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Sudhahar S, Ozer B, Chang J, Chadwick W, O'Donovan D, Campbell A, Tulip E, Thompson N, Roberts I. An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs. Nat Commun 2024; 15:5703. [PMID: 38977662 PMCID: PMC11231212 DOI: 10.1038/s41467-024-50024-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
Explaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug's therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson's disease.
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2
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Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
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Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
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3
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London A, Richter MM, Sjøberg KA, Wewer Albrechtsen NJ, Považan M, Drici L, Schaufuss A, Madsen L, Øyen J, Madsbad S, Holst JJ, van Hall G, Siebner HR, Richter EA, Kiens B, Lundsgaard A, Bojsen-Møller KN. The impact of short-term eucaloric low- and high-carbohydrate diets on liver triacylglycerol content in males with overweight and obesity: a randomized crossover study. Am J Clin Nutr 2024:S0002-9165(24)00540-9. [PMID: 38914224 DOI: 10.1016/j.ajcnut.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 06/12/2024] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Intrahepatic triacylglycerol (liver TG) content is associated with hepatic insulin resistance and dyslipidemia. Liver TG content can be modulated within days under hypocaloric conditions. OBJECTIVES We hypothesized that 4 d of eucaloric low-carbohydrate/high-fat (LC) intake would decrease liver TG content, whereas a high-carbohydrate/low-fat (HC) intake would increase liver TG content, and further that alterations in liver TG would be linked to dynamic changes in hepatic glucose and lipid metabolism. METHODS A randomized crossover trial in males with 4 d + 4 d of LC and HC, respectively, with ≥2 wk of washout. 1H-magnetic resonance spectroscopy (1H-MRS) was used to measure liver TG content, with metabolic testing before and after intake of an LC diet (11E% carbohydrate corresponding to 102 ± 12 {mean ± standard deviation [SD]) g/d, 70E% fat} and an HC diet (65E% carbohydrate corresponding to 537 ± 56 g/d, 16E% fat). Stable [6,6-2H2]-glucose and [1,1,2,3,3-D5]-glycerol tracer infusions combined with hyperinsulinemic-euglycemic clamps and indirect calorimetry were used to measure rates of hepatic glucose production and lipolysis, whole-body insulin sensitivity and substrate oxidation. RESULTS Eleven normoglycemic males with overweight or obesity (BMI 31.6 ± 3.7 kg/m2) completed both diets. The LC diet reduced liver TG content by 35.3% (95% confidence interval: -46.6, -24.1) from 4.9% [2.4-11.0] (median interquartile range) to 2.9% [1.4-6.9], whereas there was no change after the HC diet. After the LC diet, fasting whole-body fat oxidation and plasma beta-hydroxybutyrate concentration increased, whereas markers of de novo lipogenesis (DNL) diminished. Fasting plasma TG and insulin concentrations were lowered and the hepatic insulin sensitivity index increased after LC. Peripheral glucose disposal was unchanged. CONCLUSIONS Reduced carbohydrate and increased fat intake for 4 d induced a marked reduction in liver TG content and increased hepatic insulin sensitivity. Increased rates of fat oxidation and ketogenesis combined with lower rates of DNL are suggested to be responsible for lowering liver TG. This trial was registered at clinicaltrials.gov as NCT04581421.
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Affiliation(s)
- Amalie London
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark
| | - Michael M Richter
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Kim Anker Sjøberg
- Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark
| | - Nicolai J Wewer Albrechtsen
- Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Michal Považan
- Danish Research Center for Magnetic Resonance (DRCMR), Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Lylia Drici
- Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Amanda Schaufuss
- Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark
| | - Lise Madsen
- Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark; Institute of Marine Research, Bergen, Norway
| | | | - Sten Madsbad
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Jens Juul Holst
- Department of Biomedical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Gerrit van Hall
- Department of Clinical Metabolomics, Rigshospitalet, Denmark
| | - Hartwig Roman Siebner
- Danish Research Center for Magnetic Resonance (DRCMR), Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Erik A Richter
- Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark
| | - Bente Kiens
- Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark
| | - Annemarie Lundsgaard
- Department of Nutrition, Exercise and Sports, The August Krogh Section for Molecular Physiology, University of Copenhagen, Copenhagen, Denmark
| | - Kirstine Nyvold Bojsen-Møller
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Oliinyk D, Will A, Schneidmadel FR, Böhme M, Rinke J, Hochhaus A, Ernst T, Hahn N, Geis C, Lubeck M, Raether O, Humphrey SJ, Meier F. µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics. Mol Syst Biol 2024:10.1038/s44320-024-00050-9. [PMID: 38907068 DOI: 10.1038/s44320-024-00050-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 06/06/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024] Open
Abstract
Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.
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Affiliation(s)
- Denys Oliinyk
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Andreas Will
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Felix R Schneidmadel
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Maximilian Böhme
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Jenny Rinke
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Andreas Hochhaus
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Thomas Ernst
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Nina Hahn
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, 07747, Jena, Germany
| | - Christian Geis
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, 07747, Jena, Germany
| | - Markus Lubeck
- Bruker Daltonics GmbH & Co. KG, 28359, Bremen, Germany
| | | | - Sean J Humphrey
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, 3052, Victoria, Australia.
| | - Florian Meier
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany.
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany.
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Andersen JV, Marian OC, Qvist FL, Westi EW, Aldana BI, Schousboe A, Don AS, Skotte NH, Wellendorph P. Deficient brain GABA metabolism leads to widespread impairments of astrocyte and oligodendrocyte function. Glia 2024. [PMID: 38899762 DOI: 10.1002/glia.24585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/03/2024] [Accepted: 06/09/2024] [Indexed: 06/21/2024]
Abstract
The neurometabolic disorder succinic semialdehyde dehydrogenase (SSADH) deficiency leads to great neurochemical imbalances and severe neurological manifestations. The cause of the disease is loss of function of the enzyme SSADH, leading to impaired metabolism of the principal inhibitory neurotransmitter GABA. Despite the known identity of the enzymatic deficit, the underlying pathology of SSADH deficiency remains unclear. To uncover new mechanisms of the disease, we performed an untargeted integrative analysis of cerebral protein expression, functional metabolism, and lipid composition in a genetic mouse model of SSADH deficiency (ALDH5A1 knockout mice). Our proteomic analysis revealed a clear regional vulnerability, as protein alterations primarily manifested in the hippocampus and cerebral cortex of the ALDH5A1 knockout mice. These regions displayed aberrant expression of proteins linked to amino acid homeostasis, mitochondria, glial function, and myelination. Stable isotope tracing in acutely isolated brain slices demonstrated an overall maintained oxidative metabolism of glucose, but a selective decrease in astrocyte metabolic activity in the cerebral cortex of ALDH5A1 knockout mice. In contrast, an elevated capacity of oxidative glutamine metabolism was observed in the ALDH5A1 knockout brain, which may serve as a neuronal compensation of impaired astrocyte glutamine provision. In addition to reduced expression of critical oligodendrocyte proteins, a severe depletion of myelin-enriched sphingolipids was found in the brains of ALDH5A1 knockout mice, suggesting degeneration of myelin. Altogether, our study highlights that impaired astrocyte and oligodendrocyte function is intimately linked to SSADH deficiency pathology, suggesting that selective targeting of glial cells may hold therapeutic potential in this disease.
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Affiliation(s)
- Jens V Andersen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oana C Marian
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
- School of Medical Sciences, The University of Sydney, Camperdown, New South Wales, Australia
| | - Filippa L Qvist
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emil W Westi
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Blanca I Aldana
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Arne Schousboe
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anthony S Don
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
- School of Medical Sciences, The University of Sydney, Camperdown, New South Wales, Australia
| | - Niels H Skotte
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Petrine Wellendorph
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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Nygaard U, Nielsen AB, Dungu KHS, Drici L, Holm M, Ottenheijm ME, Nielsen AB, Glenthøj JP, Schmidt LS, Cortes D, Jørgensen IM, Mogensen TH, Schmiegelow K, Mann M, Vissing NH, Wewer Albrechtsen NJ. Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. Commun Biol 2024; 7:688. [PMID: 38839859 PMCID: PMC11153518 DOI: 10.1038/s42003-024-06370-8] [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: 11/12/2023] [Accepted: 05/22/2024] [Indexed: 06/07/2024] Open
Abstract
Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.
