Leveraging functional profiling datasets with machine learning to uncover proteins and cellular processes important for ageing

利用功能分析数据集和机器学习来揭示对衰老重要的蛋白质和细胞过程

• 批准号: BB/R009597/1
• 负责人: Jurg Bahler
• 金额: $ 99.29万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FR009597%2F1
• 关键词: Leveraging; functional; profiling; datasets; machine

Ageing is the largest risk factor for most human diseases in developed countries, including progressive diseases such as Alzheimer's and Parkinson's, diseases like cancer that show variable rates of onset, and catastrophic system failures such as heart-attack and stroke. While the study of specific disease processes has long been a major focus of research, there is a growing realization of the importance of studying the normal ageing process itself as an essential part of the problem, and of exploring ways to slow or reverse its effects. Ageing is a multi-factorial process that can be seen as an inevitable feature of the ravages of time. Recent discoveries, however, demonstrate that ageing can be modified in dramatic ways by simple interventions. For example, single gene knockouts can delay ageing and improve health late in the life of laboratory animals. The processes involved in ageing are similar in different organisms, and genetic mutations affecting these processes are associated with longevity in humans. A central challenge of ageing research, however, remains to tease out a complete and unified picture of the biological factors and processes determining lifespan. Ageing is highly complex and affected by diverse proteins and processes. Modern biological assays can simultaneously measure properties and interactions of thousands of proteins or genes, but it is challenging to make sense of such large datasets. Advances in computational data-analysis methods, called 'machine learning', provide exciting opportunities to get the most from large biological datasets and thus increase our understanding of complex processes like ageing. Machine Learning can find hidden patterns in data that is too complex for humans to process. Advances in computer power, algorithms and data sizes allow recent machine-learning architectures (known as 'deep learning') to accurately find and classify intricate patterns in combined datasets of different types. We plan to use fission yeast as a model organism, together with multi-step machine learning, to comprehensively identify biological processes with fundamental importance for ageing. Remarkably, many of these processes are similar from yeast to human, but are much easier to study in the simple yeast. Yeast cells enter a dormant, non-dividing state under limiting nutrients. Such dormant cells provide a useful system to analyse proteins and processes affecting the lifespan in this state. In previous studies, we have identified 116 proteins that, when absent, allow the yeast to live longer (long-lived knockout mutants). So these proteins are involved in ageing, and can be used to train machine-learning programs to predict new ageing proteins by a method known as 'guilt by association'. We will combine large systematic data on mutant features (phenotypes) with diverse existing data to empower the machine-learning predictor. We will test the predicted ageing proteins in the laboratory for lifespan effects in yeast, and feed this information back to the computer for it to learn more about ageing proteins. We will then use mutants of the new ageing proteins identified by the computer and confirmed in yeast to measure links with all other mutants. Such 'genetic-interaction' data provide rich information on functional relationships, which will be used to explore other, potentially more powerful deep-learning methods to predict the biological processes that are involved in ageing. We will then test the most attractive predictions with laboratory experiments. Moreover, we will make all the new data, methods and predictions available to interested scientists to help with their research. We anticipate that this project, using intimate cycles of experiments and machine-learning, will provide a valuable platform to better understand all the biological factors involved in ageing, to eventually develop interventions that extend healthy lifespan in humans.

