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Hakeny, K.

Publications and source records attributed to Hakeny, K..

2 recordsLinked to original sources

Aberrant inheritance of extrachromosomal DNA amplifications promotes cancer evolution

Gene amplification in the form of extrachromosomal DNA (ecDNA) is a frequent driver in multiple cancer types. As ecDNA lack centromeres, their mitotic segregation does not follow traditional inheritance principles. However, the mechanisms that govern ecDNA fate following mitosis remain unclear. We found that ecDNA undergo numerical and structural optimization under increased selective pressure, with mitotic chromosomal tethering, or detachment, dictating ecDNA fate. When tethered, ecDNA aggregates promote uneven distribution into the newly formed daughter cells, thereby driving inter-cellular numerical heterogeneity and rapid increase of amplification under selective pressure. Mitotically detached ecDNA frequently encapsulate within micronuclei of variable size and content that appear to be highly fragile. Strikingly, ecDNA enclosed in very small micronuclei, which we term nanonuclei, are being actively degraded through autophagy. Together with ongoing structural rearrangements, nanonuclear ecDNA degradation promotes their structural evolution, which facilitates cancer cell adaptation. Our work highlights ecDNA aggregation, micronucleation, and degradation, as pivotal events in directing cancer genome evolution trajectories.

cancer biology↗

GGAssembler: Economical Design of Gene Libraries with Precise Control over Mutations

Golden Gate assembly (GGA) can seamlessly generate full-length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost-effective libraries has been challenging. We present GGAssembler, a graph-theoretical method for economical design of DNA fragments that assemble a combinatorial library that encodes any desired diversity. We used GGAssembler for one-pot in vitro assembly of camelid antibody libraries comprising >105 variants with DNA costs <0.007$ per variant and dropping significantly with increased library complexity. >93% of the desired variants were present in the assembly product and >99% were represented within the expected order of magnitude as verified by deep sequencing. The GGAssembler workflow is, therefore, an accurate approach for generating complex variant libraries that may drastically reduce costs and accelerate discovery and optimization of antibodies, enzymes and other proteins. The workflow is accessible through a web interface at https://github.com/Fleishman-Lab/GGAssembler/blob/master/example/colab_oligos_design.ipynb.

molecular biology↗