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Affiliation(s)
- Ulrikka Nygaard
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Annelaura Bach Nielsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kia Hee Schultz Dungu
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lylia Drici
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Holm
- Department of Pediatrics and Adolescent Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Maud Eline Ottenheijm
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Allan Bybeck Nielsen
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Jonathan Peter Glenthøj
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital North Zealand, Hillerød, Denmark
| | - Lisbeth Samsø Schmidt
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Herlev, Herlev, Denmark
| | - Dina Cortes
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Inger Merete Jørgensen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital North Zealand, Hillerød, Denmark
| | | | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Mann
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Nadja Hawwa Vissing
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Nicolai J Wewer Albrechtsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital - Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
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8
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Devarakonda MV, Mohanty S, Sunkishala RR, Mallampalli N, Liu X. Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings. J Biomed Inform 2024; 154:104627. [PMID: 38561170 DOI: 10.1016/j.jbi.2024.104627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 02/06/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE Designing a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical trial records. This study proposes an approach based on knowledge graph embeddings and semantics-driven inductive inference for generating such recommendations. METHOD The proposed recommendation methodology is based on neural embeddings trained on first-of-its-kind knowledge graph constructed from clinical trials data. The methodology includes design of a knowledge graph for clinical trial data, evaluation of various knowledge graph embedding techniques for it, application of a novel inductive inference method using these embeddings, and generation of recommendations for clinical trial design. The study uses freely available data from clinicaltrials.gov and related sources. RESULTS The proposed approach for recommendations obtained relevance scores ranging from 70% to 83%. These scores were determined by evaluating the text similarity of recommended elements to actual elements used in clinical trials that are in progress. Furthermore, the most pertinent recommendations were consistently located towards the top of the list, indicating the effectiveness of our method. CONCLUSION Our study suggests that inductive inference using node semantics is a viable approach for generating recommendations using graphs neural embeddings, and that there is a potential for improvement in training graph embeddings using node semantics.
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Affiliation(s)
| | | | | | | | - Xiong Liu
- Biomedical Research, Novartis, Cambridge, MA, USA
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9
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Ebstrup E, Ansbøl J, Paez-Garcia A, Culp H, Chevalier J, Clemmens P, Coll NS, Moreno-Risueno MA, Rodriguez E. NBR1-mediated selective autophagy of ARF7 modulates root branching. EMBO Rep 2024; 25:2571-2591. [PMID: 38684906 PMCID: PMC11169494 DOI: 10.1038/s44319-024-00142-5] [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: 12/08/2023] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024] Open
Abstract
Auxin dictates root architecture via the Auxin Response Factor (ARF) family of transcription factors, which control lateral root (LR) formation. In Arabidopsis, ARF7 regulates the specification of prebranch sites (PBS) generating LRs through gene expression oscillations and plays a pivotal role during LR initiation. Despite the importance of ARF7 in this process, there is a surprising lack of knowledge about how ARF7 turnover is regulated and how this impacts root architecture. Here, we show that ARF7 accumulates in autophagy mutants and is degraded through NBR1-dependent selective autophagy. We demonstrate that the previously reported rhythmic changes to ARF7 abundance in roots are modulated via autophagy and might occur in other tissues. In addition, we show that the level of co-localization between ARF7 and autophagy markers oscillates and can be modulated by auxin to trigger ARF7 turnover. Furthermore, we observe that autophagy impairment prevents ARF7 oscillation and reduces both PBS establishment and LR formation. In conclusion, we report a novel role for autophagy during development, namely by enacting auxin-induced selective degradation of ARF7 to optimize periodic root branching.
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Affiliation(s)
- Elise Ebstrup
- Department of Biology, University of Copenhagen, 2200, Copenhagen N, Denmark
| | - Jeppe Ansbøl
- Department of Biology, University of Copenhagen, 2200, Copenhagen N, Denmark
| | - Ana Paez-Garcia
- Centro de Biotecnología y Genómica de Plantas (Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-CSIC (INIA/CSIC)). Campus de Montegancedo, Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Henry Culp
- Department of Biology, University of Copenhagen, 2200, Copenhagen N, Denmark
| | - Jonathan Chevalier
- Department of Biology, University of Copenhagen, 2200, Copenhagen N, Denmark
| | - Pauline Clemmens
- Department of Biology, University of Copenhagen, 2200, Copenhagen N, Denmark
| | - Núria S Coll
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Bellaterra, 08193, Spain
- Consejo Superior de Investigaciones Científicas (CSIC), Barcelona, 08001, Spain
| | - Miguel A Moreno-Risueno
- Centro de Biotecnología y Genómica de Plantas (Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-CSIC (INIA/CSIC)). Campus de Montegancedo, Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Eleazar Rodriguez
- Department of Biology, University of Copenhagen, 2200, Copenhagen N, Denmark.
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10
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Lewis CTA, Melhedegaard EG, Ognjanovic MM, Olsen MS, Laitila J, Seaborne RAE, Gronset M, Zhang C, Iwamoto H, Hessel AL, Kuehn MN, Merino C, Amigo N, Frobert O, Giroud S, Staples JF, Goropashnaya AV, Fedorov VB, Barnes B, Toien O, Drew K, Sprenger RJ, Ochala J. Remodeling of skeletal muscle myosin metabolic states in hibernating mammals. eLife 2024; 13:RP94616. [PMID: 38752835 PMCID: PMC11098559 DOI: 10.7554/elife.94616] [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] [Indexed: 05/18/2024] Open
Abstract
Hibernation is a period of metabolic suppression utilized by many small and large mammal species to survive during winter periods. As the underlying cellular and molecular mechanisms remain incompletely understood, our study aimed to determine whether skeletal muscle myosin and its metabolic efficiency undergo alterations during hibernation to optimize energy utilization. We isolated muscle fibers from small hibernators, Ictidomys tridecemlineatus and Eliomys quercinus and larger hibernators, Ursus arctos and Ursus americanus. We then conducted loaded Mant-ATP chase experiments alongside X-ray diffraction to measure resting myosin dynamics and its ATP demand. In parallel, we performed multiple proteomics analyses. Our results showed a preservation of myosin structure in U. arctos and U. americanus during hibernation, whilst in I. tridecemlineatus and E. quercinus, changes in myosin metabolic states during torpor unexpectedly led to higher levels in energy expenditure of type II, fast-twitch muscle fibers at ambient lab temperatures (20 °C). Upon repeating loaded Mant-ATP chase experiments at 8 °C (near the body temperature of torpid animals), we found that myosin ATP consumption in type II muscle fibers was reduced by 77-107% during torpor compared to active periods. Additionally, we observed Myh2 hyper-phosphorylation during torpor in I. tridecemilineatus, which was predicted to stabilize the myosin molecule. This may act as a potential molecular mechanism mitigating myosin-associated increases in skeletal muscle energy expenditure during periods of torpor in response to cold exposure. Altogether, we demonstrate that resting myosin is altered in hibernating mammals, contributing to significant changes to the ATP consumption of skeletal muscle. Additionally, we observe that it is further altered in response to cold exposure and highlight myosin as a potentially contributor to skeletal muscle non-shivering thermogenesis.
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Affiliation(s)
| | | | - Marija M Ognjanovic
- Department of Biomedical Sciences, University of CopenhagenCopenhagenDenmark
| | - Mathilde S Olsen
- Department of Biomedical Sciences, University of CopenhagenCopenhagenDenmark
| | - Jenni Laitila
- Department of Biomedical Sciences, University of CopenhagenCopenhagenDenmark
| | - Robert AE Seaborne
- Department of Biomedical Sciences, University of CopenhagenCopenhagenDenmark
- Centre for Human and Applied Physiological Sciences, Faculty of Life Sciences & Medicine, King’s College LondonLondonUnited Kingdom
| | - Magnus Gronset
- Department of Cellular and Molecular Medicine, University of CopenhagenCopenhagenDenmark
| | - Changxin Zhang
- Department of Computational Medicine and Bioinformatics, University of MichiganAnn ArborUnited States
| | - Hiroyuki Iwamoto
- Spring-8, Japan Synchrotron Radiation Research InstituteHyogoJapan
| | - Anthony L Hessel
- Institute of Physiology II, University of MuensterMuensterGermany
- Accelerated Muscle Biotechnologies ConsultantsBostonUnited States
| | - Michel N Kuehn
- Institute of Physiology II, University of MuensterMuensterGermany
- Accelerated Muscle Biotechnologies ConsultantsBostonUnited States
| | | | | | - Ole Frobert
- Department of Clinical Medicine, Faculty of Health, Aarhus UniversityAarhusDenmark
- Faculty of Health, Department of Cardiology, Örebro UniversityÖrebroSweden
| | - Sylvain Giroud
- Energetics Lab, Department of Biology, Northern Michigan UniversityMarquetteUnited States
- Research Institute of Wildlife Ecology, Department of Interdisciplinary Life Sciences, University of Veterinary Medicine ViennaViennaAustria
| | - James F Staples
- Department of Biology, University of Western OntarioLondonCanada
| | - Anna V Goropashnaya
- Center for Transformative Research in Metabolism, Institute of Arctic Biology, University of Alaska FairbanksFairbanksUnited States
| | - Vadim B Fedorov
- Center for Transformative Research in Metabolism, Institute of Arctic Biology, University of Alaska FairbanksFairbanksUnited States
| | - Brian Barnes
- Center for Transformative Research in Metabolism, Institute of Arctic Biology, University of Alaska FairbanksFairbanksUnited States
| | - Oivind Toien
- Center for Transformative Research in Metabolism, Institute of Arctic Biology, University of Alaska FairbanksFairbanksUnited States
| | - Kelly Drew
- Center for Transformative Research in Metabolism, Institute of Arctic Biology, University of Alaska FairbanksFairbanksUnited States
| | - Ryan J Sprenger
- Department of Zoology, University of British ColumbiaVancouverCanada
| | - Julien Ochala
- Department of Biomedical Sciences, University of CopenhagenCopenhagenDenmark
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11
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Valous NA, Popp F, Zörnig I, Jäger D, Charoentong P. Graph machine learning for integrated multi-omics analysis. Br J Cancer 2024:10.1038/s41416-024-02706-7. [PMID: 38729996 DOI: 10.1038/s41416-024-02706-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches-including graph neural networks-potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.
