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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [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: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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2
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Kearns NA, Iatrou A, Flood DJ, De Tissera S, Mullaney ZM, Xu J, Gaiteri C, Bennett DA, Wang Y. Dissecting the human leptomeninges at single-cell resolution. Nat Commun 2023; 14:7036. [PMID: 37923721 PMCID: PMC10624900 DOI: 10.1038/s41467-023-42825-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 10/23/2023] [Indexed: 11/06/2023] Open
Abstract
Emerging evidence shows that the meninges conduct essential immune surveillance and immune defense at the brain border, and the dysfunction of meningeal immunity contributes to aging and neurodegeneration. However, no study exists on the molecular properties of cell types within human leptomeninges. Here, we provide single nuclei profiling of dissected postmortem leptomeninges from aged individuals. We detect diverse cell types, including unique meningeal endothelial, mural, and fibroblast subtypes. For immune cells, we show that most T cells express CD8 and bear characteristics of tissue-resident memory T cells. We also identify distinct subtypes of border-associated macrophages (BAMs) that display differential gene expressions from microglia and express risk genes for Alzheimer's Disease (AD), as nominated by genome-wide association studies (GWAS). We discover cell-type-specific differentially expressed genes in individuals with Alzheimer's dementia, particularly in fibroblasts and BAMs. Indeed, when cultured, leptomeningeal cells display the signature of ex vivo AD fibroblasts upon amyloid-β treatment. We further explore ligand-receptor interactions within the leptomeningeal niche and computationally infer intercellular communications in AD. Thus, our study establishes a molecular map of human leptomeningeal cell types, providing significant insight into the border immune and fibrotic responses in AD.
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Affiliation(s)
- Nicola A Kearns
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Artemis Iatrou
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - Daniel J Flood
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Sashini De Tissera
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Zachary M Mullaney
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Psychiatry, Upstate Medical University, Syracuse, NY, 13210, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA.
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, 60612, USA.
- Rush Graduate College, Rush University, Chicago IL, 60612, USA.
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3
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Gaiteri C, Connell DR, Sultan FA, Iatrou A, Ng B, Szymanski BK, Zhang A, Tasaki S. Robust, scalable, and informative clustering for diverse biological networks. Genome Biol 2023; 24:228. [PMID: 37828545 PMCID: PMC10571258 DOI: 10.1186/s13059-023-03062-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/19/2023] [Indexed: 10/14/2023] Open
Abstract
Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.
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Affiliation(s)
- Chris Gaiteri
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA.
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.
| | - David R Connell
- Rush University Graduate College, Rush University Medical Center, Chicago, IL, USA
| | - Faraz A Sultan
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Artemis Iatrou
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Harvard University, Belmont, MA, USA
| | - Bernard Ng
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Boleslaw K Szymanski
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA
- Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, USA
- Academy of Social Sciences, Łódź, Poland
| | - Ada Zhang
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
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4
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Ng B, Tasaki S, Greathouse KM, Walker CK, Zhang A, Covitz S, Cieslak M, Adamson AB, Andrade JP, Poovey EH, Curtis KA, Muhammad HM, Seidlitz J, Satterthwaite T, Bennett DA, Seyfried NT, Vogel J, Gaiteri C, Herskowitz JH. A Molecular Basis of Human Brain Connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549895. [PMID: 37546752 PMCID: PMC10401931 DOI: 10.1101/2023.07.20.549895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Neuroimaging is commonly used to infer human brain connectivity, but those measurements are far-removed from the molecular underpinnings at synapses. To uncover the molecular basis of human brain connectivity, we analyzed a unique cohort of 98 individuals who provided neuroimaging and genetic data contemporaneous with dendritic spine morphometric, proteomic, and gene expression data from the superior frontal and inferior temporal gyri. Through cellular contextualization of the molecular data with dendritic spine morphology, we identified hundreds of proteins related to synapses, energy metabolism, and RNA processing that explain between-individual differences in functional connectivity and structural covariation. By integrating data at the genetic, molecular, subcellular, and tissue levels, we bridged the divergent fields of molecular biology and neuroimaging to identify a molecular basis of brain connectivity. One-Sentence Summary Dendritic spine morphometry and synaptic proteins unite the divergent fields of molecular biology and neuroimaging.
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5
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Fang C, Lin ZZ. Overlapping communities detection based on cluster-ability optimization. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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6
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Tasaki S, Xu J, Avey DR, Johnson L, Petyuk VA, Dawe RJ, Bennett DA, Wang Y, Gaiteri C. Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks. Nat Commun 2022; 13:655. [PMID: 35115553 PMCID: PMC8814036 DOI: 10.1038/s41467-022-28280-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 01/19/2022] [Indexed: 12/30/2022] Open
Abstract
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.
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Affiliation(s)
- Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Denis R Avey
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lynnaun Johnson
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Robert J Dawe
- 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
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
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7
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Elsisy A, Mandviwalla A, Szymanski BK, Sharkey T. A network generator for covert network structures. Inf Sci (N Y) 2022; 584:387-398. [PMID: 37927357 PMCID: PMC10620467 DOI: 10.1016/j.ins.2021.10.066] [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: 06/14/2021] [Revised: 09/12/2021] [Accepted: 10/25/2021] [Indexed: 11/26/2022]
Abstract
We focus on organizational structures in covert networks, such as criminal or terrorist networks. Their members engage in illegal activities and attempt to hide their association and interactions with these networks. Hence, data about such networks are incomplete. We introduce a novel method of rewiring covert networks parameterized by the edge connectivity standard deviation. The generated networks are statistically similar to themselves and to the original network. The higher-level organizational structures are modeled as a multi-layer network while the lowest level uses the Stochastic Block Model. Such synthetic networks provide alternative structures for data about the original network. Using them, analysts can find structures that are frequent, therefore stable under perturbations. Another application is to anonymize generated networks and use them for testing new software developed in open research facilities. The results indicate that modeling edge structure and the hierarchy together is essential for generating networks that are statistically similar but not identical to each other or the original network. In experiments, we generate many synthetic networks from two covert networks. Only a few structures of synthetics networks repeat, with the most stable ones shared by 18% of all synthetic networks making them strong candidates for the ground truth structure.
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Affiliation(s)
- Amr Elsisy
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Aamir Mandviwalla
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Boleslaw K. Szymanski
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Społeczna Akademia Nauk, Łódź, Poland
| | - Thomas Sharkey
- Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Department of Industrial Engineering, Clemson University, Clemson, SC 29631, USA
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8
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Miyoshi E, Morabito S, Swarup V. Systems biology approaches to unravel the molecular and genetic architecture of Alzheimer's disease and related tauopathies. Neurobiol Dis 2021; 160:105530. [PMID: 34634459 PMCID: PMC8616667 DOI: 10.1016/j.nbd.2021.105530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/30/2021] [Accepted: 10/07/2021] [Indexed: 11/19/2022] Open
Abstract
Over the years, genetic studies have identified multiple genetic risk variants associated with neurodegenerative disorders and helped reveal new biological pathways and genes of interest. However, genetic risk variants commonly reside in non-coding regions and may regulate distant genes rather than the nearest gene, as well as a gene's interaction partners in biological networks. Systems biology and functional genomics approaches provide the framework to unravel the functional significance of genetic risk variants in disease. In this review, we summarize the genetic and transcriptomic studies of Alzheimer's disease and related tauopathies and focus on the advantages of performing systems-level analyses to interrogate the biological pathways underlying neurodegeneration. Finally, we highlight new avenues of multi-omics analysis with single-cell approaches, which provide unparalleled opportunities to systematically explore cellular heterogeneity, and present an example of how to integrate publicly available single-cell datasets. Systems-level analysis has illuminated the function of many disease risk genes, but much work remains to study tauopathies and to understand spatiotemporal gene expression changes of specific cell types.
