Assessing the impact of HIV treatment interruptions using stochastic cellular Automata.


Journal

Journal of theoretical biology
ISSN: 1095-8541
Titre abrégé: J Theor Biol
Pays: England
ID NLM: 0376342

Informations de publication

Date de publication:
07 10 2020
Historique:
received: 30 09 2019
revised: 23 04 2020
accepted: 12 06 2020
pubmed: 24 6 2020
medline: 22 6 2021
entrez: 24 6 2020
Statut: ppublish

Résumé

Chronic HIV infection causes a progressive decrease in the ability to maintain homeostasis resulting, after some time, in eventual break down of immune functions. Recent clinical research has shed light on a significant contribution of the lymphatic tissues, where HIV causes accumulation of collagen, (fibrosis). Specifically, where tissue is populated by certain types of functional stromal cells designated Fibroblastic Reticular Cells (FRCs), these have been found to play a crucial role in balancing out apoptosis and regeneration of naïve T-cells through 2-way cellular signaling. Tissue fibrosis not only impedes this signaling, effectively reducing T-cell levels through increased apoptosis of cells of both T- and FRC type but has been found to be irreversible by current HIV standard treatment (cART). While the therapy aims to block the viral lifecycle, cART-associated increase of T-cell levels in blood appears to conceal existing FRC impairment through fibrosis. This hidden impairment can lead to adverse consequences if treatment is interrupted, e.g. due to poor adherence (missing doses) or through periods recovering from drug toxicities. Formal clinical studies on treatment interruption have indicated possible adverse effects, but quantification of those effects in relation to interruption protocol and patient predisposition remains unclear. Accordingly, the impact of treatment interruption on lymphatic tissue structure and T-cell levels is explored here by means of computer simulation. A novel Stochastic Cellular Automata model is proposed, which utilizes all sources of clinical detail available to us (though sparse in part) for model parametrization. Sources are explicitly referenced and conflicting evidence from previous studies explored. The main focus is on (i) spatial aspects of collagen build up, together with (ii) collagen increase after repeated treatment interruptions to explore the dynamics of HIV-induced fibrosis and T-cell loss.

Identifiants

pubmed: 32574568
pii: S0022-5193(20)30231-9
doi: 10.1016/j.jtbi.2020.110376
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110376

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Andreas Hillmann (A)

Advanced Research Computing Centre for Complex Systems Modelling, School of Computing, Dublin City University, Dublin, Ireland. Electronic address: andreas.hillmann2@mail.dcu.ie.

Martin Crane (M)

Advanced Research Computing Centre for Complex Systems Modelling, School of Computing, Dublin City University, Dublin, Ireland.

Heather J Ruskin (HJ)

Advanced Research Computing Centre for Complex Systems Modelling, School of Computing, Dublin City University, Dublin, Ireland.

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