In vivo dynamical interactions between CD4 Tregs, CD8 Tregs and CD4+ CD25- cells in mice.
BACKGROUND
Regulatory T cells (Tregs) were shown to be central in maintaining immunological homeostasis and preventing the development of autoimmune diseases. Several subsets of Tregs have been identified to date; however, the dynamics of the interactions between these subsets, and their implications on their regulatory functions are yet to be elucidated.


RESULTS
We employed a combination of mathematical modeling and frequent in vivo measurements of several T cell subsets. Healthy BALB/c mice received a single injection of either hCDR1--a tolerogenic peptide previously shown to induce Tregs, a control peptide or vehicle alone, and were monitored for 16 days. During this period, splenocytes from the treated mice were analyzed for the levels of CD4, CD25, CD8, CD28 and Foxp3. The collected data were then fitted to mathematical models, in order to test competing hypotheses regarding the interactions between the followed T cell subsets. In all 3 treatment groups, a significant, lasting, non-random perturbation of the immune system could be observed. Our analysis predicted the emergence of functional CD4 Tregs based on inverse oscillations of the latter and CD4(+)CD25(-) cells. Furthermore, CD4 Tregs seemed to require a sufficiently high level of CD8 Tregs in order to become functional, while conversion was unlikely to be their major source. Our results indicated in addition that Foxp3 is not a sufficient marker for regulatory activity.


CONCLUSIONS
In this work, we unraveled the dynamics of the interplay between CD4, CD8 Tregs and effector T cells, using, for the first time, a mathematical-mechanistic perspective in the analysis of Treg kinetics. Furthermore, the results obtained from this interdisciplinary approach supported the notion that CD4 Tregs need to interact with CD8 Tregs in order to become functional. Finally, we generated predictions regarding the time-dependent function of Tregs, which can be further tested empirically in future work.
Arazi
Arnon
A
Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.
Sharabi
Amir
A
Zinger
Heidy
H
Mozes
Edna
E
Neumann
Avidan U
AU
D000818:Animals; D018106:CD28 Antigens / Q000378:metabolism; D015704:CD4 Antigens / Q000378:metabolism*; D016827:CD8 Antigens / Q000378:metabolism; D018414:CD8-Positive T-Lymphocytes / Q000166:cytology* / Q000276:immunology*; D002450:Cell Communication; D005260:Female; D005434:Flow Cytometry; D051858:Forkhead Transcription Factors / Q000378:metabolism; D053645:Interleukin-2 Receptor alpha Subunit / Q000378:metabolism*; D007700:Kinetics; D016131:Lymphocyte Subsets / Q000166:cytology / Q000276:immunology; D051379:Mice; D008807:Mice, Inbred BALB C; D018448:Models, Immunological; D013154:Spleen / Q000166:cytology; D050378:T-Lymphocytes, Regulatory / Q000166:cytology* / Q000276:immunology*
D018106:CD28 Antigens; D015704:CD4 Antigens; D016827:CD8 Antigens; D051858:Forkhead Transcription Factors; C498833:Foxp3 protein, mouse; D053645:Interleukin-2 Receptor alpha Subunit