Liendo, N. | Search bioRxiv

bioRxiv2025

CD11c+ Tbet+ B cells constrain obesity- and vaccination-induced germinal center B cells and T helper cells

Obesity is a rapidly growing public health crisis associated simultaneously with increased metabolic disease and humoral immune suppression to vaccination or infection. Inflammatory CD11c+T-bet+ B cells increase in spleen and adipose tissue during obesity and exacerbate metabolic dysfunction via antibodies. We now find that during obesity Tbet+ B cells also expand in the liver but not omentum or mesenteric fat. Obese mice also develop increased splenic CXCR5+ TFH and hepatic CXCR5-TPH cells which serve as likely partners for antigen-experienced MHC-II+ CD11c+ Tbet+ B cells. We also observed that antibodies in obese mice, previously found to contribute to metabolic disease, largely circulate as inflammatory autoantigen-bound immune complexes. Obese mice lacking T-bet in B cells also develop increased autoantibody titers and expanded splenic germinal center (GC) B and T helper cells. T-bet+ B cell-deficient mice make a similarly enhanced GC, TFH, TPH response to haptenated-protein vaccination with a corresponding increase in antibody affinity, although there is no additive effect of obesity. These results are consistent with GC inhibition by expanded CD11c+ B cells demonstrated by others to occur during autoimmunity, suggesting a broadly universal mechanism which may explain reduced humoral immunity and poor clinical outcomes following infection in patients with obesity and other forms of chronic inflammation.

immunology↗

bioRxiv2024

Conformational ensemble-based framework enables rapid development of Lassa virus vaccine candidates

Lassa virus (LASV), an arenavirus endemic to West Africa, poses a significant public health threat due to its high pathogenicity and expanding geographic risk zone. LASV glycoprotein complex (GPC) is the only known target of neutralizing antibodies, but its inherent metastability and conformational flexibility have hindered the development of GPC-based vaccines. We employed a variant of AlphaFold2 (AF2), called subsampled AF2, to generate diverse structures of LASV GPC that capture an array of potential conformational states using MSA subsampling and dropout layers. Conformational ensembles identified several metamorphic domains--areas of significant conformational flexibility--that could be targeted to stabilize the GPC in its immunogenic prefusion state. ProteinMPNN was then used to redesign GPC sequences to minimize the mobility of metamorphic domains. These redesigned sequences were further filtered using subsampled AF2, leading to the identification of promising GPC variants for further testing. A small library of redesigned GPC sequences was experimentally validated and showed significantly increased protein yields compared to controls. Antigenic profiles indicated these variants preserved essential epitopes for effective immune response, suggesting their potential for broad protective efficacy. Our results demonstrate that AI-driven approaches can predict the conformational landscape of complex pathogens. This knowledge can be used to stabilize viral proteins, such as LASV GPC, in their prefusion conformation, optimizing them for stability and expression, and offering a streamlined framework for vaccine design. Our deep learning / machine learning enabled framework contributes to global efforts to combat LASV and has broader implications for vaccine design and pandemic preparedness.

immunology↗