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Middleton L, Melas I, Vasavda C, Raies A, Rozemberczki B, Dhindsa RS, Dhindsa JS, Weido B, Wang Q, Harper AR, Edwards G, Petrovski S, Vitsios D. Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data. SCIENCE ADVANCES 2024; 10:eadj1424. [PMID: 38718126 PMCID: PMC11078195 DOI: 10.1126/sciadv.adj1424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 04/04/2024] [Indexed: 05/12/2024]
Abstract
The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca's Biological Insights Knowledge Graph and numerous tabular datasets, to assess gene-disease probabilities throughout the phenome. We use graph neural networks, capturing the graph's holistic structure, and train them on hundreds of balanced datasets via a robust semi-supervised learning framework to provide gene-disease probabilities across the human exome. Mantis-ML 2.0 incorporates natural language processing to automate disease-relevant feature selection for thousands of diseases. The enhanced models demonstrate a 6.9% average classification power boost, achieving a median receiver operating characteristic (ROC) area under curve (AUC) score of 0.90 across 5220 diseases from Human Phenotype Ontology, OpenTargets, and Genomics England. Notably, Mantis-ML 2.0 prioritizes associations from an independent UK Biobank phenome-wide association study (PheWAS), providing a stronger form of triaging and mitigating against underpowered PheWAS associations. Results are exposed through an interactive web resource.
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Affiliation(s)
- Lawrence Middleton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ioannis Melas
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Chirag Vasavda
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA 02451, USA
| | - Arwa Raies
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benedek Rozemberczki
- Biological Insights Knowledge Graph (BIKG), Research D&A, R&D IT, AstraZeneca, Cambridge, UK
| | - Ryan S. Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA 02451, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Justin S. Dhindsa
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Blake Weido
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA 02451, USA
| | - Andrew R. Harper
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Gavin Edwards
- Biological Insights Knowledge Graph (BIKG), Research D&A, R&D IT, AstraZeneca, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
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Li S, Zhao S, Sinson JC, Bajic A, Rosenfeld JA, Neeley MB, Pena M, Worley KC, Burrage LC, Weisz-Hubshman M, Ketkar S, Craigen WJ, Clark GD, Lalani S, Bacino CA, Machol K, Chao HT, Potocki L, Emrick L, Sheppard J, Nguyen MTT, Khoramnia A, Hernandez PP, Nagamani SC, Liu Z, Eng CM, Lee B, Liu P. The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing. Am J Hum Genet 2024; 111:841-862. [PMID: 38593811 PMCID: PMC11080285 DOI: 10.1016/j.ajhg.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024] Open
Abstract
RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation. The approach is designed specifically to suit clinical implementation, emphasizing simplicity, cost effectiveness, turnaround time, and reproducibility. For clinical validation, we generated induced neurons (iNeurons) from 71 individuals with primary neurological phenotypes recruited to the Undiagnosed Diseases Network. The overall diagnostic yield was 25.4%. Over a quarter of the diagnostic findings benefited from transdifferentiation and could not be achieved by fibroblast RNA-seq alone. This iNeuron transcriptomic approach can be effectively integrated into diagnostic whole-transcriptome evaluation of individuals with genetic disorders.
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Affiliation(s)
- Shenglan Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sen Zhao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jefferson C Sinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Aleksandar Bajic
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Advanced Technology Cores, Baylor College of Medicine, Houston, TX, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Matthew B Neeley
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Mezthly Pena
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Kim C Worley
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Monika Weisz-Hubshman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Shamika Ketkar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - William J Craigen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Gary D Clark
- Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Seema Lalani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Carlos A Bacino
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Keren Machol
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Hsiao-Tuan Chao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Cain Pediatric Research Foundation Laboratories, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; McNair Medical Institute, The Robert and Janice McNair Foundation, Houston, TX, USA
| | - Lorraine Potocki
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Lisa Emrick
- Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Jennifer Sheppard
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - My T T Nguyen
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA
| | - Anahita Khoramnia
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Sandesh Cs Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Christine M Eng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Baylor Genetics, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Baylor Genetics, Houston, TX, USA.
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Subramanian A, Zakeri P, Mousa M, Alnaqbi H, Alshamsi FY, Bettoni L, Damiani E, Alsafar H, Saeys Y, Carmeliet P. Angiogenesis goes computational – The future way forward to discover new angiogenic targets? Comput Struct Biotechnol J 2022; 20:5235-5255. [PMID: 36187917 PMCID: PMC9508490 DOI: 10.1016/j.csbj.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.
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