Opposing roles of IgM and IgD in BCR-induced B-cell survival

Shoya Yasuda1,2, Jiping Sun2, Yang Zhou3,4

  • 1School of Computing, Tokyo Institute of Technology, Yokohama, Japan.

The B-cell receptor (BCR) transmits a tonic survival signal in the absence of antigen stimulation and an antigen-triggered survival signal. Mature B cells express two types of BCR, IgM and IgD, but it remains unclear how B-cell survival is differentially regulated by these two receptors. We found that, whereas cross-linking IgM on spleen B cells greatly enhanced their survival, cross-linking IgD did not enhance, but rather decreased, their survival. Consistently, cross-linking both IgM and IgD only moderately enhanced B-cell survival, suggesting that IgM and IgD play opposing roles in B-cell survival induced by BCR stimulation. Based on these and additional experimental results, we present a mathematical model integrating IgM- and IgD-mediated survival signals. Our model shows that IgD can transmit a tonic survival signal in the absence of antigen stimulation but cross-linking IgD not only does not generate a survival signal but also disrupts its tonic signal, resulting in inhibition of B-cell survival. These results suggest that IgD attenuates BCR-induced survival in mature B cells, presumably to restrain B-cell response to weak and/or self-antigens and prevent nonspecific B-cell activation and autoimmunity.

Related Concept Videos

Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
721
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
433
Role of Myosin in Cell Migration01:18

Role of Myosin in Cell Migration

Myosins are multimeric motor proteins involved in various cellular processes such as migration, adhesion, and proliferation. Myosin II is the most common type in animal cells, which binds and cross-links actin filaments.
Myosin II  is a hexamer comprising two heavy chains with globular heads and coiled-coil tails, two regulatory light chains, and two essential light chains. The ATPase sites on the myosin heads hydrolyze ATP, and the released phosphate generates the force for contraction....
3.4K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
798
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
605
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
622