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Guo S, Yang J. Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia. Alzheimers Res Ther 2024; 16:120. [PMID: 38824563 PMCID: PMC11144322 DOI: 10.1186/s13195-024-01488-7] [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: 07/24/2023] [Accepted: 05/27/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia. METHODS We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene. RESULTS We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD. CONCLUSION We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.
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Affiliation(s)
- Shuyi Guo
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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2
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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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3
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. Science 2024; 384:eadi5199. [PMID: 38781369 DOI: 10.1126/science.adi5199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Chicago, IL 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
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4
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
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5
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AlMansoori ME, Jemimah S, Abuhantash F, AlShehhi A. Predicting early Alzheimer's with blood biomarkers and clinical features. Sci Rep 2024; 14:6039. [PMID: 38472245 DOI: 10.1038/s41598-024-56489-1] [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: 07/05/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024] Open
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.
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Affiliation(s)
- Muaath Ebrahim AlMansoori
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
| | - Sherlyn Jemimah
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
| | - Ferial Abuhantash
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
| | - Aamna AlShehhi
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates.
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6
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Xia H, Luan X, Bao Z, Zhu Q, Wen C, Wang M, Song W. A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease. Transl Psychiatry 2024; 14:111. [PMID: 38395947 PMCID: PMC10891125 DOI: 10.1038/s41398-024-02836-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer's Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways.
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Affiliation(s)
- Huwei Xia
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China
| | - Xiaoqian Luan
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Zhengkai Bao
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Qinxin Zhu
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Weihong Song
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China.
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
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7
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Zhang Y, Shen S, Li X, Wang S, Xiao Z, Cheng J, Li R. A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction. Neurosci Lett 2024; 821:137609. [PMID: 38157927 DOI: 10.1016/j.neulet.2023.137609] [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: 08/25/2023] [Revised: 10/30/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD. METHODS In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI). RESULTS Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05). CONCLUSIONS Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.
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Affiliation(s)
- Yi Zhang
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China.
| | - Shasha Shen
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China
| | - Xiaokai Li
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China
| | - Songlin Wang
- Medical College, Panzhihua University, Panzhihua 617000, China
| | - Zongni Xiao
- Medical College, Panzhihua University, Panzhihua 617000, China
| | - Jun Cheng
- Medical College, Panzhihua University, Panzhihua 617000, China
| | - Ruifeng Li
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China
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8
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Yan R, Wang W, Yang W, Huang M, Xu W. Mitochondria-Related Candidate Genes and Diagnostic Model to Predict Late-Onset Alzheimer's Disease and Mild Cognitive Impairment. J Alzheimers Dis 2024; 99:S299-S315. [PMID: 37334608 PMCID: PMC11091583 DOI: 10.3233/jad-230314] [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] [Accepted: 05/15/2023] [Indexed: 06/20/2023]
Abstract
Background Late-onset Alzheimer's disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence. Objective Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods. Methods Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established. Results LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN. Conclusion Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.
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Affiliation(s)
- Ran Yan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjing Wang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Yang
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Masha Huang
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Xu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Ruijin Hospital, Zhoushan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
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9
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Pyun J, Park YH, Wang J, Bennett DA, Bice PJ, Kim JP, Kim S, Saykin AJ, Nho K. Transcriptional risk scores in Alzheimer's disease: From pathology to cognition. Alzheimers Dement 2024; 20:243-252. [PMID: 37563770 PMCID: PMC10840812 DOI: 10.1002/alz.13406] [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: 02/14/2023] [Revised: 06/28/2023] [Accepted: 07/03/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. METHODS We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). RESULTS Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. DISCUSSION Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. HIGHLIGHTS Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.
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Affiliation(s)
- Jung‐Min Pyun
- Department of NeurologySoonchunhyang University Seoul HospitalSoonchunhyang University College of MedicineYongsan‐guSeoulRepublic of Korea
| | - Young Ho Park
- Department of NeurologySeoul National University College of MedicineSeoul National University Bundang HospitalSeongnam‐siGyeonggi‐doRepublic of Korea
| | - Jiebiao Wang
- Department of BiostatisticsUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - David A. Bennett
- Department of Neurological ScienceRush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Paula J. Bice
- Department of Radiology and Imaging Sciencesand the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Jun Pyo Kim
- Department of Radiology and Imaging Sciencesand the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - SangYun Kim
- Department of NeurologySeoul National University College of MedicineSeoul National University Bundang HospitalSeongnam‐siGyeonggi‐doRepublic of Korea
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciencesand the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciencesand the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of Medicine, Health Information and Translational Science BuildingIndianapolisIndianaUSA
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10
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Sun Y, Zhu J, Yang Y, Zhang Z, Zhong H, Zeng G, Zhou D, Nowakowski RS, Long J, Wu C, Wu L. Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data. Transl Psychiatry 2023; 13:387. [PMID: 38092781 PMCID: PMC10719322 DOI: 10.1038/s41398-023-02695-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/16/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023] Open
Abstract
Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.
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Affiliation(s)
- Yanfa Sun
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P. R. China
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Yaohua Yang
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, 22093, USA
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Guanghua Zeng
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P. R. China
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, P.R. China
| | - Richard S Nowakowski
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, 32304, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.
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11
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Abdelwahab MM, Al-Karawi KA, Semary HE. Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data. Biomedicines 2023; 11:3304. [PMID: 38137524 PMCID: PMC10741889 DOI: 10.3390/biomedicines11123304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/02/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
Abstract
Alzheimer's disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer's disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer's disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA-CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD-CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.
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Affiliation(s)
- Mahmoud M. Abdelwahab
- Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia;
- Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis 44621, Egypt
| | - Khamis A. Al-Karawi
- School of Science, Engineering and Environment, Salford University, Salford M5 4WT, UK;
- College of Veterinary Medicine, Diyala University, Baquba 32001, Iraq
| | - Hatem E. Semary
- Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia;
- Department of Statistics and Insurance, Faculty of Commerce, Zagazig University, Zagazig 44519, Egypt
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12
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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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13
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Cai J, Hu W, Ma J, Si A, Chen S, Gong L, Zhang Y, Yan H, Chen F. Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities Data. Brain Sci 2023; 13:1535. [PMID: 38002495 PMCID: PMC10670176 DOI: 10.3390/brainsci13111535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 10/04/2023] [Accepted: 10/29/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. METHODS This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. RESULTS The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features' importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature. CONCLUSIONS The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients.
