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Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl 2024; 10:53. [PMID: 38760412 PMCID: PMC11101644 DOI: 10.1038/s41540-024-00378-w] [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] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.
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Affiliation(s)
| | - Trivadi Sundaram Ganesan
- Department of Medical Oncology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Anbumathi Palanisamy
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, India.
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2
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Manoharan SD, Abdul Hamid H, Md Hashim NF, Cheema MS, Chiroma SM, Mustapha M, Mehat MZ. Could protein phosphatase 2A and glycogen synthase kinase-3 beta be targeted by natural compounds to ameliorate Alzheimer's pathologies? Brain Res 2024; 1829:148793. [PMID: 38309553 DOI: 10.1016/j.brainres.2024.148793] [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: 09/09/2023] [Revised: 12/26/2023] [Accepted: 01/29/2024] [Indexed: 02/05/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurological disorder that impairs memory and cognitive abilities, primarily in the elderly. The burden of AD extends beyond patients, impacting families and caregivers due to the patients' reliance on assistance for daily tasks. The main features of the pathogenesis of AD are beta-amyloid plaques and neurofibrillary tangles (NFTs), that strongly correlate with oxidative stress and inflammation. NFTs result from misfolded and hyperphosphorylated tau proteins. Various studies have focused on tau phosphorylation, indicating protein phosphatase 2A (PP2A) as the primary tau phosphatase and glycogen synthase kinase-3 beta (GSK-3β) as the leading tau kinase. Experimental evidence suggests that inhibition of PP2A and increased GSK-3β activity contribute to neuroinflammation, oxidative stress, and cognitive impairment. Hence, targeting PP2A and GSK-3β with pharmacological approaches shows promise in treating AD. The use of natural compounds in the drug development for AD have been extensively studied for their antioxidant, anti-inflammatory, anti-cholinesterase, and neuroprotective properties, demonstrating therapeutic advantages in neurological diseases. Alongside the development of PP2A activator and GSK-3β inhibitor drugs, natural compounds are likely to have neuroprotective effects by increasing PP2A activity and decreasing GSK-3β levels. Therefore, based on the preclinical and clinical studies, the potential of PP2A and GSK-3β as therapeutic targets of natural compounds are highlighted in this review.
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Affiliation(s)
- Sushmitaa Dhevii Manoharan
- Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
| | - Hafizah Abdul Hamid
- Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
| | - Nur Fariesha Md Hashim
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
| | - Manraj Singh Cheema
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
| | - Samaila Musa Chiroma
- Newcastle University Medicine Malaysia (NUMed), Iskandar Puteri 79200, Johor, Malaysia.
| | - Muzaimi Mustapha
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia.
| | - Muhammad Zulfadli Mehat
- Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
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3
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Gutierrez Reyes CD, Atashi M, Fowowe M, Onigbinde S, Daramola O, Lubman DM, Mechref Y. Differential expression of N-glycopeptides derived from serum glycoproteins in mild cognitive impairment (MCI) patients. Proteomics 2024:e2300620. [PMID: 38602241 DOI: 10.1002/pmic.202300620] [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: 11/22/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities with the aging of individuals, such as language or visual/spatial comprehension. MCI is considered a prodromal phase of more complicated neurodegenerative diseases such as Alzheimer's. Therefore, accurate diagnosis and better understanding of the disease prognosis will facilitate prevention of neurodegeneration. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of the serum N-glycoproteome expression could represent an essential contributor to the overall pathophysiology of neurodegenerative diseases and be used as a potential marker to assess MCI diagnosis using less invasive procedures. In this approach, we identified N-glycopeptides with different expressions between healthy and MCI patients from serum glycoproteins. Seven of the N-glycopeptides showed outstanding AUC values, among them the antithrombin-III Asn224 + 4-5-0-2 with an AUC value of 1.00 and a p value of 0.0004. According to proteomics and ingenuity pathway analysis (IPA), our data is in line with recent publications, and the glycoproteins carrying the identified N-sites play an important role in neurodegeneration.
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Affiliation(s)
| | - Mojgan Atashi
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Mojibola Fowowe
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Sherifdeen Onigbinde
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Oluwatosin Daramola
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - David M Lubman
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan, USA
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
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4
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Zerrouk N, Alcraft R, Hall BA, Augé F, Niarakis A. Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis. NPJ Syst Biol Appl 2024; 10:10. [PMID: 38272919 PMCID: PMC10811231 DOI: 10.1038/s41540-024-00337-5] [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: 09/18/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.
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Affiliation(s)
- Naouel Zerrouk
- GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France
- Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 1, Av Pierre Brossolette, 91385, Chilly-Mazarin, France
| | - Rachel Alcraft
- Advanced Research Computing Centre, University College London, London, UK
| | - Benjamin A Hall
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Franck Augé
- Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 1, Av Pierre Brossolette, 91385, Chilly-Mazarin, France
| | - Anna Niarakis
- GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France.
- Lifeware Group, Inria Saclay, Palaiseau, France.
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5
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Rahman MA, Shuvo AA, Apu MMH, Bhakta MR, Islam F, Rahman MA, Islam MR, Reza HM. Combination of epigallocatechin 3 gallate and curcumin improves D-galactose and normal-aging associated memory impairment in mice. Sci Rep 2023; 13:12681. [PMID: 37542120 PMCID: PMC10403524 DOI: 10.1038/s41598-023-39919-4] [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: 02/21/2023] [Accepted: 08/02/2023] [Indexed: 08/06/2023] Open
Abstract
Previously, we observed curcumin improves aging-associated memory impairment in D-galactose (D-gal) and normal-aged (NA) mice. Evidence showed that multiple agents can be used in managing aging-induced memory dysfunction, drawn by the contribution of several pathways. Curcumin and Epigallocatechin 3 gallate (EGCG) combination substantially reduced the oxidative stress that commonly mediates aging. This study examined the combined effect of EGCG and curcumin on memory improvement in two recognized models, D-gal and normal-aged (NA) mice. The co-administration of EGCG and curcumin significantly (p < 0.05) increased retention time detected by passive avoidance (PA) and freezing response determined in contextual fear conditioning (CFC) compared to the discrete administration of EGCG or curcumin. Biochemical studies revealed that the combination of EGCG and curcumin remarkably ameliorated the levels (p < 0.05) of glutathione, superoxide dismutase, catalase, advanced oxidation protein products, nitric oxide, and lipid peroxidation compared to the monotherapy of EGCG or curcumin in mice hippocampi. The behavioral and biochemical studies revealed that the combination of EGCG and curcumin showed better improvement in rescuing aging-associated memory disorders in mice. EGCG and curcumin combination could serve as a better choice in managing aging-related memory disorders.
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Affiliation(s)
- Md Ashrafur Rahman
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh.
- Department of Pharmaceutical Sciences, Wilkes University, Wilkes Barre, PA, 18766, USA.
| | - Arif Anzum Shuvo
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh
| | - Md Mehedi Hasan Apu
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh
| | - Monisha Rani Bhakta
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh
| | - Farzana Islam
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh
| | - Md Atiqur Rahman
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh
| | - Md Rabiul Islam
- Department of Pharmacy, University of Asia Pacific, 74/A Green Road, Farmgate, Dhaka, 1205, Bangladesh.
- School of Pharmacy, BRAC University, 66 Mohakhali, Dhaka, 1212, Bangladesh.
| | - Hasan Mahmud Reza
- Department of Pharmaceutical Sciences, North South University, Bashundhara, Dhaka, 1229, Bangladesh.
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6
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Rollo J, Crawford J, Hardy J. A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis. Neuron 2023; 111:2126-2139. [PMID: 37172582 DOI: 10.1016/j.neuron.2023.04.018] [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: 07/07/2022] [Revised: 12/15/2022] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.
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Affiliation(s)
- Jennifer Rollo
- Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK.
| | - John Crawford
- Adam Smith Business School, University of Glasgow, Glasgow G12 8QQ, UK
| | - John Hardy
- Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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7
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Mazein A, Acencio ML, Balaur I, Rougny A, Welter D, Niarakis A, Ramirez Ardila D, Dogrusoz U, Gawron P, Satagopam V, Gu W, Kremer A, Schneider R, Ostaszewski M. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance. FRONTIERS IN BIOINFORMATICS 2023; 3:1197310. [PMID: 37426048 PMCID: PMC10325725 DOI: 10.3389/fbinf.2023.1197310] [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: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche Pour la Polyarthrite Rhumatoïde–Genhotel, University Evry, Evry, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Diana Ramirez Ardila
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, Türkiye
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Andreas Kremer
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
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8
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Zhang Y, Ghose U, Buckley NJ, Engelborghs S, Sleegers K, Frisoni GB, Wallin A, Lleó A, Popp J, Martinez-Lage P, Legido-Quigley C, Barkhof F, Zetterberg H, Visser PJ, Bertram L, Lovestone S, Nevado-Holgado AJ, Shi L. Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks. Front Aging Neurosci 2022; 14:1040001. [PMID: 36523958 PMCID: PMC9746615 DOI: 10.3389/fnagi.2022.1040001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/04/2022] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. METHODS We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. RESULTS Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. CONCLUSION Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.
