Evolution of drug resistance in Candida glabrata

光滑念珠菌耐药性的演变

• 批准号: 10531319
• 负责人: Gavin J Sherlock
• 金额: $ 7.2万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10531319
• 关键词: Accounting; Affect; Antibiotics; Antifungal Agents; Automobile Driving; Bar Codes; Candida; Candida albicans; Candida glabrata; Candidiasis; Clinical; Collection; Combination Drug Therapy; Combined Modality Therapy; Coupled; DNA; Data; Drug Controls; Drug resistance; Environment; Evolution; Exhibits; Frequencies; Fungal Drug Resistance; Generations; Genetic; Genotype; Growth; Haploidy; Human; Incidence; Individual; Infection; Intensive Care Units; Investigational Drugs; Knowledge; Maps; Measures; Minimum Inhibitory Concentration measurement; Multi-Drug Resistance; Mutation; Mycoses; Natural Selections; Outcome; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Pharmacotherapy; Population; Population Sizes; Predisposition; Procedures; Process; Resistance; Resistance profile; Resolution; System; Therapeutic; Time; Vaccines; Work; Yeasts; acute infection; arms race; asexual; base; burden of illness; chronic infection; contagion; deep sequencing; defined contribution; dosage; drug discovery; economic cost; emerging pathogen; experimental study; fitness; fungus; improved; infection rate; insight; mortality; mutant; novel therapeutic intervention; pathogen; pathogenic fungus; small molecule libraries; therapy outcome; tool; weapons; whole genome

Project Summary/Abstract The incidence and progression of chronic or acute infections reflect evolutionary processes, in which pathogens engage in an arms race with their human hosts. While vaccines and antibiotics have enabled mankind to skew the outcome of this contest, these bulwarks against contagion are being eroded. Mutation and natural selection, coupled with rapid generation times and immense population sizes, appear to give pathogens a decisive edge in the evolutionary contest. To regain the upper hand, we must devise new therapeutic strategies that better take into account how our weapons work and how pathogens adaptively evolve to subvert them. Fungal infections impose an enormous disease burden: superficial fungal infections affect as much as 25% of the human population, while each year >2,000,000 invasive fungal infections occur worldwide. Mortality rates for invasive fungal infections are high (20-95%), and their incidence and economic cost are increasing. Of particular concern is the fact that resistance to antifungal drugs is on the rise, with clinical isolates increasingly showing multidrug resistance. To rationally improve upon sequential and combination drug therapies, it is crucial that we understand the ways in which resistance to antifungal drugs arises, and the degree to which resistance to one drug may increase or decrease susceptibility to another. We propose three Specific Aims to capture the “resistome” of the human fungal pathogen, Candida glabrata. First, we will introduce a modified version of our previously developed DNA-barcode based lineage tracking system into C. glabrata, then experimentally evolve barcoded C. glabrata populations in the presence of four different antifungal agents representing all four major antifungal drug classes. Second, we will isolate hundreds of adaptive lineages from each of these evolution experiments then remeasure their fitnesses in the presence and absence of each of these antifungal drugs. This will enable us to generate a finely resolved picture of the trade-offs and cross-resistance conferred by mutations that arise under selection for resistance to a particular drug. Finally, we will whole genome sequence more than a thousand of these drug-resistant mutants, and measure for each the minimum inhibitory concentration (MIC) for each drug, generating a high-resolution map that describes the relationship between genotype, fitness and drug susceptibility. Completion of these Aims will result in the most comprehensive collection of antifungal resistance mutants ever generated, for which we will know the identities of the mutations and their fitness consequences in the presence of multiple drugs. Our data will provide unprecedented insight into the mechanisms by which resistance arises to all classes of antifungal drugs, which can be used to improve therapeutic strategies, based on the observed patterns of cross-resistance and trade-offs. Our collection of barcoded mutants will open the door to investigation of drug resistance under multi-drug and sequential drug selections and serve as a tool to screen small molecule libraries for the purpose of drug discovery.

项目总结/摘要 慢性或急性感染的发病率和进展反映了进化过程，其中 病原体与它们的人类宿主进行军备竞赛。虽然疫苗和抗生素使人类 为了扭曲这场竞赛的结果，这些抵御危机蔓延的堡垒正在受到侵蚀。突变和自然 选择，加上快速的世代时间和巨大的种群规模，似乎给病原体 在进化竞争中的决定性优势。为了重新占上风，我们必须设计新的治疗策略 这更好地考虑到我们的武器如何工作以及病原体如何适应性进化以破坏它们。 真菌感染造成巨大的疾病负担：浅表真菌感染影响多达25%的人， 全球每年发生> 2，000，000例侵袭性真菌感染。死亡率 侵袭性真菌感染很高（20-95%），其发病率和经济成本正在增加。特别 令人担忧的是，对抗真菌药物的耐药性正在上升，临床分离株越来越多地显示出 多药耐药为了合理地改进序贯和联合药物治疗，我们必须 了解抗真菌药物耐药性产生的方式，以及抗真菌药物耐药性的程度， 药物可能会增加或减少对另一种药物的敏感性。 我们提出了三个具体的目标，以捕捉人类真菌病原体，光滑念珠菌的“耐药基因组”。 首先，我们将介绍我们以前开发的基于DNA条形码的谱系跟踪的修改版本 系统转化为C. glabrata，然后实验进化条形码C. glabrata种群在四个 不同的抗真菌剂代表了所有四种主要的抗真菌药物类别。第二，我们将隔离数百名 这些进化实验的适应性谱系，然后重新测量他们的适应性， 以及缺乏这些抗真菌药物。这将使我们能够产生一个精细解决的图片， 在选择下产生的突变所带来的权衡和交叉抗性， 药最后，我们将对1000多个耐药突变体进行全基因组测序， 测量每种药物的最低抑菌浓度（MIC），生成高分辨率图谱 描述基因型、健康和药物敏感性之间的关系。完成这些目标将 导致有史以来产生的抗真菌耐药突变体的最全面的收集，为此，我们将 知道突变的身份及其在多种药物存在下的适应性后果。我们的数据 将提供前所未有的洞察机制，耐药性产生的所有类别的抗真菌药物， 根据观察到的交叉耐药模式， 和权衡。我们的条形码突变体的收集将打开大门，调查耐药性下， 多药物和顺序药物选择，并用作筛选小分子文库的工具 药物发现。

项目成果

An improved algorithm for inferring mutational parameters from bar-seq evolution experiments.

• DOI: 10.1186/s12864-023-09345-x
• 发表时间: 2023-05-06
• 期刊: BMC genomics
• 影响因子: 4.4

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

• DOI: 10.1371/journal.pbio.3001633
• 发表时间: 2022-05
• 期刊: PLOS BIOLOGY
• 影响因子: 9.8
• 作者: Avecilla, Grace;Chuong, Julie N.;Li, Fangfei;Sherlock, Gavin;Gresham, David;Ram, Yoav
• 通讯作者: Ram, Yoav

Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays.

• DOI: 10.1007/s00239-023-10098-0
• 发表时间: 2023-06
• 期刊: Journal of molecular evolution
• 影响因子: 3.9