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Affiliation(s)
- Emily Miyoshi
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Samuel Morabito
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA; Mathematical, Computational and Systems Biology (MCSB) Program, University of California, Irvine, CA 92697, USA
| | - Vivek Swarup
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA.
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9
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Tóth R, Miklós Barth A, Domonkos A, Varga V, Somogyvári Z. Do not waste your electrodes-principles of optimal electrode geometry for spike sorting. J Neural Eng 2021; 18. [PMID: 34181590 DOI: 10.1088/1741-2552/ac0f49] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 06/28/2021] [Indexed: 11/11/2022]
Abstract
Objective. This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance.Approach. The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semi-artificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampusin vivo.Main results. Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance.Significance. Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one- and two-dimensional electrode systems, targeting specific brain areas.
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Affiliation(s)
- Róbert Tóth
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Albert Miklós Barth
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Andor Domonkos
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Viktor Varga
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Zoltán Somogyvári
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,Neuromicrosystems Ltd, Budapest, Hungary
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10
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Calderer G, Kuijjer ML. Community Detection in Large-Scale Bipartite Biological Networks. Front Genet 2021; 12:649440. [PMID: 33968132 PMCID: PMC8099108 DOI: 10.3389/fgene.2021.649440] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks-such as those that represent regulatory interactions, drug-gene, or gene-disease associations-are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a "hairball" of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.
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Affiliation(s)
- Genís Calderer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Marieke L Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway.,Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
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11
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De Jager CH, White CC, Bennett DA, Ma Y. Neuroticism alters the transcriptome of the frontal cortex to contribute to the cognitive decline and onset of Alzheimer's disease. Transl Psychiatry 2021; 11:139. [PMID: 33627625 PMCID: PMC7904919 DOI: 10.1038/s41398-021-01253-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 01/06/2021] [Accepted: 01/28/2021] [Indexed: 12/18/2022] Open
Abstract
Accumulating evidence has suggested that the molecular transcriptional mechanism contributes to Alzheimer's disease (AD) and its endophenotypes of cognitive decline and neuropathological traits, β-amyloid (Aβ) and phosphorylated tangles (TAU). However, it is unknown what is the impact of the AD risk factors, personality characteristics assessed by the NEO Five-Factor Inventory, on the human brain's transcriptome. Using postmortem human brain samples from 466 subjects, we found that neuroticism has a significant overall impact on the brain transcriptome (omnibus P = 0.005) but not the other four personality characteristics. Focused on those cognitive decline related gene co-expressed modules, neuroticism has nominally significant associations (P < 0.05) with four neuronal modules, which are more related to PHFtau than Aβ across all eight brain regions. Furthermore, the effect of neuroticism on cognitive decline and AD might be mediated through the expression of module 7 and TAU pathology (P = 0.008). To conclude, neuroticism has a broad impact on the transcriptome of human brains, and its effect on cognitive decline and AD may be mediated through gene transcription programs related to TAU pathology.
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Affiliation(s)
- Céline H. De Jager
- grid.21729.3f0000000419368729Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032 USA
| | - Charles C. White
- grid.21729.3f0000000419368729Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032 USA ,grid.66859.34Cell Circuits Program, Broad Institute, 415 Main street, Cambridge, MA 02142 USA
| | - David A. Bennett
- grid.240684.c0000 0001 0705 3621Rush Alzheimer Disease Center, RUSH University Medical Center, Chicago, IL 60612 USA
| | - Yiyi Ma
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
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12
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Patrick E, Olah M, Taga M, Klein HU, Xu J, White CC, Felsky D, Agrawal S, Gaiteri C, Chibnik LB, Mostafavi S, Schneider JA, Bennett DA, Bradshaw EM, De Jager PL. A cortical immune network map identifies distinct microglial transcriptional programs associated with β-amyloid and Tau pathologies. Transl Psychiatry 2021; 11:50. [PMID: 33446646 PMCID: PMC7809035 DOI: 10.1038/s41398-020-01175-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/16/2020] [Accepted: 12/15/2020] [Indexed: 01/10/2023] Open
Abstract
Microglial dysfunction has been proposed as one of the many cellular mechanisms that can contribute to the development of Alzheimer's disease (AD). Here, using a transcriptional network map of the human frontal cortex, we identify five modules of co-expressed genes related to microglia and assess their role in the neuropathologic features of AD in 540 subjects from two cohort studies of brain aging. Two of these transcriptional programs-modules 113 and 114-relate to the accumulation of β-amyloid, while module 5 relates to tau pathology. We replicate these associations in brain epigenomic data and in two independent datasets. In terms of tau, we propose that module 5, a marker of activated microglia, may lead to tau accumulation and subsequent cognitive decline. We validate our model further by showing that three representative module 5 genes (ACADVL, TRABD, and VASP) encode proteins that are upregulated in activated microglia in AD.
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Affiliation(s)
- Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia
- The Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia
| | - Marta Olah
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Mariko Taga
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Hans-Ulrich Klein
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | | | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry & Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Sonal Agrawal
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lori B Chibnik
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara Mostafavi
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, 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
| | - Elizabeth M Bradshaw
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA.
- Cell Circuits Program, Broad Institute, Cambridge, MA, USA.
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13
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Overlapping Community Detection Based on Membership Degree Propagation. ENTROPY 2020; 23:e23010015. [PMID: 33374305 PMCID: PMC7824673 DOI: 10.3390/e23010015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 11/17/2022]
Abstract
A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.
