skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

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 dependency of the human red blood cell (RBC) metabolic network between 4C and 37C through absolutely quantified exo- and endo-metabolomics data. We used an Arrhenius-type model ( Q 10) 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 Q 10 coefficient of 2.89 + 1.03 for 48 individual metabolite concentrations, within the expected range of 2-3 for biological processes. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q 10 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 Q 10 coefficients of individual metabolites andmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4];  [5]; ORCiD logo [6];  [7]; ORCiD logo [4];  [4];  [8];  [9];  [10]
  1. Univ. of California San Diego, La Jolla, CA (United States). Dept. of Bioengineering, and Bioinformatics and Systems Biology Program
  2. Univ. of California San Diego, La Jolla, CA (United States). Dept. of Bioengineering
  3. EURAC Research, Bolzano (Italy). Inst. for Biomedicine
  4. Univ. of Iceland, Reykjavík (Iceland). Center for Systems Biology
  5. Landspítali-Univ. Hospital, Reykjavík (Iceland). The Blood Bank; Reykjavík Univ., Reykjavík (Iceland). School of Science and Engineering
  6. Univ. of California San Diego, La Jolla, CA (United States). Dept. of Bioengineering; Univ. of California San Diego, La Jolla, CA (United States). Division of Biological Sciences
  7. Sinopia Biosciences, San Diego, CA (United States)
  8. Univ. of Iceland, Reykjavík (Iceland). Center for Systems Biology; Sinopia Biosciences, San Diego, CA (United States)
  9. Landspítali-Univ. Hospital, Reykjavík (Iceland). The Blood Bank
  10. Univ. of California San Diego, La Jolla, CA (United States). Dept. of Bioengineering, and Bioinformatics and Systems Biology Program; Univ. of Iceland, Reykjavík (Iceland). Center for Systems Biology; Univ. of California San Diego, La Jolla, CA (United States). Dept. of Pediatrics; Technical Univ. of Denmark, Lyngby (Denmark). Novo Nordisk Foundation Center for Biosustainability
Publication Date:
Research Org.:
Univ. of California, San Diego, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1540307
Grant/Contract Number:  
[SC0008701]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Biological Chemistry
Additional Journal Information:
[ Journal Volume: 292; Journal Issue: 48]; Journal ID: ISSN 0021-9258
Publisher:
American Society for Biochemistry and Molecular Biology
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; biochemistry & molecular biology; computational biology; erythrocyte; metabolism; metabolomics; systems biology

Citation Formats

Yurkovich, James T., Zielinski, Daniel C., Yang, Laurence, Paglia, Giuseppe, Rolfsson, Ottar, Sigurjónsson, Ólafur E., Broddrick, Jared T., Bordbar, Aarash, Wichuk, Kristine, Brynjólfsson, Sigurður, Palsson, Sirus, Gudmundsson, Sveinn, and Palsson, Bernhard O. Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks. United States: N. p., 2017. Web. doi:10.1074/jbc.m117.804914.
Yurkovich, James T., Zielinski, Daniel C., Yang, Laurence, Paglia, Giuseppe, Rolfsson, Ottar, Sigurjónsson, Ólafur E., Broddrick, Jared T., Bordbar, Aarash, Wichuk, Kristine, Brynjólfsson, Sigurður, Palsson, Sirus, Gudmundsson, Sveinn, & Palsson, Bernhard O. Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks. United States. doi:10.1074/jbc.m117.804914.
Yurkovich, James T., Zielinski, Daniel C., Yang, Laurence, Paglia, Giuseppe, Rolfsson, Ottar, Sigurjónsson, Ólafur E., Broddrick, Jared T., Bordbar, Aarash, Wichuk, Kristine, Brynjólfsson, Sigurður, Palsson, Sirus, Gudmundsson, Sveinn, and Palsson, Bernhard O. Fri . "Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks". United States. doi:10.1074/jbc.m117.804914. https://www.osti.gov/servlets/purl/1540307.
@article{osti_1540307,
title = {Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks},
author = {Yurkovich, James T. and Zielinski, Daniel C. and Yang, Laurence and Paglia, Giuseppe and Rolfsson, Ottar and Sigurjónsson, Ólafur E. and Broddrick, Jared T. and Bordbar, Aarash and Wichuk, Kristine and Brynjólfsson, Sigurður and Palsson, Sirus and Gudmundsson, Sveinn and Palsson, Bernhard O.},
abstractNote = {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 dependency of the human red blood cell (RBC) metabolic network between 4C and 37C through absolutely quantified exo- and endo-metabolomics 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 for 48 individual metabolite concentrations, within the expected range of 2-3 for biological processes. 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.},
doi = {10.1074/jbc.m117.804914},
journal = {Journal of Biological Chemistry},
number = [48],
volume = [292],
place = {United States},
year = {2017},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 14 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Biomarkers defining the metabolic age of red blood cells during cold storage
journal, September 2016


Temperature Coefficient (Q10), Seed Germination and Other Biological Processes
journal, June 1973


Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells
journal, March 2017


Prolonged red cell storage before transfusion increases extravascular hemolysis
journal, December 2016

  • Rapido, Francesca; Brittenham, Gary M.; Bandyopadhyay, Sheila
  • Journal of Clinical Investigation, Vol. 127, Issue 1
  • DOI: 10.1172/JCI90837

Stored blood: how old is too old?
journal, December 2016

  • Lee, Janet S.; Kim-Shapiro, Daniel B.
  • Journal of Clinical Investigation, Vol. 127, Issue 1
  • DOI: 10.1172/JCI91309