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Affiliation(s)
- Jiaxin Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Weiwei Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Jiaojiao Ma
- Department of Neurology, Xi’an Gaoxin Hospital, Xi’an 710077, China;
| | - Aima Si
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Shiyu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Lingmin Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
| | - Yong Zhang
- Department of Surgical Oncology, First Affiliate Hospital of Xi’an Jiaotong University, Xi’an 710061, China;
| | - Hong Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an Jiaotong University, Xi’an 710061, China
| | - Fangyao Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, China; (J.C.); (W.H.); (A.S.); (S.C.); (L.G.)
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an Jiaotong University, Xi’an 710061, China
- Department of Radiology, First Affiliate Hospital of Xi’an Jiaotong University, Xi’an 710061, China
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14
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Krokidis MG, Vrahatis AG, Lazaros K, Skolariki K, Exarchos TP, Vlamos P. Machine Learning Analysis of Alzheimer's Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions. Curr Issues Mol Biol 2023; 45:8652-8669. [PMID: 37998721 PMCID: PMC10670182 DOI: 10.3390/cimb45110544] [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: 09/30/2023] [Revised: 10/15/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer's disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer's disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer's disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer's disease.
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Affiliation(s)
- Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (A.G.V.); (K.L.); (K.S.); (T.P.E.); (P.V.)
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15
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Akkaya UM, Kalkan H. A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease. Biomolecules 2023; 13:1563. [PMID: 38002245 PMCID: PMC10669658 DOI: 10.3390/biom13111563] [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: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data.
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Affiliation(s)
| | - Habil Kalkan
- Department of Computer Engineering, Gebze Technical University, 41400 Gebze, Turkey;
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16
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Jemimah S, AlShehhi A. c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease. BMC Med Genomics 2023; 16:244. [PMID: 37833700 PMCID: PMC10571239 DOI: 10.1186/s12920-023-01675-9] [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: 11/29/2022] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based biomarker discovery in Alzheimer's can facilitate less-invasive, routine diagnostic tests to aid early intervention. Therefore, we propose "c-Diadem" (constrained dual-input Alzheimer's disease model), a novel deep learning classifier which incorporates KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway constraints on the input genotyping data to predict disease, i.e., mild cognitive impairment (MCI)/AD or cognitively normal (CN). SHAP (SHapley Additive exPlanations) was used to explain the model and identify novel, potential blood-based genetic markers of MCI/AD. METHODS We developed a novel constrained deep learning neural network which utilizes SNPs (single nucleotide polymorphisms) and microarray data from ADNI (Alzheimer's Disease Neuroimaging Initiative) to predict the disease status of participants, i.e., CN or with disease (MCI/AD), and identify potential blood-based biomarkers for diagnosis and intervention. The dataset contains samples from 626 participants, of which 212 are CN (average age 74.6 ± 5.4 years) and 414 patients have MCI/AD (average age 72.7 ± 7.6 years). KEGG pathway information was used to generate constraints applied to the input tensors, thus enhancing the interpretability of the model. SHAP scores were used to identify genes which could potentially serve as biomarkers for diagnosis and targets for drug development. RESULTS Our model's performance, with accuracy of 69% and AUC of 70% in the test dataset, is superior to previous models. The SHAP scores show that SNPs in PRKCZ, PLCB1 and ITPR2 as well as expression of HLA-DQB1, EIF1AY, HLA-DQA1, and ZFP57 have more impact on model predictions. CONCLUSIONS In addition to predicting MCI/AD, our model has been interrogated for potential genetic biomarkers using SHAP. From our analysis, we have identified blood-based genetic markers related to Ca2+ ion release in affected regions of the brain, as well as depression. The findings from our study provides insights into disease mechanisms, and can facilitate innovation in less-invasive, cost-effective diagnostics. To the best of our knowledge, our model is the first to use pathway constraints in a multimodal neural network to identify potential genetic markers for AD.
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Affiliation(s)
- Sherlyn Jemimah
- Department of Biomedical Engineering, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Aamna AlShehhi
- Department of Biomedical Engineering, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates.
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17
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Kelly J, Moyeed R, Carroll C, Luo S, Li X. Blood biomarker-based classification study for neurodegenerative diseases. Sci Rep 2023; 13:17191. [PMID: 37821485 PMCID: PMC10567903 DOI: 10.1038/s41598-023-43956-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 09/30/2023] [Indexed: 10/13/2023] Open
Abstract
As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer's (AD) and Parkinson's disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer's (ROC AUC = 0.810) and Parkinson's (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture.
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Affiliation(s)
- Jack Kelly
- Faculty of Medicine, Biology and Health, Centre for Biostatistics, School of Health Sciences, University of Manchester, Manchester, UK.
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK.
| | - Rana Moyeed
- Faculty of Science and Engineering, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Camille Carroll
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Shouqing Luo
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Xinzhong Li
- School of Health and Life Sciences, Teesside University, Middlesbrough, TS1 3BX, UK.
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18
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Armstrong MJ, Jin Y, Vattathil SM, Huang Y, Schroeder JP, Bennet DA, Qin ZS, Wingo TS, Jin P. Role of TET1-mediated epigenetic modulation in Alzheimer's disease. Neurobiol Dis 2023; 185:106257. [PMID: 37562656 PMCID: PMC10530206 DOI: 10.1016/j.nbd.2023.106257] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/30/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder influenced by a complex interplay of environmental, epigenetic, and genetic factors. DNA methylation (5mC) and hydroxymethylation (5hmC) are DNA modifications that serve as tissue-specific and temporal regulators of gene expression. TET family enzymes dynamically regulate these epigenetic modifications in response to environmental conditions, connecting environmental factors with gene expression. Previous epigenetic studies have identified 5mC and 5hmC changes associated with AD. In this study, we performed targeted resequencing of TET1 on a cohort of early-onset AD (EOAD) and control samples. Through gene-wise burden analysis, we observed significant enrichment of rare TET1 variants associated with AD (p = 0.04). We also profiled 5hmC in human postmortem brain tissues from AD and control groups. Our analysis identified differentially hydroxymethylated regions (DhMRs) in key genes responsible for regulating the methylome: TET3, DNMT3L, DNMT3A, and MECP2. To further investigate the role of Tet1 in AD pathogenesis, we used the 5xFAD mouse model with a Tet1 KO allele to examine how Tet1 loss influences AD pathogenesis. We observed significant changes in neuropathology, 5hmC, and RNA expression associated with Tet1 loss, while the behavioral alterations were not significant. The loss of Tet1 significantly increased amyloid plaque burden in the 5xFAD mouse (p = 0.044) and lead to a non-significant trend towards exacerbated AD-associated stress response in 5xFAD mice. At the molecular level, we found significant DhMRs enriched in genes involved in pathways responsible for neuronal projection organization, dendritic spine development and organization, and myelin assembly. RNA-Seq analysis revealed a significant increase in the expression of AD-associated genes such as Mpeg1, Ctsd, and Trem2. In conclusion, our results suggest that TET enzymes, particularly TET1, which regulate the methylome, may contribute to AD pathogenesis, as the loss of TET function increases AD-associated pathology.