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14
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Li S, Weinstein G, Zare H, Teumer A, Völker U, Friedrich N, Knol MJ, Satizabal CL, Petyuk VA, Adams HHH, Launer LJ, Bennett DA, De Jager PL, Grabe HJ, Ikram MA, Gudnason V, Yang Q, Seshadri S. The genetics of circulating BDNF: towards understanding the role of BDNF in brain structure and function in middle and old ages. Brain Commun 2020; 2:fcaa176. [PMID: 33345186 PMCID: PMC7734441 DOI: 10.1093/braincomms/fcaa176] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 07/16/2020] [Accepted: 07/17/2020] [Indexed: 01/04/2023] Open
Abstract
Brain-derived neurotrophic factor (BDNF) plays an important role in brain development and function. Substantial amounts of BDNF are present in peripheral blood, and may serve as biomarkers for Alzheimer’s disease incidence as well as targets for intervention to reduce Alzheimer’s disease risk. With the exception of the genetic polymorphism in the BDNF gene, Val66Met, which has been extensively studied with regard to neurodegenerative diseases, the genetic variation that influences circulating BDNF levels is unknown. We aimed to explore the genetic determinants of circulating BDNF levels in order to clarify its mechanistic involvement in brain structure and function and Alzheimer’s disease pathophysiology in middle-aged and old adults. Thus, we conducted a meta-analysis of genome-wide association study of circulating BDNF in 11 785 middle- and old-aged individuals of European ancestry from the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES), the Framingham Heart Study (FHS), the Rotterdam Study and the Study of Health in Pomerania (SHIP-Trend). Furthermore, we performed functional annotation analysis and related the genetic polymorphism influencing circulating BDNF to common Alzheimer’s disease pathologies from brain autopsies. Mendelian randomization was conducted to examine the possible causal role of circulating BDNF levels with various phenotypes including cognitive function, stroke, diabetes, cardiovascular disease, physical activity and diet patterns. Gene interaction networks analysis was also performed. The estimated heritability of BDNF levels was 30% (standard error = 0.0246, P-value = 4 × 10−48). We identified seven novel independent loci mapped near the BDNF gene and in BRD3, CSRNP1, KDELC2, RUNX1 (two single-nucleotide polymorphisms) and BDNF-AS. The expression of BDNF was associated with neurofibrillary tangles in brain tissues from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Seven additional genes (ACAT1, ATM, NPAT, WDR48, TTC21A, SCN114 and COX7B) were identified through expression and protein quantitative trait loci analyses. Mendelian randomization analyses indicated a potential causal role of BDNF in cardioembolism. Lastly, Ingenuity Pathway Analysis placed circulating BDNF levels in four major networks. Our study provides novel insights into genes and molecular pathways associated with circulating BDNF levels and highlights the possible involvement of plaque instability as an underlying mechanism linking BDNF with brain neurodegeneration. These findings provide a foundation for a better understanding of BDNF regulation and function in the context of brain aging and neurodegenerative pathophysiology.
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Affiliation(s)
- Shuo Li
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Galit Weinstein
- School of Public Health, University of Haifa, Haifa 3498838, Israel
| | - Habil Zare
- Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX, USA.,Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, 78229 TX, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany.,Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Germany
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Germany
| | - Maria J Knol
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, 78229 TX, USA.,Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX 78229, USA.,The Framingham Study, Framingham, MA 01702, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | | | - Hieab H H Adams
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam 3015 CN, The Netherlands
| | - Lenore J Launer
- Department of Health and Human Services, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - David A Bennett
- Department of Neurology, Rush University Medical Center, Chicago, IL 60612, USA.,Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Philip L De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY 10032, USA.,Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.,German Center for Neurodegererative Diseases (DZNE), Rostock/Greifswald, Germany
| | - M Arfan Ikram
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands
| | - Vilmundur Gudnason
- Faculty of Medicine, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland.,Icelandic Heart Association, 201 Kopavogur, Iceland
| | - Qiong Yang
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, 78229 TX, USA.,The Framingham Study, Framingham, MA 01702, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
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15
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A multiresolution framework to characterize single-cell state landscapes. Nat Commun 2020; 11:5399. [PMID: 33106496 PMCID: PMC7588427 DOI: 10.1038/s41467-020-18416-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 08/21/2020] [Indexed: 12/20/2022] Open
Abstract
Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet's superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.
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16
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Heuer SE, Neuner SM, Hadad N, O'Connell KMS, Williams RW, Philip VM, Gaiteri C, Kaczorowski CC. Identifying the molecular systems that influence cognitive resilience to Alzheimer's disease in genetically diverse mice. ACTA ACUST UNITED AC 2020; 27:355-371. [PMID: 32817302 PMCID: PMC7433658 DOI: 10.1101/lm.051839.120] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/10/2020] [Indexed: 12/23/2022]
Abstract
Individual differences in cognitive decline during normal aging and Alzheimer's disease (AD) are common, but the molecular mechanisms underlying these distinct outcomes are not fully understood. We utilized a combination of genetic, molecular, and behavioral data from a mouse population designed to model human variation in cognitive outcomes to search for the molecular mechanisms behind this population-wide variation. Specifically, we used a systems genetics approach to relate gene expression to cognitive outcomes during AD and normal aging. Statistical causal-inference Bayesian modeling was used to model systematic genetic perturbations matched with cognitive data that identified astrocyte and microglia molecular networks as drivers of cognitive resilience to AD. Using genetic mapping, we identified Fgf2 as a potential regulator of the astrocyte network associated with individual differences in short-term memory. We also identified several immune genes as regulators of a microglia network associated with individual differences in long-term memory, which was partly mediated by amyloid burden. Finally, significant overlap between mouse and two different human coexpression networks provided strong evidence of translational relevance for the genetically diverse AD-BXD panel as a model of late-onset AD. Together, this work identified two candidate molecular pathways enriched for microglia and astrocyte genes that serve as causal AD cognitive biomarkers, and provided a greater understanding of processes that modulate individual and population-wide differences in cognitive outcomes during AD.
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Affiliation(s)
- Sarah E Heuer
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA.,Tufts University School of Graduate Biomedical Sciences, Boston, Massachusetts 02111, USA
| | - Sarah M Neuner
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA.,University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | - Niran Hadad
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA
| | | | - Robert W Williams
- University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA
| | | | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois 60612, USA
| | - Catherine C Kaczorowski
- The Jackson Laboratory, Bar Harbor, Maine 04609, USA.,Tufts University School of Graduate Biomedical Sciences, Boston, Massachusetts 02111, USA
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17
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Yang HS, White CC, Klein HU, Yu L, Gaiteri C, Ma Y, Felsky D, Mostafavi S, Petyuk VA, Sperling RA, Ertekin-Taner N, Schneider JA, Bennett DA, De Jager PL. Genetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology. Neuron 2020; 107:496-508.e6. [PMID: 32526197 PMCID: PMC7416464 DOI: 10.1016/j.neuron.2020.05.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 03/20/2020] [Accepted: 05/07/2020] [Indexed: 12/14/2022]
Abstract
Here, we perform a genome-wide screen for variants that regulate the expression of gene co-expression modules in the aging human brain; we discover and replicate such variants in the TMEM106B and RBFOX1 loci. The TMEM106B haplotype is known to influence the accumulation of TAR DNA-binding protein 43 kDa (TDP-43) proteinopathy, and the haplotype's large-scale transcriptomic effects include the dysregulation of lysosomal genes and alterations in synaptic gene splicing that are also seen in the pathophysiology of TDP-43 proteinopathy. Further, a variant near GRN, another TDP-43 proteinopathy susceptibility gene, shows concordant effects with the TMEM106B haplotype. Leveraging neuropathology data from the same participants, we also show that TMEM106B and APOE-amyloid-β effects converge to alter myelination and lysosomal gene expression, which then contributes to TDP-43 accumulation. These results advance our mechanistic understanding of the TMEM106B TDP-43 risk haplotype and uncover a transcriptional program that mediates the converging effects of APOE-amyloid-β and TMEM106B on TDP-43 aggregation in older adults.
