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 适应性进化（AE）既是一种“善的力量”，因为它可以帮助优化工业中的生物过程，但 当传染病利用AE来逃避宿主免疫系统或成为一种“挫败力”时，它也是一种“挫败力” 由于假设，长期以来人们认为对 AE 进行预测几乎是不可能的。 然而，随机力量和事件的主要影响是进化的可重复性。 跨性状和物种的差异比以前想象的要普遍得多，这表明 AE 在正确的数据和 事实上，我们通过对细菌病原体的实验发现，可能会（部分）变得可预测。 肺炎链球菌对抗生素的反应和抗菌素耐药性的出现， 为了使 AE 可预测，需要详细了解细菌系统的至少两个方面： 1.) 系统的遗传约束（即生物网络的架构）；以及 2.) 地点和方式； 我们通过绘制大约 25% 的细菌压力来证明这一点。 网络，确定表型和转录抗生素反应，应用网络分析来捕获 并量化网络环境中的响应，并利用实验进化来精确定位自适应 基因组突变，通过机器学习，揭示基因组中隐藏的模式成为可能 使 AE 预测变得可行的数据，这意味着网络与环境的相互作用形成。 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，从而 在此提案中，我们以这些令人兴奋的发展为基础。 目标是找出肺炎链球菌整个网络的约束并开发一台机器 学习模型可以先验地预测适应性进化，并在全基因组范围内完成。 为此，我们将 Tn-Seq、dTn-Seq 和 Drop-Seq 的目标 1 部分结合起来，最终确定一个新工具 Tn-Seq^2（Tn-Seq 平方），能够在高通量和全基因组范围内绘制遗传相互作用图谱。我们使用 Tn-Seq^2 来进行绘制。 在 20 种抗生素存在的情况下，重建肺炎链球菌的第一个全基因组遗传相互作用网络。 在目标 2 中，我们创建了 85 个 HA 标记的转录因子诱导 (TFI) 菌株，并且： a) 使用 ChIP-Seq 确定 肺炎链球菌中所有 85 个 TF 的 DNA 结合位点 b) 过表达每个 TFI 菌株，然后过表达 RNA- Seq 我们确定每个 TF 的监管特征 c) 使用转录调控因子诱导的表型筛选； 在 20 种抗生素的存在下，解开遗传和转录之间的环境特定联系 最后，在目标 3 中，我们训练和测试各种机器学习。 设计最佳模型的方法可以预测基因组中的哪些基因最有可能适应环境 这种预测性 AE 模型的开发不仅可用于预测。 抗生素耐药性的出现，但该策略对于大多数生物领域都应该有价值 从生物工程到癌症，适应性变化都很重要。