A priori adaptive evolution predictions for antibiotic resistance through genome-wide network analyses and machine learning

通过全基因组网络分析和机器学习对抗生素耐药性进行先验适应性进化预测

• 批准号: 10641700
• 负责人: Juan Cesar Federico Ortiz-Marquez
• 金额: $ 39.13万
• 依托单位: BOSTON COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10641700
• 关键词: Achievement; Affect; Antibiotic Resistance; Antibiotics; Architecture; Automobile Driving; Bacteria; Binding Sites; Biological; Biological Process; Biomass; Biomedical Engineering; ChIP-seq; Chromosome Mapping; Communicable Diseases; Complex; DNA Binding; Data; Development; Drug resistance; Ensure; Environment; Escherichia coli; Event; Evolution; Exposure to; Fermentation; Frustration; Genes; Genetic; Genetic Epistasis; Genetic Transcription; Genome; Genomics; Goals; Immune system; Immunotherapeutic agent; Industry; Life; Link; Machine Learning; Malignant Neoplasms; Maps; Microfluidics; Modeling; Mutation; Organism; Outcome; Pathway Analysis; Pattern; Phenotype; Photosynthesis; Planet Earth; Process; Repression; Resistance; Shapes; Streptococcus pneumoniae; Stress; System; Testing; Time; Training; Yeasts; design; driving force; droplet sequencing; emerging antibiotic resistance; emerging antimicrobial resistance; experience; experimental study; genetic architecture; genome-wide; genomic tools; machine learning model; machine learning prediction; network architecture; novel; overexpression; pathogenic bacteria; predictive modeling; prevent; process optimization; programs; response; tool; trait; transcription factor; transcription regulatory network; transcriptome; transcriptome sequencing; transposon sequencing

SUMMARY Adaptive evolution (AE) is both a “force of good” as it can help to optimize biological processes in industry, but it is also a “force of frustration” when infectious diseases exploit AE to escape the host immune system or become resistant to drugs. It has long been assumed close to impossible to make predictions on AE due to the presumed predominating influences of random forces and events. However, the observation that evolutionary repeatability across traits and species is far more common than previously thought, suggests that AE, with the right data and approach, may become (partially) predictable. Indeed, we found through experiments with the bacterial pathogen Streptococcus pneumoniae on its response to antibiotics and the emergence of antimicrobial resistance, that in order to make AE predictable a detailed understanding of at least two aspects of the bacterial system are required: 1.) the genetic constraints of the system (i.e. the architecture of the organismal network); and 2.) where and how in the system stress is experienced and processed. We showed that by mapping out ~25% of the bacterium's network, determining phenotypic and transcriptional antibiotic responses, applying network analyses to capture and quantify the responses in a network context, and exploiting experimental evolution to pin-point adaptive mutations in the genome it becomes possible, by means of machine learning, to uncover hidden patterns in the data that make AE predictions feasible. This means that the network in interaction with the environment shapes the adaptive landscape, it limits available solutions and makes some solutions more likely than others, thereby driving repeatability and enabling predictability. In this proposal we build on these exciting developments with the goal to map out the constraints of S. pneumoniae's entire network and develop a machine learning model that can forecast adaptive evolution a priori, and on a genome-wide scale. To accomplish this, we combine in aim 1 parts of Tn-Seq, dTn-Seq and Drop-Seq to finalize a new tool Tn-Seq^2 (Tn-Seq squared) that is able to map genetic-interactions in high-throughput and genome-wide. We use Tn-Seq^2 to reconstruct the first genome-wide genetic interaction network for S. pneumoniae in the presence of 20 antibiotics. In aim 2 we create 85 HA-tagged Transcription factor induction (TFI) strains and: a) Determine with ChIP-Seq the DNA-binding sites for all 85 TFs in S. pneumoniae; b) By overexpressing each TFI strain followed by RNA- Seq we determine each TFs regulatory signature; c) Use a Transcriptional Regulator Induced Phenotype screen in the presence of 20 antibiotics to untangle environment specific links between genetic and transcriptional perturbations and their phenotypic outcomes. Lastly, in aim 3, we train and test a variety of machine learning approaches to design an optimal model that predicts which genes in the genome are most likely to adapt in the presence of a specific antibiotic. The development of this predictive AE model, will not only be useful in predicting the emergence of antibiotic resistance, but the strategy should be valuable for most any biological field for which adaptive changes are important, ranging from biological engineering to cancer.

概括 自适应进化（AE）既是“良好的力量”，因为它可以帮助优化行业的生物过程，但是 当传染病利用AE逃脱宿主免疫系统或成为时，这也是一种“挫败感” 对药物有抵抗力。长期以来，由于预览，它几乎不可能在AE上做出预测。 随机力和事件的主要影响。但是，观察到进化可重复性 跨性状和物种比以前认为的要普遍得多，这表明AE具有正确的数据和正确的数据 方法可能会（部分）可预测。确实，我们通过细菌病原体的实验发现 肺炎链球菌对抗生素的反应和抗菌耐药性的出现， 为了使AE可以预测，需要对细菌系统的至少两个方面进行详细了解： 1.）系统的遗传约束（即有机网络的结构）； 2.）在哪里以及如何 在系统中，经历和处理压力。我们表明，通过绘制约25％的细菌 网络，确定表型和转录抗生素反应，应用网络分析以捕获 并量化网络环境中的响应，并利用实验进化对PIN点自适应 基因组中的突变是通过机器学习而变得有可能发现隐藏模式的 使AE预测可行的数据。这意味着与环境互动的网络形状 自适应景观，它限制了可用的解决方案，并使某些解决方案比其他解决方案更有可能 推动可重复性并启用可预测性。在此提案中，我们以这些令人兴奋的发展为基础 旨在绘制肺炎链球菌的整个网络的限制并开发机器 可以先验地预测自适应进化的学习模型，并以全基因组量表为单位。完成 这，我们将TN-Seq，DTN-Seq和Drop-Seq的AIM 1部分组合在一起，以最终确定一个新工具TN-Seq^2（TN-Seq 平方）能够在全基因组和全基因组中绘制遗传相互作用。我们使用tn-seq^2 在存在20种抗生素的情况下，重建了肺炎链球菌的第一个全基因组遗传相互作用网络。 在AIM 2中，我们创建了85 ha标记的转录因子诱导（TFI）菌株，a）用芯片seq确定 肺炎链球菌中所有85个TF的DNA结合位点； b）通过过表达每个TFI菌株，然后是RNA- SEQ我们确定每个TFS调节签名； c）使用转录调节剂诱导的表型屏幕 在存在20种抗生素的情况下，遗传与转录之间的特定联系 扰动及其表型结果。最后，在AIM 3中，我们训练和测试各种机器学习 设计最佳模型的方法，该模型可以预测基因组中的哪些基因最有可能适应 特定抗生素的存在。这种预测AE模型的开发不仅在预测 抗生素耐药性的出现，但该策略对于大多数生物学领域都有价值 自适应变化很重要，从生物工程到癌症。