Literature DB >> 29030425

Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

James T Yurkovich1,2, Daniel C Zielinski1, Laurence Yang1, Giuseppe Paglia3, Ottar Rolfsson4, Ólafur E Sigurjónsson5,6, Jared T Broddrick1,7, Aarash Bordbar8, Kristine Wichuk4, Sigurður Brynjólfsson4, Sirus Palsson4,8, Sveinn Gudmundsson5, Bernhard O Palsson9,2,4,10.   

Abstract

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.
© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Entities:  

Keywords:  computational biology; erythrocyte; metabolism; metabolomics; systems biology

Mesh:

Year:  2017        PMID: 29030425      PMCID: PMC5712598          DOI: 10.1074/jbc.M117.804914

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  26 in total

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Authors:  Patricia M Schulte
Journal:  J Exp Biol       Date:  2015-06       Impact factor: 3.312

2.  Comprehensive metabolomic study of platelets reveals the expression of discrete metabolic phenotypes during storage.

Authors:  Giuseppe Paglia; Ólafur E Sigurjónsson; Óttar Rolfsson; Soley Valgeirsdottir; Morten Bagge Hansen; Sigurður Brynjólfsson; Sveinn Gudmundsson; Bernhard O Palsson
Journal:  Transfusion       Date:  2014-05-19       Impact factor: 3.157

Review 3.  An update on red blood cell storage lesions, as gleaned through biochemistry and omics technologies.

Authors:  Angelo D'Alessandro; Anastasios G Kriebardis; Sara Rinalducci; Marianna H Antonelou; Kirk C Hansen; Issidora S Papassideri; Lello Zolla
Journal:  Transfusion       Date:  2014-08-06       Impact factor: 3.157

4.  Stored blood: how old is too old?

Authors:  Janet S Lee; Daniel B Kim-Shapiro
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5.  Biomarkers defining the metabolic age of red blood cells during cold storage.

Authors:  Giuseppe Paglia; Angelo D'Alessandro; Óttar Rolfsson; Ólafur E Sigurjónsson; Aarash Bordbar; Sirus Palsson; Travis Nemkov; Kirk C Hansen; Sveinn Gudmundsson; Bernhard O Palsson
Journal:  Blood       Date:  2016-08-23       Impact factor: 22.113

6.  Routine storage of red blood cell (RBC) units in additive solution-3: a comprehensive investigation of the RBC metabolome.

Authors:  Angelo D'Alessandro; Travis Nemkov; Marguerite Kelher; F Bernadette West; Rani K Schwindt; Anirban Banerjee; Ernest E Moore; Christopher C Silliman; Kirk C Hansen
Journal:  Transfusion       Date:  2014-12-30       Impact factor: 3.157

7.  Prolonged red cell storage before transfusion increases extravascular hemolysis.

Authors:  Francesca Rapido; Gary M Brittenham; Sheila Bandyopadhyay; Francesca La Carpia; Camilla L'Acqua; Donald J McMahon; Abdelhadi Rebbaa; Boguslaw S Wojczyk; Jane Netterwald; Hangli Wang; Joseph Schwartz; Andrew Eisenberger; Mark Soffing; Randy Yeh; Chaitanya Divgi; Yelena Z Ginzburg; Beth H Shaz; Sujit Sheth; Richard O Francis; Steven L Spitalnik; Eldad A Hod
Journal:  J Clin Invest       Date:  2016-12-12       Impact factor: 14.808

8.  Metabolic and cardiovascular adjustments of juvenile green turtles to seasonal changes in temperature and photoperiod.

Authors:  Amanda L Southwood; Charles A Darveau; David R Jones
Journal:  J Exp Biol       Date:  2003-12       Impact factor: 3.312

Review 9.  Control and regulation of the cellular responses to cold shock: the responses in yeast and mammalian systems.

Authors:  Mohamed B Al-Fageeh; C Mark Smales
Journal:  Biochem J       Date:  2006-07-15       Impact factor: 3.857

10.  Enzyme kinetics and the rate of biological processes.

Authors:  J L KAVANAU
Journal:  J Gen Physiol       Date:  1950-11       Impact factor: 4.086

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2.  Effects of red blood cell (RBC) transfusion on sickle cell disease recipient plasma and RBC metabolism.

Authors:  Rachel Culp-Hill; Amudan J Srinivasan; Sarah Gehrke; Reed Kamyszek; Andrea Ansari; Nirmish Shah; Ian Welsby; Angelo D'Alessandro
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Authors:  Davide Stefanoni; Xiaoyun Fu; Julie A Reisz; Tamir Kanias; Travis Nemkov; Grier P Page; Larry Dumont; Nareg Roubinian; Mars Stone; Steve Kleinman; Michael Busch; James C Zimring; Angelo D'Alessandro
Journal:  Transfusion       Date:  2020-05-08       Impact factor: 3.157

4.  Ethyl glucuronide, a marker of alcohol consumption, correlates with metabolic markers of oxidant stress but not with hemolysis in stored red blood cells from healthy blood donors.

Authors:  Angelo D'Alessandro; Xiaoyun Fu; Julie A Reisz; Mars Stone; Steve Kleinman; James C Zimring; Michael Busch
Journal:  Transfusion       Date:  2020-05-08       Impact factor: 3.157

5.  Heterogeneity of blood processing and storage additives in different centers impacts stored red blood cell metabolism as much as storage time: lessons from REDS-III-Omics.

Authors:  Angelo D'Alessandro; Rachel Culp-Hill; Julie A Reisz; Mikayla Anderson; Xiaoyun Fu; Travis Nemkov; Sarah Gehrke; Connie Zheng; Tamir Kanias; Yuelong Guo; Grier Page; Mark T Gladwin; Steve Kleinman; Marion Lanteri; Mars Stone; Michael Busch; James C Zimring
Journal:  Transfusion       Date:  2018-10-24       Impact factor: 3.157

6.  Decoding the metabolic landscape of pathophysiological stress-induced cell death in anucleate red blood cells.

Authors:  Travis Nemkov; Syed M Qadri; William P Sheffield; Angelo D'Alessandro
Journal:  Blood Transfus       Date:  2020-02-28       Impact factor: 3.443

7.  Metabolic impact of red blood cell exchange with rejuvenated red blood cells in sickle cell patients.

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8.  Hypoxic storage of red blood cells improves metabolism and post-transfusion recovery.

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Review 9.  Systems biology as an emerging paradigm in transfusion medicine.

Authors:  James T Yurkovich; Aarash Bordbar; Ólafur E Sigurjónsson; Bernhard O Palsson
Journal:  BMC Syst Biol       Date:  2018-03-07

10.  NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.

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