Billmyre R Blake, Craig Caroline J, Lyon Joshua W, Reichardt Claire, Kuhn Amy M, Eickbush Michael T, Zanders Sarah E
Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, GeorgiaUnited States of America.
Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GeorgiaUnited States of America.
PLoS Biol. 2025 May 22;23(5):e3003184. doi: 10.1371/journal.pbio.3003184. eCollection 2025 May.
Fungi can cause devastating invasive infections, typically in immunocompromised patients. Treatment is complicated both by the evolutionary similarity between humans and fungi and by the frequent emergence of drug resistance. Studies in fungal pathogens have long been slowed by a lack of high-throughput tools and community resources that are common in model organisms. Here we demonstrate a high-throughput transposon mutagenesis and sequencing (TN-seq) system in Cryptococcus neoformans that enables genome-wide determination of gene essentiality. We employed a random forest machine learning approach to classify the C. neoformans genome as essential or nonessential, predicting 1,465 essential genes, including 302 that lack human orthologs. These genes are ideal targets for new antifungal drug development. TN-seq also enables genome-wide measurement of the fitness contribution of genes to phenotypes of interest. As proof of principle, we demonstrate the genome-wide contribution of genes to growth in fluconazole, a clinically used antifungal. We show a novel role for the well-studied RIM101 pathway in fluconazole susceptibility. We also show that insertions of transposons into the 5' upstream region can drive sensitization of essential genes, enabling screenlike assays of both essential and nonessential components of the genome. Using this approach, we demonstrate a role for mitochondrial function in fluconazole sensitivity, such that tuning down many essential mitochondrial genes via 5' insertions can drive resistance to fluconazole. Our assay system will be valuable in future studies of C. neoformans, particularly in examining the consequences of genotypic diversity.
真菌可引发严重的侵袭性感染，通常发生在免疫功能低下的患者中。由于人类和真菌在进化上具有相似性，且耐药性频繁出现，治疗变得复杂。长期以来，真菌病原体的研究因缺乏在模式生物中常见的高通量工具和群体资源而进展缓慢。在此，我们展示了一种在新型隐球菌中的高通量转座子诱变和测序（TN-seq）系统，该系统能够在全基因组范围内确定基因的必需性。我们采用随机森林机器学习方法将新型隐球菌基因组分类为必需或非必需，预测出1465个必需基因，其中包括302个在人类中没有直系同源物的基因。这些基因是新型抗真菌药物开发的理想靶点。TN-seq还能够在全基因组范围内测量基因对感兴趣表型的适应性贡献。作为原理验证，我们展示了基因对临床使用的抗真菌药物氟康唑生长的全基因组贡献。我们揭示了研究充分的RIM101途径在氟康唑敏感性方面的新作用。我们还表明，转座子插入到5'上游区域可导致必需基因的敏感化，从而能够对基因组的必需和非必需成分进行类似筛选的分析。使用这种方法，我们证明了线粒体功能在氟康唑敏感性中的作用，即通过5'插入下调许多必需的线粒体基因可导致对氟康唑产生耐药性。我们的检测系统在未来新型隐球菌的研究中具有重要价值，特别是在研究基因型多样性的后果方面。