Utilizing biomarkers to forecast quantitative metabolite concentration profiles in human red blood cells
conference, August 2017

  • Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.
  • 2017 IEEE Conference on Control Technology and Applications (CCTA)
  • DOI: 10.1109/CCTA.2017.8062584

The universality of enzymatic rate–temperature dependency
journal, January 2014

  • Elias, Mikael; Wieczorek, Grzegorz; Rosenne, Shaked
  • Trends in Biochemical Sciences, Vol. 39, Issue 1
  • DOI: 10.1016/j.tibs.2013.11.001

The development of the Arrhenius equation
journal, June 1984

  • Laidler, Keith J.
  • Journal of Chemical Education, Vol. 61, Issue 6
  • DOI: 10.1021/ed061p494

METLIN: A Metabolite Mass Spectral Database
journal, January 2005


F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks
journal, April 2012

  • Larhlimi, Abdelhalim; David, Laszlo; Selbig, Joachim
  • BMC Bioinformatics, Vol. 13, Issue 1
  • DOI: 10.1186/1471-2105-13-57

Control and regulation of the cellular responses to cold shock: the responses in yeast and mammalian systems
journal, June 2006

  • Al-Fageeh, Mohamed B.; Smales, C. Mark
  • Biochemical Journal, Vol. 397, Issue 2
  • DOI: 10.1042/BJ20060166

An update on red blood cell storage lesions, as gleaned through biochemistry and omics technologies: An omics update on RBC storage
journal, August 2014

  • D'Alessandro, Angelo; Kriebardis, Anastasios G.; Rinalducci, Sara
  • Transfusion, Vol. 55, Issue 1
  • DOI: 10.1111/trf.12804

Metabolomics of ADSOL (AS-1) Red Blood Cell Storage
journal, April 2014

  • Roback, John D.; Josephson, Cassandra D.; Waller, Edmund K.
  • Transfusion Medicine Reviews, Vol. 28, Issue 2
  • DOI: 10.1016/j.tmrv.2014.01.003

Metabolic and cardiovascular adjustments of juvenile green turtles to seasonal changes in temperature and photoperiod
journal, December 2003

  • Southwood, A. L.
  • Journal of Experimental Biology, Vol. 206, Issue 24
  • DOI: 10.1242/jeb.00689

Effects of Size and Temperature on Metabolic Rate
journal, September 2001


Why does metabolism scale with temperature?
journal, April 2004


Enzyme Kinetics and the rate of Biological Processes
journal, November 1950


Red Cell Preservation in Protein-Poor Media: III. Protection Against in vitro Hemolysis
journal, November 1981


Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics
journal, April 2017

  • Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe
  • Scientific Reports, Vol. 7, Issue 1
  • DOI: 10.1038/srep46249

Enzymes of the normothermic and hibernating bat,Myotis lucifugus: Temperature as a modulator of pyruvate kinase
journal, January 1976

  • Borgmann, Anne I.; Moon, Thomas W.
  • Journal of Comparative Physiology ? B, Vol. 107, Issue 2
  • DOI: 10.1007/BF00691225

Rates of biotic interactions scale predictably with temperature despite variation
journal, May 2014

  • Burnside, William R.; Erhardt, Erik B.; Hammond, Sean T.
  • Oikos, Vol. 123, Issue 12
  • DOI: 10.1111/oik.01199

The Temperature Response of CO2 Production from Bulk Soils and Soil Fractions is Related to Soil Organic Matter Quality
journal, September 2005


Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics
journal, October 2015


Plant distribution and the temperature coefficient of metabolism
journal, March 1994


Temperature Coefficients in Biology
journal, January 1930


Temperature effects on energy metabolism: a dynamic system analysis
journal, January 2002

  • Chaui-Berlinck, José Guilherme; Alves Monteiro, Luiz Henrique; Navas, Carlos Arturo
  • Proceedings of the Royal Society of London. Series B: Biological Sciences, Vol. 269, Issue 1486
  • DOI: 10.1098/rspb.2001.1845

HMDB 3.0—The Human Metabolome Database in 2013
journal, November 2012

  • Wishart, David S.; Jewison, Timothy; Guo, An Chi
  • Nucleic Acids Research, Vol. 41, Issue D1
  • DOI: 10.1093/nar/gks1065

Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions
journal, February 2004


Routine storage of red blood cell (RBC) units in additive solution-3: a comprehensive investigation of the RBC metabolome: Metabolomics of AS-3 RBCs
journal, December 2014

  • D'Alessandro, Angelo; Nemkov, Travis; Kelher, Marguerite
  • Transfusion, Vol. 55, Issue 6
  • DOI: 10.1111/trf.12975

iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states
journal, January 2011

  • Bordbar, Aarash; Jamshidi, Neema; Palsson, Bernhard O.
  • BMC Systems Biology, Vol. 5, Issue 1
  • DOI: 10.1186/1752-0509-5-110

Comprehensive metabolomic study of platelets reveals the expression of discrete metabolic phenotypes during storage: Metabolic Properties of Stored PLTs
journal, May 2014

  • Paglia, Giuseppe; Sigurjónsson, Ólafur E.; Rolfsson, Óttar
  • Transfusion, Vol. 54, Issue 11
  • DOI: 10.1111/trf.12710

Hitchhiker's guide to the red cell storage galaxy: Omics technologies and the quality issue
journal, April 2017