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Affiliation(s)
- Matthew J Armstrong
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Yulin Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Selina M Vattathil
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Yanting Huang
- Department of Computer Science, Emory University, Atlanta, GA 30322, USA
| | - Jason P Schroeder
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - David A Bennet
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Thomas S Wingo
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Peng Jin
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
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Pan AL, Audrain M, Sakakibara E, Joshi R, Zhu X, Wang Q, Wang M, Beckmann ND, Schadt EE, Gandy S, Zhang B, Ehrlich ME, Salton SR. Dual-specificity protein phosphatase 6 (DUSP6) overexpression reduces amyloid load and improves memory deficits in male 5xFAD mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554335. [PMID: 37662269 PMCID: PMC10473733 DOI: 10.1101/2023.08.24.554335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Dual specificity protein phosphatase 6 (DUSP6) was recently identified as a key hub gene in a causal network that regulates late-onset Alzheimer's disease. Importantly, decreased DUSP6 levels are correlated with an increased clinical dementia rating in human subjects, and DUSP6 levels are additionally decreased in the 5xFAD amyloidopathy mouse model. Methods AAV5-DUSP6 or AAV5-GFP (control) were stereotactically injected into the dorsal hippocampus (dHc) of female and male 5xFAD or wild type mice to overexpress DUSP6 or GFP. Spatial learning memory of these mice was assessed in the Barnes maze, after which hippocampal tissues were isolated for downstream analysis. Results Barnes maze testing indicated that DUSP6 overexpression in the dHc of 5xFAD mice improved memory deficits and was associated with reduced amyloid plaque load, Aß 1-40 and Aß 1-42 levels, and amyloid precursor protein processing enzyme BACE1, in male but not in female mice. Microglial activation and microgliosis, which are increased in 5xFAD mice, were significantly reduced by dHc DUSP6 overexpression in both males and females. Transcriptomic profiling of female 5xFAD hippocampus revealed upregulated expression of genes involved in inflammatory and extracellular signal-regulated kinase (ERK) pathways, while dHc DUSP6 overexpression in female 5xFAD mice downregulated a subset of genes in these pathways. A limited number of differentially expressed genes (DEGs) (FDR<0.05) were identified in male mice; gene ontology analysis of DEGs (p<0.05) identified a greater number of synaptic pathways that were regulated by DUSP6 overexpression in male compared to female 5xFAD. Notably, the msh homeobox 3 gene, Msx3 , previously shown to regulate microglial M1/M2 polarization and reduce neuroinflammation, was one of the most robustly upregulated genes in female and male wild type and 5xFAD mice overexpressing DUSP6. Conclusions In summary, our data indicate that DUSP6 overexpression in dHc reduced amyloid deposition and memory deficits in male but not female 5xFAD mice, whereas reduced neuroinflammation and microglial activation were observed in both males and females. The sex-dependent regulation of synaptic pathways by DUSP6 overexpression, however, correlated with the improvement of spatial memory deficits in male but not female 5xFAD.
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Li M, Larsen PA. Single-cell sequencing of entorhinal cortex reveals widespread disruption of neuropeptide networks in Alzheimer's disease. Alzheimers Dement 2023; 19:3575-3592. [PMID: 36825405 DOI: 10.1002/alz.12979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 02/25/2023]
Abstract
INTRODUCTION Abnormalities of neuropeptides (NPs) that play important roles in modulating neuronal activities are commonly observed in Alzheimer's disease (AD). We hypothesize that NP network disruption is widespread in AD brains. METHODS Single-cell transcriptomic data from the entorhinal cortex (EC) were used to investigate the NP network disruption in AD. Bulk RNA-sequencing data generated from the temporal cortex by independent groups and machine learning were employed to identify key NPs involved in AD. The relationship between aging and AD-associated NP (ADNP) expression was studied using GTEx data. RESULTS The proportion of cells expressing NPs but not their receptors decreased significantly in AD. Neurons expressing higher level and greater diversity of NPs were disproportionately absent in AD. Increased age coincides with decreased ADNP expression in the hippocampus. DISCUSSION NP network disruption is widespread in AD EC. Neurons expressing more NPs may be selectively vulnerable to AD. Decreased expression of NPs participates in early AD pathogenesis. We predict that the NP network can be harnessed for treatment and/or early diagnosis of AD.
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Affiliation(s)
- Manci Li
- Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota, USA
- Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
| | - Peter A Larsen
- Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota, USA
- Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
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21
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Kim Y, Lee H. PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease. Front Aging Neurosci 2023; 15:1126156. [PMID: 37520124 PMCID: PMC10380929 DOI: 10.3389/fnagi.2023.1126156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 06/20/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability. Methods To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD. Results The performance of PINNet was compared with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and t-cell activation. Discussion By integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at https://github.com/DMCB-GIST/PINNet.
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Affiliation(s)
- Yeojin Kim
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Hyunju Lee
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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22
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Zhu M, Hou T, Jia L, Tan Q, Qiu C, Du Y. Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease. Neurobiol Aging 2023; 125:62-73. [PMID: 36842362 DOI: 10.1016/j.neurobiolaging.2022.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 12/05/2022] [Accepted: 12/28/2022] [Indexed: 01/02/2023]
Abstract
Current knowledge of Alzheimer's disease (AD) etiology and effective therapy remains limited. Thus, the identification of biomarkers is crucial to improve the detection and treatment of patients with AD. Using robust rank aggregation method to analyze the microarray data from Gene Expression Omnibus database, we identified 1138 differentially expressed genes in AD. We then explored 13 hub genes by weighted gene co-expression network analysis, least absolute shrinkage, and selection operator, and logistic regression in the training dataset. The detection model, which composed of CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, and TBC1D2 genes, along with APOE gene, showed that the area under the curve for detecting AD was 0.821 (95% confidence interval [CI] = 0.782-0.861) and the model was validated in ADNI dataset (area under the curve = 0.776; 95%CI = 0.686-0.865). Notably, the 13 genes in the model were highly enriched in immune function. These findings have implications for the detection and therapeutic target of AD.