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Affiliation(s)
- Nektarios A Valous
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany.
| | - Ferdinand Popp
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Inka Zörnig
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Dirk Jäger
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Pornpimol Charoentong
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
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12
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Callahan TJ, Tripodi IJ, Stefanski AL, Cappelletti L, Taneja SB, Wyrwa JM, Casiraghi E, Matentzoglu NA, Reese J, Silverstein JC, Hoyt CT, Boyce RD, Malec SA, Unni DR, Joachimiak MP, Robinson PN, Mungall CJ, Cavalleri E, Fontana T, Valentini G, Mesiti M, Gillenwater LA, Santangelo B, Vasilevsky NA, Hoehndorf R, Bennett TD, Ryan PB, Hripcsak G, Kahn MG, Bada M, Baumgartner WA, Hunter LE. An open source knowledge graph ecosystem for the life sciences. Sci Data 2024; 11:363. [PMID: 38605048 PMCID: PMC11009265 DOI: 10.1038/s41597-024-03171-w] [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: 07/26/2023] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Ignacio J Tripodi
- Computer Science Department, Interdisciplinary Quantitative Biology, University of Colorado Boulder, Boulder, CO, 80301, USA
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA
| | - Charles Tapley Hoyt
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15206, USA
| | - Scott A Malec
- Division of Translational Informatics, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA
| | - Deepak R Unni
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Marcin P Joachimiak
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Peter N Robinson
- Berlin Institute of Health at Charité-Universitatsmedizin, 10117, Berlin, Germany
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Emanuele Cavalleri
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
- ELLIS, European Laboratory for Learning and Intelligent Systems, Milan Unit, Italy
| | - Marco Mesiti
- AnacletoLab, Dipartimento di Informatica, Universit`a degli Studi di Milano, Via Celoria 18, 20133, Milan, Italy
| | - Lucas A Gillenwater
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Brook Santangelo
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Data Collaboration Center, Critical Path Institute, 1840 E River Rd. Suite 100, Tucson, AZ, 85718, USA
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael Bada
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - William A Baumgartner
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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13
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Zhou J, Gu J, Sun X, Ye Q, Wu X, Xi J, Han J, Liu Y. Supramolecular Chiral Binding Affinity-Achieved Efficient Synergistic Cancer Therapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308493. [PMID: 38380492 DOI: 10.1002/advs.202308493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/03/2024] [Indexed: 02/22/2024]
Abstract
Supramolecular chirality-mediated selective interaction among native assemblies is essential for precise disease diagnosis and treatment. Herein, to fully understand the supramolecular chiral binding affinity-achieved therapeutic efficiency, supramolecular chiral nanoparticles (WP5⊃D/L-Arg+DOX+ICG) with the chirality transfer from chiral arginine (D/L-Arg) to water-soluble pillar[5]arene (WP5) are developed through non-covalent interactions, in which an anticancer drug (DOX, doxorubicin hydrochloride) and a photothermal agent (ICG, indocyanine green) are successfully loaded. Interestingly, the WP5⊃D-Arg nanoparticles show 107 folds stronger binding capability toward phospholipid-composed liposomes compared with WP5⊃L-Arg. The enantioselective interaction further triggers the supramolecular chirality-specific drug accumulation in cancer cells. As a consequence, WP5⊃D-Arg+DOX+ICG exhibits extremely enhanced chemo-photothermal synergistic therapeutic efficacy (tumor inhibition rate of 99.4%) than that of WP5⊃L-Arg+DOX+ICG (tumor inhibition rate of 56.4%) under the same condition. This work reveals the breakthrough that supramolecular chiral assemblies can induce surprisingly large difference in cancer therapy, providing strong support for the significance of supramolecular chirality in bio-application.
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Affiliation(s)
- Jinfeng Zhou
- School of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Jiake Gu
- School of Medicine, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Xiaohuan Sun
- School of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Qianyun Ye
- School of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Xuan Wu
- School of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Juqun Xi
- School of Medicine, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Jie Han
- School of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou, Jiangsu, 225002, P. R. China
| | - Yu Liu
- College of Chemistry, State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin, 300071, P. R. China
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14
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Terranova N, Renard D, Shahin MH, Menon S, Cao Y, Hop CECA, Hayes S, Madrasi K, Stodtmann S, Tensfeldt T, Vaddady P, Ellinwood N, Lu J. Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clin Pharmacol Ther 2024; 115:658-672. [PMID: 37716910 DOI: 10.1002/cpt.3053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.
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Affiliation(s)
- Nadia Terranova
- Quantitative Pharmacology, Merck KGaA, Lausanne, Switzerland
| | - Didier Renard
- Full Development Pharmacometrics, Novartis Pharma AG, Basel, Switzerland
| | | | - Sujatha Menon
- Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA
| | - Youfang Cao
- Clinical Pharmacology and Translational Medicine, Eisai Inc., Nutley, New Jersey, USA
| | | | - Sean Hayes
- Quantitative Pharmacology & Pharmacometrics, Merck & Co. Inc., Rahway, New Jersey, USA
| | - Kumpal Madrasi
- Modeling & Simulation, Sanofi, Bridgewater, New Jersey, USA
| | - Sven Stodtmann
- Pharmacometrics, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | - Pavan Vaddady
- Quantitative Clinical Pharmacology, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | | | - James Lu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
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15
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Ghandikota SK, Jegga AG. Application of artificial intelligence and machine learning in drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:171-211. [PMID: 38789178 DOI: 10.1016/bs.pmbts.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.
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Affiliation(s)
- Sudhir K Ghandikota
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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16
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Gaudry A, Pagni M, Mehl F, Moretti S, Quiros-Guerrero LM, Cappelletti L, Rutz A, Kaiser M, Marcourt L, Queiroz EF, Ioset JR, Grondin A, David B, Wolfender JL, Allard PM. A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery. ACS CENTRAL SCIENCE 2024; 10:494-510. [PMID: 38559298 PMCID: PMC10979503 DOI: 10.1021/acscentsci.3c00800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.
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Affiliation(s)
- Arnaud Gaudry
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
| | - Marco Pagni
- Vital-IT, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Florence Mehl
- Vital-IT, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Sébastien Moretti
- Vital-IT, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Luis-Manuel Quiros-Guerrero
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
| | - Luca Cappelletti
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
| | - Adriano Rutz
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
| | - Marcel Kaiser
- Department of Medical
and Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, 4123 Allschwil, Switzerland
- Faculty of Science, University of Basel, 4002 Basel, Switzerland
| | - Laurence Marcourt
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
| | - Emerson Ferreira Queiroz
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
| | - Jean-Robert Ioset
- Drugs
for Neglected Diseases Initiative (DNDi), 1202 Geneva, Switzerland
| | - Antonio Grondin
- Green Mission Pierre Fabre, Institut de Recherche Pierre Fabre, 31562 Toulouse, France
| | - Bruno David
- Green Mission Pierre Fabre, Institut de Recherche Pierre Fabre, 31562 Toulouse, France
| | - Jean-Luc Wolfender
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
| | - Pierre-Marie Allard
- Institute of Pharmaceutical
Sciences of Western Switzerland, University
of Geneva, 1211 Geneva 4, Switzerland
- School of Pharmaceutical Sciences, University
of Geneva, 1211 Geneva 4, Switzerland
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
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17
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Luige J, Armaos A, Tartaglia GG, Ørom UAV. Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation. Nat Commun 2024; 15:2585. [PMID: 38519458 PMCID: PMC10959947 DOI: 10.1038/s41467-024-46731-9] [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: 04/06/2023] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
RNA-binding proteins are central for many biological processes and their characterization has demonstrated a broad range of functions as well as a wide spectrum of target structures. RNA G-quadruplexes are important regulatory elements occurring in both coding and non-coding transcripts, yet our knowledge of their structure-based interactions is at present limited. Here, using theoretical predictions and experimental approaches, we show that many chromatin-binding proteins bind to RNA G-quadruplexes, and we classify them based on their RNA G-quadruplex-binding potential. Combining experimental identification of nuclear RNA G-quadruplex-binding proteins with computational approaches, we build a prediction tool that assigns probability score for a nuclear protein to bind RNA G-quadruplexes. We show that predicted G-quadruplex RNA-binding proteins exhibit a high degree of protein disorder and hydrophilicity and suggest involvement in both transcription and phase-separation into membrane-less organelles. Finally, we present the G4-Folded/UNfolded Nuclear Interaction Explorer System (G4-FUNNIES) for estimating RNA G4-binding propensities at http://service.tartaglialab.com/new_submission/G4FUNNIES .