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Affiliation(s)
- Yuting Zhang
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Upamanyu Ghose
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Noel J. Buckley
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel, Brussels, Belgium
- Center for Neurociences (C4N), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kristel Sleegers
- Complex Genetics Group, VIB Center for Molecular Neurology, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Anders Wallin
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Alberto Lleó
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Julius Popp
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
- Department of Geriatric Psychiatry, University Hospital of Psychiatry and University of Zürich, Zürich, Switzerland
| | | | - Cristina Legido-Quigley
- Kings College London, London, United Kingdom
- The Systems Medicine Group, Steno Diabetes Center, Gentofte, Denmark
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
- University College London (UCL) Institutes of Neurology and Healthcare Engineering, London, United Kingdom
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, United Kingdom
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Janssen R&D, High Wycombe, United Kingdom
| | | | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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9
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Agapito G, Milano M, Cannataro M. A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies. BMC Bioinformatics 2022; 23:393. [PMID: 36167506 PMCID: PMC9516794 DOI: 10.1186/s12859-022-04936-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/23/2022] Open
Abstract
Background Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes. Results To improve the enrichment analysis results, we present a new statistical network pre-processing method by mapping DEGs and SNPs on a biological network that can improve the relevance and significance of the DEGs or SNPs of interest to incorporate pathway topology information into the PEA. The proposed methodology improves the statistical significance of the PEA analysis in terms of computed p value for each enriched pathways and limit the number of enriched pathways. This helps reduce the number of relevant biological pathways with respect to a non-specific list of genes. Conclusion The proposed method provides two-fold enhancements. Network analysis reveals fewer DEGs, by selecting only relevant DEGs and the detected DEGs improve the enriched pathways’ statistical significance, rather than simply using a general list of genes. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04936-z.
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Affiliation(s)
- Giuseppe Agapito
- Department of Law, Economics and Sociology Sciences, University Magna Græcia, 88100, Catanzaro, Italy. .,Data Analytics Research Center, University Magna Græcia, 88100, Catanzaro, Italy.
| | - Marianna Milano
- Data Analytics Research Center, University Magna Græcia, 88100, Catanzaro, Italy.,Department of Medical and Surgical Sciences, University Magna Græcia, 88100, Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia, 88100, Catanzaro, Italy.,Department of Medical and Surgical Sciences, University Magna Græcia, 88100, Catanzaro, Italy
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10
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Yang L, Ball A, Liu J, Jain T, Li YM, Akhter F, Zhu D, Wang J. Cyclic microchip assay for measurement of hundreds of functional proteins in single neurons. Nat Commun 2022; 13:3548. [PMID: 35729174 PMCID: PMC9213506 DOI: 10.1038/s41467-022-31336-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/15/2022] [Indexed: 12/02/2022] Open
Abstract
Despite the fact that proteins carry out nearly all cellular functions and mark the differences of cells, the existing single-cell tools can only analyze dozens of proteins, a scale far from full characterization of cells and tissue yet. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology that affords the comprehensive functional proteome profiling of single cells. We demonstrate the technology by detecting 182 proteins that include surface markers, neuron function proteins, neurodegeneration markers, signaling pathway proteins, and transcription factors. Further studies on cells derived from the 5XFAD mice, an Alzheimer’s Disease (AD) model, validate the utility of our technology and reveal the deep heterogeneity of brain cells. Through comparison with control mouse cells, we have identified differentially expressed proteins in AD pathology. Our technology could offer new insights into cell machinery and thus may advance many fields including drug discovery, molecular diagnostics, and clinical studies. Current single-cell tools are limited by the number of proteins they can analyse. Here the authors report a single-cell cyclic multiplex in situ tagging (CycMIST) method for functional proteome profiling of single cells, allowing multiple rounds of multiplexing of the same single cells on a microchip.
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Affiliation(s)
- Liwei Yang
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Avery Ball
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jesse Liu
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Tanya Jain
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Programs of Neurosciences, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Yue-Ming Li
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Programs of Neurosciences, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA.,Programs of Pharmacology, Weill Graduate School of Medical Sciences of Cornell University, New York, NY, USA
| | - Firoz Akhter
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Donghui Zhu
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jun Wang
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA.
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11
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Cheng K, Huang C, Hsieh T, Chiang H. Disrupted cellular calcium homeostasis is responsible for Aβ‐induced learning and memory damage and lifespan shortening in a model of Aβ transgenic fly. IUBMB Life 2022; 74:754-762. [DOI: 10.1002/iub.2621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 04/11/2022] [Indexed: 12/27/2022]
Affiliation(s)
- Kuan‐Chung Cheng
- Department of Pharmacology, College of Medicine National Cheng‐Kung University Tainan Taiwan
- Institute of Basic Medical Sciences, College of Medicine National Cheng‐Kung University Tainan Taiwan
| | - Chih‐Yuan Huang
- Division of Nephrology, Department of Internal Medicine Ditmanson Medical Foundation Chia‐Yi Christian Hospital Chiayi Taiwan
- Department of Sport Management, College of Recreation and Health Management Chia Nan University of Pharmacy and Science Tainan Taiwan
| | - Tsung‐Chi Hsieh
- Department of Pharmacology, College of Medicine National Cheng‐Kung University Tainan Taiwan
- Institute of Basic Medical Sciences, College of Medicine National Cheng‐Kung University Tainan Taiwan
- Brain Research Center National Tsing Hua University Hsinchu City Taiwan
| | - Hsueh‐Cheng Chiang
- Department of Pharmacology, College of Medicine National Cheng‐Kung University Tainan Taiwan
- Institute of Basic Medical Sciences, College of Medicine National Cheng‐Kung University Tainan Taiwan
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12
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Abubakar MB, Sanusi KO, Ugusman A, Mohamed W, Kamal H, Ibrahim NH, Khoo CS, Kumar J. Alzheimer’s Disease: An Update and Insights Into Pathophysiology. Front Aging Neurosci 2022; 14:742408. [PMID: 35431894 PMCID: PMC9006951 DOI: 10.3389/fnagi.2022.742408] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 02/25/2022] [Indexed: 12/17/2022] Open
Abstract
Alzheimer’s disease (AD) is an irreversible brain disorder associated with slow, progressive loss of brain functions mostly in older people. The disease processes start years before the symptoms are manifested at which point most therapies may not be as effective. In the hippocampus, the key proteins involved in the JAK2/STAT3 signaling pathway, such as p-JAK2-Tyr1007 and p-STAT3-Tyr705 were found to be elevated in various models of AD. In addition to neurons, glial cells such as astrocytes also play a crucial role in the progression of AD. Without having a significant effect on tau and amyloid pathologies, the JAK2/STAT3 pathway in reactive astrocytes exhibits a behavioral impact in the experimental models of AD. Cholinergic atrophy in AD has been traced to a trophic failure in the NGF metabolic pathway, which is essential for the survival and maintenance of basal forebrain cholinergic neurons (BFCN). In AD, there is an alteration in the conversion of the proNGF to mature NGF (mNGF), in addition to an increase in degradation of the biologically active mNGF. Thus, the application of exogenous mNGF in experimental studies was shown to improve the recovery of atrophic BFCN. Furthermore, it is now coming to light that the FGF7/FGFR2/PI3K/Akt signaling pathway mediated by microRNA-107 is also involved in AD pathogenesis. Vascular dysfunction has long been associated with cognitive decline and increased risk of AD. Vascular risk factors are associated with higher tau and cerebral beta-amyloid (Aβ) burden, while synergistically acting with Aβ to induce cognitive decline. The apolipoprotein E4 polymorphism is not just one of the vascular risk factors, but also the most prevalent genetic risk factor of AD. More recently, the research focus on AD shifted toward metabolisms of various neurotransmitters, major and minor nutrients, thus giving rise to metabolomics, the most important “omics” tool for the diagnosis and prognosis of neurodegenerative diseases based on an individual’s metabolome. This review will therefore proffer a better understanding of novel signaling pathways associated with neural and glial mechanisms involved in AD, elaborate potential links between vascular dysfunction and AD, and recent developments in “omics”-based biomarkers in AD.
