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.

总结 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， 当传染病利用AE逃避宿主免疫系统或成为 对药物有抵抗力。长期以来，人们一直认为几乎不可能对AE进行预测，这是由于假设的 随机力量和事件的主要影响。然而，进化的可重复性 在性状和物种之间的差异比以前认为的要普遍得多，这表明，有了正确的数据， 这可能是（部分）可预测的。事实上，我们通过对细菌病原体的实验发现， 肺炎链球菌对其抗生素的反应和耐药性的出现，表明在 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.）的人。系统的遗传约束（即生物体网络的架构）;以及2.）哪里以及如何 在系统中，压力被经历和处理。我们发现，通过绘制出约25%的细菌 网络，确定表型和转录抗生素反应，应用网络分析捕获 量化网络环境中的响应，并利用实验进化来确定自适应 通过机器学习，可以发现基因组中隐藏的模式， 使AE预测可行的数据。这意味着网络在与环境的相互作用中塑造了 适应性景观，它限制了可用的解决方案，并使一些解决方案比其他解决方案更有可能， 推动可重复性和实现可预测性。在本提案中，我们以这些令人兴奋的发展为基础， 目标是绘制出S的约束。pneumoniae的整个网络并开发一种机器 学习模型，可以预测适应性进化的先验，并在基因组范围内。完成 为此，我们将目标1中的Tn-Seq、dTn-Seq和Drop-Seq的部分内容进行联合收割机组合，最终形成一个新的工具Tn-Seq^2（Tn-Seq 平方），能够以高通量和全基因组的方式绘制遗传相互作用。我们使用Tn-Seq^2来 构建了第一个S.肺炎在20种抗生素的存在下。 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： S. B）通过过表达每种TFI菌株，然后用RNA- Seq，我们确定每个TF调控特征; 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， 干扰及其表型结果。最后，在aim 3中，我们训练和测试了各种机器学习 设计一个最佳模型来预测基因组中哪些基因最有可能适应 存在特定的抗生素。这种预测AE模型的发展，将不仅是有用的预测 抗生素耐药性的出现，但该战略应该是有价值的大多数任何生物领域， 从生物工程到癌症，适应性变化都很重要。