(C) PLOS One
This story was originally published by PLOS One and is unaltered.
. . . . . . . . . .



Interpreting T-cell search “strategies” in the light of evolution under constraints [1]

['Inge M. N. Wortel', 'Medical Biosciences', 'Radboudumc', 'Nijmegen', 'The Netherlands', 'Data Science', 'Institute For Computing', 'Information Sciences', 'Radboud University', 'Johannes Textor']

Date: 2023-04

We here employ an approach from the field of evolutionary biology to examine how cells might evolve search strategies under realistic constraints. Using a cellular Potts model (CPM), where motion arises from intracellular dynamics interacting with cell shape and a constraining environment, we simulate evolutionary optimization of a simple task: explore as much area as possible. We find that our simulated cells indeed evolve their motility patterns. But the evolved behaviors are not shaped solely by what is functionally optimal; importantly, they also reflect mechanistic constraints. Cells in our model evolve several motility characteristics previously attributed to search optimisation—even though these features are not beneficial for the task given here. Our results stress that search patterns may evolve for other reasons than being “optimal”. In part, they may be the inevitable side effects of interactions between cell shape, intracellular dynamics, and the diverse environments T cells face in vivo.

Two decades of in vivo imaging have revealed how diverse T-cell motion patterns can be. Such recordings have sparked the notion of search “strategies”: T cells may have evolved ways to search for antigen efficiently depending on the task at hand. Mathematical models have indeed confirmed that several observed T-cell migration patterns resemble a theoretical optimum; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cell’s chance of finding antigen. But the same behaviours could also arise simply because T cells cannot follow a straight, regular path through the tight spaces they navigate. Even if T cells do follow a theoretically optimal pattern, the question remains: which parts of that pattern have truly been evolved for search, and which merely reflect constraints from the cell’s migration machinery and surroundings?

Although several studies have addressed this question in mathematical models, to date, none have explicitly considered the evolutionary process itself. Here, we directly simulate evolutionary optimization of T-cell search. We find that explicitly simulating “survival of the fittest searchers” can shed new light on why T cells move the way they do. Importantly, we find that the evolving movement patterns are only in part optimized “strategies”—while other parts may merely be “side effects” stemming from the constraints arising from the cell’s molecular motor acting in a maze-like environment.

Importantly, the continuous search for pathogens means that T cells face different challenges throughout their lifetime: their needle-in-a-haystack quest for the first signs of disease in lymph nodes differs greatly from their motion in an infected lung, or from how they patrol the skin to guard against future reinfections. These observations have raised the intriguing question: have years of evolution equipped T cells with distinct search “strategies”, optimized for whichever searching tasks they might encounter?

Funding: This work was funded by a Human Frontiers Science Program (HFSP, https://www.hfsp.org/ ) grant RGP0053/2020 awarded to JT (IMNW was also supported by this grant). In addition, IMNW was supported by a Radboudumc PhD grant, and JT by a Vidi grant VI.Vidi.192.084 from the Dutch Research Council (NWO, https://www.nwo.nl/en ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

T cells have the rare ability to migrate in nearly all tissues within the human body. In lymphoid organs, such as the thymus and lymph nodes, T cells must migrate to develop and get activated; in peripheral “barrier” tissues, like the lung, the gut, and the skin, T cells continuously patrol in search of foreign invaders. Although T cells stay motile in these different contexts, they do adapt their morphology and migratory behaviour to environmental cues. Naive T cells rapidly crawl along a network of stromal cells in the lymph node, alternating between short intervals of persistent movement and random changes in direction [1–4]. This “stop-and-go” behaviour lets them cover large areas of the lymph node quickly, and seems to be a good strategy for finding rare antigens without prior information on their location [5–8]. Developing T cells adopt a similar strategy to find their specific ligand during negative selection in the thymic medulla [9, 10]. By contrast, positive selection in the thymic cortex involves migration at much lower speeds—perhaps due to the broader distribution of positively selecting ligands in the thymus [11]. This remarkably flexible behaviour has been suggested to reflect different “search strategies”, whereby T cells maximise their chance of finding antigen [5].

The idea of search strategies has interesting implications. If T-cell migration patterns are indeed optimised for some specific function (or several functions depending on context and environment), then comparing their “search efficiency” can help us make sense of how T-cell function relates to these diverse migratory behaviours [12]. However, two problems currently limit the conclusions we can draw from this work.

The first problem is that such optimality reasoning hinges on a tacit but crucial assumption: that we observe a given behaviour because it is somehow optimal and has been selected through evolution. Yet it is well known by evolutionary biologists that evolving “optimal” behaviours through natural selection is not as trivial as it appears at first glance [13]. For example, there is an indirect mapping between genotype (the genes controlling cell migration that can be transferred to the next generation) and the resulting phenotype (observed migratory behaviour). In other contexts, such indirect genotype-phenotype mappings have been shown to affect the behaviours that can evolve through natural selection in interesting and non-trivial ways [14, 15]—yet this topic has received little attention in the context of T-cell search so far.