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Affiliation(s)
- Min Zhu
- Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Tingting Hou
- Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
| | - Longfei Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qihua Tan
- Department of Public Health, Epidemiology and Biostatistics, University of Southern Denmark, Odense, Denmark
| | - Chengxuan Qiu
- Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurobiology, Care Sciences and Society, Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
| | - Yifeng Du
- Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
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23
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Jin Y, Ren Z, Wang W, Zhang Y, Zhou L, Yao X, Wu T. Classification of Alzheimer's disease using robust TabNet neural networks on genetic data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:8358-8374. [PMID: 37161202 DOI: 10.3934/mbe.2023366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers.
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Affiliation(s)
- Yu Jin
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zhe Ren
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Wenjie Wang
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yulei Zhang
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liang Zhou
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
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24
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Siecinski SK, Giamberardino SN, Spanos M, Hauser AC, Gibson JR, Chandrasekhar T, Trelles MDP, Rockhill CM, Palumbo ML, Cundiff AW, Montgomery A, Siper P, Minjarez M, Nowinski LA, Marler S, Kwee LC, Shuffrey LC, Alderman C, Weissman J, Zappone B, Mullett JE, Crosson H, Hong N, Luo S, She L, Bhapkar M, Dean R, Scheer A, Johnson JL, King BH, McDougle CJ, Sanders KB, Kim SJ, Kolevzon A, Veenstra-VanderWeele J, Hauser ER, Sikich L, Gregory SG. Genetic and epigenetic signatures associated with plasma oxytocin levels in children and adolescents with autism spectrum disorder. Autism Res 2023; 16:502-523. [PMID: 36609850 PMCID: PMC10023458 DOI: 10.1002/aur.2884] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023]
Abstract
Oxytocin (OT), the brain's most abundant neuropeptide, plays an important role in social salience and motivation. Clinical trials of the efficacy of OT in autism spectrum disorder (ASD) have reported mixed results due in part to ASD's complex etiology. We investigated whether genetic and epigenetic variation contribute to variable endogenous OT levels that modulate sensitivity to OT therapy. To carry out this analysis, we integrated genome-wide profiles of DNA-methylation, transcriptional activity, and genetic variation with plasma OT levels in 290 participants with ASD enrolled in a randomized controlled trial of OT. Our analysis identified genetic variants with novel association with plasma OT, several of which reside in known ASD risk genes. We also show subtle but statistically significant association of plasma OT levels with peripheral transcriptional activity and DNA-methylation profiles across several annotated gene sets. These findings broaden our understanding of the effects of the peripheral oxytocin system and provide novel genetic candidates for future studies to decode the complex etiology of ASD and its interaction with OT signaling and OT-based interventions. LAY SUMMARY: Oxytocin (OT) is an abundant chemical produced by neurons that plays an important role in social interaction and motivation. We investigated whether genetic and epigenetic factors contribute to variable OT levels in the blood. To this, we integrated genetic, gene expression, and non-DNA regulated (epigenetic) signatures with blood OT levels in 290 participants with autism enrolled in an OT clinical trial. We identified genetic association with plasma OT, several of which reside in known autism risk genes. We also show statistically significant association of plasma OT levels with gene expression and epigenetic across several gene pathways. These findings broaden our understanding of the factors that influence OT levels in the blood for future studies to decode the complex presentation of autism and its interaction with OT and OT-based treatment.
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Affiliation(s)
- Stephen K Siecinski
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | | | - Marina Spanos
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Annalise C Hauser
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Jason R Gibson
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Tara Chandrasekhar
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - M D Pilar Trelles
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carol M Rockhill
- Department of Psychiatry, Seattle Children’s Hospital and the University of Washington, Seattle, WA, USA
| | - Michelle L Palumbo
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Paige Siper
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mendy Minjarez
- Department of Psychiatry, Seattle Children’s Hospital and the University of Washington, Seattle, WA, USA
| | - Lisa A Nowinski
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Marler
- Department of Psychiatry, Vanderbilt University, Nashville, TN, USA
| | - Lydia C Kwee
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | | | - Cheryl Alderman
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Jordana Weissman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brooke Zappone
- Department of Psychiatry, Seattle Children’s Hospital and the University of Washington, Seattle, WA, USA
| | - Jennifer E Mullett
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Hope Crosson
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Natalie Hong
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Sheng Luo
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Lilin She
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Manjushri Bhapkar
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Russell Dean
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Abby Scheer
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Jacqueline L Johnson
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bryan H King
- Department of Psychiatry, Seattle Children’s Hospital and the University of Washington, Seattle, WA, USA
| | - Christopher J McDougle
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin B Sanders
- Department of Psychiatry, Vanderbilt University, Nashville, TN, USA
| | - Soo-Jeong Kim
- Department of Psychiatry, Seattle Children’s Hospital and the University of Washington, Seattle, WA, USA
| | - Alexander Kolevzon
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Elizabeth R Hauser
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Linmarie Sikich
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Simon G Gregory
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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25
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Pless ML. The eye as a window to the brain. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2023. [DOI: 10.47102/annals-acadmedsg.202317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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26
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Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer’s Disease. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2009545. [DOI: 10.1155/2022/2009545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Lung cancer is the most common malignancy and is responsible for the largest cancer-related mortality worldwide. Alzheimer’s disease is a degenerative neurological disease that burdens healthcare worldwide. While the two diseases are distinct, several transcriptomic studies have demonstrated they are linked. However, no concordant conclusion on how they are associated has been drawn. Since these studies utilized conventional bioinformatics methods, such as the differentially expressed gene (DEG) analysis, it is naturally expected that the proportion of DEGs having either the same or inverse directions in lung cancer and Alzheimer’s disease is substantial. This raises the inconsistency. Therefore, a novel bioinformatics method capable of determining the direction of association is desirable. In this study, the moderated t-tests were first used to identify DEGs that are shared by the two diseases. For the shared DEGs, separate autoencoder (AE) networks were trained to extract a one-dimensional representation (pseudogene) for each disease. Based on these pseudogenes, the association direction between lung cancer and Alzheimer’s disease was inferred. AE networks based on 266 shared DEGs revealed a comorbidity relationship between Alzheimer’s disease and lung cancer. Specifically, Spearman’s correlation coefficient between the predicted values using the two AE networks for the Alzheimer’s disease test set was 0.825 and for the lung cancer test set was 0.316. Novel bioinformatics methods such as an AE network may help decipher how distinct diseases are associated by providing the refined representations of dysregulated genes.