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Affiliation(s)
- Hyun-Sik Yang
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Charles C White
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hans-Ulrich Klein
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Christopher Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Yiyi Ma
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Daniel Felsky
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sara Mostafavi
- Department of Statistics, Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada; Canadian Institute for Advanced Research, Toronto, ON M5G 1M1, Canada
| | | | - Reisa A Sperling
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Nilüfer Ertekin-Taner
- Department of Neurology, Mayo Clinic, Jacksonville, FL 32224, USA; Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Philip L De Jager
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA.
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18
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Patrick E, Taga M, Ergun A, Ng B, Casazza W, Cimpean M, Yung C, Schneider JA, Bennett DA, Gaiteri C, De Jager PL, Bradshaw EM, Mostafavi S. Deconvolving the contributions of cell-type heterogeneity on cortical gene expression. PLoS Comput Biol 2020; 16:e1008120. [PMID: 32804935 PMCID: PMC7451979 DOI: 10.1371/journal.pcbi.1008120] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 08/27/2020] [Accepted: 07/02/2020] [Indexed: 12/26/2022] Open
Abstract
Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).
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Affiliation(s)
- Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia
- The Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia
| | - Mariko Taga
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Ayla Ergun
- Research and Development, Biogen, Cambridge, Massachusetts, United States of America
| | - Bernard Ng
- Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
| | - William Casazza
- Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
- The Bioinformatics Training Program, University of British Columbia, Vancouver, Canada
| | - Maria Cimpean
- Department of Pediatrics, Division of Rheumatology, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Christina Yung
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Julie A. Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Chris Gaiteri
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Elizabeth M. Bradshaw
- Department of Neurology, Columbia University Medical Center, New York City, New York, United States of America
| | - Sara Mostafavi
- Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada
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19
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Wan YW, Al-Ouran R, Mangleburg CG, Perumal TM, Lee TV, Allison K, Swarup V, Funk CC, Gaiteri C, Allen M, Wang M, Neuner SM, Kaczorowski CC, Philip VM, Howell GR, Martini-Stoica H, Zheng H, Mei H, Zhong X, Kim JW, Dawson VL, Dawson TM, Pao PC, Tsai LH, Haure-Mirande JV, Ehrlich ME, Chakrabarty P, Levites Y, Wang X, Dammer EB, Srivastava G, Mukherjee S, Sieberts SK, Omberg L, Dang KD, Eddy JA, Snyder P, Chae Y, Amberkar S, Wei W, Hide W, Preuss C, Ergun A, Ebert PJ, Airey DC, Mostafavi S, Yu L, Klein HU, Carter GW, Collier DA, Golde TE, Levey AI, Bennett DA, Estrada K, Townsend TM, Zhang B, Schadt E, De Jager PL, Price ND, Ertekin-Taner N, Liu Z, Shulman JM, Mangravite LM, Logsdon BA. Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models. Cell Rep 2020; 32:107908. [PMID: 32668255 PMCID: PMC7428328 DOI: 10.1016/j.celrep.2020.107908] [Citation(s) in RCA: 168] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 06/01/2020] [Accepted: 06/24/2020] [Indexed: 12/14/2022] Open
Abstract
We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.
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Affiliation(s)
- Ying-Wooi Wan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | - Rami Al-Ouran
- Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Carl G Mangleburg
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | | | - Tom V Lee
- Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Katherine Allison
- Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Vivek Swarup
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Mariet Allen
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL 32224, USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | | | | | | | | | | | - Hui Zheng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hongkang Mei
- Neuroscience DPU, Shanghai R&D, GlaxoSmithKline, Shanghai, China
| | - Xiaoyan Zhong
- Neuroscience DPU, Shanghai R&D, GlaxoSmithKline, Shanghai, China
| | - Jungwoo Wren Kim
- Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Valina L Dawson
- Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Adrienne Helis Malvin & Diana Helis Henry Medical Research Foundations, New Orleans, LA 70130, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ted M Dawson
- Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Adrienne Helis Malvin & Diana Helis Henry Medical Research Foundations, New Orleans, LA 70130, USA; Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ping-Chieh Pao
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Li-Huei Tsai
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jean-Vianney Haure-Mirande
- Departments of Neurology and Pediatrics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Michelle E Ehrlich
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Departments of Neurology and Pediatrics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Paramita Chakrabarty
- Evelyn F. and William L. McKnight Brain Institute, Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA
| | - Yona Levites
- Evelyn F. and William L. McKnight Brain Institute, Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA
| | - Xue Wang
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL 32224, USA; Mayo Clinic, Department of Health Sciences Research, Jacksonville, FL 32224, USA
| | - Eric B Dammer
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | | | | | | | | | | | | | | | | | - Sandeep Amberkar
- Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK; Molecular Oncology Lab, Cancer Research UK - Manchester Institute, The University of Manchester, Manchester, SK10 4TG, UK
| | - Wenbin Wei
- Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK; Department of Biosciences, Durham University, Durham, DH1 3LE, UK
| | - Winston Hide
- Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK; Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | | | - Ayla Ergun
- Translational Genome Sciences, Biogen, Cambridge, MA, USA
| | - Phillip J Ebert
- Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN 46285, USA
| | - David C Airey
- Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN 46285, USA
| | | | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Hans-Ulrich Klein
- Center for Translational & Computational Neuroimmunology, Department of Neurology and Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA; Cell Circuits Program, Broad Institute, Cambridge, MA 02142, USA
| | | | - David A Collier
- Eli Lilly & Company, Erl Wood Manor, Sunninghill Road, Windlesham, Surrey, GU20 6PH, UK
| | - Todd E Golde
- Evelyn F. and William L. McKnight Brain Institute, Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Karol Estrada
- Translational Genome Sciences, Biogen, Cambridge, MA, USA
| | | | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology and Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA; Cell Circuits Program, Broad Institute, Cambridge, MA 02142, USA
| | | | - Nilüfer Ertekin-Taner
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL 32224, USA; Mayo Clinic, Department of Neurology, Jacksonville, FL 32224, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Joshua M Shulman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurologic Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA; Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.
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20
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Gaiteri C, Dawe R, Mostafavi S, Blizinsky KD, Tasaki S, Komashko V, Yu L, Wang Y, Schneider JA, Arfanakis K, De Jager PL, Bennett DA. Gene expression and DNA methylation are extensively coordinated with MRI-based brain microstructural characteristics. Brain Imaging Behav 2020; 13:963-972. [PMID: 29934819 PMCID: PMC6309607 DOI: 10.1007/s11682-018-9910-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Cognitive function relies on both molecular levels and cellular structures. However, systematic relationships between these two components of cognitive function, and their joint contribution to disease, are largely unknown. We utilize postmortem neuroimaging in tandem with gene expression and DNA methylation, from 222 deeply-phenotyped persons in a longitudinal aging cohort. Expression of hundreds of genes and methylation at thousands of loci are related to the microstructure of extensive regions of this same set of brains, as assessed by MRI. The genes linked to brain microstructure perform functions related to cell motility, transcriptional regulation and nuclear processes, and are selectively associated with Alzheimer’s phenotypes. Similar methodology can be applied to other diseases to identify their joint molecular and structural basis, or to infer molecular levels in the brain on the basis of neuroimaging for precision medicine applications.