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Affiliation(s)
- Johanna Luige
- RNA Biology and Innovation, Institute of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Alexandros Armaos
- Centre for Human Technologies (CHT), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen, 83, 16152, Genova, Italy
| | - Gian Gaetano Tartaglia
- Centre for Human Technologies (CHT), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen, 83, 16152, Genova, Italy.
- Catalan Institution for Research and Advanced Studies ICREA Passeig Lluis Companys, 23 08010, Barcelona, Spain.
| | - Ulf Andersson Vang Ørom
- RNA Biology and Innovation, Institute of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
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18
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Stampe NK, Ottenheijm ME, Drici L, Wewer Albrechtsen NJ, Nielsen AB, Christoffersen C, Warming PE, Engstrøm T, Winkel BG, Jabbari R, Tfelt-Hansen J, Glinge C. Discovery of plasma proteins associated with ventricular fibrillation during first ST-elevation myocardial infarction via proteomics. EUROPEAN HEART JOURNAL. ACUTE CARDIOVASCULAR CARE 2024; 13:264-272. [PMID: 37811694 DOI: 10.1093/ehjacc/zuad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/23/2023] [Accepted: 10/06/2023] [Indexed: 10/10/2023]
Abstract
AIMS The underlying biological mechanisms of ventricular fibrillation (VF) during acute myocardial infarction are largely unknown. To our knowledge, this is the first proteomic study for this trait, with the aim to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI). METHODS AND RESULTS We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n = 110) and without VF (control, n = 120) before guided catheter insertion for primary percutaneous coronary intervention. The plasma proteome was investigated using mass spectrometry-based proteomics on plasma samples collected within 24 h of symptom onset, and one patient was excluded in quality control. In 229 STEMI patients {72% men, median age 62 years [interquartile range (IQR): 54-70]}, a median of 257 proteins (IQR: 244-281) were quantified per patient. A total of 26 proteins were associated with VF; these proteins were involved in several biological processes including blood coagulation, haemostasis, and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 [ACTBL2, fold change (FC) 2.25, P < 0.001, q = 0.023], and coagulation factor XIII-A (F13A1, FC 1.48, P < 0.001, q = 0.023). None of the proteins were correlated with anterior infarct location. CONCLUSION Ventricular fibrillation due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1), suggesting that they may represent novel underlying molecular VF mechanisms. Further research is needed to determine whether these proteins are predictive biomarkers or acute phase response proteins to VF during acute ischaemia.
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Affiliation(s)
- Niels Kjær Stampe
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
| | - Maud Eline Ottenheijm
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg Hospital, Copenhagen, Denmark
| | - Lylia Drici
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg Hospital, Copenhagen, Denmark
| | - Nicolai J Wewer Albrechtsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg Hospital, Copenhagen, Denmark
| | - Annelaura Bach Nielsen
- NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital Bispebjerg Hospital, Copenhagen, Denmark
| | - Christina Christoffersen
- Department of Clinical Biochemistry, Centre of Diagnostic Investigation, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peder Emil Warming
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
| | - Thomas Engstrøm
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
| | - Bo Gregers Winkel
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
| | - Reza Jabbari
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
| | - Jacob Tfelt-Hansen
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
- Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Charlotte Glinge
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital-Rigshospitalet, Inge Lehmanns Vej 7, Copenhagen 2100, Denmark
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19
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Strauss MT, Bludau I, Zeng WF, Voytik E, Ammar C, Schessner JP, Ilango R, Gill M, Meier F, Willems S, Mann M. AlphaPept: a modern and open framework for MS-based proteomics. Nat Commun 2024; 15:2168. [PMID: 38461149 PMCID: PMC10924963 DOI: 10.1038/s41467-024-46485-4] [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: 12/20/2022] [Accepted: 02/20/2024] [Indexed: 03/11/2024] Open
Abstract
In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.
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Affiliation(s)
- Maximilian T Strauss
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Isabell Bludau
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Wen-Feng Zeng
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Eugenia Voytik
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Constantin Ammar
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Julia P Schessner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | - Florian Meier
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Functional Proteomics, Jena University Hospital, Jena, Germany
| | - Sander Willems
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
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20
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Brbić M, Yasunaga M, Agarwal P, Leskovec J. Predicting drug outcome of population via clinical knowledge graph. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.06.24303800. [PMID: 38496488 PMCID: PMC10942490 DOI: 10.1101/2024.03.06.24303800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Optimal treatments depend on numerous factors such as drug chemical properties, disease biology, and patient characteristics to which the treatment is applied. To realize the promise of AI in healthcare, there is a need for designing systems that can capture patient heterogeneity and relevant biomedical knowledge. Here we present PlaNet, a geometric deep learning framework that reasons over population variability, disease biology, and drug chemistry by representing knowledge in the form of a massive clinical knowledge graph that can be enhanced by language models. Our framework is applicable to any sub-population, any drug as well drug combinations, any disease, and to a wide range of pharmacological tasks. We apply the PlaNet framework to reason about outcomes of clinical trials: PlaNet predicts drug efficacy and adverse events, even for experimental drugs and their combinations that have never been seen by the model. Furthermore, PlaNet can estimate the effect of changing population on the trial outcome with direct implications on patient stratification in clinical trials. PlaNet takes fundamental steps towards AI-guided clinical trials design, offering valuable guidance for realizing the vision of precision medicine using AI.
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Affiliation(s)
- Maria Brbić
- School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Michihiro Yasunaga
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Prabhat Agarwal
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
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21
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Li Q, Hu Z, He J, Liu X, Liu Y, Wei J, Wu B, Lu X, He H, Zhang Y, He J, Li M, Wu C, Lv L, Wang Y, Zhou L, Zhang Q, Zhang J, Cheng X, Shao H, Lu X. Deciphering the comprehensive knowledgebase landscape featuring infertility with IDDB Xtra. Comput Biol Med 2024; 170:108105. [PMID: 38330823 DOI: 10.1016/j.compbiomed.2024.108105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/15/2024] [Accepted: 02/04/2024] [Indexed: 02/10/2024]
Abstract
Infertility affects ∼15% of couples globally and half of cases are related to genetic disorders. Despite growing data and unprecedented improvements in high-throughput sequencing technologies, accumulated fertility-related issues concerning genetic diagnosis and potential treatment are urgent to be solved. However, there is a lack of comprehensive platforms that characterise various infertility-related records to provide research applications for exploring infertility in-depth and genetic counselling of infertility couple. To solve this problem, we provide IDDB Xtra by further integrating phenotypic manifestations, genomic datasets, epigenetics, modulators in collaboration with numerous interactive tools into our previous infertility database, IDDB. IDDB Xtra houses manually-curated 2369 genes of human and nine model organisms, 273 chromosomal abnormalities, 884 phenotypes, 60 genomic datasets, 464 epigenetic records, 1144 modulators relevant to infertility diagnosis and treatment. Additionally, IDDB Xtra incorporated customized graphical applications for researchers and clinicians to decipher in-depth disease mechanisms from the perspectives of developmental atlas, mutation effects, and clinical manifestations. Users can browse genes across developmental stages of human and mouse, filter candidate genes, mine potential variants and retrieve infertility biomedical network in an intuitive web interface. In summary, IDDB Xtra not only captures valuable research and data, but also provides useful applications to facilitate the genetic counselling and drug discovery of infertility. IDDB Xtra is freely available at https://mdl.shsmu.edu.cn/IDDB/and http://www.allostery.net/IDDB.
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Affiliation(s)
- Qian Li
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China; Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Zhijie Hu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China
| | - Jiayin He
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China; Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Xinyi Liu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Yini Liu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China; Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Jiale Wei
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Binjian Wu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Xun Lu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Hongxi He
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China; School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510000, China
| | - Yuqi Zhang
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China
| | - Jixiao He
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Mingyu Li
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Chengwei Wu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Lijun Lv
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Yang Wang
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Linxuan Zhou
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Quan Zhang
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China
| | - Jian Zhang
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200025, China; School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510000, China.
| | - Xiaoyue Cheng
- Center for Reproductive Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200135, China.
| | - Hongfang Shao
- Center of Reproductive Medicine, Department of Gynecology and Obstetrics, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai, 200233, China.
| | - Xuefeng Lu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200011, China.
