Revealing the dynamics of immune cells in

humans: mechanistic modelling of deuterium labelling data

Julio Jose Lahoz Beneytez, PhD

Introduction - Deuterium labelling

Key properties:

• It goes where hydrogen goes

• It is stable

• Can be measured

• Not known to be toxic

Compounds:

• 2H2O – heavy water

• [6,6-2H2]-glucose – heavy glucose

Neese et al. 2002, Proc Nat Acad Sci

Asquith and Borghans 2011

Introduction: a protocol of deuterium

labelling experiments (and its interpretation)

Macallan et al. 2009

Asquith et al. 2002

p= proliferation rate

z= loss rate

bw/g= scaling factor

Ut= label availability function

L= enrichment in DNA (%)

Am

ou

nt o

f la

be

l in

DN

A

𝑑𝐿

𝑑𝑡= 𝑝 ∙ 𝑏𝑤

𝑔 ∙ 𝑈𝑡 − 𝑧 ∙ 𝐿

Motivation

Deuterium labelling techniques have paved the way for the estimation of

the turnover parameters of immune cells in vivo in humans althoughm

its interpretation is “notoriously complicated”….

Different labelling times and different mathematical interpretations of

the system yield different turnover estimates for the same cell

populations

(Borghans and de Boer 2007, Immunol Rev,

and de Boer and Perelson 2013, J Theor Biol).

Research questions

Can a mechanistic modelling approach…

…increase our understanding of deuterium labelling data?

…solve any outstanding discrepancy in its interpretation?

…and increase our understanding of immunology?

Projects

• Physiologically based simulations of deuterated glucose for quantifying

cell turnover in humans (7 slides)

• Stem cell-like memory T cells self-renew to a great extent and provide

long-lasting protection (5 slides)

• Late-stage differentiated memory T cells continue to proliferate in vivo

(5 slides)

• Human neutrophil kinetics (4 slides)

PHYSIOLOGICALLY BASED SIMULATIONS OF DEUTERATED GLUCOSE FOR

QUANTIFYING CELL TURNOVER IN HUMANS

Can a more accurate description of label availability reconcile T cell proliferation estimates

from different labelling studies?

Ahmed et al. 2015, PLOS Comput Biol

Discrepancies in the estimates of T cell turnover

Exposure to label might have been underestimated in the

one day labelling study.

(Ahmed et al. 2015, PLOS Comput Biol).

Normalizing factor (bg = 0.73)

DNA enrichment (fractional)

Label availability function

Proliferation rate

loss rate

An understimation of label availability in the one-day study?

Physiology of the glucose-insulin

metabolism system and PBPK simulations

Schaller et al. 2013, Syst Pharmacol

Approach

or

Square pulse (classical) approach Normalizing factor (bg = 0.73)

DNA enrichment data (fractional) Proliferation rate

loss rate

Conclusion

Underestimation of label availability –although the most plausible

hypothesis (Ahmed et al. 2015, PLOS Comput Biol)- does not explain the

discrepancy in published T cell proliferation rate estimates.

STEM CELL-LIKE MEMORY T CELLS SELF-RENEW TO A GREAT EXTENT AND

PROVIDE LONG-LASTING PROTECTION

On average,

what is the half-life of a TSCM clonotype?

What is the TSCM self-renewal potential?

How many TSCM are produced per day?

Lahoz-Beneytez,

PhD thesis

Reduced Clonal Expansion Model

Deuterium data equations

Proliferation rate within TSCM pool

Number of cells resulting from clonal burst

Ratio TN:TSCM Priming constant

Source: https://www.tasciences.com/what-is-a-telomere/ date of access 09.11.2016

Telomere length serves as a proxy for the replication history of a cell population Telomere erosion is counteracted by the activity of telomerase Telomerase activity is highly active in T cells and compensates telomere loss during clonal expansion in vitro and in vivo (Bodnar et al. 1996, Exp Cell Res, Plunkett et al. 2001, Blood,

and Valenzuela an Effros 2002, Clin Immunol)

Reduced Clonal Expansion Model

(de Boer and Nost 1998, J Immunol)