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Affiliation(s)
- Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
| | - Robert Dawe
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Sara Mostafavi
- Department of Statistics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Katherine D Blizinsky
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,National Institutes of Health, National Human Genome Research Institute, Bethesda, MD, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vitalina Komashko
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Konstantinos Arfanakis
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA.,Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Philip L De Jager
- Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
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21
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Tasaki S, Gaiteri C, Petyuk VA, Blizinsky KD, De Jager PL, Buchman AS, Bennett DA. Genetic risk for Alzheimer's dementia predicts motor deficits through multi-omic systems in older adults. Transl Psychiatry 2019; 9:241. [PMID: 31582723 PMCID: PMC6776503 DOI: 10.1038/s41398-019-0577-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 05/24/2019] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease manifests with both cognitive and motor deficits. However, the degree to which genetic risk of Alzheimer's dementia contributes to late-life motor impairment, and the specific molecular systems underlying these associations, are uncertain. Here, we adopted an integrative multi-omic approach to assess genetic influence on motor impairment in older adults and identified key molecular pathways that may mediate this risk. We built a polygenic risk score for clinical diagnosis of Alzheimer's dementia (AD-PRS) and examined its relationship to several motor phenotypes in 1885 older individuals from two longitudinal aging cohorts. We found that AD-PRS was associated with a previously validated composite motor scores and their components. The major genetic risk factor for sporadic Alzheimer's dementia, the APOE/TOMM40 locus, was not a major driver of these associations. To identify specific molecular features that potentially medicate the genetic risk into motor dysfunction, we examined brain multi-omics, including transcriptome, DNA methylation, histone acetylation (H3K9AC), and targeted proteomics, as well as diverse neuropathologies. We found that a small number of factors account for the majority of the influence of AD-PRS on motor function, which comprises paired helical filament tau-tangle density, H3K9AC in specific chromosomal regions encoding genes involved in neuromuscular process. These multi-omic factors have the potential to elucidate key molecular mechanisms developing motor impairment in the context of Alzheimer's dementia.
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Affiliation(s)
- Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Katherine D Blizinsky
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
- Cell Circuits Program, Broad Institute, Cambridge, MA, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
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22
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Abstract
Motivation Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes. Results To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS2), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS2 is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes. Availability and implementation The NBS2 package is freely available at: https://github.com/wzhang1984/NBSS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wei Zhang
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jianzhu Ma
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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23
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De Beurs D, Fried EI, Wetherall K, Cleare S, O' Connor DB, Ferguson E, O'Carroll RE, O' Connor RC. Exploring the psychology of suicidal ideation: A theory driven network analysis. Behav Res Ther 2019; 120:103419. [PMID: 31238299 DOI: 10.1016/j.brat.2019.103419] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 05/05/2019] [Accepted: 06/03/2019] [Indexed: 12/24/2022]
Abstract
Two leading theories within the field of suicide prevention are the interpersonal psychological theory of suicidal behaviour (IPT) and the integrated motivational-volitional (IMV) model. The IPT posits that suicidal thoughts emerge from high levels of perceived burdensomeness and thwarted belongingness. The IMV model is a multivariate framework that conceptualizes defeat and entrapment as key drivers of suicide ideation. We applied network analysis to cross-sectional data collected as part of the Scottish Wellbeing Study, in which a nationally representative sample of 3508 young adults (18-34 years) completed a battery of psychological measures. Network analysis can help us to understand how the different theoretical components interact and how they relate to suicide ideation. Within a network that included only the core factors from both models, internal entrapment and perceived burdensomeness were most strongly related to suicide ideation. The core constructs defeat, external entrapment and thwarted belonginess were mainly related to other factors than suicide ideation. Within the network of all available psychological factors, 12 of the 20 factors were uniquely related to suicide ideation, with perceived burdensomeness, internal entrapment, depressive symptoms and history of suicide ideation explaining the most variance. None of the factors was isolated, and we identified four larger clusters: mental wellbeing, interpersonal needs, personality, and suicide-related factors. Overall, the results suggest that relationships between suicide ideation and psychological risk factors are complex, with some factors contributing direct risk, and others having indirect impact.
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Affiliation(s)
- D De Beurs
- Netherlands Institute for Health Services Research, Otterstraat, 118-124, Utrecht, the Netherlands.
| | - E I Fried
- Leiden University, Clinical Psychology, Netherlands
| | - K Wetherall
- Suicidal Behaviour Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, UK
| | - S Cleare
- Suicidal Behaviour Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, UK
| | | | - E Ferguson
- School of Psychology, University of Nottingham, UK
| | - R E O'Carroll
- Division of Psychology, School of Natural Sciences, University of Stirling, UK
| | - R C O' Connor
- Suicidal Behaviour Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, UK
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24
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Felsky D, Roostaei T, Nho K, Risacher SL, Bradshaw EM, Petyuk V, Schneider JA, Saykin A, Bennett DA, De Jager PL. Neuropathological correlates and genetic architecture of microglial activation in elderly human brain. Nat Commun 2019; 10:409. [PMID: 30679421 PMCID: PMC6345810 DOI: 10.1038/s41467-018-08279-3] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 11/20/2018] [Indexed: 11/10/2022] Open
Abstract
Microglia, the resident immune cells of the brain, have important roles in brain health. However, little is known about the regulation and consequences of microglial activation in the aging human brain. Here we report that the proportion of morphologically activated microglia (PAM) in postmortem cortical tissue is strongly associated with β-amyloid, tau-related neuropathology, and the rate of cognitive decline. Effect sizes for PAM measures are substantial, comparable to that of APOE ε4, the strongest genetic risk factor for Alzheimer's disease, and mediation models support an upstream role for microglial activation in Alzheimer's disease via accumulation of tau. Further, we identify a common variant (rs2997325) influencing PAM that also affects in vivo microglial activation measured by [11C]-PBR28 PET in an independent cohort. Thus, our analyses begin to uncover pathways regulating resident neuroinflammation and identify overlaps of PAM's genetic architecture with those of Alzheimer's disease and several other traits.
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Affiliation(s)
- Daniel Felsky
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10032, USA
- Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA, 02141, USA
| | - Tina Roostaei
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10032, USA
| | - Kwangsik Nho
- Indiana Alzheimer's Disease Center, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 355 West 16th Street, Indianapolis, IN, 46202, USA
| | - Shannon L Risacher
- Indiana Alzheimer's Disease Center, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 355 West 16th Street, Indianapolis, IN, 46202, USA
| | - Elizabeth M Bradshaw
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10032, USA
| | - Vlad Petyuk
- Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Julie A Schneider
- Department of Neurology, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL, 60612, USA
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL, 60612, USA
| | - Andrew Saykin
- Indiana Alzheimer's Disease Center, Center for Neuroimaging, Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 355 West 16th Street, Indianapolis, IN, 46202, USA
| | - David A Bennett
- Department of Neurology, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL, 60612, USA
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10032, USA.
- Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA, 02141, USA.