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22
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Lewis CTA, Melhedegaard EG, Ognjanovic MM, Olsen MS, Laitila J, Seaborne RAE, Gronset MN, Zhang C, Iwamoto H, Hessel AL, Kuehn MN, Merino C, Amigo N, Frobert O, Giroud S, Staples JF, Goropashnaya AV, Fedorov VB, Barnes BM, Toien O, Drew KL, Sprenger RJ, Ochala J. Remodelling of Skeletal Muscle Myosin Metabolic States in Hibernating Mammals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.14.566992. [PMID: 38014200 PMCID: PMC10680686 DOI: 10.1101/2023.11.14.566992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Hibernation is a period of metabolic suppression utilized by many small and large mammal species to survive during winter periods. As the underlying cellular and molecular mechanisms remain incompletely understood, our study aimed to determine whether skeletal muscle myosin and its metabolic efficiency undergo alterations during hibernation to optimize energy utilization. We isolated muscle fibers from small hibernators, Ictidomys tridecemlineatus and Eliomys quercinus and larger hibernators, Ursus arctos and Ursus americanus. We then conducted loaded Mant-ATP chase experiments alongside X-ray diffraction to measure resting myosin dynamics and its ATP demand. In parallel, we performed multiple proteomics analyses. Our results showed a preservation of myosin structure in U. arctos and U. americanus during hibernation, whilst in I. tridecemlineatus and E. quercinus, changes in myosin metabolic states during torpor unexpectedly led to higher levels in energy expenditure of type II, fast-twitch muscle fibers at ambient lab temperatures (20°C). Upon repeating loaded Mant-ATP chase experiments at 8°C (near the body temperature of torpid animals), we found that myosin ATP consumption in type II muscle fibers was reduced by 77-107% during torpor compared to active periods. Additionally, we observed Myh2 hyper-phosphorylation during torpor in I. tridecemilineatus, which was predicted to stabilize the myosin molecule. This may act as a potential molecular mechanism mitigating myosin-associated increases in skeletal muscle energy expenditure during periods of torpor in response to cold exposure. Altogether, we demonstrate that resting myosin is altered in hibernating mammals, contributing to significant changes to the ATP consumption of skeletal muscle. Additionally, we observe that it is further altered in response to cold exposure and highlight myosin as a potentially contributor to skeletal muscle non-shivering thermogenesis.
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23
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Chen Y, Wang B, Zhao Y, Shao X, Wang M, Ma F, Yang L, Nie M, Jin P, Yao K, Song H, Lou S, Wang H, Yang T, Tian Y, Han P, Hu Z. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer. Nat Commun 2024; 15:1657. [PMID: 38395893 PMCID: PMC10891053 DOI: 10.1038/s41467-024-46043-y] [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: 05/09/2023] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.
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Affiliation(s)
- Yangzi Chen
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Bohong Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yizi Zhao
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Xinxin Shao
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Mingshuo Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Fuhai Ma
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
- Department of General Surgery, Department of Gastrointestinal Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Laishou Yang
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Meng Nie
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Peng Jin
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
- Department of Gastroenterology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Ke Yao
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Haibin Song
- Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Shenghan Lou
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Hang Wang
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Tianshu Yang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
- Shanghai Qi Zhi Institute, Shanghai, 200438, China
| | - Yantao Tian
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
| | - Peng Han
- Department of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China.
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, 150081, China.
| | - Zeping Hu
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, 100084, China.
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24
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Malusare A, Aggarwal V. Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model. ARXIV 2024:arXiv:2402.08790v1. [PMID: 38410649 PMCID: PMC10896363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilization of extensive biomedical knowledge, often systematized within knowledge graphs, whose potential to inform and enhance generative processes has not been realized. In this paper, we present a novel approach that bridges this divide by developing a framework for knowledge-enhanced generative models called K-DReAM. We develop a scalable methodology to extend the functionality of knowledge graphs while preserving semantic integrity, and incorporate this contextual information into a generative framework to guide a diffusion-based model. The integration of knowledge graph embeddings with our generative model furnishes a robust mechanism for producing novel drug candidates possessing specific characteristics while ensuring validity and synthesizability. K-DReAM outperforms state-of-the-art generative models on both unconditional and targeted generation tasks.
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Affiliation(s)
- Aditya Malusare
- School of Industrial Engineering, Purdue University, USA
- Purdue Institute for Cancer Research, Purdue University, USA
| | - Vaneet Aggarwal
- School of Industrial Engineering, Purdue University, USA
- Purdue Institute for Cancer Research, Purdue University, USA
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25
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Rajendran S, Pan W, Sabuncu MR, Chen Y, Zhou J, Wang F. Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation. PATTERNS (NEW YORK, N.Y.) 2024; 5:100913. [PMID: 38370129 PMCID: PMC10873158 DOI: 10.1016/j.patter.2023.100913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C4) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C4 approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C4 in health care, including its present stage, potential opportunities, and associated challenges.
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Affiliation(s)
- Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Cornell University, Ithaca, NY, USA
| | - Weishen Pan
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Mert R. Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
- Cornell Tech, Cornell University, New York, NY, USA
- Department of Radiology, Weill Cornell Medical School, New York, NY, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Fei Wang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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26
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Fernández-Irigoyen J, Santamaría E. Special Issue "Deployment of Proteomics Approaches in Biomedical Research". Int J Mol Sci 2024; 25:1717. [PMID: 38338994 PMCID: PMC10855870 DOI: 10.3390/ijms25031717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].
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Affiliation(s)
| | - Enrique Santamaría
- Proteomics Platform, Clinical Neuroproteomics Unit, Navarrabiomed, Hospitalario Universitario de Navarra (HUN), Navarra Institute for Health Research (IDISNA), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain
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27
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Farzan R. Artificial intelligence in Immuno-genetics. Bioinformation 2024; 20:29-35. [PMID: 38352901 PMCID: PMC10859949 DOI: 10.6026/973206300200029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.
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Affiliation(s)
- Raed Farzan
- Department of Clinical Laboratory Sciences, College of Applied Medical Scienecs, King Saud University, Riyadh - 11433, Saudi Arabia
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh - 11433, Saudi Arabia
- Medical and Molecular Genetics Research, King Saud University, Riyadh-11433, Saudi Arabia
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28
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Han S, Lee JE, Kang S, So M, Jin H, Lee JH, Baek S, Jun H, Kim TY, Lee YS. Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Brief Bioinform 2024; 25:bbae035. [PMID: 38349059 PMCID: PMC10862655 DOI: 10.1093/bib/bbae035] [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: 11/07/2023] [Revised: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Ji Eun Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Seolhee Kang
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Minyoung So
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hee Jin
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Jang Ho Lee
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Sunghyeob Baek
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hyungjin Jun
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Tae Yong Kim
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Yun-Sil Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
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Hu J, Huang Z, Ge X, Shen Y, Xu Y, Zhang Z, Zhou G, Wang J, Lu S, Yu Y, Wan C, Zhang X, Huang R, Liu Y, Cheng G. Development and application of Chinese medical ontology for diabetes mellitus. BMC Med Inform Decis Mak 2024; 24:18. [PMID: 38243204 PMCID: PMC10799385 DOI: 10.1186/s12911-023-02405-y] [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: 10/05/2023] [Accepted: 12/12/2023] [Indexed: 01/21/2024] Open
Abstract
OBJECTIVE To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. MATERIALS AND METHODS We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering. RESULTS The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes. CONCLUSION CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.
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Affiliation(s)
- Jie Hu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zixian Huang
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Xuewen Ge
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yulin Shen
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Yihan Xu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zirui Zhang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guangyin Zhou
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shan Lu
- Outpatient Department of the First Affiliated Hospital of Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China
| | - Yun Yu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Cheng Wan
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xin Zhang
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China
| | - Ruochen Huang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, Jiangsu, China.
| | - Gong Cheng
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
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Chaturvedi J, Wang T, Velupillai S, Stewart R, Roberts A. Development of a Knowledge Graph Embeddings Model for Pain. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:299-308. [PMID: 38222382 PMCID: PMC10785867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.
