An age-dependent mathematical model of neurofilament trafficking in healthy conditions.

Alessio Paris, Pranami Bora, Silvia Parolo, Michael Monine, Xiao Tong, Satish Eraly, Eric Masson, Toby Ferguson, Alexander McCampbell, Danielle Graham, Enrico Domenici, Ivan Nestorov, Luca Marchetti
Author Information
  1. Alessio Paris: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
  2. Pranami Bora: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
  3. Silvia Parolo: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
  4. Michael Monine: Biogen, Inc., Cambridge, Massachusetts, USA.
  5. Xiao Tong: Biogen, Inc., Cambridge, Massachusetts, USA.
  6. Satish Eraly: Biogen, Inc., Cambridge, Massachusetts, USA.
  7. Eric Masson: Biogen, Inc., Cambridge, Massachusetts, USA.
  8. Toby Ferguson: Biogen, Inc., Cambridge, Massachusetts, USA.
  9. Alexander McCampbell: Biogen, Inc., Cambridge, Massachusetts, USA.
  10. Danielle Graham: Biogen, Inc., Cambridge, Massachusetts, USA.
  11. Enrico Domenici: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
  12. Ivan Nestorov: Biogen, Inc., Cambridge, Massachusetts, USA.
  13. Luca Marchetti: Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.

Abstract

Neurofilaments (Nfs) are the major structural component of neurons. Their role as a potential biomarker of several neurodegenerative diseases has been investigated in past years with promising results. However, even under physiological conditions, little is known about the leaking of Nfs from the neuronal system and their detection in the cerebrospinal fluid (CSF) and blood. This study aimed at developing a mathematical model of Nf transport in healthy subjects in the 20-90 age range. The model was implemented as a set of ordinary differential equations describing the trafficking of Nfs from the nervous system to the periphery. Model parameters were calibrated on typical Nf levels obtained from the literature. An age-dependent function modeled on CSF data was also included and validated on data measured in serum. We computed a global sensitivity analysis of model rates and volumes to identify the most sensitive parameters affecting the model's steady state. Age, Nf synthesis, and degradation rates proved to be relevant for all model variables. Nf levels in the CSF and in blood were observed to be sensitive to the Nf leakage rates from neurons and to the blood clearance rate, and CSF levels were also sensitive to rates representing CSF turnover. An additional parameter perturbation analysis was also performed to investigate possible transient effects on the model variables not captured by the sensitivity analysis. The model provides useful insights into Nf transport and constitutes the basis for implementing quantitative system pharmacology extensions to investigate Nf trafficking in neurodegenerative diseases.

References

  1. Neurobiol Dis. 2010 Jan;37(1):13-25 [PMID: 19664713]
  2. J Neuroimmunol. 1987 Mar;14(2):149-60 [PMID: 3029175]
  3. Front Neurol. 2019 Jan 18;9:1167 [PMID: 30713520]
  4. Neurology. 2017 Jun 13;88(24):2302-2309 [PMID: 28500227]
  5. J Neuroinflammation. 2018 Jul 18;15(1):209 [PMID: 30021640]
  6. IET Syst Biol. 2011 Nov;5(6):336-6 [PMID: 22129029]
  7. JAMA Neurol. 2017 May 1;74(5):557-566 [PMID: 28346578]
  8. Curr Opin Cell Biol. 2021 Feb;68:181-191 [PMID: 33454158]
  9. J Neurosci Res. 2000 Aug 1;61(3):338-49 [PMID: 10900081]
  10. Nat Commun. 2020 Feb 10;11(1):812 [PMID: 32041951]
  11. JAMA Neurol. 2016 Jan;73(1):60-7 [PMID: 26524180]
  12. Cold Spring Harb Perspect Biol. 2017 Apr 3;9(4): [PMID: 28373358]
  13. Clin Chem Lab Med. 2019 Sep 25;57(10):1556-1564 [PMID: 31251725]
  14. Clin Chem Lab Med. 2015 Sep 1;53(10):1575-84 [PMID: 25720124]
  15. Ann Clin Transl Neurol. 2019 Apr 17;6(5):932-944 [PMID: 31139691]
  16. Clin Chem Lab Med. 2016 Oct 1;54(10):1655-61 [PMID: 27071153]
  17. J Neurosci. 2012 Jan 11;32(2):746-58 [PMID: 22238110]
  18. Brief Bioinform. 2020 Mar 23;21(2):527-540 [PMID: 30753281]
  19. J Neurosci. 2009 Sep 9;29(36):11316-29 [PMID: 19741138]
  20. Brain. 2018 Aug 1;141(8):2382-2391 [PMID: 29860296]
  21. J Neurol Neurosurg Psychiatry. 2013 Feb;84(2):208-12 [PMID: 23243216]
  22. Lancet Neurol. 2017 Aug;16(8):601-609 [PMID: 28601473]
  23. Expert Rev Mol Diagn. 2017 Aug;17(8):761-770 [PMID: 28598205]
  24. Mol Psychiatry. 2015 Aug;20(8):986-94 [PMID: 25869803]
  25. CPT Pharmacometrics Syst Pharmacol. 2022 Apr;11(4):447-457 [PMID: 35146969]
  26. PLoS One. 2013 Sep 20;8(9):e75091 [PMID: 24073237]
  27. Sci Rep. 2018 Nov 26;8(1):17368 [PMID: 30478269]
  28. J Cell Sci. 2004 Feb 29;117(Pt 6):861-9 [PMID: 14762113]
  29. Neurology. 2018 May 15;90(20):e1780-e1788 [PMID: 29653990]
  30. Nat Rev Neurol. 2018 Oct;14(10):577-589 [PMID: 30171200]
  31. J Neurol Neurosurg Psychiatry. 2018 Apr;89(4):367-373 [PMID: 29054919]
  32. Neurology. 2017 May 9;88(19):1788-1794 [PMID: 28404801]
  33. N Engl J Med. 2020 Jul 9;383(2):109-119 [PMID: 32640130]
  34. PLoS Comput Biol. 2015 Aug 18;11(8):e1004406 [PMID: 26285012]
  35. Mol Neurobiol. 2008 Aug;38(1):27-65 [PMID: 18649148]

MeSH Term

Biomarkers
Humans
Intermediate Filaments
Models, Theoretical
Neurodegenerative Diseases
Neurofilament Proteins

Chemicals

Biomarkers
Neurofilament Proteins

Word Cloud

Created with Highcharts 10.0.0modelNfCSFratesNfssystembloodtraffickinglevelsalsoanalysissensitiveneuronsneurodegenerativediseasesconditionsmathematicaltransporthealthyparametersage-dependentdatasensitivityvariablesinvestigateNeurofilamentsmajorstructuralcomponentrolepotentialbiomarkerseveralinvestigatedpastyearspromisingresultsHoweverevenphysiologicallittleknownleakingneuronaldetectioncerebrospinalfluidstudyaimeddevelopingsubjects20-90agerangeimplementedsetordinarydifferentialequationsdescribingnervousperipheryModelcalibratedtypicalobtainedliteraturefunctionmodeledincludedvalidatedmeasuredserumcomputedglobalvolumesidentifyaffectingmodel'ssteadystateAgesynthesisdegradationprovedrelevantobservedleakageclearanceraterepresentingturnoveradditionalparameterperturbationperformedpossibletransienteffectscapturedprovidesusefulinsightsconstitutesbasisimplementingquantitativepharmacologyextensionsneurofilament

Similar Articles

Cited By