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25
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Ji JL, Spronk M, Kulkarni K, Repovš G, Anticevic A, Cole MW. Mapping the human brain's cortical-subcortical functional network organization. Neuroimage 2019; 185:35-57. [PMID: 30291974 PMCID: PMC6289683 DOI: 10.1016/j.neuroimage.2018.10.006] [Citation(s) in RCA: 267] [Impact Index Per Article: 53.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 09/30/2018] [Accepted: 10/02/2018] [Indexed: 01/04/2023] Open
Abstract
Understanding complex systems such as the human brain requires characterization of the system's architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brain's large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently-identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly-organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, 06511, USA
| | - Marjolein Spronk
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Kaustubh Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, 1000, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, 06511, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA.
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26
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Wang H, Li Y, Ryder JW, Hole JT, Ebert PJ, Airey DC, Qian HR, Logsdon B, Fisher A, Ahmed Z, Murray TK, Cavallini A, Bose S, Eastwood BJ, Collier DA, Dage JL, Miller BB, Merchant KM, O'Neill MJ, Demattos RB. Genome-wide RNAseq study of the molecular mechanisms underlying microglia activation in response to pathological tau perturbation in the rTg4510 tau transgenic animal model. Mol Neurodegener 2018; 13:65. [PMID: 30558641 PMCID: PMC6296031 DOI: 10.1186/s13024-018-0296-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 11/28/2018] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Activation of microglia, the resident immune cells of the central nervous system, is a prominent pathological hallmark of Alzheimer's disease (AD). However, the gene expression changes underlying microglia activation in response to tau pathology remain elusive. Furthermore, it is not clear how murine gene expression changes relate to human gene expression networks. METHODS Microglia cells were isolated from rTg4510 tau transgenic mice and gene expression was profiled using RNA sequencing. Four age groups of mice (2-, 4-, 6-, and 8-months) were analyzed to capture longitudinal gene expression changes that correspond to varying levels of pathology, from minimal tau accumulation to massive neuronal loss. Statistical and system biology approaches were used to analyze the genes and pathways that underlie microglia activation. Differentially expressed genes were compared to human brain co-expression networks. RESULTS Statistical analysis of RNAseq data indicated that more than 4000 genes were differentially expressed in rTg4510 microglia compared to wild type microglia, with the majority of gene expression changes occurring between 2- and 4-months of age. These genes belong to four major clusters based on their temporal expression pattern. Genes involved in innate immunity were continuously up-regulated, whereas genes involved in the glutamatergic synapse were down-regulated. Up-regulated innate inflammatory pathways included NF-κB signaling, cytokine-cytokine receptor interaction, lysosome, oxidative phosphorylation, and phagosome. NF-κB and cytokine signaling were among the earliest pathways activated, likely driven by the RELA, STAT1 and STAT6 transcription factors. The expression of many AD associated genes such as APOE and TREM2 was also altered in rTg4510 microglia cells. Differentially expressed genes in rTg4510 microglia were enriched in human neurodegenerative disease associated pathways, including Alzheimer's, Parkinson's, and Huntington's diseases, and highly overlapped with the microglia and endothelial modules of human brain transcriptional co-expression networks. CONCLUSION This study revealed temporal transcriptome alterations in microglia cells in response to pathological tau perturbation and provides insight into the molecular changes underlying microglia activation during tau mediated neurodegeneration.
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Affiliation(s)
- Hong Wang
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA.
| | - Yupeng Li
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - John W Ryder
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Justin T Hole
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Philip J Ebert
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - David C Airey
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Hui-Rong Qian
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Alice Fisher
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Zeshan Ahmed
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Tracey K Murray
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Annalisa Cavallini
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Suchira Bose
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Brian J Eastwood
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - David A Collier
- Eli Lilly and Company Limited, Lilly Research Centre, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Jeffrey L Dage
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Bradley B Miller
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Michael J O'Neill
- Present address: AbbVie Deutschland GmbH & Co. K.G, Ludwigshafen, Germany
| | - Ronald B Demattos
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
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27
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Tasaki S, Gaiteri C, Mostafavi S, De Jager PL, Bennett DA. The Molecular and Neuropathological Consequences of Genetic Risk for Alzheimer's Dementia. Front Neurosci 2018; 12:699. [PMID: 30349450 PMCID: PMC6187226 DOI: 10.3389/fnins.2018.00699] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/18/2018] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's dementia commonly impacts the health of older adults and lacks any preventative therapy. While Alzheimer's dementia risk has a substantial genetic component, the specific molecular mechanisms and neuropathologies triggered by most of the known genetic variants are unclear. Resultantly, they have shown limited influence on drug development portfolios to date. To facilitate our understanding of the consequences of Alzheimer's dementia susceptibility variants, we examined their relationship to a wide range of clinical, molecular and neuropathological features. Because the effect size of individual variants is typically small, we utilized a polygenic (overall) risk approach to identify the global impact of Alzheimer's dementia susceptibility variants. Under this approach, each individual has a polygenic risk score (PRS) that we related to clinical, molecular and neuropathological phenotypes. Applying this approach to 1,272 individuals who came to autopsy from one of two longitudinal aging cohorts, we observed that an individual's PRS was associated with cognitive decline and brain pathologies including beta-amyloid, tau-tangles, hippocampal sclerosis, and TDP-43, MIR132, four proteins including VGF, IGFBP5, and STX1A, and many chromosomal regions decorated with acetylation on histone H3 lysine 9 (H3K9Ac). While excluding the APOE/TOMM40 region (containing the single largest genetic risk factor for late-onset Alzheimer's dementia) in the calculation of the PRS resulted in a slightly weaker association with the molecular signatures, results remained significant. These PRS-associated brain pathologies and molecular signatures appear to mediate genetic risk, as they attenuated the association of the PRS with cognitive decline. Notably, the PRS induced changes in H3K9Ac throughout the genome, implicating it in large-scale chromatin changes. Thus, the PRS for Alzheimer's dementia (AD-PRS) showed effects on diverse clinical, molecular, and pathological systems, ranging from the epigenome to specific proteins. These convergent targets of a large number of genetic risk factors for Alzheimer's dementia will help define the experimental systems and models needed to test therapeutic targets, which are expected to be broadly effective in the aging population that carries diverse genetic risks for Alzheimer's dementia.
