Biology subjects

Kotzen, B.

Publications and source records attributed to Kotzen, B..

3 recordsLinked to original sources

bioRxiv2026

Transition from infectivity and immune escape to pure escape as an evolutionary strategy during the COVID-19 pandemic.

New SARS-CoV-2 variants have undergone repeated selective sweeps since the beginning of the COVID-19 pandemic, but the fitness advantages and mechanisms driving these sweeps are not fully understood. We developed a probabilistic modeling framework to analyze pandemic growth, infectivity, and immune escape, explicitly accounting for seven immune exposure histories in 5,732 experiments and estimating the effects of 835 mutations. We found infectivity was important for early variants, but as gains became zero-sum, growth became driven by consistent increase in immune escape conferred by a primarily additive effect of mutational accumulation. While phenotypic tradeoffs exist for individual mutations, successful viral strains boast assemblages of mutations that do not sacrifice infectivity for escape. Thus, during an apparent transition to endemicity, SARS-CoV-2 evolution ascended along an evolutionary ridge in the mutational space defined by infectivity and escape, with infectivity reaching an early peak and antigenicity continuing to evolve.

evolutionary biology↗

bioRxiv2025

Complex exchanges among plasmids and clonal expansion of lineages shape the population structure and virulence of Borrelia burgdorferi

The many plasmids (commonly >20 per strain) of Borrelia burgdorferi (Bb) pose challenges for studies of Lyme disease. The genetic content of most plasmids cannot be resolved using short-read sequencing. We generated long-read assemblies (LRA) of 183 isolates of human-derived Bb and analyzed patterns of genome variation in the Lyme spirochete. LRAs confirm that populations consist of strongly-structured genotypes with nearly-clonal structure and tight-knit blocks of accessory genome elements. Notably, these patterns of linkage were statistical but not physical, with linkage blocks distributed across multiple plasmids. A consequence of this structure is that plasmid name and/or plasmid subtype does not capture strain-specific genetic content. We used network methods to characterize patterns of genetic linkage. We demonstrate that co-occurring gene networks, here termed genetic modules, are the fundamental unit of genome variation in Bb and designate genetic modules consisting of co-occurring genes. We linked genetic modules to Bb phenotype by identifying modules that influence dissemination in humans. This modular decomposition clarifies previously observed associations between strain and virulence. For example, virulent RST1/OspC type A strains are distinguished by the presence of virulence-associated modules containing gene content from lp28-1, lp56, and the chromosome along with the absence of gene content on lp28-1 and lp28-4 associated with localized disease. LRAs also demonstrate that the well-established statistical linkage between physical unlinked genetic markers (e.g. RST and OspC) is a general pattern among accessory genome elements. In summary, genetic modules containing genes linked across multiple replicons, rather than strain-defining plasmids, organize the Bb accessory genome and the strain-specific variation responsible for differences in human virulence.

microbiology↗

bioRxiv2023

Deep generative models predict SARS-CoV-2 Spike infectivity and foreshadow neutralizing antibody escape

Recurrent waves of SARS-CoV-2 infection, driven by the periodic emergence of new viral variants, highlight the need for vaccines and therapeutics that remain effective against future strains. Yet, our ability to proactively evaluate such therapeutics is limited to assessing their effectiveness against previous or circulating variants, which may differ significantly in their antibody escape from future viral evolution. To address this challenge, we develop a deep learning method to predict the effect of mutations on fitness and escape from neutralizing antibodies. We use this model to engineer 83 unique SARS-CoV-2 Spike proteins incorporating novel combinations of up to 46 amino acid changes relative to the ancestral B.1 variant. The designed constructs were infectious and evaded neutralization by nine well-characterized panels of human polyclonal anti-SARS-CoV-2 immune sera (from vaccinated, boosted, bivalent boosted, and breakthrough infection individuals). Designed constructs on contemporary SARS-CoV-2 strains displayed similar levels of antibody neutralization escape and similar antigenic profiles as variants seen subsequently (up to 12 months later) during the COVID-19 pandemic despite differences in exact mutations. Our approach provides targeted datasets of antigenically diverse escape variants for an early evaluation of the protective ability of vaccines and therapeutics to inhibit not only currently circulating but also future variants. This approach is generalizable to other viral pathogens.

immunology↗