Modelling microbial evolution: predicting evolutionary escape

细菌和病毒等微生物对人类健康和疾病至关重要，是湖泊、海洋和森林健康的基础，也是农业和全球营养循环的基础。由于种群规模大，世代时间短，微生物进化非常快;随着细菌对每种新抗生素的耐药性不断进化，这一点也被反复观察到。我的研究小组使用数学模型来预测微生物如何进化。我们通过计算机模拟微生物种群的进化来验证我们的数学工作。我们还与实验科学家密切合作，以更好地了解微生物在受控实验室环境中进化时获得的数据。每当微生物的生长受到人类免疫系统、医疗干预或其他微生物的控制时，进化通常会绕过这种限制，这样一个适应的微生物种群就会恢复并生长;这种通过适应的恢复被称为“进化逃避”。我的目标是开发模型和模拟，使我们能够更好地预测三种情况下的进化逃逸：1。逃离免疫如果一个种群中有足够多的个体对某种传染性微生物具有免疫力，这种传染病就不会传播。不幸的是，微生物可以进化以避免被免疫系统识别;这就是为什么很难设计流感疫苗。在以前的工作中，我们发现，在大多数情况下，如果种群以许多不同的方式对微生物免疫，这种进化逃避就会延迟。然而，我们最近发现，免疫多样性使逃跑更有可能。我的目标是使用数学模型来识别这些意外的和潜在的危险情况，在这些情况下，多样性促进了逃避。2.逃避序列特异性治疗。一种令人兴奋的新型抗生素疗法正在出现，它可以针对特定的基因序列进行编程。例如，这将使我们能够杀死感染中的有害细菌，或者只杀死环境中的抗药性细菌。不幸的是，与以前的抗生素一样，进化逃避是不可避免的。我的目标是预测序列特异性药物的预期逃逸率;这项工作将是开发这类新疗法的关键一步。3.共同进化的逃避感染性微生物和它们感染的种群可以在一系列进化逃逸中共同进化：“宿主”种群通过发展免疫力来逃避微生物;微生物共同进化以逃避免疫压力。基因序列可以在微生物和它们的宿主之间移动，使这种情况变得更加复杂。以细菌和病毒为模型系统，我计划使用进化模型来预测在微生物和它们的宿主共同进化时在它们之间移动的基因序列的命运。最终，这项工作将帮助我们理解和解释我们从感染我们的病毒中获得的8%的人类基因组。

Microbes, such as bacteria and viruses, are critical to both human health and disease, underpin the health of our lakes, oceans and forests, and are also foundational to agriculture and global nutrient cycling. With large population sizes and short generation times, microbes evolve very quickly; this is repeatedly observed as bacteria evolve resistance to each new antibiotic. My research group uses mathematical models to predict how microbes evolve. We verify our mathematical work by using computer simulations of evolving microbial populations. We also work closely with experimental scientists to better understand the data obtained when microbes evolve in controlled laboratory settings. Whenever microbial growth is held in check - by the human immune system, by medical intervention, or by other microbes - evolution typically circumvents the restriction, such that an adapted microbial population recovers and grows; this recovery through adaptation is called "evolutionary escape". My goal is to develop models and simulations that allow us to better predict evolutionary escape in three situations: 1. Escape from Immunity. If enough individuals in a population are immune to an infectious microbe, the infection will not spread. Unfortunately, the microbe can evolve to avoid recognition by the immune system; this is why it is difficult to design the influenza vaccine. In previous work we found that in most situations, this evolutionary escape is delayed if the population is immune to the microbe in many different ways. We have recently discovered, however, situations in which immune diversity makes escape more likely. My goal is to use mathematical models to identify these unexpected and potentially dangerous situations in which diversity promotes escape. 2. Escape from Sequence-Specific Therapy. An exciting new class of antibiotic therapies is emerging that can be programmed to target specific gene sequences. This will allow us to kill, for example, only the harmful bacteria in an infection, or only the antibiotic-resistant bacteria in the environment. Unfortunately, as with previous antibiotics, evolutionary escape is inevitable. My goal is to predict the expected rate of escape from sequence-specific agents; this work will be a critical step in the development of this new class of therapy. 3. Co-evolutionary Escape. Infectious microbes and the populations they infect can co-evolve in a series of evolutionary escapes: the "host" population escapes the microbe by developing immunity; the microbe co-evolves to escape immune pressure. This situation is made more complex by gene sequences that can move between microbes and their hosts. Using bacteria and viruses as a model system, I plan to use evolutionary models to predict the fate of gene sequences that move between microbes and their hosts as they co-evolve. Ultimately, this work will help us understand and explain the 8% of the human genome that we have adopted from the viruses that infect us.