Physical extraction of antigen and information
Edited by William Bialek, Princeton University, Princeton, NJ; received November 21, 2023; accepted July 18, 2024
Significance
To sense and adapt, cells must capture and internalize external signals. Whereas information flow inside the cell has caught much attention, signal acquisition via cell–cell interfaces is less examined. Motivated by antigen extraction by immune cells using active mechanics, we present a physical-information framework of stochastic antigen transfer, which explains why receptor signaling gives way to physical extraction as cells are subject to natural selection for higher antigen-binding affinities. Our framework reveals complementary recognition modes, establishes a quantitative link from information gain to selection fidelity, and yields falsifiable predictions including optimal contact patterns. Altogether, our work elucidates the physical basis for persistent evolution of humoral immunity from an information perspective.
Abstract
To respond and adapt, cells use surface receptors to sense environmental cues. While biochemical signal processing inside the cell is studied in depth, less is known about how physical processes during cell–cell contact impact signal acquisition. New experiments found that fast-evolving immune B cells in germinal centers (GCs) apply force to acquire antigen clusters prior to internalization, suggesting adaptive benefits of physical information extraction. We present a theory of stochastic antigen transfer and show that maximizing information gain via physical extraction can explain the dramatic phenotypic transition from naive to GC B cells—attenuated receptor signaling, enhanced force usage, and decentralized contact architecture. Our model suggests that binding-lifetime measurement and physical extraction serve as complementary modes of antigen recognition, greatly extending the dynamic range of affinity discrimination when combined. This physical-information framework further predicts that the optimal size of receptor clusters decreases as affinity improves, rationalizing the use of a multifocal synaptic pattern seen in GC B cells. By linking extraction dynamics to selection fidelity via discriminatory performance, we propose that cells may physically enhance information acquisition to sustain adaptive evolution.
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Acknowledgments
We are grateful for funding support from the Bhaumik Institute for Theoretical Physics at University of California, Los Angeles, the NSF Grant MCB-2225947 and an NSF CAREER Award PHY-2146581.
Author contributions
S.W. designed research; H.J. performed research; H.J. and S.W. analyzed data; and S.W. wrote the paper.
Competing interests
The authors declare no competing interest.
Supporting Information
Appendix 01 (PDF)
- Download
- 2.12 MB
References
1
E. F. Keller, L. A. Segel, Model for chemotaxis. J. Theor. Biol. 30, 225–234 (1971).
2
G. H. Wadhams, J. P. Armitage, Making sense of it all: Bacterial chemotaxis. Nat. Rev. Mol. Cell Biol. 5, 1024–1037 (2004).
3
E. T. Roussos, J. S. Condeelis, A. Patsialou, Chemotaxis in cancer. Nat. Rev. Cancer 11, 573–587 (2011).
4
A. Shellard, R. Mayor, Collective durotaxis along a self-generated stiffness gradient in vivo. Nature 600, 690–694 (2021).
5
R. Sunyer et al., Collective cell durotaxis emerges from long-range intercellular force transmission. Science 353, 1157–1161 (2016).
6
L. Chin, Y. Xia, D. E. Discher, P. A. Janmey, Mechanotransduction in cancer. Curr. Opin. Chem. Eng. 11, 77–84 (2016).
7
E. M. Hedgecock, R. L. Russell, Normal and mutant thermotaxis in the nematode Caenorhabditis elegans. Proc. Natl. Acad. Sci. U.S.A. 72, 4061–4065 (1975).
8
A. Bahat et al., Thermotaxis of mammalian sperm cells: A potential navigation mechanism in the female genital tract. Nat. Med. 9, 149–150 (2003).
9
R. Milo et al., Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002).
10
Y. E. Antebi et al., Combinatorial signal perception in the bmp pathway. Cell 170, 1184–1196 (2017).
11
T. J. Perkins, P. S. Swain, Strategies for cellular decision-making. Mol. Syst. Biol. 5, 326 (2009).
12
S. Tripathi, H. Levine, M. K. Jolly, The physics of cellular decision making during epithelial-mesenchymal transition. Annu. Rev. Biophys. 49, 1–18 (2020).
13
T. Y. C. Tsai et al., Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science 321, 126–129 (2008).
14
G. Balazsi, A. van Oudenaarden, J. J. Collins, Cellular decision making and biological noise: From microbes to mammals. Cell 144, 910–925 (2011).
15
H. C. Berg, E. M. Purcell, Physics of chemoreception. Biophys. J. 20, 193–219 (1977).
16
R. G. Endres, N. S. Wingreen, Accuracy of direct gradient sensing by single cells. Proc. Natl. Acad. Sci. U.S.A. 105, 15749–15754 (2008).
17
T. Mora, N. S. Wingreen, Limits of sensing temporal concentration changes by single cells. Phys. Rev. Lett. 104, 248101 (2010).
