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Khan AF, Iturria-Medina Y. Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms. Transl Psychiatry 2024; 14:386. [PMID: 39313512 PMCID: PMC11420368 DOI: 10.1038/s41398-024-03073-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 08/20/2024] [Accepted: 08/27/2024] [Indexed: 09/25/2024] Open
Abstract
From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.
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Affiliation(s)
- Ahmed Faraz Khan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Canada
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada.
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, Canada.
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2
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He B, Wu R, Sangani N, Pugalenthi PV, Patania A, Risacher SL, Nho K, Apostolova LG, Shen L, Saykin AJ, Yan J. Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease. Alzheimers Dement 2024. [PMID: 39285750 DOI: 10.1002/alz.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 07/24/2024] [Accepted: 08/15/2024] [Indexed: 09/21/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODS Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTS Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. DISCUSSION Our risk score holds great potential to improve the precision of early risk assessment. HIGHLIGHTS Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.
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Affiliation(s)
- Bing He
- Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA
| | - Ruiming Wu
- Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Neel Sangani
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Pradeep Varathan Pugalenthi
- Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA
| | - Alice Patania
- Department of Mathematics Statistics, University of Vermont, Burlington, Vermont, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Liana G Apostolova
- Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Li Shen
- Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jingwen Yan
- Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA
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3
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Lüleci HB, Jones A, Çakır T. Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease. Eur J Neurosci 2024; 60:4922-4936. [PMID: 39072881 DOI: 10.1111/ejn.16482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 05/14/2024] [Accepted: 07/13/2024] [Indexed: 07/30/2024]
Abstract
Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.
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Affiliation(s)
| | - Attila Jones
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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4
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Kioko M, Mwangi S, Pance A, Ochola-Oyier LI, Kariuki S, Newton C, Bejon P, Rayner JC, Abdi AI. The mRNA content of plasma extracellular vesicles provides a window into molecular processes in the brain during cerebral malaria. SCIENCE ADVANCES 2024; 10:eadl2256. [PMID: 39151016 PMCID: PMC11328904 DOI: 10.1126/sciadv.adl2256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 07/10/2024] [Indexed: 08/18/2024]
Abstract
The impact of cerebral malaria on the transcriptional profiles of cerebral tissues is difficult to study using noninvasive approaches. We isolated plasma extracellular vesicles (EVs) from patients with cerebral malaria and community controls and sequenced their mRNA content. Deconvolution analysis revealed that EVs from cerebral malaria are enriched in transcripts of brain origin. We ordered the patients with cerebral malaria based on their EV-transcriptional profiles from cross-sectionally collected samples and inferred disease trajectory while using healthy community controls as a starting point. We found that neuronal transcripts in plasma EVs decreased with disease trajectory, whereas transcripts from glial, endothelial, and immune cells increased. Disease trajectory correlated positively with severity indicators like death and was associated with increased VEGFA-VEGFR and glutamatergic signaling, as well as platelet and neutrophil activation. These data suggest that brain tissue responses in cerebral malaria can be studied noninvasively using EVs circulating in peripheral blood.
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Affiliation(s)
- Mwikali Kioko
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Open University, Milton Keynes, UK
| | - Shaban Mwangi
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Alena Pance
- Pathogens and Microbes Programme, Wellcome Sanger Institute, Cambridge, UK
- School of Life and Medical Science, University of Hertfordshire, Hatfield, UK
| | - Lynette Isabella Ochola-Oyier
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Symon Kariuki
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Charles Newton
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Philip Bejon
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Julian C Rayner
- Cambridge Institute of Medical Research, University of Cambridge, Cambridge, UK
| | - Abdirahman I Abdi
- Bioscience Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pwani University Biosciences Research Centre, Pwani University, Kilifi, Kenya
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Agrawal A, Rachleff VM, Travaglini KJ, Mukherjee S, Crane PK, Hawrylycz M, Keene CD, Lein E, Mena GE, Gabitto MI. B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.10.597236. [PMID: 38915664 PMCID: PMC11195076 DOI: 10.1101/2024.06.10.597236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Throughout an organism's life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimer's Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimer's Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.