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Affiliation(s)
- Jaya Chaturvedi
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Tao Wang
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Sumithra Velupillai
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Robert Stewart
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Angus Roberts
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
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Liu T, Feenstra KA, Huang Z, Heringa J. Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph. Bioinformatics 2024; 40:btad771. [PMID: 38147362 PMCID: PMC10769815 DOI: 10.1093/bioinformatics/btad771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 11/06/2023] [Accepted: 12/25/2023] [Indexed: 12/27/2023] Open
Abstract
MOTIVATION Up-to-date pathway knowledge is usually presented in scientific publications for human reading, making it difficult to utilize these resources for semantic integration and computational analysis of biological pathways. We here present an approach to mining knowledge graphs by combining manual curation with automated named entity recognition and automated relation extraction. This approach allows us to study pathway-related questions in detail, which we here show using the ketamine pathway, aiming to help improve understanding of the role of gut microbiota in the antidepressant effects of ketamine. RESULTS The thus devised ketamine pathway 'KetPath' knowledge graph comprises five parts: (i) manually curated pathway facts from images; (ii) recognized named entities in biomedical texts; (iii) identified relations between named entities; (iv) our previously constructed microbiota and pre-/probiotics knowledge bases; and (v) multiple community-accepted public databases. We first assessed the performance of automated extraction of relations between named entities using the specially designed state-of-the-art tool BioKetBERT. The query results show that we can retrieve drug actions, pathway relations, co-occurring entities, and their relations. These results uncover several biological findings, such as various gut microbes leading to increased expression of BDNF, which may contribute to the sustained antidepressant effects of ketamine. We envision that the methods and findings from this research will aid researchers who wish to integrate and query data and knowledge from multiple biomedical databases and literature simultaneously. AVAILABILITY AND IMPLEMENTATION Data and query protocols are available in the KetPath repository at https://dx.doi.org/10.5281/zenodo.8398941 and https://github.com/tingcosmos/KetPath.
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Affiliation(s)
- Ting Liu
- Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
- Learning & Reasoning Group, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - K Anton Feenstra
- Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Zhisheng Huang
- Learning & Reasoning Group, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jaap Heringa
- Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
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Venugopal S. Teaching Scientific Literature Analysis: A Systematic Adoption of Skill-Building Methods to Enrich Research Training for Undergraduate Students. JOURNAL OF UNDERGRADUATE NEUROSCIENCE EDUCATION : JUNE : A PUBLICATION OF FUN, FACULTY FOR UNDERGRADUATE NEUROSCIENCE 2023; 22:A74-A81. [PMID: 38322405 PMCID: PMC10768817 DOI: 10.59390/awcf3324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/24/2023] [Accepted: 11/14/2023] [Indexed: 02/08/2024]
Abstract
Teaching scientific literature analysis skills is a critical step in research training. Here I describe a 6-week skill-building module on understanding scientific literature, incorporated into a 10-week undergraduate honors research practice course in Neuroscience. Key pedagogical components include: 1) student-centered active-learning, skill-building and community-building activities; 2) persistent adoption of a proven CREATE method and a novel curate scientific summary (CSS) method for teaching scientific literature analysis skills; 3) collaborative class organization consisting of persistent learning pods (PLPs) to facilitate student-driven participation and peer learning; and, 4) role play of a real research lab. Skill development was assessed using a self-assessment survey (SAS) and longitudinal evaluation of the CREATE and CSS methods application by the PLPs to analyze primary research articles (PRAs) over four weeks. Outcomes demonstrate alleviation of pre-existing student anxiety to read complex scientific literature and advancement of critical-thinking and collaborative skills. Specifically, the SAS responses indicate that student perception about reading scientific literature transformed from being a daunting task to an enjoyable activity; this enhanced their confidence in evaluating scientific literature. PLPs fostered student engagement, peer instruction, and community building, and contributed to skill development. Weekly assessment of CREATE and CSS application highlighted marked improvements in students' abilities to analyze and critique complicated scientific material. Role playing a research lab setting with a focused research theme facilitated integrative understanding of a frontier topic in Neuroscience. The outlined innovative approach can be adopted in Course-based Undergraduate Research Experience (CURE) and should help contribute to systematizing didactic practices to train neuroscientists.
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Affiliation(s)
- Sharmila Venugopal
- Department of Integrative Biology and Physiology, Division of Life Sciences
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095
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Heylen D, Peeters J, Aerts J, Ertaylan G, Hooyberghs J. BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation. PLoS One 2023; 18:e0295361. [PMID: 38096184 PMCID: PMC10721075 DOI: 10.1371/journal.pone.0295361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/19/2023] [Indexed: 12/17/2023] Open
Abstract
One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.
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Affiliation(s)
- Dries Heylen
- Theory Lab, Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jannes Peeters
- Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
| | - Jan Aerts
- Visual Data Analysis Lab, Department of Biostystems KU Leuven, Leuven, Belgium
| | - Gökhan Ertaylan
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jef Hooyberghs
- Theory Lab, Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
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Wang H, Lim KP, Kong W, Gao H, Wong BJH, Phua SX, Guo T, Goh WWB. MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects. Sci Data 2023; 10:858. [PMID: 38042886 PMCID: PMC10693559 DOI: 10.1038/s41597-023-02779-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/23/2023] [Indexed: 12/04/2023] Open
Abstract
Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.
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Affiliation(s)
- He Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Kai Peng Lim
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Huanhuan Gao
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang, 310030, China
| | - Bertrand Jern Han Wong
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Ser Xian Phua
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang, 310030, China
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
- School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore.
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, 636921, Singapore.
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Rostgaard N, Olsen MH, Lolansen SD, Nørager NH, Plomgaard P, MacAulay N, Juhler M. Ventricular CSF proteomic profiles and predictors of surgical treatment outcome in chronic hydrocephalus. Acta Neurochir (Wien) 2023; 165:4059-4070. [PMID: 37857909 PMCID: PMC10739511 DOI: 10.1007/s00701-023-05832-y] [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: 07/07/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND By applying an unbiased proteomic approach, we aimed to search for cerebrospinal fluid (CSF) protein biomarkers distinguishing between obstructive and communicating hydrocephalus in order to improve appropriate surgical selection for endoscopic third ventriculostomy vs. shunt implants. Our second study purpose was to look for potential CSF biomarkers distinguishing between patients with adult chronic hydrocephalus benefitting from surgery (responders) vs. those who did not (non-responders). METHODS Ventricular CSF samples were collected from 62 patients with communicating hydrocephalus and 28 patients with obstructive hydrocephalus. CSF was collected in relation to the patients' surgical treatment. As a control group, CSF was collected from ten patients with unruptured aneurysm undergoing preventive surgery (vascular clipping). RESULTS Mass spectrometry-based proteomic analysis of the samples identified 1251 unique proteins. No proteins differed significantly between the communicating hydrocephalus group and the obstructive hydrocephalus group. Four proteins were found to be significantly less abundant in CSF from communicating hydrocephalus patients compared to control subjects. A PCA plot revealed similar proteomic CSF profiles of obstructive and communicating hydrocephalus and control samples. For obstructive hydrocephalus, ten proteins were found to predict responders from non-responders. CONCLUSION Here, we show that the proteomic profile of ventricular CSF from patients with hydrocephalus differs slightly from control subjects. Furthermore, we find ten predictors of response to surgical outcome (endoscopic third ventriculostomy or ventriculo-peritoneal shunt) in patients with obstructive hydrocephalus.
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Affiliation(s)
- Nina Rostgaard
- Department of Neurosurgery, The Neuroscience Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Markus Harboe Olsen
- Department of Neuroanaesthesiology, The Neuroscience Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sara Diana Lolansen
- Department of Neurosurgery, The Neuroscience Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicolas Hernandez Nørager
- Department of Neurosurgery, The Neuroscience Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Peter Plomgaard
- Department of Clinical Biochemistry, Centre of Diagnostic Investigations, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Nanna MacAulay
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marianne Juhler
- Department of Neurosurgery, The Neuroscience Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Werner T, Fahrner M, Schilling O. Using proteomics for stratification and risk prediction in patients with solid tumors. PATHOLOGIE (HEIDELBERG, GERMANY) 2023; 44:176-182. [PMID: 37999758 DOI: 10.1007/s00292-023-01261-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/19/2023] [Indexed: 11/25/2023]
Abstract
Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.
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Affiliation(s)
- Tilman Werner
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Centre Freiburg, University of Freiburg, Breisacher Str. 115a, 79106, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
| | - Matthias Fahrner
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Centre Freiburg, University of Freiburg, Breisacher Str. 115a, 79106, Freiburg, Germany
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Centre Freiburg, University of Freiburg, Breisacher Str. 115a, 79106, Freiburg, Germany.
- German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
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Davis S, Scott C, Oetjen J, Charles PD, Kessler BM, Ansorge O, Fischer R. Deep topographic proteomics of a human brain tumour. Nat Commun 2023; 14:7710. [PMID: 38001067 PMCID: PMC10673928 DOI: 10.1038/s41467-023-43520-8] [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: 03/01/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.
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Affiliation(s)
- Simon Davis
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Connor Scott
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Janina Oetjen
- Bruker Daltonics GmbH & Co. KG, Fahrenheitstraße 4, 28359, Bremen, Germany
| | - Philip D Charles
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Olaf Ansorge
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Roman Fischer
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.