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27
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Lan AY, Corces MR. Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases. Front Aging Neurosci 2022; 14:1027224. [PMID: 36466610 PMCID: PMC9716280 DOI: 10.3389/fnagi.2022.1027224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022] Open
Abstract
Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.
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Affiliation(s)
- Alexander Y. Lan
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - M. Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
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28
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Seto M, Weiner RL, Dumitrescu L, Mahoney ER, Hansen SL, Janve V, Khan OA, Liu D, Wang Y, Menon V, De Jager PL, Schneider JA, Bennett DA, Gifford KA, Jefferson AL, Hohman TJ. RNASE6 is a novel modifier of APOE-ε4 effects on cognition. Neurobiol Aging 2022; 118:66-76. [PMID: 35896049 PMCID: PMC9721357 DOI: 10.1016/j.neurobiolaging.2022.06.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/25/2022] [Accepted: 06/27/2022] [Indexed: 02/06/2023]
Abstract
Apolipoprotein E4 (APOE-ε4), the strongest common genetic risk factor for Alzheimer's disease (AD), contributes to worse cognition in older adults. However, many APOE-ε4 carriers remain cognitively normal throughout life, suggesting that neuroprotective factors may be present in these individuals. In this study, we leverage whole-blood RNA sequencing (RNAseq) from 324 older adults to identify genetic modifiers of APOE-ε4 effects on cognition. Expression of RNASE6 interacted with APOE-ε4 status (p = 4.35 × 10-8) whereby higher RNASE6 expression was associated with worse memory at baseline among APOE-ε4 carriers. This interaction was replicated using RNAseq data from the prefrontal cortex in an independent dataset (N = 535; p = 0.002), suggesting the peripheral effect of RNASE6 is also present in brain tissue. RNASE6 encodes an antimicrobial peptide involved in innate immune response and has been previously observed in a gene co-expression network module with other AD-related inflammatory genes, including TREM2 and MS4A. Together, these data implicate neuroinflammation in cognitive decline, and suggest that innate immune signaling may be detectable in blood and confer differential susceptibility to AD depending on APOE-ε4.
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Affiliation(s)
- Mabel Seto
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Rebecca L Weiner
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emily R Mahoney
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shania L Hansen
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Vaibhav Janve
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Omair A Khan
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dandan Liu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA; Cell Circuits Program, Broad Institute, Cambridge, MA, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Katherine A Gifford
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN, USA.
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29
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Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome. Cells 2022; 11:cells11182867. [PMID: 36139449 PMCID: PMC9496853 DOI: 10.3390/cells11182867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/26/2022] [Accepted: 09/10/2022] [Indexed: 12/02/2022] Open
Abstract
Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.
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Kim J, Yoo G, Lee T, Kim JH, Seo DM, Kim J. Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis. BIOLOGY 2022; 11:biology11091310. [PMID: 36138789 PMCID: PMC9495746 DOI: 10.3390/biology11091310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 11/21/2022]
Abstract
Simple Summary Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe complications in patients with diabetic foot ulcers (DFUs). Although NF and OM often cause results including limb amputation and death, definite diagnoses of these are challenging. To aid the prompt and proper diagnosis of NF and OM in patients with DFU, we developed and evaluated a novel prediction model based on machine learning technology. In summary, our prediction model appropriately discriminated the NF and OM from diabetic foot. Moreover, this prediction model has advantages in that it is based on the demographic data and routine laboratory results, which requires no additional examinations which are complicated or expensive. Abstract Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.
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Affiliation(s)
- Jiye Kim
- Department of Plastic Surgery, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Gilsung Yoo
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Taesic Lee
- Division of Data Mining and Computational Biology, Institute of Global Health Care and Development, Wonju Severance Christian Hospital, Wonju 26411, Korea
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
- Center for Precision Medicine and Genomics, Wonju Severance Christian Hospital, Wonju 26411, Korea
| | - Jeong Ho Kim
- Department of Plastic Surgery, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Dong Min Seo
- Department of Medical Information, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
| | - Juwon Kim
- Department of Laboratory Medicine, Yonsei University Wonju College of Medicine, Wonju 26411, Korea
- Center for Precision Medicine and Genomics, Wonju Severance Christian Hospital, Wonju 26411, Korea
- Correspondence: ; Tel.: +82-33-741-1596; Fax: +82-33-741-1780
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Song L, Yang YT, Guo Q, Zhao XM. Cellular transcriptional alterations of peripheral blood in Alzheimer's disease. BMC Med 2022; 20:266. [PMID: 36031604 PMCID: PMC9422129 DOI: 10.1186/s12916-022-02472-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD. METHODS To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion. RESULTS We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains. CONCLUSIONS We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.
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Affiliation(s)
- Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | | | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China. .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China. .,Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China. .,International Human Phenome Institutes (Shanghai), Shanghai, 200433, China.
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Kalkan H, Akkaya UM, Inal-Gültekin G, Sanchez-Perez AM. Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene Expression. Genes (Basel) 2022; 13:genes13081406. [PMID: 36011317 PMCID: PMC9407775 DOI: 10.3390/genes13081406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/03/2022] [Accepted: 08/04/2022] [Indexed: 11/16/2022] Open
Abstract
Early intervention can delay the progress of Alzheimer’s Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes’ expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection.
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Affiliation(s)
- Habil Kalkan
- Department of Computer Engineering, Gebze Technical University, 41400 Kocaeli, Turkey
- Correspondence: (H.K.); (A.M.S.-P.)
| | - Umit Murat Akkaya
- Department of Computer Engineering, Gebze Technical University, 41400 Kocaeli, Turkey
| | - Güldal Inal-Gültekin
- Department of Physiology, Faculty of Medicine, Istanbul Okan University, 34959 Istanbul, Turkey
| | - Ana Maria Sanchez-Perez
- Faculty of Health Science and Institute of Advanced Materials (INAM), University Jaume I, 12071 Castellon, Spain
- Correspondence: (H.K.); (A.M.S.-P.)