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Yuan X, Li W, Liu Q, Long Q, Yan Q, Zhang P. Genomic characteristics of adipose-derived stromal cells induced into neurons based on single-cell RNA sequencing. Heliyon 2024; 10:e33079. [PMID: 38984299 PMCID: PMC11231542 DOI: 10.1016/j.heliyon.2024.e33079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
Adipose-derived stromal cells (ADSCs) can be induced to differentiate into neurons, representing the most promising avenue for cell therapy. However, the molecular mechanism and genomic characteristics of the differentiation of ADSCs into neurons remain poorly understood. In this study, cells from the adult ADSCs group, induction 1h, 3h, 5h, 6h, and 8h groups were selected for single-cell RNA sequencing (scRNA-Seq). Samples from these seven-time points were sequenced and analyzed. The expression of neuron marker genes, including NES, MAP2, TMEM59L, PTK2B, CHN1, DNM1, NRSN2, FBLN2, SCAMP1, SLC1A1, DLG4, CDK5, and ENO2, was found to be low in the ADSCs group, but highly expressed in differentiated cell clusters. The expression of stem cell marker genes, including CCND1, IL1B, MMP1, MMP3, MYO10, and BMP2, was the highest in the ADSCs cluster. This expression decreased significantly with the extension of induction time. Gene ontology (GO) enrichment analysis of upregulated genes in the induced samples showed that the biological processes related to neuronal differentiation and development, such as neuronal differentiation, projection, and apoptosis, were significantly upregulated with a longer induction time during cell cluster differentiation. The results of the cell communication analysis demonstrated the gradual formation of complex neural network connections between ADSC-derived neurons through receptor and ligand pairs at 5h after the induction of differentiation.
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Affiliation(s)
- Xiaodong Yuan
- Department of Neurology of Kailuan General Hospital Affiliated North China University of Science and Technology, China
- Hebei Provincial Key Laboratory of Neurobiological Function, China
| | - Wen Li
- Department of Neurology of Kailuan General Hospital Affiliated North China University of Science and Technology, China
- Hebei Provincial Key Laboratory of Neurobiological Function, China
| | - Qing Liu
- Department of Neurology of Kailuan General Hospital Affiliated North China University of Science and Technology, China
- Hebei Provincial Key Laboratory of Neurobiological Function, China
| | - Qingxi Long
- Department of Neurology of Kailuan General Hospital Affiliated North China University of Science and Technology, China
- Hebei Provincial Key Laboratory of Neurobiological Function, China
| | - Qi Yan
- Department of Neurology of Kailuan General Hospital Affiliated North China University of Science and Technology, China
- Hebei Provincial Key Laboratory of Neurobiological Function, China
| | - Pingshu Zhang
- Department of Neurology of Kailuan General Hospital Affiliated North China University of Science and Technology, China
- Hebei Provincial Key Laboratory of Neurobiological Function, China
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Stroh A, Schweiger S, Ramirez JM, Tüscher O. The selfish network: how the brain preserves behavioral function through shifts in neuronal network state. Trends Neurosci 2024; 47:246-258. [PMID: 38485625 DOI: 10.1016/j.tins.2024.02.005] [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: 08/19/2023] [Revised: 01/31/2024] [Accepted: 02/19/2024] [Indexed: 04/12/2024]
Abstract
Neuronal networks possess the ability to regulate their activity states in response to disruptions. How and when neuronal networks turn from physiological into pathological states, leading to the manifestation of neuropsychiatric disorders, remains largely unknown. Here, we propose that neuronal networks intrinsically maintain network stability even at the cost of neuronal loss. Despite the new stable state being potentially maladaptive, neural networks may not reverse back to states associated with better long-term outcomes. These maladaptive states are often associated with hyperactive neurons, marking the starting point for activity-dependent neurodegeneration. Transitions between network states may occur rapidly, and in discrete steps rather than continuously, particularly in neurodegenerative disorders. The self-stabilizing, metastable, and noncontinuous characteristics of these network states can be mathematically described as attractors. Maladaptive attractors may represent a distinct pathophysiological entity that could serve as a target for new therapies and for fostering resilience.
