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Toga AW, Neu S, Sheehan ST, Crawford K. The informatics of ADNI. Alzheimers Dement 2024. [PMID: 39140398 DOI: 10.1002/alz.14099] [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: 05/02/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 08/15/2024]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNI established a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNI set a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approach not only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer's disease. Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.
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Affiliation(s)
- Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Scott Neu
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sidney Taiko Sheehan
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Karen Crawford
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
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2
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Gómez-Pascual A, Rocamora-Pérez G, Ibanez L, Botía JA. Targeted co-expression networks for the study of traits. Sci Rep 2024; 14:16675. [PMID: 39030261 PMCID: PMC11271532 DOI: 10.1038/s41598-024-67329-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 07/10/2024] [Indexed: 07/21/2024] Open
Abstract
Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .
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Affiliation(s)
- A Gómez-Pascual
- Communications Engineering and Information Department, University of Murcia, 30100, Murcia, Spain
| | - G Rocamora-Pérez
- Department of Genetics and Genomic Medicine Research and Teaching, UCL GOS Institute of Child Health, London, WC1N 1EH, UK
| | - L Ibanez
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - J A Botía
- Communications Engineering and Information Department, University of Murcia, 30100, Murcia, Spain.
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3
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Li X, Owen LA, Taylor KD, Ostmo S, Chen YDI, Coyner AS, Sonmez K, Hartnett ME, Guo X, Ipp E, Roll K, Genter P, Chan RVP, DeAngelis MM, Chiang MF, Campbell JP, Rotter JI. Genome-wide association identifies novel ROP risk loci in a multiethnic cohort. Commun Biol 2024; 7:107. [PMID: 38233474 PMCID: PMC10794688 DOI: 10.1038/s42003-023-05743-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 12/26/2023] [Indexed: 01/19/2024] Open
Abstract
We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10-8) and 9 with significance of p < 5×10-6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10-9); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.
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Affiliation(s)
- Xiaohui Li
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Leah A Owen
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, USA.
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA.
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA.
- Department of Ophthalmology, University at Buffalo the State University of New York, Buffalo, NY, USA.
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Aaron S Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Kemal Sonmez
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | | | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Eli Ipp
- Division of Endocrinology and Metabolism, Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kathryn Roll
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Pauline Genter
- Division of Endocrinology and Metabolism, Department of Medicine, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Margaret M DeAngelis
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Department of Ophthalmology, University at Buffalo the State University of New York, Buffalo, NY, USA
- Department of Biochemistry; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, NY, USA
- Department of Neuroscience; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, NY, USA
- Department of Genetics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, NY, USA
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation; Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA.
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4
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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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5
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Chandrashekar PB, Alatkar S, Wang J, Hoffman GE, He C, Jin T, Khullar S, Bendl J, Fullard JF, Roussos P, Wang D. DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction. Genome Med 2023; 15:88. [PMID: 37904203 PMCID: PMC10617196 DOI: 10.1186/s13073-023-01248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/16/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. METHOD To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. RESULTS We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). CONCLUSION We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.
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Affiliation(s)
- Pramod Bharadwaj Chandrashekar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Sayali Alatkar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Chenfeng He
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA.
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53076, USA.
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6
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Bucholc M, James C, Al Khleifat A, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial Intelligence for Dementia Research Methods Optimization. ARXIV 2023:arXiv:2303.01949v1. [PMID: 36911275 PMCID: PMC10002770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
INTRODUCTION Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J. Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M. Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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7
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Shen J, Li H, Yu X, Bai L, Dong Y, Cao J, Lu K, Tang Z. Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder. Front Oncol 2023; 12:1091767. [PMID: 36703783 PMCID: PMC9872139 DOI: 10.3389/fonc.2022.1091767] [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: 11/07/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model's original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.
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Affiliation(s)
- Junjie Shen
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Huijun Li
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Xinghao Yu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China,Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Lu Bai
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Jianping Cao
- School of Radiation Medicine and Protection and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, China
| | - Ke Lu
- Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, China,*Correspondence: Zaixiang Tang, ; Ke Lu,
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China,*Correspondence: Zaixiang Tang, ; Ke Lu,
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8
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Xie F, Xu Y, Ma M, Pang X. A safe acceleration method for multi-task twin support vector machine. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-021-01481-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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New insights into the role of fibroblast growth factors in Alzheimer's disease. Mol Biol Rep 2021; 49:1413-1427. [PMID: 34731369 DOI: 10.1007/s11033-021-06890-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD), acknowledged as the most common progressive neurodegenerative disorder, is the leading cause of dementia in the elderly. The characteristic pathologic hallmarks of AD-including the deposition of extracellular senile plaques (SP) formation, intracellular neurofibrillary tangles, and synaptic loss, along with prominent vascular dysfunction and cognitive impairment-have been observed in patients. Fibroblast growth factors (FGFs), originally characterized as angiogenic factors, are a large family of signaling molecules that are implicated in a wide range of biological functions in brain development, maintenance and repair, as well as in the pathogenesis of brain-related disorders including AD. Many studies have focused on the implication of FGFs in AD pathophysiology. In this review, we will provide a summary of recent findings regarding the role of FGFs and their receptors in the pathogenesis of AD, and discuss the possible opportunities for targeting these molecules as novel treatment strategies in AD.
