AI assisted genomic profiling for the personalisation of treatment and control of infections

人工智能辅助基因组分析，实现个性化治疗和感染控制

• 批准号: MR/X005895/1
• 负责人: Taane Clark
• 金额: $ 11.59万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX005895%2F1
• 关键词: AI; assisted; genomic; profiling; personalisation

Cost-effective and rapid whole-genome sequencing (WGS) technologies are now being rolled-out in clinical settings to prevent disease, diagnose and personalise treatment of patients. WGS has become a routine, fast and affordable diagnostic tool used in infectious disease settings, revolutionizing clinical decision making, public health surveillance and infection control. This utility has been demonstrated during the COVID-19 pandemic, where rapid WGS of SARS-CoV-2 genomes has assisted the detection of clinically important variants (e.g., omicron), informed transmission dynamics, and aided vaccine development. More generally, analysis of WGS data can rapidly infer pathogen "virulent" strain-types, predict drug or antimicrobial resistance (AMR), and identify outbreaks. To assist this WGS-based analysis, molecular barcodes to profile pathogens for AMR, geographical source (e.g., for identification of importation events) and transmissibility can be derived and linked to fast informatic software tools. However, with increasing WGS use in clinical settings, there is a need for AI methods to mine the resulting big data to update barcodes and infer transmission dynamics in (near) real time. Our WGS profiling work in malaria and tuberculosis disease has established informative barcoding mutations, and developed world-leading informatics platforms (e.g., TB-Profiler) that have been applied globally (>100k tuberculosis bacteria with WGS, profiled across >35 countries). We have also applied AI methods (e.g., neural networks) to detect known and identify novel genes linked to AMR, thereby improving knowledge of underlying resistance mechanisms to improve barcodes. Here, we will integrate AI-based AMR mutation and transmission discovery tools into our informatic profiling software, making them dynamic and potentially improving clinical and infection control decision making. Working within established collaborations involving The UK Health Security Agency (UKHSA) and Health ministries in Asia (Philippines, Thailand, Vietnam), which are routinely using WGS-based diagnostics, we will implement the resulting AI-informatics platforms in UK and infectious disease endemic settings, with the potential of extending them to other infections, leading to associated health and economic benefits. Further, all WGS data generated, and AI and informatics software developed, will put in the public domain, leading to positive impacts in other biomedical research and healthcare areas.

具有成本效益和快速的全基因组测序（WGS）技术目前正在临床环境中推广，以预防疾病，诊断和个性化治疗患者。WGS已成为传染病环境中使用的常规，快速和负担得起的诊断工具，彻底改变了临床决策，公共卫生监测和感染控制。这种实用性已经在COVID-19大流行期间得到了证明，其中SARS-CoV-2基因组的快速WGS有助于检测临床上重要的变体（例如，omicron），告知传播动态，并协助疫苗开发。更一般地说，WGS数据的分析可以快速推断病原体“毒性”菌株类型，预测药物或抗菌素耐药性（AMR），并确定爆发。为了帮助这种基于WG的分析，分子条形码可以描述AMR的病原体，地理来源（例如，用于识别进口事件）和可传播性可以推导并链接到快速信息软件工具。然而，随着WGS在临床环境中的使用越来越多，需要人工智能方法来挖掘产生的大数据，以更新条形码并（接近）真实的时间推断传输动态。我们在疟疾和结核病中的WGS分析工作已经建立了信息条形码突变，并开发了世界领先的信息学平台（例如，TB-Profiler）（> 10万个结核菌WGS，分布于>35个国家）。我们还应用了人工智能方法（例如，神经网络）来检测已知的和鉴定与AMR相关的新基因，从而提高对潜在耐药机制的认识，以改进条形码。在这里，我们将把基于人工智能的AMR突变和传播发现工具整合到我们的信息分析软件中，使它们成为动态的，并有可能改善临床和感染控制决策。在涉及英国卫生安全局（UKHSA）和亚洲卫生部（菲律宾，泰国，越南）的既定合作范围内工作，这些部门经常使用基于WGS的诊断，我们将在英国和传染病流行环境中实施由此产生的AI信息学平台，并有可能将其扩展到其他感染，从而带来相关的健康和经济效益。此外，WGS生成的所有数据以及开发的人工智能和信息学软件都将进入公共领域，从而对其他生物医学研究和医疗保健领域产生积极影响。

项目成果

Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.

• DOI: 10.1093/bioinformatics/btad428
• 发表时间: 2023-07-01
• 期刊: Bioinformatics (Oxford, England)

Population-based genomic study of Plasmodium vivax malaria in seven Brazilian states and across South America.

• DOI: 10.1016/j.lana.2022.100420
• 发表时间: 2023-03
• 期刊: LANCET REGIONAL HEALTH-AMERICAS
• 作者: Ibrahim, Amy;Manko, Emilia;Dombrowski, Jamille G.;Campos, Monica;Benavente, Ernest Diez;Nolder, Debbie;Sutherland, Colin J.;Nosten, Francois;Fernandez, Diana;Velez-Tobon, Gabriel;Castano, Alberto Tobon;Aguiar, Anna Caroline C.;Pereira, Dhelio Batista;Santos, Simone da Silva;Suarez-Mutis, Martha;Di Santi, Silvia Maria;Baptista, Andrea Regina de Souza;Machado, Ricardo Luiz Dantas;Marinho, Claudio R. F.;Clark, Taane G.;Campino, Susana
• 通讯作者: Campino, Susana

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.

• DOI: 10.1038/s41598-022-27254-z
• 发表时间: 2022-12-31
• 期刊: Scientific reports
• 影响因子: 4.6

Population dynamics and drug resistance mutations in Plasmodium falciparum on the Bijagós Archipelago, Guinea-Bissau.

• DOI: 10.1038/s41598-023-33176-1
• 发表时间: 2023-04-18
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Moss S;Mańko E;Vasileva H;Da Silva ET;Goncalves A;Osborne A;Phelan J;Rodrigues A;Djata P;D'Alessandro U;Mabey D;Krishna S;Last A;Clark TG;Campino S
• 通讯作者: Campino S

Large-scale genomic analysis of Mycobacterium tuberculosis reveals extent of target and compensatory mutations linked to multi-drug resistant tuberculosis.

• DOI: 10.1038/s41598-023-27516-4
• 发表时间: 2023-01-12
• 期刊: Scientific reports
• 影响因子: 4.6