Reference: Monge M, et al. (2025) Highly replicated experiments studying complex genotypes using nested DNA barcodes. G3 (Bethesda)

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Abstract


Many biological experiments involve studying the differences caused by genetic modifications, including genotypes composed of modifications at more than 1 locus. However, as the genotypes increase in number and complexity, it becomes a major challenge to independently generate and track the necessary number of biological replicate samples. A major development in genetic studies of large numbers of genotypes has been the use of barcode tracking. Inspired by such high-throughput studies, we developed a barcode-based method to track large numbers of independent replicates of a small number of combinatorial genotypes in a pooled format, enabling robust detection of subtle phenotypic differences. To construct a plasmid library of combinatorial genotypes, we utilized a nested serial cloning process to combine gene variants of interest that have associated DNA barcodes. The final plasmids each contain variants of multiple genes of interest, and a combined barcode that specifies the genotype of all the genes while also encoding a random sequence for tracking individual replicates. Sequencing of the pool of barcodes by next-generation sequencing allows the whole population to be studied in a single flask, enabling a high degree of replication even for complex genotypes. Using this approach, we tested the functionality of combinations of yeast, human, and null orthologs of the nucleotide excision repair factor I (NEF-1) complex and found that yeast cells expressing all 3 yeast NEF-1 subunits had superior growth in DNA-damaging conditions. We also assessed the sensitivity of our method by simulating downsampling of barcodes across different degrees of phenotypic differentiation. Our results demonstrate the utility of NICR (nested identification combined with replication) barcodes for high-throughput combinatorial genetic screens and provide a scalable framework for exploring complex genotype-phenotype relationships.

Reference Type
Journal Article
Authors
Monge M, Giovanetti SM, Ravishankar A, Sadhu MJ
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