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10
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Sridharan PS, Lu Y, Rice RC, Pieper AA, Rajadhyaksha AM. Loss of Cav1.2 channels impairs hippocampal theta burst stimulation-induced long-term potentiation. Channels (Austin) 2021; 14:287-293. [PMID: 32799605 PMCID: PMC7515572 DOI: 10.1080/19336950.2020.1807851] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
CACNA1 C, which codes for the Cav1.2 isoform of L-type Ca2+ channels (LTCCs), is a prominent risk gene in neuropsychiatric and neurodegenerative conditions. A role forLTCCs, and Cav1.2 in particular, in transcription-dependent late long-term potentiation (LTP) has long been known. Here, we report that elimination of Cav1.2 channels in glutamatergic neurons also impairs theta burst stimulation (TBS)-induced LTP in the hippocampus, known to be transcription-independent and dependent on N-methyl D-aspartate receptors (NMDARs) and local protein synthesis at synapses. Our expansion of the established role of Cav1.2channels in LTP broadens understanding of synaptic plasticity and identifies a new cellular phenotype for exploring treatment strategies for cognitive dysfunction.
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Affiliation(s)
- Preethy S Sridharan
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center , Cleveland, OH, USA.,Department of Psychiatry and Department of Neuroscience, Case Western Reserve University , Cleveland, OH, USA
| | - Yuan Lu
- Department of Psychiatry, University of Iowa Carver College of Medicine , Iowa City, IA, USA
| | - Richard C Rice
- Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University , New York, NY, USA.,Pediatric Neurology, Pediatrics, Weill Cornell Medicine of Cornell University , New York, NY, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospitals Cleveland Medical Center , Cleveland, OH, USA.,Department of Psychiatry and Department of Neuroscience, Case Western Reserve University , Cleveland, OH, USA.,Department of Psychiatry, University of Iowa Carver College of Medicine , Iowa City, IA, USA.,Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University , New York, NY, USA.,Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center , Cleveland, OH, USA
| | - Anjali M Rajadhyaksha
- Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University , New York, NY, USA.,Pediatric Neurology, Pediatrics, Weill Cornell Medicine of Cornell University , New York, NY, USA.,Feil Family Brain and Mind and Research Institute, Weill Cornell Medicine of Cornell University , New York, NY, USA
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11
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Xie F, Pang X, Xu Y. Pinball loss-based multi-task twin support vector machine and its safe acceleration method. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06173-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Wen C, Yang Y, Xiao Q, Huang M, Pan W. Genome-wide association studies of brain imaging data via weighted distance correlation. Bioinformatics 2021; 36:4942-4950. [PMID: 32619001 DOI: 10.1093/bioinformatics/btaa612] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 06/17/2020] [Accepted: 06/26/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships. RESULTS To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI). AVAILABILITY AND IMPLEMENTATION Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Canhong Wen
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Yuhui Yang
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Quan Xiao
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
| | - Meiyan Huang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Wenliang Pan
- Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020; 11:1567-1584. [PMID: 33269107 PMCID: PMC7673858 DOI: 10.14336/ad.2020.0312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.
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Affiliation(s)
- Rohan Mishra
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
| | - Bin Li
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
- Georgetown University Medical Center, Washington D.C. 20057, USA
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Zhou J, Qiu Y, Chen S, Liu L, Liao H, Chen H, Lv S, Li X. A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions. Front Genet 2020; 11:572350. [PMID: 33193677 PMCID: PMC7542238 DOI: 10.3389/fgene.2020.572350] [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: 06/13/2020] [Accepted: 08/17/2020] [Indexed: 12/17/2022] Open
Abstract
Motivation: At present, a number of correlation analysis methods between SNPs and ROIs have been devised to explore the pathogenic mechanism of Alzheimer's disease. However, some of the deficiencies inherent in these methods, including lack of statistical efficacy and biological meaning. This study aims at addressing issues: insufficient correlation by previous methods (relative high regression error) and the lack of biological meaning in association analysis. Results: In this paper, a novel three-stage SNPs and ROIs correlation analysis framework is proposed. Firstly, clustering algorithm is applied to remove the potential linkage unbalanced structure of two SNPs. Then, the group sparse model is used to introduce prior information such as gene structure and linkage unbalanced structure to select feature SNPs. After the above steps, each SNP has a weight vector corresponding to each ROI, and the importance of SNPs can be judged according to the weights in the feature vector, and then the feature SNPs can be selected. Finally, for the selected feature SNPS, a support vector machine regression model is used to implement the prediction of the ROIs phenotype values. The experimental results under multiple performance measures show that the proposed method has better accuracy than other methods.