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Oh SL, Zhou M, Chin EWM, Amarnath G, Cheah CH, Ng KP, Kandiah N, Goh ELK, Chiam KH. Alzheimer's Disease Blood Biomarkers Associated With Neuroinflammation as Therapeutic Targets for Early Personalized Intervention. Front Digit Health 2022; 4:875895. [PMID: 35899035 PMCID: PMC9309434 DOI: 10.3389/fdgth.2022.875895] [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: 02/14/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022] Open
Abstract
The definitive diagnosis of Alzheimer's Disease (AD) without the need for neuropathological confirmation remains a challenge in AD research today, despite efforts to uncover the molecular and biological underpinnings of the disease process. Furthermore, the potential for therapeutic intervention is limited upon the onset of symptoms, providing motivation for studying and treating the AD precursor mild cognitive impairment (MCI), the prodromal stage of AD instead. Applying machine learning classification to transcriptomic data of MCI, AD, and cognitively normal (CN) control patients, we identified differentially expressed genes that serve as biomarkers for the characterization and classification of subjects into MCI or AD groups. Predictive models employing these biomarker genes exhibited good classification performances for CN, MCI, and AD, significantly above random chance. The PI3K-Akt, IL-17, JAK-STAT, TNF, and Ras signaling pathways were also enriched in these biomarker genes, indicating their diagnostic potential and pathophysiological roles in MCI and AD. These findings could aid in the recognition of MCI and AD risk in clinical settings, allow for the tracking of disease progression over time in individuals as part of a therapeutic approach, and provide possible personalized drug targets for early intervention of MCI and AD.
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Affiliation(s)
- Sher Li Oh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, Singapore
| | - Meikun Zhou
- Bioinformatics Institute, ASTAR, Singapore, Singapore
| | - Eunice W. M. Chin
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Gautami Amarnath
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Chee Hoe Cheah
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kok Pin Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nagaendran Kandiah
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Eyleen L. K. Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- *Correspondence: Eyleen L. K. Goh
| | - Keng-Hwee Chiam
- Bioinformatics Institute, ASTAR, Singapore, Singapore
- Keng-Hwee Chiam
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Gestational age-specific serum creatinine can predict adverse pregnancy outcomes. Sci Rep 2022; 12:11224. [PMID: 35780246 PMCID: PMC9250531 DOI: 10.1038/s41598-022-15450-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 06/23/2022] [Indexed: 12/04/2022] Open
Abstract
Serum creatinine level (SCr) typically decreases during pregnancy due to physiologic glomerular hyperfiltration. Therefore, the clinical practice of estimated glomerular filtration rate (eGFR) based on SCr concentrations might be inapplicable to pregnant women with kidney disease since it does not take into account of the pregnancy-related biological changes. We integrated the Wonju Severance Christian Hospital (WSCH)-based findings and prior knowledge from big data to reveal the relationship between the abnormal but hidden SCr level and adverse pregnancy outcomes. We analyzed 4004 pregnant women who visited in WSCH. Adverse pregnancy outcomes included preterm birth, preeclampsia, fetal growth retardation, and intrauterine fetal demise. We categorized the pregnant women into four groups based on the gestational age (GA)-unadjusted raw distribution (Q1–4raw), and then GA-specific (Q1–4adj) SCr distribution. Linear regression analysis revealed that Q1-4adj groups had better predictive outcomes than the Q1–4raw groups. In logistic regression model, the Q1–4adj groups exhibited a robust non-linear U-shaped relationship with the risk of adverse pregnancy outcomes, compared to the Q1–4raw groups. The integrative analysis on SCr with respect to GA-specific distribution could be used to screen out pregnant women with a normal SCr coupled with a decreased renal function.
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Ueno M, Yoshino Y, Mori H, Funahashi Y, Kumon H, Ochi S, Ozaki T, Tachibana A, Yoshida T, Shimizu H, Mori T, Iga JI, Ueno SI. Association Study and Meta-Analysis of Polymorphisms and Blood mRNA Expression of the ALDH2 Gene in Patients with Alzheimer’s Disease. J Alzheimers Dis 2022; 87:863-871. [PMID: 35404279 PMCID: PMC9198735 DOI: 10.3233/jad-215627] [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] [Indexed: 11/15/2022]
Abstract
Background: Late-onset Alzheimer’s disease (LOAD) is a complex disease in which neuroinflammation plays an important pathophysiological role, and exposure to neurotoxic substrates such as aldehydes may contribute. Blood mRNA expression levels of neuroinflammation-related genes appear to be potential biological markers of LOAD. A relationship between ALDH2 and LOAD has been suggested. Objective: Our objective was to examine blood ALDH2 expression in Japanese LOAD patients, conduct a genetic association study, and add new studies to an extended meta-analysis of the Asian population. Methods: A blood expression study (45 AD subjects, 54 controls) in which total RNA was isolated from whole peripheral blood samples and ALDH2 expression measured was conducted. In addition, a genetic association study (271 AD subjects, 492 controls) using genomic DNA from whole peripheral blood samples was conducted. Finally, a meta-analysis examined the relationship between ALDH2*2 frequency and the risk of LOAD. Results: ALDH2 mRNA expression was significantly higher in LOAD than in controls, and also higher in men with LOAD than in women with LOAD (p = 0.043). The genotypes in the two classified groups and the allele frequency were significantly different between AD and control subjects. The meta-analysis showed a significant difference in the ALDH2*2 allele, with an increased AD risk (OR = 1.38; 95% CI = 1.02–1.85; p = 0.0348, I2 = 81.1%). Conclusion: There was a significant increase in blood ALDH2 expression, and a genetic association with ALDH2*2 in LOAD. ALDH2 may have significant roles in the pathogenesis of LOAD in the Asian population.
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Affiliation(s)
- Mariko Ueno
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Yuta Yoshino
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Hiroaki Mori
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Yu Funahashi
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Hiroshi Kumon
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Shinichiro Ochi
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Tomoki Ozaki
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Ayumi Tachibana
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Taku Yoshida
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Hideaki Shimizu
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Takaaki Mori
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Jun-ichi Iga
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
| | - Shu-ichi Ueno
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Shitsukawa, Toon, Ehime, Japan
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Dobromyslin VI, Megherbi DB. Augmenting Imaging Biomarker Performance with Blood-Based Gene Expression Levels for Predicting Alzheimer’s Disease Progression. J Alzheimers Dis 2022; 87:583-594. [DOI: 10.3233/jad-215640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Structural brain imaging metrics and gene expression biomarkers have previously been used for Alzheimer’s disease (AD) diagnosis and prognosis, but none of these studies explored integration of imaging and gene expression biomarkers for predicting mild cognitive impairment (MCI)-to-AD conversion 1-2 years into the future. Objective: We investigated advantages of combining gene expression and structural brain imaging features for predicting MCI-to-AD conversion. Selection of the differentially expressed genes (DEGs) for classifying cognitively normal (CN) controls and AD patients was benchmarked against previously reported results. Methods: The current work proposes integrating brain imaging and blood gene expression data from two public datasets (ADNI and ANM) to predict MCI-to-AD conversion. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated in the two independents patient cohorts. Results: Combining DEGs and imaging biomarkers for predicting MCI-to-AD conversion yielded 0.832-0.876 receiver operating characteristic (ROC) area under the curve (AUC), which exceeded the 0.808-0.840 AUC from using the imaging features alone. With using only three DEGs, the CN versus AD predictive model achieved 0.718, 0.858, and 0.873 cross-validation AUC for the ADNI, ANM1, and ANM2 datasets. Conclusion: For the first time we show that combining gene expression and imaging biomarkers yields better predictive performance than using imaging metrics alone. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated to produce consistent results in the two independents patient cohorts. Using an improved feature selection, we show that predictive models with fewer gene expression probes can achieve competitive performance.