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Affiliation(s)
- Murtala Bello Abubakar
- Department of Physiology, Faculty of Basic Medical Sciences, College of Health Sciences, Usmanu Danfodiyo University, Sokoto, Nigeria
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Kamaldeen Olalekan Sanusi
- Department of Physiology, Faculty of Basic Medical Sciences, College of Health Sciences, Usmanu Danfodiyo University, Sokoto, Nigeria
- Centre for Advanced Medical Research and Training, Usmanu Danfodiyo University, Sokoto, Nigeria
| | - Azizah Ugusman
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Wael Mohamed
- Department of Basic Medical Science, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
- Department of Clinical Pharmacology, Faculty of Medicine, Menoufia University, Shebin El-Kom, Egypt
| | - Haziq Kamal
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Nurul Husna Ibrahim
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Ching Soong Khoo
- Neurology Unit, Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
- *Correspondence: Jaya Kumar,
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13
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Shim Y, Han HJ, Park KW, Kim BC, Park KH, Park MY, Kim HJ, Moon SY, Choi SH, Park KW, Yang DW, Yoon SJ, Kim SY, Youn YC, Choi HJ, Yoon KE, Cho HJ, Han SH. A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Phase IIb Clinical Study to Evaluate the Safety and Efficacy of DHP1401 in Patients with Mild to Moderate Alzheimer's Disease Treated with Donepezil: DHP1401 Randomized Trial in Mild to Moderate Alzheimer's Disease (DRAMA). J Alzheimers Dis 2022; 87:391-403. [PMID: 35275529 DOI: 10.3233/jad-215277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Preclinical studies in transgenic models of Alzheimer's disease (AD) suggest that DHP1401 has neuroprotective and memory-enhancing effects. OBJECTIVE To evaluate the efficacy and safety of DHP1401 in AD patients treated with donepezilMethods:Methods: In a double-blind study, patients with mild-to-moderate AD were randomized (1:1:1) to receive a twice daily total dose of 500 mg or 1000 mg DHP1401 or placebo for 24 weeks. Tolerability and safety were monitored at baseline and weeks 12 and 24. RESULTS total of 180 patients were randomized to Active 1 (500 mg: n = 62), Active 2 (1000 mg: n = 53), and control groups (n = 65) in 16 sites in Korea. There was no significant difference in the Alzheimer's Disease Assessment Scale (ADAS-cog) score, the primary efficacy endpoint, from baseline. However, in the subgroup with mild AD patients (MMSE, 20-26) who received the high dose of DHP1401 and the group that received donepezil 5 mg, the ADAS-cog scores improved. MMSE and K-TMT-e type B were significant in both active groups at week 24. The most frequently observed symptom was dizziness (2.78%), and the most commonly observed reactions were related to metabolism and nutrition disorders (5.00%). No remarkable adverse events were observed for 24 weeks. CONCLUSION Although the effectiveness of DHP1401 was not proved to be superior as the primary efficacy endpoint, the secondary endpoints, MMSE and K-TMT-e type B, showed significant beneficial effects. Also, the subgroups showed that ADAS-cog scores significantly were improved. DHP1401 could be proven beneficial for the AD treatment by further clinical trials.
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Affiliation(s)
- YongSoo Shim
- Department of Neurology, The Catholic University of Korea Eunpyeong St. Mary's Hospital, Seoul, Republic of Korea
| | - Hyun Jeong Han
- Department of Neurology, Myongji Hospital, Hanyang University College of Medicine, Goyang, Republic of Korea
| | - Kyung Won Park
- Department of Neurology, Dong-A University College of Medicine and Department of Translational Biomedical Sciences, Graduate School of Dong-A University, Busan, Republic of Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Kee Hyung Park
- Department of Neurology, College of Medicine, Gachon University, Gil Medical Center, Incheon, Republic of Korea
| | - Mee Young Park
- Department of Neurology, Yeungnam University College of Medicine, Daegu, Republic of Korea
| | - Hee-Jin Kim
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - So Young Moon
- Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Kun Woo Park
- Department of Neurology, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Dong Won Yang
- Department of Neurology, The Catholic University of Korea, Seoul St. Mary's hospital, Seoul, Republic of Korea
| | - Soo Jin Yoon
- Department of Neurology, Daejeon Eulji Medical Center, Eulji University, Daejeon, Republic of Korea
| | - Sang Yun Kim
- Department of Neurology, Seoul National University Bundang Hospital & Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University, College of Medicine, Seoul, Republic of Korea
| | - Ho Jin Choi
- Department of Neurology, Hanyang University College of Medicine, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Koung Eun Yoon
- Clinical Trial Team, Daehwa Pharmaceutical Co., Ltd, Seoul, Republic of Korea
| | - Hyun Ju Cho
- Clinical Trial Team, Daehwa Pharmaceutical Co., Ltd, Seoul, Republic of Korea
| | - Seol-Heui Han
- Department of Neurology, Konkuk University Medical Center, Seoul, Republic of Korea
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14
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Pereira C, Mazein A, Farinha CM, Gray MA, Kunzelmann K, Ostaszewski M, Balaur I, Amaral MD, Falcao AO. CyFi-MAP: an interactive pathway-based resource for cystic fibrosis. Sci Rep 2021; 11:22223. [PMID: 34782688 PMCID: PMC8592983 DOI: 10.1038/s41598-021-01618-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/27/2021] [Indexed: 12/11/2022] Open
Abstract
Cystic fibrosis (CF) is a life-threatening autosomal recessive disease caused by more than 2100 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating variability in disease severity among individuals with CF sharing the same CFTR genotype. Systems biology can assist in the collection and visualization of CF data to extract additional biological significance and find novel therapeutic targets. Here, we present the CyFi-MAP-a disease map repository of CFTR molecular mechanisms and pathways involved in CF. Specifically, we represented the wild-type (wt-CFTR) and the F508del associated processes (F508del-CFTR) in separate submaps, with pathways related to protein biosynthesis, endoplasmic reticulum retention, export, activation/inactivation of channel function, and recycling/degradation after endocytosis. CyFi-MAP is an open-access resource with specific, curated and continuously updated information on CFTR-related pathways available online at https://cysticfibrosismap.github.io/ . This tool was developed as a reference CF pathway data repository to be continuously updated and used worldwide in CF research.
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Affiliation(s)
- Catarina Pereira
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
- LASIGE, Faculty of Sciences, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- CIRI UMR5308, CNRS-ENS-UCBL-INSERM, European Institute for Systems Biology and Medicine, Université de Lyon, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Carlos M Farinha
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Michael A Gray
- Biosciences Institute, University Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | | | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- CIRI UMR5308, CNRS-ENS-UCBL-INSERM, European Institute for Systems Biology and Medicine, Université de Lyon, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Margarida D Amaral
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Andre O Falcao
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal.
- LASIGE, Faculty of Sciences, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal.
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15
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Tan MS, Cheah PL, Chin AV, Looi LM, Chang SW. A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Comput Biol Med 2021; 139:104947. [PMID: 34678481 DOI: 10.1016/j.compbiomed.2021.104947] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 12/26/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.
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Affiliation(s)
- Mei Sze Tan
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Phaik-Leng Cheah
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Ai-Vyrn Chin
- Division of Geriatric Medicine, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Lai-Meng Looi
- Department of Pathology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
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16
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Glavaški M, Velicki L. Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation. BioData Min 2021; 14:45. [PMID: 34600580 PMCID: PMC8487578 DOI: 10.1186/s13040-021-00279-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/14/2021] [Indexed: 11/25/2022] Open
Abstract
Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-021-00279-2.
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Affiliation(s)
- Mila Glavaški
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.,Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
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17
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Shea TB. Improvement of cognitive performance by a nutraceutical formulation: Underlying mechanisms revealed by laboratory studies. Free Radic Biol Med 2021; 174:281-304. [PMID: 34352370 DOI: 10.1016/j.freeradbiomed.2021.07.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 12/28/2022]
Abstract
Cognitive decline, decrease in neuronal function and neuronal loss that accompany normal aging and dementia are the result of multiple mechanisms, many of which involve oxidative stress. Herein, we review these various mechanisms and identify pharmacological and non-pharmacological approaches, including modification of diet, that may reduce the risk and progression of cognitive decline. The optimal degree of neuronal protection is derived by combinations of, rather than individual, compounds. Compounds that provide antioxidant protection are particularly effective at delaying or improving cognitive performance in the early stages of Mild Cognitive Impairment and Alzheimer's disease. Laboratory studies confirm alleviation of oxidative damage in brain tissue. Lifestyle modifications show a degree of efficacy and may augment pharmacological approaches. Unfortunately, oxidative damage and resultant accumulation of biomarkers of neuronal damage can precede cognitive decline by years to decades. This underscores the importance of optimization of dietary enrichment, antioxidant supplementation and other lifestyle modifications during aging even for individuals who are cognitively intact.