老龄化是发达国家大多数人类疾病的最大风险因素，包括阿尔茨海默氏症和帕金森氏症等渐进性疾病，癌症等发病率不一的疾病，以及心脏病发作和中风等灾难性系统故障。虽然对特定疾病过程的研究长期以来一直是研究的主要焦点，但人们越来越认识到研究正常衰老过程本身作为问题的重要组成部分的重要性，并探索减缓或逆转其影响的方法。老龄化是一个多因素的过程，可被视为时间破坏的一个不可避免的特征。然而，最近的发现表明，衰老可以通过简单的干预以戏剧性的方式改变。例如，单基因敲除可以延缓衰老，改善实验室动物生命后期的健康状况。衰老过程在不同的生物体中是相似的，影响这些过程的基因突变与人类的长寿有关。然而，老龄化研究的一个核心挑战仍然是梳理出决定寿命的生物因素和过程的完整和统一的图景。衰老是非常复杂的，受到不同蛋白质和过程的影响。现代生物测定可以同时测量数千种蛋白质或基因的特性和相互作用，但要理解如此大的数据集是具有挑战性的。计算数据分析方法的进步，称为“机器学习”，提供了令人兴奋的机会，可以从大型生物数据集中获得最大的信息，从而增加我们对衰老等复杂过程的理解。机器学习可以发现数据中隐藏的模式，这些模式对于人类来说太复杂了。计算机能力、算法和数据大小的进步使得最近的机器学习架构（称为“深度学习”）能够在不同类型的组合数据集中准确地发现和分类复杂的模式。我们计划使用裂变酵母作为模型生物，结合多步机器学习，全面识别对衰老具有根本重要性的生物过程。值得注意的是，这些过程中有许多是类似的从酵母到人类，但更容易在简单的酵母中研究。酵母细胞在有限的营养下进入休眠、不分裂状态。这种休眠细胞提供了一个有用的系统来分析影响这种状态下寿命的蛋白质和过程。在以前的研究中，我们已经确定了116种蛋白质，当不存在时，可以让酵母活得更长（长寿敲除突变体）。因此，这些蛋白质与衰老有关，可以用来训练机器学习程序，通过一种被称为“关联内疚”的方法来预测新的衰老蛋白质。我们将联合收割机将突变特征（表型）的大型系统数据与各种现有数据相结合，以增强机器学习预测器的能力。我们将在实验室中测试预测的老化蛋白质对酵母寿命的影响，并将此信息反馈给计算机，以了解更多关于老化蛋白质的信息。然后，我们将使用由计算机识别并在酵母中确认的新老化蛋白的突变体来测量与所有其他突变体的联系。这种“遗传相互作用”数据提供了关于功能关系的丰富信息，这些信息将用于探索其他可能更强大的深度学习方法，以预测衰老所涉及的生物过程。然后，我们将用实验室实验来检验最有吸引力的预测。此外，我们将向感兴趣的科学家提供所有新的数据，方法和预测，以帮助他们的研究。我们预计，该项目利用实验和机器学习的亲密循环，将提供一个有价值的平台，以更好地了解与衰老有关的所有生物因素，最终开发出延长人类健康寿命的干预措施。

项目成果

Broad functional profiling of fission yeast proteins using phenomics and machine learning

使用表型组学和机器学习对裂殖酵母蛋白进行广泛的功能分析

• DOI: 10.7554/elife.88229.3
• 发表时间: 2023
• 期刊: eLife
• 影响因子: 7.7
• 作者: Bordin N

Clustering FunFams using sequence embeddings improves EC purity.

• DOI: 10.1093/bioinformatics/btab371
• 发表时间: 2021-10-25
• 期刊: Bioinformatics (Oxford, England)
• 作者: Littmann M;Bordin N;Heinzinger M;Schütze K;Dallago C;Orengo C;Rost B
• 通讯作者: Rost B

Fission stories: using PomBase to understand Schizosaccharomyces pombe biology.

• DOI: 10.1093/genetics/iyab222
• 发表时间: 2022-04-04
• 期刊: GENETICS
• 影响因子: 3.3
• 作者: Harris, Midori A.;Rutherford, Kim M.;Hayles, Jacqueline;Lock, Antonia;Bahler, Jurg;Oliver, Stephen G.;Mata, Juan;Wood, Valerie
• 通讯作者: Wood, Valerie

SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals.

• DOI: 10.1038/s41598-020-71936-5
• 发表时间: 2020-10-05
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Lam SD;Bordin N;Waman VP;Scholes HM;Ashford P;Sen N;van Dorp L;Rauer C;Dawson NL;Pang CSM;Abbasian M;Sillitoe I;Edwards SJL;Fraternali F;Lees JG;Santini JM;Orengo CA
• 通讯作者: Orengo CA

Closely related Lak megaphages replicate in the microbiomes of diverse animals.

• DOI: 10.1016/j.isci.2021.102875
• 发表时间: 2021-08-20
• 期刊: iScience
• 影响因子: 5.8
• 作者: Crisci MA;Chen LX;Devoto AE;Borges AL;Bordin N;Sachdeva R;Tett A;Sharrar AM;Segata N;Debenedetti F;Bailey M;Burt R;Wood RM;Rowden LJ;Corsini PM;van Winden S;Holmes MA;Lei S;Banfield JF;Santini JM
• 通讯作者: Santini JM