Biology subjects

Sage, P. T.

Publications and source records attributed to Sage, P. T..

3 recordsLinked to original sources

bioRxiv2026

Autoreactive antibody production by intrarenal B cells in mouse kidney allograft rejection

Antibody-mediated rejection (AMR) is a major cause of kidney allograft failure, with donor HLA-specific antibodies (DSA) recognized as primary drivers of AMR pathology. However, a significant proportion of AMR diagnoses lack detectable DSA, thus implicating DSA-independent mechanisms. In support, we previously reported on the accumulation of autoreactive B cells in rejecting renal biopsies, which raised the possibility that autoreactive IgG produced within the allograft contributes to graft pathology. In this study, we used mouse models to show that rejecting kidney allografts preferentially promoted a breach in autoreactive B cell tolerance, leading to the local production of autoreactive antibodies. Notably, autoreactive IgG responses were uncoupled from DSA production, indicative of distinct regulation. Organoid cultures confirmed that autoantibody production was observed in the rejecting kidney, whereas DSAs were produced in both the lymph node and graft. Intrarenal B cells expressing Nur77 were enriched for autoreactivity, consistent with in situ antigen recognition. Finally, autoreactive antibodies are pathogenic, since inhibiting autoantibody production with transient anti-IL-15 and CTLA-4Ig led to preserved kidney allografts. These unique features of in situ autoantibody responses may be relevant to diverse diseases with chronic tissue inflammation beyond transplantation.

immunology↗

bioRxiv2025

Comparative ScRNA-Seq Profiling of Antigen-Specific CD4+ T cells in Semi-Allogeneic Transplantation and Pregnancy Reveals Intersecting Signatures of Rejection and Tolerance

Transplantation of allogeneic organs requires lifelong immunosuppression to prevent rejection. Prior sensitization and resultant memory T cells are barriers to achieving successful transplant tolerance. In reproductive immunology by contrast, pregnancy represents a spontaneous model of tolerance where the semi-allogeneic fetus evades rejection even in multiparous or rejection-sensitized mothers. CD8+ T cell phenotypes of tolerance and rejection have been previously reported in transplant and pregnancy, but the transcriptional states of donor and fetus-specific CD4+ T cells remain poorly defined. Here, we performed Single-cell RNA-sequencing on endogenous, antigen-specific CD4+ T cells across models of allogeneic heart transplants and naive or paternal skin-sensitized pregnancy. We identified expanded T follicular helper (Tfh) and non-follicular effectors in transplant rejection absent in tolerance. Naive pregnancy resulted in a modest expansion of effector clusters with transcriptional quiescence that mirrored virgin mice. Successful sensitized pregnancy resulted in expanded Tfh clusters consistent with increased fetal-specific antibodies and limited non-Tfh effector responses. Most striking were the extensive changes imposed on donor-specific Foxp3pos regulatory T cells (Tregs) resulting in the co-clustering together with Foxp3neg T conventional cells (Tconvs) in transplant tolerance and the emergence of a Foxp3neg Type I Regulatory cluster observed in pregnancy of sensitized dams. Finally, we showed that these transcriptomes were relevant and enriched in human datasets of health and disease respectively. Thus, the context-dependent signatures of antigen-specific CD4+ T cells provide new insights into their divergent responses to allogeneic conflict at the intersection of transplant and reproductive immunology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/663404v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@1b30a86org.highwire.dtl.DTLVardef@17820cforg.highwire.dtl.DTLVardef@1b1fbeaorg.highwire.dtl.DTLVardef@11ff17_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Integration of Diverse Transcriptomics Datasets using Random Forest to Predict Universal Functional Pathways in Tfr Cells

MotivationT follicular regulatory (Tfr) cells are a specialized cell subset that controls humoral immunity. Despite a number of individual transcriptomic studies on these cells, core functional pathways have been difficult to uncover due to the substantial transcriptional overlap of these cells with other effector cell types, as well as transcriptional changes occurring due to disease settings. Developing a core transcriptional module for Tfr cells that integrates multiple cell type comparisons as well as diverse disease settings will allow a more accurate prediction of functional pathways. Researchers studying allergic reactions, immune responses to vaccines, autoimmunity and cancer could use this gene set to better understand the roles of Tfr cells in controlling disease progression. Additional cell types beyond Tfr cells that have similar features of transcriptomic complexity within diverse disease settings may also be studied using similar approaches. High-throughput sequencing technologies allow the generation of large datasets that require specific tools to best interpret the data. The development of a core transcriptional module for Tfr cells will allow investigators to determine if Tfr cells may have functional roles within their biological systems with little knowledge of Tfr biology. With this work, we have addressed the need of core gene modules to define specific subsets of immune cells. ResultsWe introduce an integrated "core Tfr cell gene module" that can be incorporated into GSEA analysis using various input sizes. The integrated core Tfr gene module was built using transcriptomic studies in Tfr cells from several different tissues, disease settings, and cell type comparisons. Random forest was used to integrate the transcriptomic studies to generate the core gene module. A GSEA gene set was formulated from the integrated core Tfr gene module for incorporation into end-user friendly GSEA. The gene sets are presented along with random genes taken from the GTEX data set and are presented as GMT files. The user can upload the gene set to the GSEA website or any gene set tool which takes GMT files. We also present the full results of the model including p-values calculated by random forest. This provides users with more flexibility in choosing a p-value cutoff that is most appropriate for the experimental setting. AvailabilityThe core Tfr gene sets are freely available at: https://github.com/alosdiallo/TFR_Model. We have also included all of the code and data used in developing these gene sets. The code and results are released under an MIT license. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