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Affiliation(s)
- Thomas B Shea
- Laboratory for Neuroscience, Department of Biological Sciences, University of Massachusetts Lowell, Lowell, MA, 01854, USA.
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18
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Khullar S, Wang D. Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer's disease phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021. [PMID: 34189529 DOI: 10.1101/2021.06.21.449165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes. METHOD First, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes. RESULTS Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.
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19
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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20
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Cheng KC, Chen YH, Wu CL, Lee WP, Cheung CHA, Chiang HC. Rac1 and Akt Exhibit Distinct Roles in Mediating Aβ-Induced Memory Damage and Learning Impairment. Mol Neurobiol 2021; 58:5224-5238. [PMID: 34273104 DOI: 10.1007/s12035-021-02471-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 06/24/2021] [Indexed: 11/25/2022]
Abstract
Accumulated beta-amyloid (Aβ) in the brain is the hallmark of Alzheimer's disease (AD). Despite Aβ accumulation is known to trigger cellular dysfunctions and learning and memory damage, the detailed molecular mechanism remains elusive. Recent studies have shown that the onset of memory impairment and learning damage in the AD animal is different, suggesting that the underlying mechanism of the development of memory impairment and learning damage may not be the same. In the current study, with the use of Aβ42 transgenic flies as models, we found that Aβ induces memory damage and learning impairment via differential molecular signaling pathways. In early stage, Aβ activates both Ras and PI3K to regulate Rac1 activity, which affects mostly on memory performance. In later stage, PI3K-Akt is strongly activated by Aβ, which leads to learning damage. Moreover, reduced Akt, but not Rac1, activity promotes cell viability in the Aβ42 transgenic flies, indicating that Akt and Rac1 exhibit differential roles in Aβ regulating toxicity. Taken together, different molecular and cellular mechanisms are involved in Aβ-induced learning damage and memory decline; thus, caution should be taken during the development of therapeutic intervention in the future.
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Affiliation(s)
- Kuan-Chung Cheng
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
- Institute of Basic Medical Sciences, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Ying-Hao Chen
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-Lin Wu
- Department of Biochemistry and Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Wang-Pao Lee
- Department of Biochemistry and Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chun Hei Antonio Cheung
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
- Institute of Basic Medical Sciences, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Hsueh-Cheng Chiang
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan.
- Institute of Basic Medical Sciences, College of Medicine, National Cheng-Kung University, Tainan, Taiwan.
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21
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Nielsen SS, Ostaszewski M, McGee F, Hoksza D, Zorzan S. Machine Learning to Support the Presentation of Complex Pathway Graphs. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1130-1141. [PMID: 31484128 DOI: 10.1109/tcbb.2019.2938501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.
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Anti-Alzheimer's Molecules Derived from Marine Life: Understanding Molecular Mechanisms and Therapeutic Potential. Mar Drugs 2021; 19:md19050251. [PMID: 33925063 PMCID: PMC8146595 DOI: 10.3390/md19050251] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/19/2021] [Accepted: 04/26/2021] [Indexed: 02/08/2023] Open
Abstract
Alzheimer’s disease (AD) is a devastating neurodegenerative disease and the most common cause of dementia. It has been confirmed that the pathological processes that intervene in AD development are linked with oxidative damage to neurons, neuroinflammation, tau phosphorylation, amyloid beta (Aβ) aggregation, glutamate excitotoxicity, and cholinergic deficit. Still, there is no available therapy that can cure AD. Available therapies only manage some of the AD symptoms at the early stages of AD. Various studies have revealed that bioactive compounds derived from marine organisms and plants can exert neuroprotective activities with fewer adverse events, as compared with synthetic drugs. Furthermore, marine organisms have been identified as a source of novel compounds with therapeutic potential. Thus, there is a growing interest regarding bioactive compounds derived from marine sources that have anti-AD potentials. Various marine drugs including bryostatin-1, homotaurine, anabaseine and its derivative, rifampicins, anhydroexfoliamycin, undecylprodigioisin, gracilins, 13-desmethyl spirolide-C, and dictyostatin displayed excellent bioavailability and efficacy against AD. Most of these marine drugs were found to be well-tolerated in AD patients, along with no significant drug-associated adverse events. In this review, we focus on the drugs derived from marine life that can be useful in AD treatment and also summarize the therapeutic agents that are currently used to treat AD.
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Patel D, Zhang X, Farrell JJ, Lunetta KL, Farrer LA. Set-Based Rare Variant Expression Quantitative Trait Loci in Blood and Brain from Alzheimer Disease Study Participants. Genes (Basel) 2021; 12:419. [PMID: 33804025 PMCID: PMC7999141 DOI: 10.3390/genes12030419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/04/2021] [Accepted: 03/10/2021] [Indexed: 12/12/2022] Open
Abstract
Because studies of rare variant effects on gene expression have limited power, we investigated set-based methods to identify rare expression quantitative trait loci (eQTL) related to Alzheimer disease (AD). Gene-level and pathway-level cis rare-eQTL mapping was performed genome-wide using gene expression data derived from blood donated by 713 Alzheimer's Disease Neuroimaging Initiative participants and from brain tissues donated by 475 Religious Orders Study/Memory and Aging Project participants. The association of gene or pathway expression with a set of all cis potentially regulatory low-frequency and rare variants within 1 Mb of genes was evaluated using SKAT-O. A total of 65 genes expressed in the brain were significant targets for rare expression single nucleotide polymorphisms (eSNPs) among which 17% (11/65) included established AD genes HLA-DRB1 and HLA-DRB5. In the blood, 307 genes were significant targets for rare eSNPs. In the blood and the brain, GNMT, LDHC, RBPMS2, DUS2, and HP were targets for significant eSNPs. Pathway enrichment analysis revealed significant pathways in the brain (n = 9) and blood (n = 16). Pathways for apoptosis signaling, cholecystokinin receptor (CCKR) signaling, and inflammation mediated by chemokine and cytokine signaling were common to both tissues. Significant rare eQTLs in inflammation pathways included five genes in the blood (ALOX5AP, CXCR2, FPR2, GRB2, IFNAR1) that were previously linked to AD. This study identified several significant gene- and pathway-level rare eQTLs, which further confirmed the importance of the immune system and inflammation in AD and highlighted the advantages of using a set-based eQTL approach for evaluating the effect of low-frequency and rare variants on gene expression.
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Affiliation(s)
- Devanshi Patel
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA;
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - John J. Farrell
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA;
| | - Lindsay A. Farrer
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA;
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA;
- Department of Ophthalmology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
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Rodriguez S, Hug C, Todorov P, Moret N, Boswell SA, Evans K, Zhou G, Johnson NT, Hyman BT, Sorger PK, Albers MW, Sokolov A. Machine learning identifies candidates for drug repurposing in Alzheimer's disease. Nat Commun 2021; 12:1033. [PMID: 33589615 PMCID: PMC7884393 DOI: 10.1038/s41467-021-21330-0] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 01/21/2021] [Indexed: 01/31/2023] Open
Abstract
Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.
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Affiliation(s)
- Steve Rodriguez
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Clemens Hug
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Petar Todorov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Nienke Moret
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Sarah A Boswell
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Kyle Evans
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - George Zhou
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Nathan T Johnson
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Mark W Albers
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
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25
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Tan SZK, Zhao RC, Chakrabarti S, Stambler I, Jin K, Lim LW. Interdisciplinary Research in Alzheimer's Disease and the Roles International Societies Can Play. Aging Dis 2021; 12:36-41. [PMID: 33532125 PMCID: PMC7801283 DOI: 10.14336/ad.2020.0602] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/02/2020] [Indexed: 01/01/2023] Open
Abstract
An ever-increasing ageing population has elevated Alzheimer's disease to be one of the biggest challenges in modern medicine. Alzheimer's disease is highly complex, and we are still no closer to understanding the causes, let alone an effective treatment. The lack of good experimental models and lack of critical understanding has led to high failure rates of clinical trials with high associated costs, as well as difficulties in implementing treatments. The multifaceted nature of this disease highlights the need for an interdisciplinary approach to address these concerns. In this essay, we suggest how collaborative work can be useful in addressing some of the above issues. We then propose that international organisations and publishers need to support interdisciplinary research by creating platforms, lobbying funders, and pushing for interdisciplinary publications. We further highlight some of the issues involved in implementing these suggestions and argue that willpower of the research community, together with a re-evaluation of evaluation metrics and incentive systems, are needed in order to foster interdisciplinary research. Overall, we emphasise the need for interdisciplinary research in Alzheimer's disease and suggest that international societies should play a huge role in this endeavour.
