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Biology subjects

Mankowski, M. C.

Publications and source records attributed to Mankowski, M. C..

2 recordsLinked to original sources

Antibodies, Memory B Cells, and Antigen Valency Reshape B Cell Responses to Drifted Influenza Virus Vaccination

Antigenic drift in influenza A virus hemagglutinin (HA) limits humoral protective immunity. Here, we combine cell fate mapping with adoptive transfer of antigenic-site-specific antibodies (Abs) and memory B cells (MBCs) with moderately drifted HA vaccination in mice to better understand how this influences immune escape and protective responses. We demonstrate that drift in vaccine antigens affects MBC reactivation and naive B cell responses in germinal centers (GC). Strikingly, passively transferred monoclonal and polyclonal Abs suppress cognate epitope-specific GC B cell responses only when the vaccine antigen was multivalent while responses to monovalent recombinant trimeric HA remain unaffected. Using MBC and Abs co-transfer we unveil that antigenic site-specific suppression is more potent in blocking MBC rather than naive B cells entry into GC. In addition, we show that MBC hamper naive B cell recruitment to GC even in the absence of antibody transfer through local differentiation and Ab release in the responding lymph node. Altogether, our study reveals that serum Ab feedback depends on vaccine valency, while pre-existing MBC alone without Abs present can reshape immunodominance of naive B cells, with critical practical implications for rational universal influenza vaccine design.

immunology↗

Top-down engineering of complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.

evolutionary biology↗