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Affiliation(s)
- Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Sara Mostafavi
- Department of Statistics, Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Philip L. De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, United States
- Cell Circuits Program, Broad Institute, Cambridge, MA, United States
| | - David A. Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
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Lim ASP, Gaiteri C, Yu L, Sohail S, Swardfager W, Tasaki S, Schneider JA, Paquet C, Stuss DT, Masellis M, Black SE, Hugon J, Buchman AS, Barnes LL, Bennett DA, De Jager PL. Seasonal plasticity of cognition and related biological measures in adults with and without Alzheimer disease: Analysis of multiple cohorts. PLoS Med 2018; 15:e1002647. [PMID: 30180184 PMCID: PMC6122787 DOI: 10.1371/journal.pmed.1002647] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 07/30/2018] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND There are few data concerning the association between season and cognition and its neurobiological correlates in older persons-effects with important translational and therapeutic implications for the diagnosis and treatment of Alzheimer disease (AD). We aimed to measure these effects. METHODS AND FINDINGS We analyzed data from 3,353 participants from 3 observational community-based cohort studies of older persons (the Rush Memory and Aging Project [MAP], the Religious Orders Study [ROS], and the Minority Aging Research Study [MARS]) and 2 observational memory-clinic-based cohort studies (Centre de Neurologie Cognitive [CNC] study at Lariboisière Hospital and the Sunnybrook Dementia Study [SDS]). We performed neuropsychological testing and, in subsets of participants, evaluated cerebrospinal fluid AD biomarkers, standardized structured autopsy measures, and/or prefrontal cortex gene expression by RNA sequencing. We examined the association between season and these variables using nested multiple linear and logistic regression models. There was a robust association between season and cognition that was replicated in multiple cohorts (amplitude = 0.14 SD [a measure of the magnitude of seasonal variation relative to overall variability; 95% CI 0.07-0.23], p = 0.007, in the combined MAP, ROS, and MARS cohorts; amplitude = 0.50 SD [95% CI 0.07-0.66], p = 0.017, in the SDS cohort). Average composite global cognitive function was higher in the summer and fall compared to winter and spring, with the difference equivalent in cognitive effect to 4.8 years' difference in age (95% CI 2.1-8.4, p = 0.002). Further, the odds of meeting criteria for mild cognitive impairment or dementia were higher in the winter and spring (odds ratio 1.31 [95% CI 1.10-1.57], p = 0.003). These results were robust against multiple potential confounders including depressive symptoms, sleep, physical activity, and thyroid status and persisted in cases with AD pathology. Moreover, season had a marked effect on cerebrospinal fluid Aβ 42 level (amplitude 0.30 SD [95% CI 0.10-0.64], p = 0.003), which peaked in the summer, and on the brain expression of 4 cognition-associated modules of co-expressed genes (m6: amplitude = 0.44 SD [95% CI 0.21-0.65], p = 0.0021; m13: amplitude = 0.46 SD [95% CI 0.27-0.76], p = 0.0009; m109: amplitude = 0.43 SD [95% CI 0.24-0.67], p = 0.0021; and m122: amplitude 0.46 SD [95% CI 0.20-0.71], p = 0.0012), which were in phase or anti-phase to the rhythms of cognition and which were in turn associated with binding sites for several seasonally rhythmic transcription factors including BCL11A, CTCF, EGR1, MEF2C, and THAP1. Limitations include the evaluation of each participant or sample once per annual cycle, reliance on self-report for measurement of environmental and behavioral factors, and potentially limited generalizability to individuals in equatorial regions or in the southern hemisphere. CONCLUSIONS Season has a clinically significant association with cognition and its neurobiological correlates in older adults with and without AD pathology. There may be value in increasing dementia-related clinical resources in the winter and early spring, when symptoms are likely to be most pronounced. Moreover, the persistence of robust seasonal plasticity in cognition and its neurobiological correlates, even in the context of concomitant AD pathology, suggests that targeting environmental or behavioral drivers of seasonal cognitive plasticity, or the key transcription factors and genes identified in this study as potentially mediating these effects, may allow us to substantially improve cognition in adults with and without AD.
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Affiliation(s)
- Andrew S. P. Lim
- Division of Neurology, Department of Medicine, Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Chris Gaiteri
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
| | - Lei Yu
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
| | - Shahmir Sohail
- Division of Neurology, Department of Medicine, Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Walter Swardfager
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Shinya Tasaki
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
| | - Julie A. Schneider
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
| | - Claire Paquet
- Centre de Neurologie Cognitive, Hôpitaux Saint-Louis Lariboisière Fernand-Widal, Assistance Publique–Hôpitaux de Paris, University of Paris Diderot, Paris, France
- Inserm U942, Paris, France
| | - Donald T. Stuss
- Division of Neurology, Department of Medicine, Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mario Masellis
- Division of Neurology, Department of Medicine, Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E. Black
- Division of Neurology, Department of Medicine, Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jacques Hugon
- Centre de Neurologie Cognitive, Hôpitaux Saint-Louis Lariboisière Fernand-Widal, Assistance Publique–Hôpitaux de Paris, University of Paris Diderot, Paris, France
- Inserm U942, Paris, France
| | - Aron S. Buchman
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
| | - Lisa L. Barnes
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
- Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois, United States of America
| | - David A. Bennett
- Rush Alzheimer Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
- Department of Neurological Sciences, Rush University, Chicago, Illinois, United States of America
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, New York, United States of America
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Felsky D, Patrick E, Schneider JA, Mostafavi S, Gaiteri C, Patsopoulos N, Bennett DA, De Jager PL. Polygenic analysis of inflammatory disease variants and effects on microglia in the aging brain. Mol Neurodegener 2018; 13:38. [PMID: 30041668 PMCID: PMC6057096 DOI: 10.1186/s13024-018-0272-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 07/13/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The role of the innate immune system in Alzheimer's disease (AD) and neurodegenerative disease susceptibility has recently been highlighted in genetic studies. However, we do not know whether risk for inflammatory disease predisposes unaffected individuals to late-life cognitive deficits or AD-related neuropathology. We investigated whether genetic risk scores for seven immune diseases and central nervous system traits were related to cognitive decline (nmax = 1601), classical AD neuropathology (nmax = 985), or microglial density (nmax = 184). METHODS Longitudinal cognitive decline, postmortem amyloid and tau neuropathology, microglial density, and gene module expression from bulk brain tissue were all measured in participants from two large cohorts (the Rush Religious Orders Study and Memory and Aging Project; ROS/MAP) of elderly subjects (mean age at entry 78 +/- 8.7 years). We analyzed data primarily using robust regression methods. Neuropathologists were blind to clinical data. RESULTS The AD genetic risk scores, including and excluding APOE effects, were strongly associated with cognitive decline in all domains (min Puncor = 3.2 × 10- 29). Multiple sclerosis (MS), Parkinson's disease, and schizophrenia risk did not influence cognitive decline in older age, but the rheumatoid arthritis (RA) risk score alone was significantly associated with microglial density after correction (t146 = - 3.88, Puncor = 1.6 × 10- 4). Post-hoc tests found significant effects of the RA genetic risk score in multiple regions and stages of microglial activation (min Puncor = 1.5 × 10- 6). However, these associations were driven by only one or two variants, rather than cumulative polygenicity. Further, individual MS (Pone-sided < 8.4 × 10- 4) and RA (Pone-sided = 3 × 10- 4) variants associated with higher microglial density were also associated with increased expression of brain immune gene modules. CONCLUSIONS Our results demonstrate that global risk of inflammatory disease does not strongly influence aging-related cognitive decline but that susceptibility variants that influence peripheral immune function also alter microglial density and immune gene expression in the aging brain, opening a new perspective on the control of microglial and immune responses within the central nervous system. Further study on the molecular mechanisms of peripheral immune disease risk influencing glial cell activation will be required to identify key regulators of these pathways.