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Affiliation(s)
- Shawn Zheng Kai Tan
- Neuromodulation Laboratory, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Robert Chunhua Zhao
- International Society on Aging and Disease (ISOAD), Fort Worth, Texas, USA.
- The Executive Committee on Anti-aging and Disease Prevention in the framework of Science and Technology, Pharmacology and Medicine Themes under an Interactive Atlas along the Silk Roads, UNESCO, Paris, France.
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Sasanka Chakrabarti
- International Society on Aging and Disease (ISOAD), Fort Worth, Texas, USA.
- The Executive Committee on Anti-aging and Disease Prevention in the framework of Science and Technology, Pharmacology and Medicine Themes under an Interactive Atlas along the Silk Roads, UNESCO, Paris, France.
- Department of Biochemistry and Central Research Cell, M M Institute of Medical Sciences and Research, Mullana, India.
| | - Ilia Stambler
- International Society on Aging and Disease (ISOAD), Fort Worth, Texas, USA.
- The Executive Committee on Anti-aging and Disease Prevention in the framework of Science and Technology, Pharmacology and Medicine Themes under an Interactive Atlas along the Silk Roads, UNESCO, Paris, France.
- The Geriatric Medical Center "Shmuel Harofe", Beer Yaakov, affiliated to Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Kunlin Jin
- International Society on Aging and Disease (ISOAD), Fort Worth, Texas, USA.
- The Executive Committee on Anti-aging and Disease Prevention in the framework of Science and Technology, Pharmacology and Medicine Themes under an Interactive Atlas along the Silk Roads, UNESCO, Paris, France.
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Texas, USA.
| | - Lee Wei Lim
- Neuromodulation Laboratory, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- International Society on Aging and Disease (ISOAD), Fort Worth, Texas, USA.
- The Executive Committee on Anti-aging and Disease Prevention in the framework of Science and Technology, Pharmacology and Medicine Themes under an Interactive Atlas along the Silk Roads, UNESCO, Paris, France.
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Nishi A, Ohbuchi K, Kaifuchi N, Shimobori C, Kushida H, Yamamoto M, Kita Y, Tokuoka SM, Yachie A, Matsuoka Y, Kitano H. LimeMap: a comprehensive map of lipid mediator metabolic pathways. NPJ Syst Biol Appl 2021; 7:6. [PMID: 33504811 PMCID: PMC7840682 DOI: 10.1038/s41540-020-00163-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 12/11/2020] [Indexed: 01/30/2023] Open
Abstract
Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance. Against this background, a knowledge-based pathway map of lipid mediators would be useful to visualize and understand the overall interactions of these factors. Here, we have built a precise map of lipid mediator metabolic pathways (LimeMap) to visualize the comprehensive profiles of lipid mediators that change dynamically in various disorders. We constructed the map by focusing on ω-3 and ω-6 fatty acid metabolites and their respective metabolic pathways, with manual curation of referenced information from public databases and relevant studies. Ultimately, LimeMap comprises 282 factors (222 mediators, and 60 enzymes, receptors, and ion channels) and 279 reactions derived from 102 related studies. Users will be able to modify the map and visualize measured data specific to their purposes using CellDesigner and VANTED software. We expect that LimeMap will contribute to elucidating the comprehensive functional relationships and pathways of lipid mediators.
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Affiliation(s)
- Akinori Nishi
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Katsuya Ohbuchi
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Noriko Kaifuchi
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Chika Shimobori
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Hirotaka Kushida
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Masahiro Yamamoto
- grid.510132.4Tsumura Kampo Research Laboratories, Tsumura & Co., Ibaraki, Japan
| | - Yoshihiro Kita
- grid.26999.3d0000 0001 2151 536XLife Sciences Core Facility, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan ,grid.26999.3d0000 0001 2151 536XDepartment of Lipidomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Suzumi M. Tokuoka
- grid.26999.3d0000 0001 2151 536XDepartment of Lipidomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ayako Yachie
- grid.452864.9The Systems Biology Institute, Shinagawa, Tokyo Japan
| | - Yukiko Matsuoka
- grid.452864.9The Systems Biology Institute, Shinagawa, Tokyo Japan
| | - Hiroaki Kitano
- grid.452864.9The Systems Biology Institute, Shinagawa, Tokyo Japan
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27
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Park JC, Jang SY, Lee D, Lee J, Kang U, Chang H, Kim HJ, Han SH, Seo J, Choi M, Lee DY, Byun MS, Yi D, Cho KH, Mook-Jung I. A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids. Nat Commun 2021; 12:280. [PMID: 33436582 PMCID: PMC7804132 DOI: 10.1038/s41467-020-20440-5] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/19/2020] [Indexed: 01/29/2023] Open
Abstract
Developing effective drugs for Alzheimer's disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood-brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs.
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Affiliation(s)
- Jong-Chan Park
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.83440.3b0000000121901201Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG United Kingdom
| | - So-Yeong Jang
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Dongjoon Lee
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Jeongha Lee
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Uiryong Kang
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Hongjun Chang
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Haeng Jun Kim
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Sun-Ho Han
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Jinsoo Seo
- grid.417736.00000 0004 0438 6721Department of Brain and Cognitive Science, Daegu Gyeongbuk Institute of Sciences and Technology (DGIST), Daegu, 42988 Republic of Korea
| | - Murim Choi
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Dong Young Lee
- grid.31501.360000 0004 0470 5905Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, College of medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080 Republic of Korea
| | - Min Soo Byun
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, 13620 Republic of Korea
| | - Dahyun Yi
- grid.31501.360000 0004 0470 5905Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080 Republic of Korea
| | - Kwang-Hyun Cho
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Inhee Mook-Jung
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
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Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1513] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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29
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Henry V, Moszer I, Dameron O, Vila Xicota L, Dubois B, Potier MC, Hofmann-Apitius M, Colliot O. Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6137817. [PMID: 33590873 DOI: 10.1093/database/baab004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 01/17/2021] [Accepted: 01/27/2021] [Indexed: 11/12/2022]
Abstract
Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders' pathophysiology at the molecular level adapted to omics measurements. However, the expressiveness of disease maps could be increased to help in avoiding ambiguities and misinterpretations and to reinforce their interoperability with other knowledge resources. Ontology is an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the Disease Map Ontology (DMO), an ontological upper model based on systems biology terms. We then propose to apply DMO to Alzheimer's disease (AD). Specifically, we use it to drive the conversion of AlzPathway, a disease map devoted to AD, into a formal ontology: Alzheimer DMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Furthermore, we show that it can store and manage multi-omics data. Finally, we expand the model using elements from other resources, such as clinical features contained in the AD Ontology, resulting in an enriched model called ADMO-plus. The current versions of DMO, ADMO and ADMO-plus are freely available at http://bioportal.bioontology.org/ontologies/ADMO.
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Affiliation(s)
- Vincent Henry
- Inria Paris, Aramis Project-Team, Paris 75013, France.,Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,ICONICS Core Facility, Paris Brain Institute, Paris 75013, France
| | - Ivan Moszer
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,ICONICS Core Facility, Paris Brain Institute, Paris 75013, France
| | - Olivier Dameron
- Univ Rennes, CNRS, Inria, IRISA-UMR 6074, Rennes 35000, France
| | - Laura Vila Xicota
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, Paris 75013, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Department of Neurology, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris 75013, France
| | - Marie-Claude Potier
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, Paris 75013, France
| | | | - Olivier Colliot
- Inria Paris, Aramis Project-Team, Paris 75013, France.,Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France
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30
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hiPSCs for predictive modelling of neurodegenerative diseases: dreaming the possible. Nat Rev Neurol 2021; 17:381-392. [PMID: 33658662 PMCID: PMC7928200 DOI: 10.1038/s41582-021-00465-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2021] [Indexed: 02/07/2023]
Abstract
Human induced pluripotent stem cells (hiPSCs) were first generated in 2007, but the full translational potential of this valuable tool has yet to be realized. The potential applications of hiPSCs are especially relevant to neurology, as brain cells from patients are rarely available for research. hiPSCs from individuals with neuropsychiatric or neurodegenerative diseases have facilitated biological and multi-omics studies as well as large-scale screening of chemical libraries. However, researchers are struggling to improve the scalability, reproducibility and quality of this descriptive disease modelling. Addressing these limitations will be the first step towards a new era in hiPSC research - that of predictive disease modelling - involving the correlation and integration of in vitro experimental data with longitudinal clinical data. This approach is a key element of the emerging precision medicine paradigm, in which hiPSCs could become a powerful diagnostic and prognostic tool. Here, we consider the steps necessary to achieve predictive modelling of neurodegenerative disease with hiPSCs, using Huntington disease as an example.