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Affiliation(s)
- Albrecht Stroh
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Susann Schweiger
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Human Genetics, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Germany
| | - Jan-Marino Ramirez
- Center for Integrative Brain Research at the Seattle Children's Research Institute, University of Washington, Seattle, USA
| | - Oliver Tüscher
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Germany; Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
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Cary GA, Wiley JC, Gockley J, Keegan S, Amirtha Ganesh SS, Heath L, Butler RR, Mangravite LM, Logsdon BA, Longo FM, Levey A, Greenwood AK, Carter GW. Genetic and multi-omic risk assessment of Alzheimer's disease implicates core associated biological domains. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2024; 10:e12461. [PMID: 38650747 PMCID: PMC11033838 DOI: 10.1002/trc2.12461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/23/2024] [Accepted: 02/09/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. METHODS Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. RESULTS The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. DISCUSSION This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Frank M. Longo
- Stanford University School of MedicineStanfordCaliforniaUSA
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9
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Roussos P, Kosoy R, Fullard J, Bendl J, Kleopoulos S, Shao Z, Argyriou S, Mathur D, Vicari J, Ma Y, Humphrey J, Brophy E, Raj T, Katsel P, Voloudakis G, Lee D, Bennett D, Haroutunian V, Hoffman G. Alzheimer's disease transcriptional landscape in ex-vivo human microglia. RESEARCH SQUARE 2024:rs.3.rs-3851590. [PMID: 38343831 PMCID: PMC10854306 DOI: 10.21203/rs.3.rs-3851590/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Microglia are resident immune cells of the brain and are implicated in the etiology of Alzheimer's Disease (AD) and other diseases. Yet the cellular and molecular processes regulating their function throughout the course of the disease are poorly understood. Here, we present the transcriptional landscape of primary microglia from 189 human postmortem brains, including 58 healthy aging individuals and 131 with a range of disease phenotypes, including 63 patients representing the full spectrum of clinical and pathological severity of AD. We identified transcriptional changes associated with multiple AD phenotypes, capturing the severity of dementia and neuropathological lesions. Transcript-level analyses identified additional genes with heterogeneous isoform usage and AD phenotypes. We identified changes in gene-gene coordination in AD, dysregulation of co-expression modules, and disease subtypes with distinct gene expression. Taken together, these data further our understanding of the key role of microglia in AD biology and nominate candidates for therapeutic intervention.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Yixuan Ma
- Icahn School of Medicine at Mount Sinai
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10
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Tayler HM, MacLachlan R, Güzel Ö, Fisher RA, Skrobot OA, Abulfadl MA, Kehoe PG, Miners JS. Altered Gene Expression Within the Renin-Angiotensin System in Normal Aging and Dementia. J Gerontol A Biol Sci Med Sci 2024; 79:glad241. [PMID: 37813091 PMCID: PMC10733177 DOI: 10.1093/gerona/glad241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Indexed: 10/11/2023] Open
Abstract
The renin-angiotensin system (RAS) is dysregulated in Alzheimer's disease (AD). In this study, we have explored the hypothesis that an -age--related imbalance in brain RAS is a trigger for RAS dysregulation in AD. We characterized RAS gene expression in the frontal cortex from (i) a cohort of normal aging (n = 99, age range = 19-96 years) and (ii) a case-control cohort (n = 209) including AD (n = 66), mixed dementia (VaD + AD; n = 50), pure vascular dementia (VaD; n = 42), and age-matched controls (n = 51). The AD, mixed dementia, and age-matched controls were further stratified by Braak tangle stage (BS): BS0-II (n = 48), BSIII-IV (n = 44), and BSV-VI (n = 85). Gene expression was calculated by quantitative PCR (qPCR) for ACE1, AGTR1, AGTR2, ACE2, LNPEP, and MAS1 using the 2-∆∆Cq method, after adjustment for reference genes (RPL13 and UBE2D2) and cell-specific calibrator genes (NEUN, GFAP, PECAM). ACE1 and AGTR1, markers of classical RAS signaling, and AGTR2 gene expression were elevated in normal aging and gene expression in markers of protective downstream regulatory RAS signaling, including ACE2, MAS1, and LNPEP, were unchanged. In AD and mixed dementia, AGTR1 and AGTR2 gene expression were elevated in BSIII-IV and BSV-VI, respectively. MAS1 gene expression was reduced at BSV-VI and was inversely related to parenchymal Aβ and tau load. LNPEP gene expression was specifically elevated in VaD. These data provide novel insights into RAS signaling in normal aging and dementia.
