Sport and Health Sciences, Technische Universität München, Munich, Germany
| | - Werner Römisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Henning Wackerhage
- Exercise Biology Group, Department of Sport and Health Sciences, Technische Universität München, Munich, Germany.
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Watanabe LP, Riddle NC. Characterization of the Rotating Exercise Quantification System (REQS), a novel Drosophila exercise quantification apparatus. PLoS One 2017; 12:e0185090. [PMID: 29016615 PMCID: PMC5634558 DOI: 10.1371/journal.pone.0185090] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 09/06/2017] [Indexed: 02/06/2023] Open
Abstract
Obesity is a disease that has reached epidemic proportions in the United States and has prompted international legislation in an attempt to curtail its prevalence. Despite the fact that one of the most prescribed treatment options for obesity is exercise, the genetic mechanisms underlying exercise response in individuals are still largely unknown. The fruit fly Drosophila melanogaster is a promising new model for studying exercise genetics. Currently, the lack of an accurate method to quantify the amount of exercise performed by the animals is limiting the utility of the Drosophila model for exercise genetics research. To address this limitation, we developed the Rotational Exercise Quantification System (REQS), a novel apparatus that is able to simultaneously induce exercise in flies while recording their activity levels. Thus, the REQS provides a method to standardize Drosophila exercise and ensure that all animals irrespective of genotype and sex experience the same level of exercise. Here, we provide a basic characterization of the REQS, validate its measurements using video-tracking technology, illustrate its potential use by presenting a comparison of two different exercise regimes, and demonstrate that it can be used to detect genotype-dependent variation in activity levels.
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Affiliation(s)
- Louis Patrick Watanabe
- Department of Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Nicole C. Riddle
- Department of Biology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- * E-mail:
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Baumgartner C. The Era of Big Data: From Data-Driven Research to Data-Driven Clinical Care. TRANSLATIONAL BIOINFORMATICS 2016. [DOI: 10.1007/978-94-017-7543-4_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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