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31
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Pandey M, Choudhury H, Verma RK, Chawla V, Bhattamisra SK, Gorain B, Raja MAG, Amjad MW. Nanoparticles Based Intranasal Delivery of Drug to Treat Alzheimer's Disease: A Recent Update. CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS 2020; 19:648-662. [PMID: 32819251 DOI: 10.2174/1871527319999200819095620] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 05/23/2020] [Accepted: 07/10/2020] [Indexed: 11/22/2022]
Abstract
Alzheimer Association Report (2019) stated that the 6th primary cause of death in the USA is Alzheimer's Disease (AD), which leads to behaviour and cognitive impairment. Nearly 5.8 million peoples of all ages in the USA have suffered from this disease, including 5.6 million elderly populations. The statistics of the progression of this disease is similar to the global scenario. Still, the treatment of AD is limited to a few conventional oral drugs, which often fail to deliver an adequate amount of the drug in the brain. The reduction in the therapeutic efficacy of an anti-AD drug is due to poor solubility, existence to the blood-brain barrier and low permeability. In this context, nasal drug delivery emerges as a promising route for the delivery of large and small molecular drugs for the treatment of AD. This promising pathway delivers the drug directly into the brain via an olfactory route, which leads to the low systemic side effect, enhanced bioavailability, and higher therapeutic efficacy. However, few setbacks, such as mucociliary clearance and poor drug mucosal permeation, limit its translation from the laboratory to the clinic. The above stated limitation could be overcome by the adaption of nanoparticle as a drug delivery carrier, which may lead to prolong delivery of drugs with better permeability and high efficacy. This review highlights the latest work on the development of promising Nanoparticles (NPs) via the intranasal route for the treatment of AD. Additionally, the current update in this article will draw the attention of the researcher working on these fields and facing challenges in practical applicability.
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Affiliation(s)
- Manisha Pandey
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University-Bukit Jalil 57000, Kuala Lumpur, Malaysia
| | - Hira Choudhury
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University-Bukit Jalil 57000, Kuala Lumpur, Malaysia
| | - Rohit Kumar Verma
- Department of Pharmacy Practice, School of Pharmacy, International Medical University- Bukit Jalil 57000, Kuala Lumpur, Malaysia
| | - Viney Chawla
- University Institute of Pharmaceutical Sciences and Research, Baba Farid University of Health Sciences, Faridkot, India
| | - Subrat Kumar Bhattamisra
- Department of Life sciences, School of Pharmacy, International Medical University-Bukit Jalil 57000, Kuala Lumpur, Malaysia
| | - Bapi Gorain
- School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Selangor 47500, Malaysia
| | | | - Muhammad Wahab Amjad
- Department of Pharmaceutics, Faculty of Pharmacy, Northern Border University, Saudi Arabia
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Kim YW, Al‐Ramahi I, Koire A, Wilson SJ, Konecki DM, Mota S, Soleimani S, Botas J, Lichtarge O. Harnessing the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer's disease. Alzheimers Dement 2020; 17:831-846. [PMID: 33576571 PMCID: PMC8247413 DOI: 10.1002/alz.12240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/08/2020] [Accepted: 10/22/2020] [Indexed: 01/05/2023]
Abstract
The strongest genetic risk factor for idiopathic late‐onset Alzheimer's disease (LOAD) is apolipoprotein E (APOE) ɛ4, while the APOE ɛ2 allele is protective. However, there are paradoxical APOE ɛ4 carriers who remain disease‐free and APOE ɛ2 carriers with LOAD. We compared exomes of healthy APOE ɛ4 carriers and APOE ɛ2 Alzheimer's disease (AD) patients, prioritizing coding variants based on their predicted functional impact, and identified 216 genes with differential mutational load between these two populations. These candidate genes were significantly dysregulated in LOAD brains, and many modulated tau‐ or β42‐induced neurodegeneration in Drosophila. Variants in these genes were associated with AD risk, even in APOE ɛ3 homozygotes, showing robust predictive power for risk stratification. Network analyses revealed involvement of candidate genes in brain cell type‐specific pathways including synaptic biology, dendritic spine pruning and inflammation. These potential modifiers of LOAD may constitute novel biomarkers, provide potential therapeutic intervention avenues, and support applying this approach as larger whole exome sequencing cohorts become available.
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Affiliation(s)
- Young Won Kim
- Program in Integrative Molecular and Biomedical SciencesBaylor College of MedicineHoustonTexasUSA
| | - Ismael Al‐Ramahi
- Jan and Dan Duncan Neurological Research InstituteHoustonTexasUSA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Amanda Koire
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
- Medical Scientist Training ProgramBaylor College of MedicineHoustonTexasUSA
| | - Stephen J. Wilson
- Biochemistry and Molecular BiologyBaylor College of MedicineHoustonTexasUSA
| | - Daniel M. Konecki
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
| | - Samantha Mota
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Shirin Soleimani
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
| | - Juan Botas
- Jan and Dan Duncan Neurological Research InstituteHoustonTexasUSA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
| | - Olivier Lichtarge
- Program in Integrative Molecular and Biomedical SciencesBaylor College of MedicineHoustonTexasUSA
- Jan and Dan Duncan Neurological Research InstituteHoustonTexasUSA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Graduate Program in Quantitative and Computational BiosciencesBaylor College of MedicineHoustonTexasUSA
- Medical Scientist Training ProgramBaylor College of MedicineHoustonTexasUSA
- Biochemistry and Molecular BiologyBaylor College of MedicineHoustonTexasUSA
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Uddin MS, Al Mamun A, Kabir MT, Ashraf GM, Bin-Jumah MN, Abdel-Daim MM. Multi-Target Drug Candidates for Multifactorial Alzheimer's Disease: AChE and NMDAR as Molecular Targets. Mol Neurobiol 2020; 58:281-303. [PMID: 32935230 DOI: 10.1007/s12035-020-02116-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) is one of the most common forms of dementia among elder people, which is a progressive neurodegenerative disease that results from a chronic loss of cognitive activities. It has been observed that AD is multifactorial, hence diverse pharmacological targets that could be followed for the treatment of AD. The Food and Drug Administration has approved two types of medications for AD treatment such as cholinesterase inhibitors (ChEIs) and N-methyl-D-aspartic acid receptor (NMDAR) antagonists. Rivastigmine, donepezil, and galantamine are the ChEIs that have been approved to treat AD. On the other hand, memantine is the only non-competitive NMDAR antagonist approved in AD treatment. As compared with placebo, it has been revealed through clinical studies that many single-target therapies are unsuccessful to treat multifactorial Alzheimer's symptoms or disease progression. Therefore, due to the complex nature of AD pathophysiology, diverse pharmacological targets can be hunted. In this article, based on the entwined link of acetylcholinesterase (AChE) and NMDAR, we represent several multifunctional compounds in the rational design of new potential AD medications. This review focus on the significance of privileged scaffolds in the generation of the multi-target lead compound for treating AD, investigating the idea and challenges of multi-target drug design. Furthermore, the most auspicious elementary units for designing as well as synthesizing hybrid drugs are demonstrated as pharmacological probes in the rational design of new potential AD therapeutics.
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Affiliation(s)
- Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh.
- Pharmakon Neuroscience Research Network, Dhaka, Bangladesh.
| | - Abdullah Al Mamun
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh
- Pharmakon Neuroscience Research Network, Dhaka, Bangladesh
| | | | - Ghulam Md Ashraf
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - May N Bin-Jumah
- Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11474, Saudi Arabia
| | - Mohamed M Abdel-Daim
- Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
- Pharmacology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt
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Yu X, Yu W, Yang W, Lü Y. Usage and adherence of antidementia drugs in a memory clinic cohort in Chongqing, Southwest China. Psychogeriatrics 2020; 20:706-712. [PMID: 32500567 DOI: 10.1111/psyg.12568] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 04/14/2020] [Accepted: 05/04/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND To investigate the use and adherence of antidementia drugs in elderly patients with dementia from the Memory Clinic of The First Affiliated Hospital of Chongqing Medical University. METHODS Patients were recruited from the Memory Clinic of The First Affiliated Hospital of Chongqing Medical University from December 2010 to December 2018. Medical charts were reviewed, including diagnosis, dosage of antidementia medicines, neuropsychological testing scores, and the further questionnaires were conducted via face-to-face or telephone, included duration of treatment, types of antidementia drugs, and reasons for treatment discontinuation. RESULTS The data from 422 patients were analysed retrospectively for this study. Three hundred and fifteen were diagnosed with Alzheimer's disease (AD), 67 with mild cognitive impairment (MCI), and 40 with other types of dementia. From the 422 patients, 26.8% were treated with original donepezil (n = 113), 11.6% with generic donepezil (n = 49), 24.6% with memantine (n = 104), 13.3% with huperzine A (n = 56), and 23.7% with a combination of drugs (n = 100). However, 73% of patients discontinued treatment within 1 year of initiation. Patients treated for more than 36 months (37.8%) were more likely to choose combined medication, as compared with patients treated for less than 36 months. Patients with less than 9 years of education (odds ratio (OR): 2.394; 95% CI: 1.508-3.801) were more likely to discontinue treatment than patients with more than 9 years of education. Patients with elevated physical self-maintenance scale (PSMS) scores (OR: 1.195; 95% CI: 1.086-1.316) had a high risk of discontinuation. CONCLUSIONS Overall treatment compliance is relatively poor in memory clinics in Chongqing. Our study demonstrates that higher education may lead to better treatment adherence in dementia care. Combination therapy may increase treatment time. However, poorer PSMS scores are a significant risk factor for treatment discontinuation.