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Gonzalez-Cavazos AC, Tanska A, Mayers M, Carvalho-Silva D, Sridharan B, Rewers PA, Sankarlal U, Jagannathan L, Su AI. DrugMechDB: A Curated Database of Drug Mechanisms. Sci Data 2023; 10:632. [PMID: 37717042 PMCID: PMC10505144 DOI: 10.1038/s41597-023-02534-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.
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Affiliation(s)
- Adriana Carolina Gonzalez-Cavazos
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Anna Tanska
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Michael Mayers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Denise Carvalho-Silva
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Brindha Sridharan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Patrick A Rewers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Umasri Sankarlal
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Lakshmanan Jagannathan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Andrew I Su
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
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Hartman E, Scott AM, Karlsson C, Mohanty T, Vaara ST, Linder A, Malmström L, Malmström J. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis. Nat Commun 2023; 14:5359. [PMID: 37660105 PMCID: PMC10475049 DOI: 10.1038/s41467-023-41146-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/22/2023] [Indexed: 09/04/2023] Open
Abstract
The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).
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Affiliation(s)
- Erik Hartman
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
| | - Aaron M Scott
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Christofer Karlsson
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Tirthankar Mohanty
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Suvi T Vaara
- Department of Perioperative and Intensive Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Adam Linder
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
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Smirnov D, Konstantinovskiy N, Prokisch H. Integrative omics approaches to advance rare disease diagnostics. J Inherit Metab Dis 2023; 46:824-838. [PMID: 37553850 DOI: 10.1002/jimd.12663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/10/2023]
Abstract
Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.
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Affiliation(s)
- Dmitrii Smirnov
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Nikita Konstantinovskiy
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Holger Prokisch
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
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Imbert B, Kreplak J, Flores RG, Aubert G, Burstin J, Tayeh N. Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes. Front Artif Intell 2023; 6:1191122. [PMID: 37601035 PMCID: PMC10435283 DOI: 10.3389/frai.2023.1191122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.
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Affiliation(s)
- Baptiste Imbert
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Jonathan Kreplak
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Raphaël-Gauthier Flores
- Université Paris-Saclay, INRAE, URGI, Versailles, France
- Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, Versailles, France
| | - Grégoire Aubert
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Judith Burstin
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Nadim Tayeh
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
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Lobentanzer S, Aloy P, Baumbach J, Bohar B, Carey VJ, Charoentong P, Danhauser K, Doğan T, Dreo J, Dunham I, Farr E, Fernandez-Torras A, Gyori BM, Hartung M, Hoyt CT, Klein C, Korcsmaros T, Maier A, Mann M, Ochoa D, Pareja-Lorente E, Popp F, Preusse M, Probul N, Schwikowski B, Sen B, Strauss MT, Turei D, Ulusoy E, Waltemath D, Wodke JAH, Saez-Rodriguez J. Democratizing knowledge representation with BioCypher. Nat Biotechnol 2023; 41:1056-1059. [PMID: 37337100 DOI: 10.1038/s41587-023-01848-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Affiliation(s)
- Sebastian Lobentanzer
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Balazs Bohar
- Earlham Institute, Norwich, UK
- Biological Research Centre, Szeged, Hungary
| | - Vincent J Carey
- Channing Division of Network Medicine, Mass General Brigham, Harvard Medical School, Boston, MA, USA
| | - Pornpimol Charoentong
- Centre for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Heidelberg, Germany
- Department of Medical Oncology, National Centre for Tumour Diseases (NCT), Heidelberg University Hospital (UKHD), Heidelberg, Germany
| | - Katharina Danhauser
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Munich, Germany
| | - Tunca Doğan
- Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey
- Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Johann Dreo
- Computational Systems Biomedicine Lab, Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université Paris Cité, Paris, France
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Elias Farr
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Adrià Fernandez-Torras
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Christoph Klein
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Munich, Germany
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, UK
- Imperial College London, London, UK
- Quadram Institute Bioscience, Norwich, UK
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Matthias Mann
- Proteomics Program, Novo Nordisk Foundation Centre for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Elena Pareja-Lorente
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ferdinand Popp
- Applied Tumour Immunity Clinical Cooperation Unit, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Martin Preusse
- German Centre for Diabetes Research (DZD), Neuherberg, Germany
| | - Niklas Probul
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
| | - Bünyamin Sen
- Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey
- Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Maximilian T Strauss
- Proteomics Program, Novo Nordisk Foundation Centre for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Denes Turei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Erva Ulusoy
- Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey
- Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Dagmar Waltemath
- Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
| | - Judith A H Wodke
- Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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Li F, Nian Y, Sun Z, Tao C. Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions. Yearb Med Inform 2023; 32:215-224. [PMID: 38147863 PMCID: PMC10751115 DOI: 10.1055/s-0043-1768735] [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] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVES Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research. METHODS We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality. RESULTS A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions. CONCLUSIONS GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research.
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Affiliation(s)
- Fang Li
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yi Nian
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zenan Sun
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Cui Tao
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
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Hou Y, Yeung J, Xu H, Su C, Wang F, Zhang R. From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs. RESEARCH SQUARE 2023:rs.3.rs-3185632. [PMID: 37577545 PMCID: PMC10418534 DOI: 10.21203/rs.3.rs-3185632/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Purpose Large Language Models (LLMs) have shown exceptional performance in various natural language processing tasks, benefiting from their language generation capabilities and ability to acquire knowledge from unstructured text. However, in the biomedical domain, LLMs face limitations that lead to inaccurate and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable resources for organizing structured information. Biomedical Knowledge Graphs (BKGs) have gained significant attention for managing diverse and large-scale biomedical knowledge. The objective of this study is to assess and compare the capabilities of ChatGPT and existing BKGs in question-answering, biomedical knowledge discovery, and reasoning tasks within the biomedical domain. Methods We conducted a series of experiments to assess the performance of ChatGPT and the BKGs in various aspects of querying existing biomedical knowledge, knowledge discovery, and knowledge reasoning. Firstly, we tasked ChatGPT with answering questions sourced from the "Alternative Medicine" sub-category of Yahoo! Answers and recorded the responses. Additionally, we queried BKG to retrieve the relevant knowledge records corresponding to the questions and assessed them manually. In another experiment, we formulated a prediction scenario to assess ChatGPT's ability to suggest potential drug/dietary supplement repurposing candidates. Simultaneously, we utilized BKG to perform link prediction for the same task. The outcomes of ChatGPT and BKG were compared and analyzed. Furthermore, we evaluated ChatGPT and BKG's capabilities in establishing associations between pairs of proposed entities. This evaluation aimed to assess their reasoning abilities and the extent to which they can infer connections within the knowledge domain. Results The results indicate that ChatGPT with GPT-4.0 outperforms both GPT-3.5 and BKGs in providing existing information. However, BKGs demonstrate higher reliability in terms of information accuracy. ChatGPT exhibits limitations in performing novel discoveries and reasoning, particularly in establishing structured links between entities compared to BKGs. Conclusions To address the limitations observed, future research should focus on integrating LLMs and BKGs to leverage the strengths of both approaches. Such integration would optimize task performance and mitigate potential risks, leading to advancements in knowledge within the biomedical field and contributing to the overall well-being of individuals.
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Géci I, Bober P, Filová E, Amler E, Sabo J. The Role of ARHGAP1 in Rho GTPase Inactivation during Metastasizing of Breast Cancer Cell Line MCF-7 after Treatment with Doxorubicin. Int J Mol Sci 2023; 24:11352. [PMID: 37511111 PMCID: PMC10379778 DOI: 10.3390/ijms241411352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/05/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is the most prevalent cancer type in women worldwide. It proliferates rapidly and can metastasize into farther tissues at any stage due to the gradual invasiveness and motility of the tumor cells. These crucial properties are the outcome of the weakened intercellular adhesion, regulated by small guanosine triphosphatases (GTPases), which hydrolyze to the guanosine diphosphate (GDP)-bound conformation. We investigated the inactivating effect of ARHGAP1 on Rho GTPases involved signaling pathways after treatment with a high dose of doxorubicin. Label-free quantitative proteomic analysis of the proteome isolated from the MCF-7 breast cancer cell line, treated with 1 μM of doxorubicin, identified RAC1, CDC42, and RHOA GTPases that were inactivated by the ARHGAP1 protein. Upregulation of the GTPases involved in the transforming growth factor-beta (TGF-beta) signaling pathway initiated epithelial-mesenchymal transitions. These findings demonstrate a key role of the ARHGAP1 protein in the disruption of the cell adhesion and simultaneously allow for a better understanding of the molecular mechanism of the reduced cell adhesion leading to the subsequent metastasis. The conclusions of this study corroborate the hypothesis that chemotherapy with doxorubicin may increase the risk of metastases in drug-resistant breast cancer cells.