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Affiliation(s)
- Juan Zhou
- School of Software, East China Jiaotong University, Nanchang, China
| | - Yangping Qiu
- School of Software, East China Jiaotong University, Nanchang, China
| | - Shuo Chen
- School of Software, East China Jiaotong University, Nanchang, China
| | - Liyue Liu
- School of Software, East China Jiaotong University, Nanchang, China
| | - Huifa Liao
- School of Software, East China Jiaotong University, Nanchang, China
| | - Hongli Chen
- School of Software, East China Jiaotong University, Nanchang, China
| | - Shanguo Lv
- School of Software, East China Jiaotong University, Nanchang, China
| | - Xiong Li
- School of Software, East China Jiaotong University, Nanchang, China
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Wang X, Chen H, Yan J, Nho K, Risacher SL, Saykin AJ, Shen L, Huang H. Quantitative trait loci identification for brain endophenotypes via new additive model with random networks. Bioinformatics 2019; 34:i866-i874. [PMID: 30423101 DOI: 10.1093/bioinformatics/bty557] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Motivation The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation An executable is available at https://github.com/littleq1991/additive_FNNRW.
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Affiliation(s)
- Xiaoqian Wang
- Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hong Chen
- Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingwen Yan
- Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Heng Huang
- Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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Wu W, Xu Y. Accelerating improved twin support vector machine with safe screening rule. INT J MACH LEARN CYB 2019. [DOI: 10.1007/s13042-019-00946-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Hsu WCJ, Wildburger NC, Haidacher SJ, Nenov MN, Folorunso O, Singh AK, Chesson BC, Franklin WF, Cortez I, Sadygov RG, Dineley KT, Rudra JS, Taglialatela G, Lichti CF, Denner L, Laezza F. PPARgamma agonists rescue increased phosphorylation of FGF14 at S226 in the Tg2576 mouse model of Alzheimer's disease. Exp Neurol 2017; 295:1-17. [PMID: 28522250 DOI: 10.1016/j.expneurol.2017.05.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 04/13/2017] [Accepted: 05/13/2017] [Indexed: 12/13/2022]
Abstract
BACKGROUND Cognitive impairment in humans with Alzheimer's disease (AD) and in animal models of Aβ-pathology can be ameliorated by treatments with the nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARγ) agonists, such as rosiglitazone (RSG). Previously, we demonstrated that in the Tg2576 animal model of AD, RSG treatment rescued cognitive deficits and reduced aberrant activity of granule neurons in the dentate gyrus (DG), an area critical for memory formation. METHODS We used a combination of mass spectrometry, confocal imaging, electrophysiology and split-luciferase assay and in vitro phosphorylation and Ingenuity Pathway Analysis. RESULTS Using an unbiased, quantitative nano-LC-MS/MS screening, we searched for potential molecular targets of the RSG-dependent rescue of DG granule neurons. We found that S226 phosphorylation of fibroblast growth factor 14 (FGF14), an accessory protein of the voltage-gated Na+ (Nav) channels required for neuronal firing, was reduced in Tg2576 mice upon treatment with RSG. Using confocal microscopy, we confirmed that the Tg2576 condition decreased PanNav channels at the AIS of the DG, and that RSG treatment of Tg2576 mice reversed the reduction in PanNav channels. Analysis from previously published data sets identified correlative changes in action potential kinetics in RSG-treated T2576 compared to untreated and wildtype controls. In vitro phosphorylation and mass spectrometry confirmed that the multifunctional kinase GSK-3β, a downstream target of insulin signaling highly implicated in AD, phosphorylated FGF14 at S226. Assembly of the FGF14:Nav1.6 channel complex and functional regulation of Nav1.6-mediated currents by FGF14 was impaired by a phosphosilent S226A mutation. Bioinformatics pathway analysis of mass spectrometry and biochemistry data revealed a highly interconnected network encompassing PPARγ, FGF14, SCN8A (Nav 1.6), and the kinases GSK-3 β, casein kinase 2β, and ERK1/2. CONCLUSIONS These results identify FGF14 as a potential PPARγ-sensitive target controlling Aβ-induced dysfunctions of neuronal activity in the DG underlying memory loss in early AD.