It is true that cells probably can control migratory traits such as “speed” and “persistence”, at least to some extent, by evolving their genetic background or gene expression. But if they cannot tune these traits independently, they may not be able to evolve the one without affecting the other. Indeed, this seems to be the case: a “universal coupling between speed and persistence” (UCSP) has been described in which faster cells move more persistently [16–18]. This phenomenon is thought to apply across many types of migrating cells because actin polymerisation and polarisation are inherently coupled in any type of actomyosin-driven motion [18]. Thus, the cell’s migration machinery already poses constraints on the motion patterns cells can adopt. These constraints are strengthened further by the complex, crowded environment T cells typically migrate in, which can also strongly affect T-cell shapes and migration patterns [3, 19, 20]. All of these constraints mean that (evolving) T cells can only “choose” from a limited range of motion patterns, and that their behaviour will in many cases reflect some kind of compromise rather than a true optimum. The question then becomes: how do we untangle these different influences?

The second problem is that, to determine how “optimal” a migration pattern is, we must make assumptions; after all, even though it may be very useful for a searching T cell to be in two places at once or to move at the speed of light, we typically do not consider these options in a search for optimal behaviours. Put simply: we can only assess the “efficiency” of a strategy relative to a set of other strategies we think the cell can adopt [13, 21]. Studies investigating immune cell search mostly use (variations of) random walk models for this purpose [5, 22–26]. These mathematical or agent-based models can produce different motility patterns depending on parameters, which directly impose properties like speed and turning behaviour on the cell. For a given dataset, fitting these parameters yields a model of the “observed” strategy whose search performance we can assess on imaginary targets in silico. Thus, we learn whether the observed motion pattern was a good strategy for some searching task.

Such models, however, are hard to interpret. Model selection is difficult because the same data can often be explained by multiple models depending on exactly how migration is quantified [25, 27, 28]: for example, while Harris et al. have claimed that T cells in the brain follow Lévy flights to find rare pathogens [22], others [29] recently cautioned that similar statistics may arise through other mechanisms. Furthermore, the search efficiency found in such models can again strongly depend on the structure of the environment [30]—and even models that differ only slightly can still make very different predictions of the area cells can explore on larger time scales [31]. But most importantly, even if these models indeed show that a behaviour benefits some T-cell function, they cannot tell us whether the same behaviour could also have arisen for another reason altogether.

To unravel which migratory patterns truly are optimised for search, it does therefore not suffice to construct a random walk model showing that they are beneficial in some context or other. Instead, there are other crucial points to consider—Is the proposed “optimal” strategy something a cell could realistically adopt or evolve, given the biophysical constraints of its internal migration mechanism and environment? Which migration pattern would these constraints impose on the cell if no evolutionary pressures existed? To what extent do we need an evolutionary explanation for the pattern in question, or could it simply be a side effect of dynamic cell motion in a complex environment?

These additional questions have received little attention in the field of T-cell search, which so far has mostly taken a “top-down” view of evolution: given an observed migration behavior, they have tested whether it could theoretically be optimal for some function [22–26]. This approach considers evolution only implicitly, as the assumed driving force behind the patterns observed. Yet evolutionary theory has benefited from a complementary “bottom-up” approach, where evolution is simulated explicitly to ask: which behaviors might we expect to evolve from known or assumed basic interactions? [14, 32]

Here, we apply this bottom-up approach to the problem of T-cell search. Examining which migration patterns emerge spontaneously from the cell’s migration machinery and/or the environment, we ask to what extent cells might still evolve or tune search strategies within those constraints. We turned to a cellular Potts model (CPM) called the Act-CPM [33], in which migration arises from a machinery where cell shape, environment, and motility interact. This model naturally captures many of the constraints acting on a migrating T cell: it reproduces the UCSP, explains how cell shape dynamics limit possible migratory patterns, and can simulate (T–)cell migration in a realistic tissue environment [34]. We now use this model to simulate an evolutionary process where cells optimise a simple task: exploring as much area as possible. Although cells do optimise their migratory behaviour for this task to some extent, they also spontaneously develop behaviours that were previously interpreted as optimal search strategies but are not beneficial for the task given here. We discuss what these results mean for the interpretation of T-cell migration patterns as “search strategies”.

[END]
---
[1] Url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010918

Published and (C) by PLOS One
Content appears here under this condition or license: Creative Commons - Attribution BY 4.0.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/

You are viewing proxied material from magical.fish. The copyright of proxied material belongs to its original authors. Any comments or complaints in relation to proxied material should be directed to the original authors of the content concerned. Please see the disclaimer for more details.