Biology subjects

Tsuda, B.

Publications and source records attributed to Tsuda, B..

3 recordsLinked to original sources

bioRxiv2025

Human Interleukin-4-Dependent Facilitation of Human IgG Production in PBL-NOG-hIL-4-Tg mice

Immune-humanized mice provide valuable experimental models for evaluating immune-based therapies, yet the induction of human antigen-specific IgG production remains limited due to species-specific incompatibilities. Our previous work demonstrated that NOG-hIL-4-Tg mice, which express human interleukin-4 (IL-4), support human T and B cell maintenance and enable antigen-specific IgG production following transplantation of human peripheral blood mononuclear cells (PBMCs). In this study, we investigated how IL-4 enhances antibody responses in this model. Flow cytometry and histological analysis revealed that human B cell maintenance was associated with specific plasma hIL-4 concentration ranges at supraphysiological levels. T and B cell receptor repertoire analysis using next-generation sequencing showed that clonal diversity remained largely conserved for one month and decreased to three months post-engraftment. Immunoglobulin repertoire profiling confirmed IgG class switching in an IL-4-concentration-dependent manner. Among the IgG subclasses, IgG3 increased during the first and second months and then decreased thereafter. In contrast, IgG1 tended to increase over time; however, the proportion of IgG subclass varied among individual donors. Following immunization with two distinct peptide antigens, the mice produced enhanced levels of antigen-reactive IgG. However, many B cell clones also exhibited weak responses to unrelated third-party antigens, likely reflecting insufficient affinity maturation. Histological evaluation showed tertiary lymphoid structure (TLS)-like accumulations of B and T cells in the spleen, although fully developed germinal centers were absent. Taken together, these findings demonstrate that NOG-hIL-4-Tg mice maintain human B cells within a regulated IL-4 environment, promote early formation of splenic tertiary lymphoid structures, and support IgG production characterized by clonal expansion, class switching, and somatic hypermutation. These results confirm that human IL-4 expression supports human antibody responses in the PBL-NOG-hIL-4-Tg mouse system. (278)

immunology↗

bioRxiv2021

Neuromodulators enable overlapping synaptic memory regimes and nonlinear transition dynamics in recurrent neural networks

Neuromodulators are critical controllers of neural states, with dysfunctions linked to various neuropsychiatric disorders. Although many biological aspects of neuromodulation have been studied, the computational principles underlying how neuromodulation of distributed neural populations controls brain states remain unclear. Compared with specific contextual inputs, neuromodulation is a single scalar signal that is broadcast broadly to many neurons. We model the modulation of synaptic weight in a recurrent neural network model and show that neuromodulators can dramatically alter the function of a network, even when highly simplified. We find that under structural constraints like those in brains, this provides a fundamental mechanism that can increase the computational capability and flexibility of a neural network. Diffuse synaptic weight modulation enables storage of multiple memories using a common set of synapses that are able to generate diverse, even diametrically opposed, behaviors. Our findings help explain how neuromodulators "unlock" specific behaviors by creating task-specific hyperchannels in the space of neural activities and motivate more flexible, compact and capable machine learning architectures. SignificanceNeuromodulation through the release of molecules like serotonin and dopamine provides a control mechanism that allows brains to shift into distinct behavioral modes. We use an artificial neural network model to show how the action of neuromodulatory molecules acting as a broadcast signal on synaptic connections enables flexible and smooth behavioral shifting. We find that individual networks exhibit idiosyncratic sensitivities to neuromodulation under identical training conditions, highlighting a principle underlying behavioral variability. Network sensitivity is tied to the geometry of network activity dynamics, which provides an explanation for why different types of neuromodulation (molecular vs direct current modulation) have different behavioral effects. Our work suggests experiments to test biological hypotheses and paths forward in the development of flexible artificial intelligence systems.

neuroscience↗

bioRxiv2020

A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex

The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underlying schema encoding and storage. Yet how the brain is able to create new schemas while preserving and utilizing old schemas remains unclear. Here we propose a simple neural network framework based on a modification of the mixture of experts architecture to model the prefrontal cortexs ability to flexibly encode and use multiple disparate schemas. We show how incorporation of gating naturally leads to transfer learning and robust memory savings. We then show how phenotypic impairments observed in patients with prefrontal damage are mimicked by lesions of our network. Our architecture, which we call DynaMoE, provides a fundamental framework for how the prefrontal cortex may handle the abundance of schemas necessary to navigate the real world.

neuroscience↗