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Affiliation(s)
- Vitaly I. Dobromyslin
- Center for Computer Machine/Human Intelligence Networking and Distributed Systems, University of Massachusetts, Lowell, MA, USA
| | - Dalila B. Megherbi
- Center for Computer Machine/Human Intelligence Networking and Distributed Systems, University of Massachusetts, Lowell, MA, USA
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Machine Learning Framework for the Prediction of Alzheimer’s Disease Using Gene Expression Data Based on Efficient Gene Selection. Symmetry (Basel) 2022. [DOI: 10.3390/sym14030491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
In recent years, much research has focused on using machine learning (ML) for disease prediction based on gene expression (GE) data. However, many diseases have received considerable attention, whereas some, including Alzheimer’s disease (AD), have not, perhaps due to data shortage. The present work is intended to fill this gap by introducing a symmetric framework to predict AD from GE data, with the aim to produce the most accurate prediction using the smallest number of genes. The framework works in four stages after it receives a training dataset: pre-processing, gene selection (GS), classification, and AD prediction. The symmetry of the model is manifested in all of its stages. In the pre-processing stage gene columns in the training dataset are pre-processed identically. In the GS stage, the same user-defined filter metrics are invoked on every gene individually, and so are the same user-defined wrapper metrics. In the classification stage, a number of user-defined ML models are applied identically using the minimal set of genes selected in the preceding stage. The core of the proposed framework is a meticulous GS algorithm which we have designed to nominate eight subsets of the original set of genes provided in the training dataset. Exploring the eight subsets, the algorithm selects the best one to describe AD, and also the best ML model to predict the disease using this subset. For credible results, the framework calculates performance metrics using repeated stratified k-fold cross validation. To evaluate the framework, we used an AD dataset of 1157 cases and 39,280 genes, obtained by combining a number of smaller public datasets. The cases were split in two partitions, 1000 for training/testing, using 10-fold CV repeated 30 times, and 157 for validation. From the testing/training phase, the framework identified only 1058 genes to be the most relevant and the support vector machine (SVM) model to be the most accurate with these genes. In the final validation, we used the 157 cases that were never seen by the SVM classifier. For credible performance evaluation, we evaluated the classifier via six metrics, for which we obtained impressive values. Specifically, we obtained 0.97, 0.97, 0.98, 0.945, 0.972, and 0.975 for the sensitivity (recall), specificity, precision, kappa index, AUC, and accuracy, respectively.
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Hung SJ, Tsai HP, Wang YF, Ko WC, Wang JR, Huang SW. Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning. Front Cell Infect Microbiol 2022; 12:831281. [PMID: 35223554 PMCID: PMC8866709 DOI: 10.3389/fcimb.2022.831281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.
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Affiliation(s)
- Su-Jhen Hung
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan
| | - Huey-Pin Tsai
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ya-Fang Wang
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Tainan, Taiwan
| | - Wen-Chien Ko
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jen-Ren Wang
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Tainan, Taiwan
- Center of Infectious Disease and Signaling Research, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Wen Huang
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan
- *Correspondence: Sheng-Wen Huang,
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40
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Artificial Intelligence in Blood Transcriptomics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_262] [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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41
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Raihan SMS, Ahmed M, Sharma A, Hossain MS, Islam RU, Andersson K. A Belief Rule Based Expert System to Diagnose Alzheimer’s Disease Using Whole Blood Gene Expression Data. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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42
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Mahendran N, Vincent PMDR, Srinivasan K, Chang CY. Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning. Front Genet 2021; 12:784814. [PMID: 34868275 PMCID: PMC8632950 DOI: 10.3389/fgene.2021.784814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - P M Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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43
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Milanesi E, Dobre M, Cucos CA, Rojo AI, Jiménez-Villegas J, Capetillo-Zarate E, Matute C, Piñol-Ripoll G, Manda G, Cuadrado A. Whole Blood Expression Pattern of Inflammation and Redox Genes in Mild Alzheimer's Disease. J Inflamm Res 2021; 14:6085-6102. [PMID: 34848989 PMCID: PMC8612672 DOI: 10.2147/jir.s334337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 09/29/2021] [Indexed: 12/21/2022] Open
Abstract
Background Although Alzheimer’s disease (AD) is associated with alterations of the central nervous system, this disease has an echo in blood that might represent a valuable source of biomarkers for improved diagnosis, prognosis and for monitoring drug response. Methods We performed a targeted transcriptomics study on 38 mild Alzheimer’s disease (AD) patients and 38 matched controls for evaluating the expression levels of 136 inflammation and 84 redox genes in whole blood. Patients were diagnosed as mild AD based on altered levels of total TAU, phospho-TAU and Abeta(1–42) in cerebrospinal fluid, and Abeta(1–40), Abeta(1–42) and total TAU levels in plasma. Whenever possible, blood and brain comparisons were made using public datasets. Results We found 48 inflammation and 34 redox genes differentially expressed in the blood of AD patients vs controls (FC >1.5, p < 0.01), out of which 22 pro-inflammatory and 12 redox genes exhibited FC >2 and p < 0.001. Receiver operating characteristic (ROC) analysis identified nine inflammation and seven redox genes that discriminated between AD patients and controls (area under the curve >0.9). Correlations of the dysregulated inflammation and redox transcripts indicated that RELA may regulate several redox genes including DUOX1 and GSR. Based on the gene expression profile, we have found that the master regulators of inflammation and redox homeostasis, NFκB and NRF2, were significantly disturbed in the blood of AD patients, as well as several zinc finger and helix-loop-helix transcription factors. Conclusion The selected inflammation and redox genes might be useful biomarkers for monitoring anti-inflammatory therapy in mild AD.
