Next-Generation Antimicrobial Diagnostics: Speed and Precision via Whole Genome Sequencing

下一代抗菌诊断：通过全基因组测序实现速度和精度

• 批准号: 508401-2017
• 负责人: Guttman, David
• 金额: $ 15.81万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=621901
• 关键词: Next; Generation; Antimicrobial; Diagnostics; Speed

Bacterial infections cause millions of infections and take tens of thousands of lives every year,and without antibiotics the death toll would skyrocket. Unfortunately, antibiotics are rapidlybecoming less effective due to the evolution of resistance. On top of this, doctors often have adifficult time identifying what drugs will be most effective against infecting pathogens a timelymanner. For these reasons there is desperate need for new antimicrobial diagnostic methodsthat can rapidly provide physicians accurate information to guide their treatments. One verypromising new approach to this problem is to sequence the genomes of the infectingpathogens and use this information predict how they will respond to treatment. We propose toproduce genome sequence data and use sophisticated genome-wide association andpredictive modeling methods to develop a new antimicrobial diagnostic tool. We willspecifically focus on stopping a bacterial pathogen called Pseudomonas aeruginosa thatcauses a disproportionate number of hospital infections and deaths. We will work incollaboration with our partners at the Hospital for Sick Children in Toronto to generate thisstate-of-the-art diagnostic tool and then develop procedures to move it into the clinic. Thesuccess of this project will speed diagnosis, improve patient care, reduce healthcare costs,and enhance stewardship of antibiotics; thereby, preserving their efficacy for the future.

细菌感染每年造成数百万人感染，并夺走数万人的生命，如果没有抗生素，死亡人数将飙升。不幸的是，由于耐药性的进化，抗生素的效果正在迅速下降。最重要的是，医生经常很难及时确定哪些药物对感染病原体最有效。由于这些原因，迫切需要新的抗菌诊断方法，可以迅速为医生提供准确的信息，以指导他们的治疗。解决这个问题的一个非常有前途的新方法是对感染病原体的基因组进行测序，并利用这些信息预测它们对治疗的反应。我们建议产生基因组序列数据，并使用复杂的全基因组关联和预测建模方法来开发一种新的抗菌诊断工具。我们将特别关注阻止一种名为绿脓杆菌的细菌病原体，这种病原体会导致不成比例的医院感染和死亡。我们将与我们在多伦多的病童医院的合作伙伴共同开发这种最先进的诊断工具，然后开发出将其应用于临床的程序。该项目的成功将加快诊断速度，改善患者护理，降低医疗成本，并加强抗生素的管理;从而为未来保留其功效。