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Affiliation(s)
- Hannah M Tayler
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robert MacLachlan
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Özge Güzel
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robert A Fisher
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Olivia A Skrobot
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mohamed A Abulfadl
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Patrick G Kehoe
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - J Scott Miners
- Dementia Research Group, Clinical Neurosciences, Bristol Medical School, University of Bristol, Bristol, UK
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Kotredes KP, Pandey RS, Persohn S, Elderidge K, Burton CP, Miner EW, Haynes KA, Santos DFS, Williams SP, Heaton N, Ingraham CM, Lloyd C, Garceau D, O’Rourke R, Herrick S, Rangel-Barajas C, Maharjan S, Wang N, Sasner M, Lamb BT, Territo PR, Sukoff Rizzo SJ, Carter GW, Howell GR, Oblak AL. Characterizing Molecular and Synaptic Signatures in mouse models of Late-Onset Alzheimer's Disease Independent of Amyloid and Tau Pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.571985. [PMID: 38187716 PMCID: PMC10769232 DOI: 10.1101/2023.12.19.571985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
INTRODUCTION MODEL-AD is creating and distributing novel mouse models with humanized, clinically relevant genetic risk factors to more accurately mimic LOAD than commonly used transgenic models. METHODS We created the LOAD2 model by combining APOE4, Trem2*R47H, and humanized amyloid-beta. Mice aged up to 24 months were subjected to either a control diet or a high-fat/high-sugar diet (LOAD2+HFD) from two months of age. We assessed disease-relevant outcomes, including in vivo imaging, biomarkers, multi-omics, neuropathology, and behavior. RESULTS By 18 months, LOAD2+HFD mice exhibited cortical neuron loss, elevated insoluble brain Aβ42, increased plasma NfL, and altered gene/protein expression related to lipid metabolism and synaptic function. In vivo imaging showed age-dependent reductions in brain region volume and neurovascular uncoupling. LOAD2+HFD mice also displayed deficits in acquiring touchscreen-based cognitive tasks. DISCUSSION Collectively the comprehensive characterization of LOAD2+HFD mice reveal this model as important for preclinical studies that target features of LOAD independent of amyloid and tau.