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Affiliation(s)
- Elena Milanesi
- "Victor Babes" National Institute of Pathology, Bucharest, 050096, Romania
| | - Maria Dobre
- "Victor Babes" National Institute of Pathology, Bucharest, 050096, Romania
| | | | - Ana I Rojo
- Department of Endocrine Physiology and Nervous System, Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Madrid, 28029, Spain.,Department of Biochemistry, Faculty of Medicine, Autonomous University of Madrid, Madrid, 28049, Spain.,Neuroscience Section, Instituto de Investigación Sanitaria La Paz (IDIPAZ), Madrid, 28046, Spain.,Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, 28031, Spain
| | - José Jiménez-Villegas
- Department of Endocrine Physiology and Nervous System, Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Madrid, 28029, Spain.,Department of Biochemistry, Faculty of Medicine, Autonomous University of Madrid, Madrid, 28049, Spain
| | - Estibaliz Capetillo-Zarate
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, 28031, Spain.,IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain.,Department of Neuroscience, University of the Basque Country UPV/EHU, Achucarro Basque Center for Neuroscience, Leioa, Spain
| | - Carlos Matute
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain.,Department of Neuroscience, University of the Basque Country UPV/EHU, Achucarro Basque Center for Neuroscience, Leioa, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastons Cognitius, Hospital Universitari Santa Maria-IRB Leida, Lleida, 25198, Spain
| | - Gina Manda
- "Victor Babes" National Institute of Pathology, Bucharest, 050096, Romania
| | - Antonio Cuadrado
- "Victor Babes" National Institute of Pathology, Bucharest, 050096, Romania.,Department of Endocrine Physiology and Nervous System, Instituto de Investigaciones Biomédicas "Alberto Sols" UAM-CSIC, Madrid, 28029, Spain.,Department of Biochemistry, Faculty of Medicine, Autonomous University of Madrid, Madrid, 28049, Spain.,Neuroscience Section, Instituto de Investigación Sanitaria La Paz (IDIPAZ), Madrid, 28046, Spain.,Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid, 28031, Spain
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44
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Lee T, Lee H. Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis. Biomedicines 2021; 9:biomedicines9111525. [PMID: 34829754 PMCID: PMC8614900 DOI: 10.3390/biomedicines9111525] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- Correspondence: ; Tel.: +82-62-715-2213
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45
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Beebe-Wang N, Celik S, Weinberger E, Sturmfels P, De Jager PL, Mostafavi S, Lee SI. Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies. Nat Commun 2021; 12:5369. [PMID: 34508095 PMCID: PMC8433314 DOI: 10.1038/s41467-021-25680-7] [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: 01/12/2021] [Accepted: 07/14/2021] [Indexed: 02/08/2023] Open
Abstract
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer's Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require "harmonized" phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer's Disease.
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Affiliation(s)
- Nicasia Beebe-Wang
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Safiye Celik
- Recursion Pharmaceuticals, Salt Lake City, UT, USA
| | - Ethan Weinberger
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Pascal Sturmfels
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Philip L De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
| | - Sara Mostafavi
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada.
| | - Su-In Lee
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
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46
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Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. Int J Mol Sci 2021; 22:7911. [PMID: 34360676 PMCID: PMC8347529 DOI: 10.3390/ijms22157911] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/21/2022] Open
Abstract
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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47
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A machine learning approach to unmask novel gene signatures and prediction of Alzheimer's disease within different brain regions. Genomics 2021; 113:1778-1789. [PMID: 33878365 DOI: 10.1016/j.ygeno.2021.04.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/14/2021] [Indexed: 01/11/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unraveled the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.
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48
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Liskova A, Samec M, Koklesova L, Kudela E, Kubatka P, Golubnitschaja O. Mitochondriopathies as a Clue to Systemic Disorders-Analytical Tools and Mitigating Measures in Context of Predictive, Preventive, and Personalized (3P) Medicine. Int J Mol Sci 2021; 22:ijms22042007. [PMID: 33670490 PMCID: PMC7922866 DOI: 10.3390/ijms22042007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/11/2021] [Accepted: 02/14/2021] [Indexed: 02/06/2023] Open
Abstract
The mitochondrial respiratory chain is the main site of reactive oxygen species (ROS) production in the cell. Although mitochondria possess a powerful antioxidant system, an excess of ROS cannot be completely neutralized and cumulative oxidative damage may lead to decreasing mitochondrial efficiency in energy production, as well as an increasing ROS excess, which is known to cause a critical imbalance in antioxidant/oxidant mechanisms and a "vicious circle" in mitochondrial injury. Due to insufficient energy production, chronic exposure to ROS overproduction consequently leads to the oxidative damage of life-important biomolecules, including nucleic acids, proteins, lipids, and amino acids, among others. Different forms of mitochondrial dysfunction (mitochondriopathies) may affect the brain, heart, peripheral nervous and endocrine systems, eyes, ears, gut, and kidney, among other organs. Consequently, mitochondriopathies have been proposed as an attractive diagnostic target to be investigated in any patient with unexplained progressive multisystem disorder. This review article highlights the pathomechanisms of mitochondriopathies, details advanced analytical tools, and suggests predictive approaches, targeted prevention and personalization of medical services as instrumental for the overall management of mitochondriopathy-related cascading pathologies.
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Affiliation(s)
- Alena Liskova
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Marek Samec
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Lenka Koklesova
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Erik Kudela
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia; (A.L.); (M.S.); (L.K.); (E.K.)
| | - Peter Kubatka
- Department of Medical Biology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 036 01 Martin, Slovakia
- European Association for Predictive, Preventive and Personalised Medicine, EPMA, 1160 Brussels, Belgium
- Correspondence: (P.K.); (O.G.)
| | - Olga Golubnitschaja
- European Association for Predictive, Preventive and Personalised Medicine, EPMA, 1160 Brussels, Belgium
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
- Correspondence: (P.K.); (O.G.)
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49
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Lee T, Lee H. Shared Blood Transcriptomic Signatures between Alzheimer's Disease and Diabetes Mellitus. Biomedicines 2021; 9:34. [PMID: 33406707 PMCID: PMC7823888 DOI: 10.3390/biomedicines9010034] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/29/2020] [Accepted: 12/30/2020] [Indexed: 12/29/2022] Open
Abstract
Alzheimer's disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
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50
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Warnat-Herresthal S, Oestreich M, Schultze JL, Becker M. Artificial Intelligence in Blood Transcriptomics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_262-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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