Understanding bacterial resistance by machine learning from genetic data

通过遗传数据的机器学习了解细菌耐药性

• 批准号: 2599501
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2599501
• 关键词: Understanding; bacterial; resistance; machine; learning

Motivation-Bacterial resistance to antibiotics remains one of the biggest challenges in medicine. Although more drugs have become available over the past decade, it is crucial that the use of these drugs is optimised through novel laboratory tests that allow individualised antibiotic therapy for patients.State-of-the-art-Current treatment of patients with infection usually starts with a best guess antibiotic. At the same time, samples are taken and the laboratory attempts to culture the infecting bacterium. If this is successful, the bacterium is exposed in vitro to various antibiotics to determine which antibiotics are likely to effectively treat the infection. Clinicians use these results to change antibiotic treatment as appropriate. Although these tests are cheap and easy, they provide a narrow representation of complex underlying resistance mechanisms.Whole genome sequencing continues to become more accessible and is now routinely used in the NHS in the management of certain infections. This technology allows laboratories to read the whole genetic code of bacteria and is used for the management of outbreaks and, in some cases, to detect antibiotic resistance. However, there is a poor understanding of how genomic data are linked to traditional bacterial culture techniques (phenotypic tests), which have a long track record of being able to predict the likelihood of an antibiotic to cure an infection.As data produced by novel genomic tests become more accessible, we need a better understanding of how they can be used to optimise what antibiotics to give to patients. Past research has shown promising results and plausibility of this approach, however, datasets are limited to certain organisms such as Mycobacterium tuberculosis. Most studies represented antibiotic resistance in a binary form, rather than by a range of susceptibility.Problem statement-The aim is to discover how certain parts of bacterial genomes affect the bacterial resistance to antibiotics. Since any genome is a long sequence and we already have many bacteria of interest, a machine learning approach is needed to find exact correlations between relevant entries of every genome and a minimum inhibitory concentration of given antibiotic to suppress bacterial growth. The ultimate goal is to replace slow experiments in the lab by faster and mathematically justified algorithmic predictions of susceptibility to antibiotics by using only the bacterial genome.Work plan-In the early stages of the project, the candidate will collaborate with Liverpool Clinical Laboratories (LCL) and Liverpool University Foundation Trust to collect a representative bank of approximately 500 bacterial isolates that are important causes of infection.Through collaboration with the Centre for Genomic Research, the bacterial isolates will be sequenced using state-of-the-art Next Generation Sequencing techniques.The isolates will be tested for susceptibility to a panel of antibiotics using traditional techniques that will be used as the gold-standard test.One of the challenges will be the high dimensions of data produced for each bacterial isolate from genome sequencing. Therefore, the data will need to undergo dimensionality reduction using information about genes known to confer resistance to the antibiotics of interest.The data produced from this process will be used as an input to a machine learning model that will predict susceptibility of bacteria to a panel of antibiotics - with the output being a measure of antibiotic concentration required to suppress bacterial growth (minimum inhibitory concentration).The study will explore the relationship of this model to patient clinical features and outcomes, through data available from LCL.Expected Deliverables-This project will reveal an explicit relationship between genomes of bacteria and their resistance to antibiotics. A practical outcome will be an implemented algorithm to reliably predict a minimum inhibitory concent

动机-细菌对抗生素的耐药性仍然是医学界面临的最大挑战之一。尽管在过去十年中有更多的药物可用，但至关重要的是，通过新颖的实验室测试来优化这些药物的使用，使患者能够进行个性化的抗生素治疗。目前对感染患者的治疗通常从最好的猜测抗生素开始。同时，采集样本，实验室尝试培养感染细菌。如果成功，细菌将在体外接触各种抗生素，以确定哪些抗生素可能有效地治疗感染。临床医生利用这些结果适当地改变抗生素治疗。虽然这些测试既便宜又简单，但它们只能提供复杂的潜在耐药机制的狭隘代表。全基因组测序继续变得更容易获得，现在NHS经常用于某些感染的管理。这项技术使实验室能够读取细菌的整个遗传密码，并用于管理疫情，在某些情况下，还用于检测抗生素耐药性。然而，人们对基因组数据如何与传统的细菌培养技术(表型测试)联系在一起缺乏了解，传统的细菌培养技术(表型测试)在预测抗生素治愈感染的可能性方面有着长期的记录。随着新的基因组测试产生的数据变得更容易获得，我们需要更好地理解如何使用这些数据来优化给患者服用什么抗生素。过去的研究已经显示了这种方法有希望的结果和似是而非的结果，然而，数据集仅限于某些生物，如结核分枝杆菌。大多数研究以二元形式表示抗生素耐药性，而不是通过一系列的敏感性。问题陈述-目的是发现细菌基因组的某些部分如何影响细菌对抗生素的耐药性。由于任何基因组都是一个长序列，而且我们已经有许多感兴趣的细菌，因此需要一种机器学习方法来找到每个基因组的相关条目与给定抗生素的最低抑制浓度之间的确切关联，以抑制细菌生长。最终目标是通过只使用细菌基因组，用更快的和数学上合理的抗生素敏感性算法预测来取代实验室中缓慢的实验。工作计划-在项目的早期阶段，候选人将与利物浦临床实验室(LCL)和利物浦大学基金会信托基金合作，收集一个具有代表性的约500个细菌分离株，这些细菌是重要的感染原因。通过与基因组研究中心的合作，这些细菌分离物将使用最先进的下一代测序技术进行测序。分离物将使用传统技术进行对一组抗生素的敏感性测试，这将被用作黄金测试。其中一个挑战将是从基因组测序中为每个细菌分离物产生的高维数据。因此，数据将需要使用已知的与感兴趣的抗生素耐药有关的基因信息进行降维。从这个过程产生的数据将被用作机器学习模型的输入，该模型将预测细菌对一组抗生素的敏感性-输出是抑制细菌生长所需的抗生素浓度(最小抑制浓度)的测量。研究将通过LC提供的数据来探索该模型与患者临床特征和结果的关系。预期交付成果-该项目将揭示细菌基因组和它们对抗生素的耐药性之间的明确关系。一个实用的结果将是一个实现的算法，以可靠地预测最小抑制浓度