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Affiliation(s)
- Kevin P. Kotredes
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
| | - Ravi S. Pandey
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, United States 06032
| | - Scott Persohn
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
| | - Kierra Elderidge
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
| | - Charles P Burton
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
| | - Ethan W. Miner
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
| | - Kathryn A. Haynes
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, 100 Technology Drive, Pittsburgh, PA Pittsburgh, PA, United States 15219
| | - Diogo Francisco S. Santos
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, 100 Technology Drive, Pittsburgh, PA Pittsburgh, PA, United States 15219
| | - Sean-Paul Williams
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, 100 Technology Drive, Pittsburgh, PA Pittsburgh, PA, United States 15219
| | - Nicholas Heaton
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, 100 Technology Drive, Pittsburgh, PA Pittsburgh, PA, United States 15219
| | - Cynthia M. Ingraham
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
| | - Christopher Lloyd
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
| | - Dylan Garceau
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
| | - Rita O’Rourke
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
| | - Sarah Herrick
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
| | - Claudia Rangel-Barajas
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W. 10 St., HITS 4000, Indianapolis, IN, United States 46202
| | - Surendra Maharjan
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
- Department of Radiology & Imaging Sciences, Indiana University School of Medicine, 550 University Blvd, Indianapolis, IN, United States 46202
| | - Nian Wang
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
- Department of Radiology & Imaging Sciences, Indiana University School of Medicine, 550 University Blvd, Indianapolis, IN, United States 46202
| | - Michael Sasner
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
| | - Bruce T. Lamb
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W. 10 St., HITS 4000, Indianapolis, IN, United States 46202
| | - Paul R. Territo
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, 545 Barnhill Drive, Indianapolis, IN, United States 46202
| | - Stacey J. Sukoff Rizzo
- Department of Medicine, University of Pittsburgh Aging Institute, University of Pittsburgh School of Medicine, 100 Technology Drive, Pittsburgh, PA Pittsburgh, PA, United States 15219
| | - Gregory W. Carter
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, United States 06032
- Tufts University Graduate School of Biomedical Sciences, 136 Harrison Ave #813, Boston, MA, United States 02111
- Graduate School of Biomedical Sciences and Engineering, University of Maine, 5775 Stodder Hall, Orono, Maine, United States 04469
| | - Gareth R. Howell
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, United States, 04609
- Tufts University Graduate School of Biomedical Sciences, 136 Harrison Ave #813, Boston, MA, United States 02111
- Graduate School of Biomedical Sciences and Engineering, University of Maine, 5775 Stodder Hall, Orono, Maine, United States 04469
| | - Adrian L. Oblak
- Indiana University School of Medicine, 340 W 10 Street, Indianapolis, IN, United States 46202
- Stark Neurosciences Research Institute, 320 W 15 Street, Indianapolis, IN, United States 46202
- Department of Radiology & Imaging Sciences, Indiana University School of Medicine, 550 University Blvd, Indianapolis, IN, United States 46202
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12
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Hou W, Ji Z, Chen Z, Wherry EJ, Hicks SC, Ji H. A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples. Nat Commun 2023; 14:7286. [PMID: 37949861 PMCID: PMC10638410 DOI: 10.1038/s41467-023-42841-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Zhicheng Ji
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Zeyu Chen
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - E John Wherry
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephanie C Hicks
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
| | - Hongkai Ji
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
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13
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Girdhar K, Bendl J, Baumgartner A, Therrien K, Venkatesh S, Mathur D, Dong P, Rahman S, Kleopoulos SP, Misir R, Reach SM, Auluck PK, Marenco S, Lewis DA, Haroutunian V, Funk C, Voloudakis G, Hoffman GE, Fullard JF, Roussos P. The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.02.23296067. [PMID: 37873320 PMCID: PMC10593028 DOI: 10.1101/2023.10.02.23296067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.
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Affiliation(s)
- Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Sanan Venkatesh
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Deepika Mathur
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah M Reach
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pavan K Auluck
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
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14
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Girdhar K, Bendl J, Baumgartner A, Therrien K, Venkatesh S, Mathur D, Dong P, Rahman S, Kleopoulos SP, Misir R, Reach SM, Auluck PK, Marenco S, Lewis DA, Haroutunian V, Funk C, Voloudakis G, Hoffman GE, Fullard JF, Roussos P. The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development. RESEARCH SQUARE 2023:rs.3.rs-3393581. [PMID: 37886514 PMCID: PMC10602154 DOI: 10.21203/rs.3.rs-3393581/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.