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Affiliation(s)
- Xingyan Yu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weihua Yu
- Institute of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Wenkai Yang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Albertini C, Salerno A, Sena Murteira Pinheiro P, Bolognesi ML. From combinations to multitarget‐directed ligands: A continuum in Alzheimer's disease polypharmacology. Med Res Rev 2020; 41:2606-2633. [DOI: 10.1002/med.21699] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/01/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Claudia Albertini
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
| | - Alessandra Salerno
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
| | - Pedro Sena Murteira Pinheiro
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
- Programa de Pós‐Graduação em Farmacologia e Química Medicinal, Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil
| | - Maria L. Bolognesi
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
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Cummings JL, Tong G, Ballard C. Treatment Combinations for Alzheimer's Disease: Current and Future Pharmacotherapy Options. J Alzheimers Dis 2020; 67:779-794. [PMID: 30689575 PMCID: PMC6398562 DOI: 10.3233/jad-180766] [Citation(s) in RCA: 286] [Impact Index Per Article: 71.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although Alzheimer’s disease (AD) is the world’s leading cause of dementia and the population of patients with AD continues to grow, no new therapies have been approved in more than a decade. Many clinical trials of single-agent therapies have failed to affect disease progression or symptoms compared with placebo. The complex pathophysiology of AD may necessitate combination treatments rather than monotherapy. The goal of this narrative literature review is to describe types of combination therapy, review the current clinical evidence for combination therapy regimens (both symptomatic and disease-modifying) in the treatment of AD, describe innovative clinical trial study designs that may be effective in testing combination therapy, and discuss the regulatory and drug development landscape for combination therapy. Successful combination therapies in other complex disorders, such as human immunodeficiency virus, may provide useful examples of a potential path forward for AD treatment.
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Affiliation(s)
| | | | - Clive Ballard
- University of Exeter Medical School, St Luke's Campus, Exeter, UK
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Fernández-Martínez JL, Álvarez-Machancoses Ó, deAndrés-Galiana EJ, Bea G, Kloczkowski A. Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning. Int J Mol Sci 2020; 21:ijms21103594. [PMID: 32438758 PMCID: PMC7279419 DOI: 10.3390/ijms21103594] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/09/2020] [Accepted: 05/13/2020] [Indexed: 12/21/2022] Open
Abstract
We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.
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Affiliation(s)
- Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
- DeepBioInsights, C/Federico García Lorca, 18, 33007 Oviedo, Spain
- Correspondence:
| | - Óscar Álvarez-Machancoses
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
- DeepBioInsights, C/Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
- Department of Informatics and Computer Science, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Guillermina Bea
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA;
- Department of Pediatrics, The Ohio State University, Columbus, OH 43205, USA
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Ravel JM, Monraz Gomez LC, Sompairac N, Calzone L, Zhivotovsky B, Kroemer G, Barillot E, Zinovyev A, Kuperstein I. Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases. Cancers (Basel) 2020; 12:E990. [PMID: 32316560 PMCID: PMC7226067 DOI: 10.3390/cancers12040990] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 03/10/2020] [Indexed: 12/25/2022] Open
Abstract
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.
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Affiliation(s)
- Jean-Marie Ravel
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Laboratoire de génétique médicale, CHRU-Nancy, F-54000 Nancy, France
- Inserm, NGERE, Université de Lorraine, F-54000 Nancy, France
| | - L. Cristobal Monraz Gomez
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Centre de Recherches Interdisciplinaires, Université Paris Descartes, 75006 Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Boris Zhivotovsky
- Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Division of Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Box 210, 17177 Stockholm, Sweden
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, 75006 Paris, France;
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, 94805 Villejuif, France
- Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
- Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou 215163, China
- Karolinska Institute, Department of Women’s and Children’s Health, Karolinska University Hospital, 171 77 Stockholm, Sweden
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
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Humayun F, Domingo-Fernández D, Paul George AA, Hopp MT, Syllwasschy BF, Detzel MS, Hoyt CT, Hofmann-Apitius M, Imhof D. A Computational Approach for Mapping Heme Biology in the Context of Hemolytic Disorders. Front Bioeng Biotechnol 2020; 8:74. [PMID: 32211383 PMCID: PMC7069124 DOI: 10.3389/fbioe.2020.00074] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/28/2020] [Indexed: 01/07/2023] Open
Abstract
Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme's role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme's interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.
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Affiliation(s)
- Farah Humayun
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Ajay Abisheck Paul George
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Marie-Thérèse Hopp
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Benjamin F. Syllwasschy
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Milena S. Detzel
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Diana Imhof
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
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Singh V, Kalliolias GD, Ostaszewski M, Veyssiere M, Pilalis E, Gawron P, Mazein A, Bonnet E, Petit-Teixeira E, Niarakis A. RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis. Database (Oxford) 2020; 2020:baaa017. [PMID: 32311035 PMCID: PMC7170216 DOI: 10.1093/database/baaa017] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 01/21/2020] [Accepted: 02/13/2020] [Indexed: 02/07/2023]
Abstract
Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.
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Affiliation(s)
- Vidisha Singh
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - George D Kalliolias
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
- Weill Cornell Medical Center, Weill Department of Medicine, 525 East 68th Street, New York, NY 10065, USA
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Maëva Veyssiere
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Eleftherios Pilalis
- eNIOS Applications P.C., R&D department, Alexandrou Pantou 25, 17671, Kallithea-Athens, Greece
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Eric Bonnet
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA, 2 rue Gaston Crémieux, CP5706 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Elisabeth Petit-Teixeira
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
| | - Anna Niarakis
- Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde - Genhotel, Univ Evry, Université Paris-Saclay, 2, rue Gaston Crémieux, 91057 EVRY-GENOPOLE cedex, Evry, France
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Zolotareva O, Kleine M. A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2018-0069/jib-2018-0069.xml. [PMID: 31494632 PMCID: PMC7074139 DOI: 10.1515/jib-2018-0069] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 07/12/2019] [Indexed: 12/16/2022] Open
Abstract
Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.
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Affiliation(s)
- Olga Zolotareva
- Bielefeld University, Faculty of Technology and Center for Biotechnology, International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes" and Genome Informatics, Universitätsstraße 25, Bielefeld, Germany
| | - Maren Kleine
- Bielefeld University, Faculty of Technology, Bioinformatics/Medical Informatics Department, Universitätsstraße 25, Bielefeld, Germany
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42
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Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R. Community-driven roadmap for integrated disease maps. Brief Bioinform 2019; 20:659-670. [PMID: 29688273 PMCID: PMC6556900 DOI: 10.1093/bib/bby024] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 03/02/2018] [Indexed: 01/07/2023] Open
Abstract
The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Ugur Dogrusoz
- Computer Engineering Department, Faculty of Engineering, Bilkent University, Ankara 06800, Turkey
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Ronan M T Fleming
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, Netherlands
| | - Nicolas Le Novère
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, United Kingdom
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Thomas Ligon
- Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany
| | - Anna Niarakis
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, Evry 91025, France
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
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Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Brief Bioinform 2019; 20:609-623. [PMID: 29684165 PMCID: PMC6556902 DOI: 10.1093/bib/bby025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/05/2018] [Indexed: 12/14/2022] Open
Abstract
Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.