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Affiliation(s)
- Imrich Géci
- Department of Medical and Clinical Biophysics, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Trieda SNP 1, 04011 Košice, Slovakia
| | - Peter Bober
- Department of Medical and Clinical Biophysics, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Trieda SNP 1, 04011 Košice, Slovakia
| | - Eva Filová
- Institute of Experimental Medicine, Czech Academy of Sciences, Vídeňská 1083, 142 00 Prague, Czech Republic
| | - Evžen Amler
- Institute of Experimental Medicine, Czech Academy of Sciences, Vídeňská 1083, 142 00 Prague, Czech Republic
| | - Ján Sabo
- Department of Medical and Clinical Biophysics, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Trieda SNP 1, 04011 Košice, Slovakia
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Lu J, Shen J, Xiong B, Ma W, Staab S, Yang C. HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL. ANNUAL INTERNATIONAL ACMSIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL 2023; 2023:2052-2056. [PMID: 38352127 PMCID: PMC10863609 DOI: 10.1145/3539618.3591997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.
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Affiliation(s)
| | | | - Bo Xiong
- University of Stuttgart, Germany
| | | | - Steffen Staab
- University of Stuttgart, Germany, University of Southampton, UK
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Caufield JH, Putman T, Schaper K, Unni DR, Hegde H, Callahan TJ, Cappelletti L, Moxon SAT, Ravanmehr V, Carbon S, Chan LE, Cortes K, Shefchek KA, Elsarboukh G, Balhoff J, Fontana T, Matentzoglu N, Bruskiewich RM, Thessen AE, Harris NL, Munoz-Torres MC, Haendel MA, Robinson PN, Joachimiak MP, Mungall CJ, Reese JT. KG-Hub-building and exchanging biological knowledge graphs. Bioinformatics 2023; 39:btad418. [PMID: 37389415 PMCID: PMC10336030 DOI: 10.1093/bioinformatics/btad418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/09/2023] [Accepted: 06/29/2023] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION https://kghub.org.
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Affiliation(s)
- J Harry Caufield
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Kevin Schaper
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Deepak R Unni
- SIB Swiss Institute of Bioinformatics, Basel 1015, Switzerland
| | - Harshad Hegde
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Luca Cappelletti
- Department of Computer Science, University of Milano, Milan 20126, Italy
| | - Sierra A T Moxon
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Vida Ravanmehr
- Department of Lymphoma-Myeloma, MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Seth Carbon
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, United States
| | - Katherina Cortes
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Kent A Shefchek
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Glass Elsarboukh
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Jim Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, United States
| | - Tommaso Fontana
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy
| | | | | | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | | | - Melissa A Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, United States
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, United States
| | - Marcin P Joachimiak
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Justin T Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
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Hou Y, Yeung J, Xu H, Su C, Wang F, Zhang R. From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291208. [PMID: 37398259 PMCID: PMC10312889 DOI: 10.1101/2023.06.09.23291208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Large Language Models (LLMs) have demonstrated exceptional performance in various natural language processing tasks, utilizing their language generation capabilities and knowledge acquisition potential from unstructured text. However, when applied to the biomedical domain, LLMs encounter limitations, resulting in erroneous and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable resources for structured information representation and organization. Specifically, Biomedical Knowledge Graphs (BKGs) have attracted significant interest in managing large-scale and heterogeneous biomedical knowledge. This study evaluates the capabilities of ChatGPT and existing BKGs in question answering, knowledge discovery, and reasoning. Results indicate that while ChatGPT with GPT-4.0 surpasses both GPT-3.5 and BKGs in providing existing information, BKGs demonstrate superior information reliability. Additionally, ChatGPT exhibits limitations in performing novel discoveries and reasoning, particularly in establishing structured links between entities compared to BKGs. To overcome these limitations, future research should focus on integrating LLMs and BKGs to leverage their respective strengths. Such an integrated approach would optimize task performance and mitigate potential risks, thereby advancing knowledge in the biomedical field and contributing to overall well-being.
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Affiliation(s)
- Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jeremy Yeung
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, Yale University, New Haven, Connecticut, USA
| | - Chang Su
- Department of Health Service Administration and Policy, Temple University, Philadelphia, PA, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Rui Zhang
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
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Shen Y, Kioumourtzoglou MA, Wu H, Vokonas P, Spiro A, Navas-Acien A, Baccarelli AA, Gao F. Cohort Network: A Knowledge Graph toward Data Dissemination and Knowledge-Driven Discovery for Cohort Studies. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:8236-8244. [PMID: 37224396 PMCID: PMC10597774 DOI: 10.1021/acs.est.2c08174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Contemporary environmental health sciences draw on large-scale longitudinal studies to understand the impact of environmental exposures and behavior factors on the risk of disease and identify potential underlying mechanisms. In such studies, cohorts of individuals are assembled and followed up over time. Each cohort generates hundreds of publications, which are typically neither coherently organized nor summarized, hence limiting knowledge-driven dissemination. Hence, we propose a Cohort Network, a multilayer knowledge graph approach to extract exposures, outcomes, and their connections. We applied the Cohort Network on 121 peer-reviewed papers published over the past 10 years from the Veterans Affairs (VA) Normative Aging Study (NAS). The Cohort Network visualized connections between exposures and outcomes across different publications and identified key exposures and outcomes, such as air pollution, DNA methylation, and lung function. We demonstrated the utility of the Cohort Network for new hypothesis generation, e.g., identification of potential mediators of exposure-outcome associations. The Cohort Network can be used by investigators to summarize the cohort's research and facilitate knowledge-driven discovery and dissemination.
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Affiliation(s)
- Yike Shen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Marianthi-Anna Kioumourtzoglou
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Haotian Wu
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Pantel Vokonas
- VA Normative Aging Study, VA Boston Healthcare System, Boston, Massachusetts 02130, United States
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts 02118, United States
| | - Avron Spiro
- VA Normative Aging Study, VA Boston Healthcare System, Boston, Massachusetts 02130, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts 02118, United States
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts 02118, United States
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Feng Gao
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
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50
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Zhao X, Wang Y, Li P, Xu J, Sun Y, Qiu M, Pang G, Wen T. The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data. Front Pharmacol 2023; 14:1147677. [PMID: 37324451 PMCID: PMC10264574 DOI: 10.3389/fphar.2023.1147677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/19/2023] [Indexed: 06/17/2023] Open
Abstract
Background: The complexity and rapid progression of lesions in diabetic kidney disease pose significant challenges for clinical diagnosis and treatment. The advantages of Traditional Chinese Medicine (TCM) in diagnosing and treating this condition have gradually become evident. However, due to the disease's complexity and the individualized approach to diagnosis and treatment in Traditional Chinese Medicine, Traditional Chinese Medicine guidelines have limitations in guiding the treatment of diabetic kidney disease. Most medical knowledge is currently stored in the process of recording medical records, which hinders the understanding of diseases and the acquisition of diagnostic and treatment knowledge among young doctors. Consequently, there is a lack of sufficient clinical knowledge to support the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine. Objective: To build a comprehensive knowledge graph for the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine, utilizing clinical guidelines, consensus, and real-world clinical data. On this basis, the knowledge of Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was systematically combed and mined. Methods: Normative guideline data and actual medical records were used to construct a knowledge graph of Traditional Chinese Medicine diagnosis and treatment for diabetic kidney disease and the results obtained by data mining techniques enrich the relational attributes. Neo4j graph database was used for knowledge storage, visual knowledge display, and semantic query. Utilizing multi-dimensional relations with hierarchical weights as the core, a reverse retrieval verification process is conducted to address the critical problems of diagnosis and treatment put forward by experts. Results: 903 nodes and 1670 relationships were constructed under nine concepts and 20 relationships. Preliminarily a knowledge graph for Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was constructed. Based on the multi-dimensional relationships, the diagnosis and treatment questions proposed by experts were validated through multi-hop queries of the graphs. The results were confirmed by experts and showed good outcomes. Conclusion: This study systematically combed the Traditional Chinese Medicine diagnosis and treatment knowledge of diabetic kidney disease by constructing the knowledge graph. Furthermore, it effectively solved the problem of "knowledge island". Through visual display and semantic retrieval, the discovery and sharing of diagnosis and treatment knowledge of diabetic kidney disease were realized.
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Affiliation(s)
- Xiaoliang Zhao
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yifei Wang
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Penghui Li
- Kaifeng Hospital of Traditional Chinese Medicine, Henan, China
| | - Julia Xu
- The University of Melbourne, Melbourne, SA, Australia
| | - Yao Sun
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Science, Beijing, China
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Moyan Qiu
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Guoming Pang
- Kaifeng Hospital of Traditional Chinese Medicine, Henan, China
| | - Tiancai Wen
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Science, Beijing, China
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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