18
M. Skoge, Y. Meir, N. S. Wingreen, Dynamics of cooperativity in chemical sensing among cell-surface receptors. Phys. Rev. Lett. 107, 178101 (2011).
19
F. Beroz et al., Physical limits to biomechanical sensing in disordered fibre networks. Nat. Commun. 8, 1–11 (2017).
20
P. R. ten Wolde, N. B. Becker, T. E. Ouldridge, A. Mugler, Fundamental limits to cellular sensing. J. Stat. Phys. 162, 1395–1424 (2016).
21
P. Mehta, D. J. Schwab, Energetic costs of cellular computation. Proc. Natl. Acad. Sci. U.S.A. 109, 17978–17982 (2012).
22
P. Sartori, L. Granger, C. F. Lee, J. M. Horowitz, Thermodynamic costs of information processing in sensory adaptation. PLoS Comput. Biol. 10, e1003974 (2014).
23
A. C. Barato, D. Hartich, U. Seifert, Efficiency of cellular information processing. New J. Phys. 16, 103024 (2014).
24
C. C. Govern, P. R. ten Wolde, Energy dissipation and noise correlations in biochemical sensing. Phys. Rev. Lett. 113, 258102 (2014).
25
E. Natkanski et al., B cells use mechanical energy to discriminate antigen affinities. Science 340, 1587–1590 (2013).
26
G. D. Victora, M. C. Nussenzweig, Germinal centers. Annu. Rev. Immunol. 30, 429–457 (2012).
27
H. N. Eisen, G. W. Siskind, Variations in affinities of antibodies during the immune response. Biochemistry 3, 996–1008 (1964).
28
A. D. Gitlin, Z. Shulman, M. C. Nussenzweig, Clonal selection in the germinal centre by regulated proliferation and hypermutation. Nature 509, 637–640 (2014).
29
C. R. Nowosad, K. M. Spillane, P. Tolar, Germinal center B cells recognize antigen through a specialized immune synapse architecture. Nat. Immunol. 17, 870–877 (2016).
30
W. Luo et al., The AKT kinase signaling network is rewired by PTEN to control proximal BCR signaling in germinal center B cells. Nat. Immunol. 20, 736–746 (2019).
31
A. Khalil, J. Cambier, M. Shlomchik, B cell receptor signal transduction in the GC is short-circuited by high phosphatase activity. Science 336, 1178–1181 (2012).
32
H. Jiang, S. Wang, Immune cells use active tugging forces to distinguish affinity and accelerate evolution. Proc. Natl. Acad. Sci. U.S.A. 120, e2213067120 (2023).
33
H. Jiang, S. Wang, Molecular tug of war reveals adaptive potential of an immune cell repertoire. Phys. Rev. X 13, 021022 (2023).
34
M. Knežević, H. Jiang, S. Wang, Active tuning of synaptic patterns enhances immune discrimination. Phys. Rev. Lett. 121, 238101 (2018).
35
K. M. Spillane, P. Tolar, B cell antigen extraction is regulated by physical properties of antigen-presenting cells. J. Cell Biol. 216, 217–230 (2017).
36
G. I. Bell, Models for the specific adhesion of cells to cells: A theoretical framework for adhesion mediated by reversible bonds between cell surface molecules. Science 200, 618–627 (1978).
37
P. Hänggi, P. Talkner, M. Borkovec, Reaction-rate theory: Fifty years after kramers. Rev. Mod. Phys. 62, 251 (1990).
38
F. Schwesinger et al., Unbinding forces of single antibody-antigen complexes correlate with their thermal dissociation rates. Proc. Natl. Acad. Sci. U.S.A. 97, 9972–9977 (2000).
39
P. François, G. Voisinne, E. D. Siggia, G. Altan-Bonnet, M. Vergassola, Phenotypic model for early T-cell activation displaying sensitivity, specificity, and antagonism. Proc. Natl. Acad. Sci. U.S.A. 110, E888–E897 (2013).
40
D. Depoil, M. L. Dustin, Force and affinity in ligand discrimination by the TCR. Trends Immunol. 35, 597–603 (2014).
41
T. W. McKeithan, Kinetic proofreading in T-cell receptor signal transduction. Proc. Natl. Acad. Sci. U.S.A. 92, 5042–5046 (1995).
42
O. Dushek, R. Das, D. Coombs, A role for rebinding in rapid and reliable T cell responses to antigen. PLoS Comput. Biol. 5, e1000578 (2009).
43
M. J. Taylor, K. Husain, Z. J. Gartner, S. Mayor, R. D. Vale, A DNA-based T cell receptor reveals a role for receptor clustering in ligand discrimination. Cell 169, 108–119 (2017).
44
O. S. Yousefi et al., Optogenetic control shows that kinetic proofreading regulates the activity of the T cell receptor. eLife 8, e42475 (2019).