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Affiliation(s)
- Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Sanan Venkatesh
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Deepika Mathur
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah M Reach
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pavan K Auluck
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, New York, 10468, USA
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15
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Choi I, Wang M, Yoo S, Xu P, Seegobin SP, Li X, Han X, Wang Q, Peng J, Zhang B, Yue Z. Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence. Nat Cell Biol 2023; 25:963-974. [PMID: 37231161 PMCID: PMC10950302 DOI: 10.1038/s41556-023-01158-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 04/25/2023] [Indexed: 05/27/2023]
Abstract
Dysfunctional autophagy has been implicated in the pathogenesis of Alzheimer's disease (AD). Previous evidence suggested disruptions of multiple stages of the autophagy-lysosomal pathway in affected neurons. However, whether and how deregulated autophagy in microglia, a cell type with an important link to AD, contributes to AD progression remains elusive. Here we report that autophagy is activated in microglia, particularly of disease-associated microglia surrounding amyloid plaques in AD mouse models. Inhibition of microglial autophagy causes disengagement of microglia from amyloid plaques, suppression of disease-associated microglia, and aggravation of neuropathology in AD mice. Mechanistically, autophagy deficiency promotes senescence-associated microglia as evidenced by reduced proliferation, increased Cdkn1a/p21Cip1, dystrophic morphologies and senescence-associated secretory phenotype. Pharmacological treatment removes autophagy-deficient senescent microglia and alleviates neuropathology in AD mice. Our study demonstrates the protective role of microglial autophagy in regulating the homeostasis of amyloid plaques and preventing senescence; removal of senescent microglia is a promising therapeutic strategy.
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Affiliation(s)
- Insup Choi
- Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven P Seegobin
- Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xianting Li
- Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xian Han
- Departments of Structural Biology and Developmental Neurobiology, Saint Jude Children's Research Hospital, Memphis, TN, USA
- Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, Saint Jude Children's Research Hospital, Memphis, TN, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhenyu Yue
- Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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16
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Huang K, Zhang Y, Gong H, Qiao Z, Wang T, Zhao W, Huang L, Zhou X. Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression. PLoS Comput Biol 2023; 19:e1011122. [PMID: 37228122 DOI: 10.1371/journal.pcbi.1011122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.
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Affiliation(s)
- Kexin Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
- West China Biomedical Big Data Centre, West China Hospital of Sichuan University, Chengdu, China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haoran Gong
- West China Biomedical Big Data Centre, West China Hospital of Sichuan University, Chengdu, China
| | - Zhengzheng Qiao
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Tiangang Wang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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17
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Ruan Y, Lv W, Li S, Cheng Y, Wang D, Zhang C, Shimizu K. Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis. Comput Biol Med 2023; 159:106922. [PMID: 37094463 DOI: 10.1016/j.compbiomed.2023.106922] [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: 12/19/2022] [Revised: 03/07/2023] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease that is strongly associated with aging. Telomeres are DNA sequences that protect chromosomes from damage and shorten with age. Telomere-related genes (TRGs) may play a role in AD's pathogenesis. OBJECTIVES To identify TRGs related to aging clusters in AD patients, explore their immunological characteristics, and build a TRG-based prediction model for AD and AD subtypes. METHODS We analyzed the gene expression profiles of 97 AD samples from the GSE132903 dataset, using aging-related genes (ARGs) as clustering variables. We also assessed immune-cell infiltration in each cluster. We performed a weighted gene co-expression network analysis to identify cluster-specific differentially expressed TRGs. We compared four machine-learning models (random forest, generalized linear model [GLM], gradient boosting model, and support vector machine) for predicting AD and AD subtypes based on TRGs and validated TRGs by conducting an artificial neural network (ANN) analysis and a nomogram model. RESULTS We identified two aging clusters in AD patients with distinct immunological features: Cluster A had higher immune scores than Cluster B. Cluster A and the immune system are intimately associated, and this association could affect immunological function and result in AD via the digestive system. The GLM predicted AD and AD subtypes most accurately and was validated by the ANN analysis and nomogram model. CONCLUSION Our analyses revealed novel TRGs associated with aging clusters in AD patients and their immunological characteristics. We also developed a promising prediction model based on TRGs for assessing AD risk.