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Affiliation(s)
- Daniel Felsky
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, 630 West 168th Street, PH 19 – 302, New York, NY 10032 USA
- Department of Neurology, Brigham and Woman’s Hospital, 75 Francis Street, Boston, MA 02115 USA
- Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115 USA
- Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142 USA
| | - Ellis Patrick
- Department of Statistics, University of Sydney, Camperdown, NSW 2006 Australia
| | - Julie A. Schneider
- Department of Neurology, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL 60612 USA
- Rush Alzheimer’s Disease Center, Rush University Medical Center, 600 South Paulina Street, Chicago, IL 60612 USA
| | - Sara Mostafavi
- Department of Statistics, University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4 Canada
| | - Chris Gaiteri
- Department of Neurology, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL 60612 USA
- Rush Alzheimer’s Disease Center, Rush University Medical Center, 600 South Paulina Street, Chicago, IL 60612 USA
| | - Nikolaos Patsopoulos
- Department of Neurology, Brigham and Woman’s Hospital, 75 Francis Street, Boston, MA 02115 USA
- Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115 USA
| | - David A. Bennett
- Department of Neurology, Rush University Medical Center, 1653 West Congress Parkway, Chicago, IL 60612 USA
- Rush Alzheimer’s Disease Center, Rush University Medical Center, 600 South Paulina Street, Chicago, IL 60612 USA
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, 630 West 168th Street, PH 19 – 302, New York, NY 10032 USA
- Department of Neurology, Brigham and Woman’s Hospital, 75 Francis Street, Boston, MA 02115 USA
- Department of Neurology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115 USA
- Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142 USA
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30
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Mostafavi S, Gaiteri C, Sullivan SE, White CC, Tasaki S, Xu J, Taga M, Klein HU, Patrick E, Komashko V, McCabe C, Smith R, Bradshaw EM, Root DE, Regev A, Yu L, Chibnik LB, Schneider JA, Young-Pearse TL, Bennett DA, De Jager PL. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer's disease. Nat Neurosci 2018; 21:811-819. [PMID: 29802388 PMCID: PMC6599633 DOI: 10.1038/s41593-018-0154-9] [Citation(s) in RCA: 319] [Impact Index Per Article: 53.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 04/20/2018] [Indexed: 02/07/2023]
Abstract
There is a need for new therapeutic targets with which to prevent Alzheimer’s disease (AD), a major contributor to aging-related cognitive decline. Here, we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets as well as in epigenomic data. We also illustrate the use of the network in prioritizing amyloid-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases. Systems biology analysis of RNA sequencing data from the aging human cortex identifies a molecular network which prioritizes groups of genes that influence cognitive decline or neuropathology in Alzheimer’s disease.
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Affiliation(s)
- Sara Mostafavi
- Department of Statistics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.,Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Sarah E Sullivan
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Jishu Xu
- Broad Institute, Cambridge, MA, USA
| | - Mariko Taga
- Broad Institute, Cambridge, MA, USA.,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Hans-Ulrich Klein
- Broad Institute, Cambridge, MA, USA.,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Vitalina Komashko
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | | | - Robert Smith
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth M Bradshaw
- Broad Institute, Cambridge, MA, USA.,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | | | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lori B Chibnik
- Broad Institute, Cambridge, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Tracy L Young-Pearse
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
| | - Philip L De Jager
- Broad Institute, Cambridge, MA, USA. .,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
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A semi-synchronous label propagation algorithm with constraints for community detection in complex networks. Sci Rep 2017; 7:45836. [PMID: 28374836 PMCID: PMC5379178 DOI: 10.1038/srep45836] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 03/03/2017] [Indexed: 01/12/2023] Open
Abstract
Community structure is an important feature of a complex network, where detection of the community structure can shed some light on the properties of such a complex network. Amongst the proposed community detection methods, the label propagation algorithm (LPA) emerges as an effective detection method due to its time efficiency. Despite this advantage in computational time, the performance of LPA is affected by randomness in the algorithm. A modified LPA, called CLPA-GNR, was proposed recently and it succeeded in handling the randomness issues in the LPA. However, it did not remove the tendency for trivial detection in networks with a weak community structure. In this paper, an improved CLPA-GNR is therefore proposed. In the new algorithm, the unassigned and assigned nodes are updated synchronously while the assigned nodes are updated asynchronously. A similarity score, based on the Sørensen-Dice index, is implemented to detect the initial communities and for breaking ties during the propagation process. Constraints are utilised during the label propagation and community merging processes. The performance of the proposed algorithm is evaluated on various benchmark and real-world networks. We find that it is able to avoid trivial detection while showing substantial improvement in the quality of detection.
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32
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DeMaere MZ, Darling AE. Deconvoluting simulated metagenomes: the performance of hard- and soft- clustering algorithms applied to metagenomic chromosome conformation capture (3C). PeerJ 2016; 4:e2676. [PMID: 27843713 PMCID: PMC5103821 DOI: 10.7717/peerj.2676] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 10/11/2016] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Chromosome conformation capture, coupled with high throughput DNA sequencing in protocols like Hi-C and 3C-seq, has been proposed as a viable means of generating data to resolve the genomes of microorganisms living in naturally occuring environments. Metagenomic Hi-C and 3C-seq datasets have begun to emerge, but the feasibility of resolving genomes when closely related organisms (strain-level diversity) are present in the sample has not yet been systematically characterised. METHODS We developed a computational simulation pipeline for metagenomic 3C and Hi-C sequencing to evaluate the accuracy of genomic reconstructions at, above, and below an operationally defined species boundary. We simulated datasets and measured accuracy over a wide range of parameters. Five clustering algorithms were evaluated (2 hard, 3 soft) using an adaptation of the extended B-cubed validation measure. RESULTS When all genomes in a sample are below 95% sequence identity, all of the tested clustering algorithms performed well. When sequence data contains genomes above 95% identity (our operational definition of strain-level diversity), a naive soft-clustering extension of the Louvain method achieves the highest performance. DISCUSSION Previously, only hard-clustering algorithms have been applied to metagenomic 3C and Hi-C data, yet none of these perform well when strain-level diversity exists in a metagenomic sample. Our simple extension of the Louvain method performed the best in these scenarios, however, accuracy remained well below the levels observed for samples without strain-level diversity. Strain resolution is also highly dependent on the amount of available 3C sequence data, suggesting that depth of sequencing must be carefully considered during experimental design. Finally, there appears to be great scope to improve the accuracy of strain resolution through further algorithm development.
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Affiliation(s)
- Matthew Z. DeMaere
- ithree institute, University of Technology Sydney, Sydney, NSW, Australia
| | - Aaron E. Darling
- ithree institute, University of Technology Sydney, Sydney, NSW, Australia
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Chin JH, Ratnavelu K. Detecting Community Structure by Using a Constrained Label Propagation Algorithm. PLoS One 2016; 11:e0155320. [PMID: 27176470 PMCID: PMC4866740 DOI: 10.1371/journal.pone.0155320] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 04/27/2016] [Indexed: 11/18/2022] Open
Abstract
Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA). The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR), Relaxed Caveman (RC) and Girvan-Newman (GN) benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results.
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Affiliation(s)
- Jia Hou Chin
- Institute of Mathematical Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Kuru Ratnavelu
- Institute of Mathematical Science, University of Malaya, Kuala Lumpur, Malaysia
- * E-mail:
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