Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.10.26.513854

High-Throughput Screening of the Saccharomyces cerevisiae Genome for 2-Amino-3-Methylimidazo Quinoline Resistance Identifies Colon Cancer-Associated Genes

Abstract

Heterocyclic aromatic amines (HAAs) are potent carcinogenic agents found in charred meats and cigarette smoke. However, few eukaryotic resistance genes have been identified. We used Saccharomyces cerevisiae (budding yeast) to identify genes that confer resistance to 2-amino-3-methylimidazo[4,5-f]quinoline (IQ). CYP1A2 and NAT2 activate IQ to become a mutagenic nitrenium compound. We introduced an expression vector that contains human CYP1A2 and NAT2 genes into selected mutant strains and the diploid yeast deletion collection. The deletion libraries expressing CYP1A2 and NAT2 or no human genes were exposed to either 400 or 800 M IQ for five or ten generations. DNA barcodes were sequenced using the Illumina HiSeq 2500 platform and statistical significance was determined for exactly matched barcodes. Four screens for IQ resistance in the "humanized" collection identified 1160 unique ORFs, of which 337 were validated or duplicated in at least two screens. Two screens of the original yeast library identified 101 genes that overlap with the 337 previously identified. Selected genes were validated by growth curves, competitive growth assays, or trypan blue assays. Prominent among both sets are ribosomal protein genes, while nitrogen metabolism, cell wall synthesis, and phosphatase genes were identified among the "humanized" library. Protein complexes identified included the casein kinase 2 (CK2) and histone chaperone (HIR) complex. DNA repair genes included NTG1, RAD18, RAD9, PSY2 and UBC13. Polymorphisms in human NTHL1, the NTG1 ortholog, and RAD18 are risk factors for colon cancer. These studies thus provoke questions of whether genetic risk factors for colon cancer confer more HAA-associated toxicity.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dolan, M., Zaidi, F., St. John, N., Doyle, F., Fasullo, M.. 2022-10-27. High-Throughput Screening of the Saccharomyces cerevisiae Genome for 2-Amino-3-Methylimidazo Quinoline Resistance Identifies Colon Cancer-Associated Genes. https://doi.org/10.1101/2022.10.26.513854

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Generation of a transgenic cephalopod

Coleoid cephalopods (cuttlefish, octopus, and squid) are marine mollusks with elaborate nervous systems that support a diverse repertoire of complex behaviors. These include the neural control of the color, pattern, and texture of the skin, facilitating both adaptive camouflage and innate patterning that may reflect internal state. The development of transgenic cephalopods expressing fluorescent proteins, optogenetic actuators, and reporters of neural activity would contribute a new and important technology to cephalopod biology. The generation of transgenic cephalopods, however, has remained a major challenge. Here, we report the development of stable transgenic dwarf cuttlefish (Ascarosepion bandense) expressing ubiquitous nuclear-localized mScarlet, a red fluorescent protein. We evaluated multiple strategies for transgenesis, and established cuttlefish lines using both CRISPR and the transposons Sleeping Beauty and Minos. The stable expression of transgenes enabled live imaging of cell dynamics during embryonic development. The Minos transposon emerged as the most efficient transgenesis strategy and is adaptable to promoters and transgenes of choice. These strategies now enable the generation of diverse genetic tools for mechanistic studies of cephalopod biology.

genetics↗

Large language model-based bibliometric evaluation of population descriptors in human genetics

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

genetics↗

Mitigating biases of rescaling in forward-in-time population genetic simulations

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

genetics↗