Deuterium data equations

Telomere data equation

2k=C

Conclusions

Average half-life TSCM clonotypes is about

• 1.68 years (95% IC 0.51-2.86 years) for CD4+ T cells

• 1.73 years (95% IC 0-5.22 years) for CD8+ T cells

Daily production1 of TSCM cells is about

• 450 million for CD4+ T cells

• 20 million for CD8+ T cells

Self-renewal TSCM clonotypes is about

• 90% for CD4+ T cells

• 85% for CD8+ T cells

Lahoz-Beneytez, PhD thesis

TN

TSCM

Representative fit (CD4):

Manuscript in preparation

Costa del Amo P, Lahoz-Beneytez J, Boelen L, …, Ahmed R, Baird DM,

Price DA, Ladell K, Macallan D, Asquith B. Human stem cell-like

memory T cell dynamics are compatible with long-lived immunological

memory. In preparation.

LATE-STAGE DIFFERENTIATED MEMORY T CELLS

CONTINUE TO PROLIFERATE IN VIVO

Are late-stage differentiated (CD57+) memory T cells senescent (i.e. do not divide)?

What is the driving force that triggers CD57 expression in vivo?

Bayer 16:9 Template 2010 • March 2016

T cell ageing

(or the rise of late-stage differentiated/senescent cells)

• human life-expectancy increasing > important to understand how an “aged” immune system is

regulated

• The ageing of the T cell population coincides with the accumulation of oligoclonal CD57+ cells

• Some reports suggest that these cells are senescent, i.e. do not divide

(Brencheley et al. 2003, Blood, and Palmer et al. 2005, J. Immunol)

• Whilst others suggest the opposite

(Chong et al. 2008, Eur. J. Immunol, and Lutz et al. 2011, . Immunol)

• No longitudinal studies in vivo in humans available

Results (AICc distance) ∆AIC

c

∆AIC

c

mA mB mC mA mB mC p2=0 p2=0 p2=0

mA mB mC mA mB mC p2=0 p2=0 p2=0

Conclusions

• Late-stage differentiated cells (CD57+) do divide in vivo and

have not reached replicative senescence.

• We could not discern the driving force that triggers CD57

expression in vivo.

HUMAN NEUTROPHIL KINETICS

What is the blood half-life of a neutrophil?

What’s the blood half-life of a neutrophil?

Athens et al. 1961, Cartwright et al. 1964,

Dancey et al. 1976 and others

Pillay et al. 2010

~7h?

>3days?

A physiologically based model of neutrophil turnover

p = proliferation rate

q = transition to post mitotic pool rate

s = marrow egress rate

z = loss rate

Parameters taken from literature:

• Np pool size

• Nb pool size

Analysis of labeling data from 18 individuals yielded estimates of human neutrophil half-lives of 13 hours.

RECAPITULATION

Can a mechanistic modelling approach…

…increase our understanding of deuterium labelling data?

…solve any outstanding discrepancy in its interpretation?

…and increase our understanding of immunology?

1some pathogen-specific figure were already available

2we confirm this finding for the first time in vivo in humans and address literature discrepancies

3in vitro evidence shows that CD57 expression is antigen-driven; thus it may coincide with clonal expansion

4which closer to historical estimates

Question Previous answer Answer given here

Can a more accurate description of label availability reconcile

estimates of T cell turnover rates? V likely No

On average, what is the half-life of TSCM-mediated memory? Inconclusive/

not known1 1.7 years

What is the TSCM self-renewal potential? not known 90% (CD4)

85% (CD8)

How many TSCM are produced per day? not known 450 million (CD4)

20 million (CD8)

Are late-stage differentiated (CD57+) memory T cells senescent

(i.e. do not divide)? Inconclusive No2

What is the driving force that triggers CD57 expression in vivo? Not known3 Inconclusive

What is the blood half-life of a neutrophil? 3.5days 13h4

Question Answer

Can the mechanistic modelling increase our understanding of deuterium

labelling data?

☑

…and solve any outstanding discrepancy in the interpretation of this data? ✘☑ …and increase our understanding of immunology? ☑

Christoph Niederalt

Becca Asquith

Derek Macallan

Arafa Salam

Raya Ahmed

Yan Zhang

Arne Schenk

Andre Dallman

Benjamin Ballnus

Christian Müller

Sai Gadham Setty

Pedro Costa

Lies Boelen

David Vickers

Thomas Eissing

Stephan Schaller

Sebastian Frechen

Pavel Balazki

Acknowledgements

Thanks for your attention.