Sequencing HIV-neutralizing antibody exons and introns reveals detailed aspects of lineage maturation.

The developmental pathways of broadly neutralizing antibodies (bNAbs) against HIV are of great importance for the design of immunogens that can elicit protective responses. Here we show the maturation features of the HIV-neutralizing anti-V1V2 VRC26 lineage by simultaneously sequencing the exon together with the downstream intron of VRC26 members. Using the mutational landscapes of both segments and the selection-free nature of the intron region, we identify multiple events of amino acid mutational convergence in the complementarity-determining region 3 (CDR3) of VRC26 members, and determine potential intermediates with diverse CDR3s to a late stage bNAb from 2 years prior to its isolation. Moreover, we functionally characterize the earliest neutralizing intermediates with critical CDR3 mutations, with some emerging only 14 weeks after initial lineage detection and containing only ~6% V gene mutations. Our results thus underscore the utility of analyzing exons and introns simultaneously for studying antibody maturation and repertoire selection.

Discovery of high affinity anti-ricin antibodies by B cell receptor sequencing and by yeast display of combinatorial VH:VL libraries from immunized animals.

Ricin is a toxin that could potentially be used as a bioweapon. We identified anti-ricin A chain antibodies by sequencing the antibody repertoire from immunized mice and by selecting high affinity antibodies using yeast surface display. These methods led to the isolation of multiple antibodies with high (sub-nanomolar) affinity. Interestingly, the antibodies identified by the 2 independent approaches are from the same clonal lineages, indicating for the first time that yeast surface display can identify native antibodies. The new antibodies represent well-characterized reagents for biodefense diagnostics and therapeutics development.

Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires.

Elucidating how antigen exposure and selection shape the human antibody repertoire is fundamental to our understanding of B-cell immunity. We sequenced the paired heavy- and light-chain variable regions (VH and VL, respectively) from large populations of single B cells combined with computational modeling of antibody structures to evaluate sequence and structural features of human antibody repertoires at unprecedented depth. Analysis of a dataset comprising 55,000 antibody clusters from CD19(+)CD20(+)CD27(-) IgM-naive B cells, >120,000 antibody clusters from CD19(+)CD20(+)CD27(+) antigen-experienced B cells, and >2,000 RosettaAntibody-predicted structural models across three healthy donors led to a number of key findings: (i) VH and VL gene sequences pair in a combinatorial fashion without detectable pairing restrictions at the population level; (ii) certain VH:VL gene pairs were significantly enriched or depleted in the antigen-experienced repertoire relative to the naive repertoire; (iii) antigen selection increased antibody paratope net charge and solvent-accessible surface area; and (iv) public heavy-chain third complementarity-determining region (CDR-H3) antibodies in the antigen-experienced repertoire showed signs of convergent paired light-chain genetic signatures, including shared light-chain third complementarity-determining region (CDR-L3) amino acid sequences and/or Vκ,λ-Jκ,λ genes. The data reported here address several longstanding questions regarding antibody repertoire selection and development and provide a benchmark for future repertoire-scale analyses of antibody responses to vaccination and disease.

Facile Discovery of a Diverse Panel of Anti-Ebola Virus Antibodies by Immune Repertoire Mining.

The ongoing evolution of Ebolaviruses poses significant challenges to the development of immunodiagnostics for detecting emergent viral variants. There is a critical need for the discovery of monoclonal antibodies with distinct affinities and specificities for different Ebolaviruses. We developed an efficient technology for the rapid discovery of a plethora of antigen-specific monoclonal antibodies from immunized animals by mining the VH:VL paired antibody repertoire encoded by highly expanded B cells in the draining popliteal lymph node (PLN). This approach requires neither screening nor selection for antigen-binding. Specifically we show that mouse immunization with Ebola VLPs gives rise to a highly polarized antibody repertoire in CD138(+) antibody-secreting cells within the PLN. All highly expanded antibody clones (7/7 distinct clones/animal) were expressed recombinantly, and shown to recognize the VLPs used for immunization. Using this approach we obtained diverse panels of antibodies including: (i) antibodies with high affinity towards GP; (ii) antibodies which bound Ebola VLP Kissidougou-C15, the strain circulating in the recent West African outbreak; (iii) non-GP binding antibodies that recognize wild type Sudan or Bundibugyo viruses that have 39% and 37% sequence divergence from Ebola virus, respectively and (iv) antibodies to the Reston virus GP for which no antibodies have been reported.

Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum.

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12[Formula: see text], closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03[Formula: see text]. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.