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Affiliation(s)
- Mansoor Saqi
- Mansoor Saqi Data Science Institute, Imperial College London, UK
| | - Artem Lysenko
- Artem Lysenko Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yi-Ke Guo
- Yi-Ke Guo Data Science Institute, Imperial College London, UK
| | - Tatsuhiko Tsunoda
- Tatsuhiko Tsunoda Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan CREST, JST, Tokyo, Japan Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Charles Auffray
- Charles Auffray European Institute for Systems Biology and Medicine, Lyon, France
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44
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Rezazadeh M, Hosseinzadeh H, Moradi M, Salek Esfahani B, Talebian S, Parvin S, Gharesouran J. Genetic discoveries and advances in late‐onset Alzheimer’s disease. J Cell Physiol 2019; 234:16873-16884. [DOI: 10.1002/jcp.28372] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/20/2019] [Accepted: 01/24/2019] [Indexed: 12/26/2022]
Affiliation(s)
- Maryam Rezazadeh
- Department of Medical Genetics Faculty of Medicine, Tabriz University of Medical Sciences Tabriz Iran
- Division of Medical Genetics Tabriz Children’s Hospital, Tabriz University of Medical Sciences Tabriz Iran
| | | | - Mohsen Moradi
- Department of Medical Genetics Faculty of Medicine, Tabriz University of Medical Sciences Tabriz Iran
| | - Behnaz Salek Esfahani
- Department of Medical Genetics Faculty of Medicine, Tabriz University of Medical Sciences Tabriz Iran
| | - Shahrzad Talebian
- Department of Medical Genetics Faculty of Medicine, Tabriz University of Medical Sciences Tabriz Iran
| | - Shaho Parvin
- Department of Medical Genetics Faculty of Medicine, Tabriz University of Medical Sciences Tabriz Iran
| | - Jalal Gharesouran
- Department of Medical Genetics Faculty of Medicine, Tabriz University of Medical Sciences Tabriz Iran
- Division of Medical Genetics Tabriz Children’s Hospital, Tabriz University of Medical Sciences Tabriz Iran
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45
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Clausznitzer D, Pichardo-Almarza C, Relo AL, van Bergeijk J, van der Kam E, Laplanche L, Benson N, Nijsen M. Quantitative Systems Pharmacology Model for Alzheimer Disease Indicates Targeting Sphingolipid Dysregulation as Potential Treatment Option. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:759-770. [PMID: 30207429 PMCID: PMC6263662 DOI: 10.1002/psp4.12351] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 08/16/2018] [Indexed: 12/31/2022]
Abstract
Alzheimer disease (AD) is a devastating neurodegenerative disorder with high unmet medical need. Drug development is hampered by limited understanding of the disease and its driving factors. Quantitative Systems Pharmacology (QSP) modeling provides a comprehensive quantitative framework to evaluate the relevance of biological mechanisms in the context of disease and to predict the efficacy of novel treatments. Here, we report a QSP model for AD with a particular focus on investigating the relevance of dysregulation of cholesterol and sphingolipids. We show that our model captures the modulation of several biomarkers in subjects with AD, as well as the response to pharmacological interventions. We evaluate the impact of targeting the sphingosine-1-phosphate 5 receptor (S1PR5) as a potential novel treatment option for AD, and model predictions increase our confidence in this novel disease pathway. Future applications for the QSP model are in validation of further targets and identification of potential treatment response biomarkers.
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Affiliation(s)
| | | | | | | | | | | | - Neil Benson
- Certara QSP, Innovation centre, Unit 43, Canterbury, UK
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46
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Ostaszewski M, Kieffer E, Danoy G, Schneider R, Bouvry P. Clustering approaches for visual knowledge exploration in molecular interaction networks. BMC Bioinformatics 2018; 19:308. [PMID: 30157777 PMCID: PMC6116538 DOI: 10.1186/s12859-018-2314-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 08/14/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-Belval, Luxembourg
| | - Emmanuel Kieffer
- Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg
| | - Grégoire Danoy
- Computer Science and Communications Research Unit, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-Belval, Luxembourg
| | - Pascal Bouvry
- Computer Science and Communications Research Unit, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg
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47
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Mazein A, Knowles RG, Adcock I, Chung KF, Wheelock CE, Maitland‐van der Zee AH, Sterk PJ, Auffray C. AsthmaMap: An expert‐driven computational representation of disease mechanisms. Clin Exp Allergy 2018; 48:916-918. [DOI: 10.1111/cea.13211] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Alexander Mazein
- European Institute for Systems Biology and Medicine CIRI UMR5308 CNRS‐ENS‐UCBL‐INSERM Université de Lyon Lyon France
| | | | - Ian Adcock
- Airway Disease National Heart & Lung Institute Imperial College London London UK
| | - Kian Fan Chung
- Airway Disease National Heart & Lung Institute Imperial College London London UK
| | - Craig E Wheelock
- Division of Physiological Chemistry 2 Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden
| | | | - Peter J Sterk
- Respiratory Medicine Academic Medical Center Amsterdam The Netherlands
| | - Charles Auffray
- European Institute for Systems Biology and Medicine CIRI UMR5308 CNRS‐ENS‐UCBL‐INSERM Université de Lyon Lyon France
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48
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Glaab E. Computational systems biology approaches for Parkinson's disease. Cell Tissue Res 2018; 373:91-109. [PMID: 29185073 PMCID: PMC6015628 DOI: 10.1007/s00441-017-2734-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/06/2017] [Indexed: 12/26/2022]
Abstract
Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.
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Affiliation(s)
- Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
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49
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Mazein A, Ostaszewski M, Kuperstein I, Watterson S, Le Novère N, Lefaudeux D, De Meulder B, Pellet J, Balaur I, Saqi M, Nogueira MM, He F, Parton A, Lemonnier N, Gawron P, Gebel S, Hainaut P, Ollert M, Dogrusoz U, Barillot E, Zinovyev A, Schneider R, Balling R, Auffray C. Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms. NPJ Syst Biol Appl 2018; 4:21. [PMID: 29872544 PMCID: PMC5984630 DOI: 10.1038/s41540-018-0059-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/26/2018] [Accepted: 05/04/2018] [Indexed: 12/18/2022] Open
Abstract
The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
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Affiliation(s)
- Alexander Mazein
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Marek Ostaszewski
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Steven Watterson
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nicolas Le Novère
- 8The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT UK
| | - Diane Lefaudeux
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Bertrand De Meulder
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Johann Pellet
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Irina Balaur
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Mansoor Saqi
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Maria Manuela Nogueira
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg
| | - Andrew Parton
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nathanaël Lemonnier
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Piotr Gawron
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Pierre Hainaut
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg.,11Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis, University of Southern Denmark, Odense, Denmark
| | - Ugur Dogrusoz
- 12Faculty of Engineering, Computer Engineering Department, Bilkent University, Ankara, 06800 Turkey
| | - Emmanuel Barillot
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Andrei Zinovyev
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Reinhard Schneider
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Rudi Balling
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Charles Auffray
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
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50
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De Meulder B, Lefaudeux D, Bansal AT, Mazein A, Chaiboonchoe A, Ahmed H, Balaur I, Saqi M, Pellet J, Ballereau S, Lemonnier N, Sun K, Pandis I, Yang X, Batuwitage M, Kretsos K, van Eyll J, Bedding A, Davison T, Dodson P, Larminie C, Postle A, Corfield J, Djukanovic R, Chung KF, Adcock IM, Guo YK, Sterk PJ, Manta A, Rowe A, Baribaud F, Auffray C. A computational framework for complex disease stratification from multiple large-scale datasets. BMC SYSTEMS BIOLOGY 2018; 12:60. [PMID: 29843806 PMCID: PMC5975674 DOI: 10.1186/s12918-018-0556-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 02/21/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
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Affiliation(s)
- Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
| | - Diane Lefaudeux
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Aruna T Bansal
- Acclarogen Ltd, St John's Innovation Centre, Cambridge, CB4 OWS, UK
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Amphun Chaiboonchoe
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Hassan Ahmed
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Irina Balaur
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Nathanaël Lemonnier
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Kai Sun
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Ioannis Pandis
- Data Science Institute, Imperial College, London, SW7 2AZ, UK.,Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Xian Yang
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | | | | | | | | | - Timothy Davison
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Paul Dodson
- AstraZeneca Ltd, Alderley Park, Macclesfield, SK10 4TG, UK
| | | | - Anthony Postle
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Julie Corfield
- AstraZeneca R & D, 43150, Mölndal, Sweden.,Arateva R & D Ltd, Nottingham, NG1 1GF, UK
| | - Ratko Djukanovic
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Kian Fan Chung
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Ian M Adcock
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Yi-Ke Guo
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Peter J Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, AZ1105, The Netherlands
| | - Alexander Manta
- Research Informatics, Roche Diagnostics GmbH, 82008, Unterhaching, Germany
| | - Anthony Rowe
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | | | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
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