Bryan D. Bryson

The paper identifies bottlenecks in MHC-I antigen presentation by Mtb, showing that only a small fraction of predicted binders are actually presented, and develops computational models to improve prediction.

This study identifies Rabgef1 as a regulator of gasdermin pore formation by controlling membrane cholesterol, showing cholesterol depletion restores pyroptosis in cancer cells and tumors.

The paper identifies E-sensors, HLA-E-restricted CD8+ T cells that recognize increased self-VL9/HLA-E, mediating immunoregulation and antiviral responses in HIV control.

This study uses machine learning to guide directed evolution of plant Rubisco, identifying variants with improved catalytic efficiency and carboxylation rates.

The paper shows human memory CD4+ T cells recognize both Mtb-infected and bystander macrophages, with some TCRs specifically recognizing infected cells.

This study compares IFNγ-induced iNOS activity in human and murine tuberculosis models, finding minimal iNOS expression in human myeloid cells and identifying epithelial cells as primary iNOS sources.

This paper introduces a lysis-independent method for joint host-pathogen single-cell profiling using bacterial surface display, enabling analysis of both host and pathogen features in infected cells.

This study identifies IL-10 and type I IFN signaling as mechanisms that restrict MHC-II trafficking in Mtb-infected M2-like macrophages, limiting CD4+ T cell activation.

This study compares the metabolic states of Mycobacterium tuberculosis in human and mouse macrophages, revealing species-specific lipid metabolism and identifying a barrier to lipid access in human macrophages.

The paper shows neutrophils store and release IFN-I from the nucleolus, revealing a new role for nucleolar stress in immune responses.

This paper introduces a yeast surface display pipeline to screen for class I peptide MHC binders across multiple alleles and applies it to pathogen proteomes to identify potential T cell antigens.

The paper shows that Mycobacterium tuberculosis uses its ESX-1 secretion system to present antigens on MHC-I, bypassing host pathways like autophagy.

This study identifies MHC-II antigens from Mycobacterium tuberculosis using mass spectrometry and evaluates mRNA vaccine immunogens to enhance T cell recognition.

This study examines the effects of EGFR kinase inhibitors on Mtb-macrophage interactions, finding heterogeneous outcomes and both direct and host-dependent mechanisms of action.

PathMHC improves identification of pathogen-derived MHC peptides for vaccine targets by combining computational analysis with targeted mass spectrometry.

This study identifies TREM2+ lipid-laden macrophages in alveolar TB pneumonia that support Mtb survival, contrasting with granulomas, and shows pharmacological reversal of this immunometabolic state.

This study identifies protective antibodies against Mycobacterium tuberculosis and shows that their Fc-effector function and Fab domains collaborate to restrict infection through immune cell reorganization.

This study identifies hypoxia as a key driver of immune dysfunction in TB granulomas, correlating with bacterial persistence.

This paper introduces AbMAP, a transfer learning framework for antibody sequence analysis, showing it can predict structure, binding, and paratope with high accuracy and enable antibody optimization.

This study identifies Mtb outer membrane proteins using protease shaving and mass spectrometry, revealing novel vaccine targets and methods to deliver cargoes to the Mtb surface.

This study introduces PhagoID, a proximity-labeling method to analyze the phagosome lumen proteome in murine and human macrophages, revealing both conserved and species-specific proteins.

This study uses multi-omics approaches to analyze myeloid cell diversity in TB granulomas and identifies IFN-γ, TGF-β, and time as factors driving macrophage diversity.

The paper introduces a method to compress phenotypic screens using computational deconvolution, demonstrating increased efficiency and applicability to complex multicellular models.

The paper identifies genetic risk factors for tuberculosis, including HLA alleles and INPP5A variants, and shows smoking as a lifestyle risk factor.

The paper develops a recurrent neural network to simulate intracellular signaling by predicting transcription factor activity from ligand concentrations, validated with synthetic and experimental data.

The paper uses neural language models to predict viral escape mutations by modeling semantic change and grammaticality in viral sequences.

The paper demonstrates how uncertainty quantification in machine learning improves biological discovery by enabling robust predictions and iterative experimental design across diverse domains.

The paper introduces Trajectorama, an algorithm that uses gene coexpression to integrate cellular trajectories across studies, revealing developmental and disease-related processes.

The paper introduces a geometric sketching method to compactly summarize scRNA-seq data, revealing rare cell types and accelerating analysis tasks.

Scanorama matches mutual nearest neighbors across all scRNA-seq data set pairs, rapidly merging 105,476 cells from 26 experiments while keeping data set-specific cell types separate.