Adaptive Immunity: Maintaining naivety of T cells
Adaptive immunity develops with exposure to pathogens. Unlike the innate immune system, which supplies a fast, general response against threats, the adaptive immune system can recognise and remember specific pathogens, thus providing a long-lasting protection against infections. Its key components, B and T cells, can specifically target the germs causing an infection. However, before these cells can attack specific pathogens, they need to encounter a molecule, known as an antigen, which they recognise and respond to. Until then, these cells are ‘naive’.
Naive T cells are produced in the thymus, an organ that shrinks with age. As the thymus becomes smaller, the number of newly produced naive T cells declines rapidly; however, the body is somehow able to maintain a source of naive T cells throughout its life. So far, it has been unclear how the body does this. A population of cells that has no external source of new cells and is subject to death can be sustained by two intrinsic processes: the remaining cells can adjust their division rates to replace lost cells; or they can die less frequently by extending their lifespan.
As with many areas in biology, studying the immune system is challenging, due to its inherent complexity. Immune cells are motile and can be found in many organs. Moreover, an acute adaptive immune response mobilises many cellular actors, and their offspring can still be active for extended periods following infection. Experimental set ups often lack the ability to monitor all of these factors continuously. Now, in eLife, Sanket Rane (Columbia University), Thea Hogan (University College London; UCL), Edward Lee (Yale University), Benedict Seddon (UCL) and Andrew Yates (Columbia) report on mathematical models that can bridge the gap (Rane et al., 2022).
To question how naive T cell populations in mice are maintained throughout life, and how host age, cell age and cell numbers influence both the proliferation and decrease of naive T cells, the researchers developed mathematical models of the number of cells and their different types. Data from various experimental systems were analysed and several competing hypotheses evaluated.
T cells can be divided into two varieties that can be distinguished by the type of transmembrane glycoprotein they express: CD4+T cells increase the activity of other immune cells by releasing cytokines, while cytotoxic CD8+T cells are involved in killing infected cells and their pathogens. The models revealed that both types of cell appear to be able to regulate their lifespan independently from external factors, dividing rarely in adult mice, and living longer as the mice get older. The model accurately predicted the population dynamics of CD4+T cells in both young and adult mice. However, CD8+T cells appear to have distinct dynamics in newborn mice up to three weeks of age, which seem to lose CD8+T cells at a higher rates than adult mice. These results support a traditional understanding in which the thymus drives the maintenance of a naive T cell pool early on, while later in life, cells regulate their survival rates independently.
As with all scientific studies, there are some caveats that highlight the need for further investigation. First, even if data appear to be consistent with a hypothesis, it does not mean that the hypothesis is true – merely that there is insufficient evidence to reject it. As new data become available, accepted hypotheses should be re-challenged. However, one of the advantages of the statistical approach taken by Rane et al. is that additional predictions from the mathematical models can be readily made, facilitating any further re-examination in light of new data.
Second, the data used throughout the analyses were obtained from laboratory mice living in a sterile environment. In the wild, however, mice are exposed to a wide range of naturally occurring infections. The response to these pathogens results in a far more mature immune system with a substantial immune memory, which may have implications for naive populations (Willyard, 2018; Rosshart et al., 2019).
Lastly, it remains to be seen if the findings obtained from studies in mice also apply to humans. This is particularly relevant here, as evidence suggests that the maintenance of naive T cells may differ fundamentally between the two species (den Braber et al., 2012). Regardless, the scientific method used by Rane et al. is certain to make a significant contribution towards a better understanding of human cell dynamics.
References
Article and author information
Author details
Publication history
Copyright
© 2022, Duffy
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 626
- views
-
- 93
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Neuroscience
Hypothalamic kisspeptin (Kiss1) neurons are vital for pubertal development and reproduction. Arcuate nucleus Kiss1 (Kiss1ARH) neurons are responsible for the pulsatile release of gonadotropin-releasing hormone (GnRH). In females, the behavior of Kiss1ARH neurons, expressing Kiss1, neurokinin B (NKB), and dynorphin (Dyn), varies throughout the ovarian cycle. Studies indicate that 17β-estradiol (E2) reduces peptide expression but increases Slc17a6 (Vglut2) mRNA and glutamate neurotransmission in these neurons, suggesting a shift from peptidergic to glutamatergic signaling. To investigate this shift, we combined transcriptomics, electrophysiology, and mathematical modeling. Our results demonstrate that E2 treatment upregulates the mRNA expression of voltage-activated calcium channels, elevating the whole-cell calcium current that contributes to high-frequency burst firing. Additionally, E2 treatment decreased the mRNA levels of canonical transient receptor potential (TPRC) 5 and G protein-coupled K+ (GIRK) channels. When Trpc5 channels in Kiss1ARH neurons were deleted using CRISPR/SaCas9, the slow excitatory postsynaptic potential was eliminated. Our data enabled us to formulate a biophysically realistic mathematical model of Kiss1ARH neurons, suggesting that E2 modifies ionic conductances in these neurons, enabling the transition from high-frequency synchronous firing through NKB-driven activation of TRPC5 channels to a short bursting mode facilitating glutamate release. In a low E2 milieu, synchronous firing of Kiss1ARH neurons drives pulsatile release of GnRH, while the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons.
-
- Computational and Systems Biology
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study bias affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.