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Affiliation(s)
- Wei-Chun J Hsu
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Biochemistry and Molecular Biology Graduate Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; M.D./Ph.D. Combined Degree Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Norelle C Wildburger
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Neuroscience Graduate Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Department of Neurology, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, United States
| | - Sigmund J Haidacher
- Department of Internal Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Miroslav N Nenov
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Oluwarotimi Folorunso
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Aditya K Singh
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Brent C Chesson
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Whitney F Franklin
- Neuroscience Graduate Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Department of Neurology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Ibdanelo Cortez
- Neuroscience Graduate Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Rovshan G Sadygov
- Biochemistry and Molecular Biology Graduate Program, Graduate School of Biomedical Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Sealy Center for Molecular Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Kelly T Dineley
- Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Department of Neurology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Center for Addiction Research, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Jay S Rudra
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Giulio Taglialatela
- Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Department of Neurology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Cheryl F Lichti
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Larry Denner
- Sealy Center for Molecular Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Department of Internal Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Center for Addiction Research, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States
| | - Fernanda Laezza
- Department of Pharmacology & Toxicology, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Mitchell Center for Neurodegenerative Diseases, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Center for Addiction Research, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States; Center for Biomedical Engineering, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, United States.
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Di Re J, Wadsworth PA, Laezza F. Intracellular Fibroblast Growth Factor 14: Emerging Risk Factor for Brain Disorders. Front Cell Neurosci 2017; 11:103. [PMID: 28469558 PMCID: PMC5396478 DOI: 10.3389/fncel.2017.00103] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 03/28/2017] [Indexed: 01/31/2023] Open
Abstract
The finely tuned regulation of neuronal firing relies on the integrity of ion channel macromolecular complexes. Minimal disturbances of these tightly regulated networks can lead to persistent maladaptive plasticity of brain circuitry. The intracellular fibroblast growth factor 14 (FGF14) belongs to the nexus of proteins interacting with voltage-gated Na+ (Nav) channels at the axonal initial segment. Through isoform-specific interactions with the intracellular C-terminal tail of neuronal Nav channels (Nav1.1, Nav1.2, Nav1.6), FGF14 controls channel gating, axonal targeting and phosphorylation in neurons effecting excitability. FGF14 has been also involved in synaptic transmission, plasticity and neurogenesis in the cortico-mesolimbic circuit with cognitive and affective behavioral outcomes. In translational studies, interest in FGF14 continues to rise with a growing list of associative links to diseases of the cognitive and affective domains such as neurodegeneration, depression, anxiety, addictive behaviors and recently schizophrenia, suggesting its role as a converging node in the etiology of complex brain disorders. Yet, a full understanding of FGF14 function in neurons is far from being complete and likely to involve other functions unrelated to the direct regulation of Nav channels. The goal of this Mini Review article is to provide a summary of studies on the emerging role of FGF14 in complex brain disorders.
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Affiliation(s)
- Jessica Di Re
- Neuroscience Graduate Program, University of Texas Medical BranchGalveston, TX, USA.,Department of Pharmacology and Toxicology, University of Texas Medical BranchGalveston, TX, USA
| | - Paul A Wadsworth
- Biochemistry and Molecular Biology Graduate Program, The University of Texas Medical BranchGalveston, TX, USA
| | - Fernanda Laezza
- Department of Pharmacology and Toxicology, University of Texas Medical BranchGalveston, TX, USA.,Mitchell Center for Neurodegenerative Diseases, The University of Texas Medical BranchGalveston, TX, USA.,Center for Addiction Research, The University of Texas Medical BranchGalveston, TX, USA
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Tao C, Nichols TE, Hua X, Ching CRK, Rolls ET, Thompson PM, Feng J. Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications. Neuroimage 2016; 144:35-57. [PMID: 27666385 DOI: 10.1016/j.neuroimage.2016.08.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Revised: 08/01/2016] [Accepted: 08/14/2016] [Indexed: 11/18/2022] Open
Abstract
We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.
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Affiliation(s)
- Chenyang Tao
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK
| | | | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Interdepartmental Neuroscience Graduate Program, UCLA School of Medicine, Los Angeles, CA, USA
| | - Edmund T Rolls
- Department of Computer Science, Warwick University, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Jianfeng Feng
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai 200433, PR China.
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