Immunological imprinting shapes the specificity of human antibody responses against SARS-CoV-2 variants.

The spike glycoprotein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to accumulate substitutions, leading to breakthrough infections of vaccinated individuals. It remains unclear if exposures to antigenically distant SARS-CoV-2 variants can overcome memory B cell biases established by initial SARS-CoV-2 encounters. We determined the specificity and functionality of antibody and B cell responses following exposure to BA.5 and XBB variants in individuals who received ancestral SARS-CoV-2 mRNA vaccines. BA.5 exposures elicited antibody responses that targeted epitopes conserved between the BA.5 and ancestral spike. XBB exposures also elicited antibody responses that primarily targeted epitopes conserved between the XBB.1.5 and ancestral spike. However, unlike BA.5, a single XBB exposure elicited low frequencies of XBB.1.5-specific antibodies and B cells in some individuals. Pre-existing cross-reactive B cells and antibodies were correlated with stronger overall responses to XBB but weaker XBB-specific responses, suggesting that baseline immunity influences the activation of variant-specific SARS-CoV-2 responses.

Immunological imprinting shapes the specificity of human antibody responses against SARS-CoV-2 variants.

The spike glycoprotein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to accumulate substitutions, leading to breakthrough infections of vaccinated individuals and prompting the development of updated booster vaccines. Here, we determined the specificity and functionality of antibody and B cell responses following exposure to BA.5 and XBB variants in individuals who received ancestral SARS-CoV-2 mRNA vaccines. BA.5 exposures elicited antibody responses that primarily targeted epitopes conserved between the BA.5 and ancestral spike, with poor reactivity to the XBB.1.5 variant. XBB exposures also elicited antibody responses that targeted epitopes conserved between the XBB.1.5 and ancestral spike. However, unlike BA.5, a single XBB exposure elicited low levels of XBB.1.5-specific antibodies and B cells in some individuals. Pre-existing cross-reactive B cells and antibodies were correlated with stronger overall responses to XBB but weaker XBB-specific responses, suggesting that baseline immunity influences the activation of variant-specific SARS-CoV-2 responses.

Structure-Activity Relationships of Natural and Semisynthetic Plecomacrolides Suggest Distinct Pathways for HIV-1 Immune Evasion and Vacuolar ATPase-Dependent Lysosomal Acidification.

The human immunodeficiency virus (HIV)-encoded accessory protein Nef enhances pathogenicity by reducing major histocompatibility complex I (MHC-I) cell surface expression, protecting HIV-infected cells from immune recognition. Nef-dependent downmodulation of MHC-I can be reversed by subnanomolar concentrations of concanamycin A (1), a well-known inhibitor of vacuolar ATPase, at concentrations below those that interfere with lysosomal acidification or degradation. We conducted a structure-activity relationship study that assessed 76 compounds for Nef inhibition, 24 and 72 h viability, and lysosomal neutralization in Nef-expressing primary T cells. This analysis demonstrated that the most potent compounds were natural concanamycins and their derivatives. Comparison against a set of new, semisynthetic concanamycins revealed that substituents at C-8 and acylation of C-9 significantly affected Nef potency, target cell viability, and lysosomal neutralization. These findings provide important progress toward understanding the mechanism of action of these compounds and the identification of an advanced lead anti-HIV Nef inhibitory compound.

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.