45
H. S. Seung, H. Sompolinsky, Simple models for reading neuronal population codes. Proc. Natl. Acad. Sci. U.S.A. 90, 10749–10753 (1993).
46
B. Hu, W. Chen, W. J. Rappel, H. Levine, Physical limits on cellular sensing of spatial gradients. Phys. Rev. Lett. 105, 048104 (2010).
47
T. E. Ouldridge, C. C. Govern, P. R. ten Wolde, Thermodynamics of computational copying in biochemical systems. Phys. Rev. X 7, 021004 (2017).
48
C. C. Govern, P. R. Ten Wolde, Optimal resource allocation in cellular sensing systems. Proc. Natl. Acad. Sci. U.S.A. 111, 17486–17491 (2014).
49
K. Suzuki, I. Grigorova, T. G. Phan, L. M. Kelly, J. G. Cyster, Visualizing B cell capture of cognate antigen from follicular dendritic cells. J. Exp. Med. 206, 1485–1493 (2009).
50
F. D. Batista, M. S. Neuberger, Affinity dependence of the b cell response to antigen: A threshold, a ceiling, and the importance of off-rate. Immunity 8, 751–759 (1998).
51
E. T. Boder, K. S. Midelfort, K. D. Wittrup, Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity. Proc. Natl. Acad. Sci. U.S.A. 97, 10701 (2000).
52
K. Kwak et al., Intrinsic properties of human germinal center B cells set antigen affinity thresholds. Sci. Immunol. 3, eaau6598 (2018).
53
M. Stein, A. Mezghani, J. A. Nossek, A lower bound for the fisher information measure. IEEE Signal Process. Lett. 21, 796–799 (2014).
54
J. B. Lalanne, P. François, Principles of adaptive sorting revealed by in silico evolution. Phys. Rev. Lett. 110, 218102 (2013).
55
R. Shinnakasu et al., Regulated selection of germinal-center cells into the memory B cell compartment. Nat. Immunol. 17, 861–869 (2016).
56
Z. Wan et al., The activation of IgM-or isotype-switched IgG-and IgG-BCR exhibits distinct mechanical force sensitivity and threshold. eLife 4, e06925 (2015).
57
I. Vikstrom et al., Mcl-1 is essential for germinal center formation and B cell memory. Science 330, 1095–1099 (2010).
58
F. J. Weisel, G. V. Zuccarino-Catania, M. Chikina, M. J. Shlomchik, A temporal switch in the germinal center determines differential output of memory B and plasma cells. Immunity 44, 116–130 (2016).
59
J. Sheng, S. Wang, Coevolutionary transitions emerging from flexible molecular recognition and eco-evolutionary feedback. iScience 24, 102861 (2021).
60
J. Merkenschlager et al., Dynamic regulation of TFH selection during the germinal centre reaction. Nature 591, 458–463 (2021).
61
B. Liu, W. Chen, B. D. Evavold, C. Zhu, Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling. Cell 157, 357–368 (2014).
Information & Authors
Information
Published in
Cover image: Pictured are rescue tigers (Panthera tigris) at In-Sync Exotics Wildlife Rescue and Educational Center in Wylie, Texas. Ellie Armstrong, Jazlyn Mooney et al. explored the genomic diversity of the privately owned captive tiger population in the United States based on genomic sequencing of 138 individuals. The study population had ancestry from all six extant wild tiger subspecies and measures of genetic diversity and inbreeding that were comparable to wild tiger populations. The results could help inform tiger conservation efforts and illegal trafficking investigations. See the article by Armstrong, Mooney et al. e2402924121. Image credit: Keri Ostermann (In-Sync Exotics Wildlife Rescue and Educational Center, Wylie, TX).
Classifications
Copyright
Copyright © 2024 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Data, Materials, and Software Availability
All study data are included in the article and/or SI Appendix.
Submission history
Received: November 21, 2023
Accepted: July 18, 2024
Published online: September 20, 2024
Published in issue: September 24, 2024
Keywords
Acknowledgments
We are grateful for funding support from the Bhaumik Institute for Theoretical Physics at University of California, Los Angeles, the NSF Grant MCB-2225947 and an NSF CAREER Award PHY-2146581.
Author Contributions
S.W. designed research; H.J. performed research; H.J. and S.W. analyzed data; and S.W. wrote the paper.
Competing Interests
The authors declare no competing interest.
Notes
This article is a PNAS Direct Submission.
Authors
Metrics & Citations
Metrics
Citation statements
Altmetrics
Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
View Options
View options
PDF format
Download this article as a PDF file
DOWNLOAD PDFGet Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Personal login Institutional LoginRecommend to a librarian
Recommend PNAS to a LibrarianPurchase options
Purchase this article to access the full text.