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Affiliation(s)
- Yang Ruan
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan
| | - Weichao Lv
- Sino-Jan Joint Lab of Natural Health Products Research, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Shuaiyu Li
- Saigo Laboratory, School of Information Science, Kyushu University, Fukuoka, 819-0395, Japan
| | - Yuzhong Cheng
- Joint Graduate School of Mathematics for Innovation, Kyushu University, Fukuoka, 819-0395, Japan
| | - Duanyang Wang
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan
| | - Chaofeng Zhang
- Sino-Jan Joint Lab of Natural Health Products Research, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Kuniyoshi Shimizu
- Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan.
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18
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Panes J, Nguyen TKO, Gao H, Christensen TA, Stojakovic A, Trushin S, Salisbury JL, Fuentealba J, Trushina E. Partial Inhibition of Complex I Restores Mitochondrial Morphology and Mitochondria-ER Communication in Hippocampus of APP/PS1 Mice. Cells 2023; 12:1111. [PMID: 37190020 PMCID: PMC10137328 DOI: 10.3390/cells12081111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/29/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023] Open
Abstract
Alzheimer's disease (AD) has no cure. Earlier, we showed that partial inhibition of mitochondrial complex I (MCI) with the small molecule CP2 induces an adaptive stress response, activating multiple neuroprotective mechanisms. Chronic treatment reduced inflammation, Aβ and pTau accumulation, improved synaptic and mitochondrial functions, and blocked neurodegeneration in symptomatic APP/PS1 mice, a translational model of AD. Here, using serial block-face scanning electron microscopy (SBFSEM) and three-dimensional (3D) EM reconstructions combined with Western blot analysis and next-generation RNA sequencing, we demonstrate that CP2 treatment also restores mitochondrial morphology and mitochondria-endoplasmic reticulum (ER) communication, reducing ER and unfolded protein response (UPR) stress in the APP/PS1 mouse brain. Using 3D EM volume reconstructions, we show that in the hippocampus of APP/PS1 mice, dendritic mitochondria primarily exist as mitochondria-on-a-string (MOAS). Compared to other morphological phenotypes, MOAS have extensive interaction with the ER membranes, forming multiple mitochondria-ER contact sites (MERCS) known to facilitate abnormal lipid and calcium homeostasis, accumulation of Aβ and pTau, abnormal mitochondrial dynamics, and apoptosis. CP2 treatment reduced MOAS formation, consistent with improved energy homeostasis in the brain, with concomitant reductions in MERCS, ER/UPR stress, and improved lipid homeostasis. These data provide novel information on the MOAS-ER interaction in AD and additional support for the further development of partial MCI inhibitors as a disease-modifying strategy for AD.
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Affiliation(s)
- Jessica Panes
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology, Universidad de Concepcion, Concepción 4030000, Chile
| | | | - Huanyao Gao
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Trace A. Christensen
- Microscopy and Cell Analysis Core Facility, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Sergey Trushin
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jeffrey L. Salisbury
- Microscopy and Cell Analysis Core Facility, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jorge Fuentealba
- Department of Physiology, Universidad de Concepcion, Concepción 4030000, Chile
- Centro de Investigaciones Avanzadas en Biomedicina (CIAB-UdeC), Universidad de Concepción, Concepción 4030000, Chile
| | - Eugenia Trushina
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
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19
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Aloi MS, Prater KE, Sánchez REA, Beck A, Pathan JL, Davidson S, Wilson A, Keene CD, de la Iglesia H, Jayadev S, Garden GA. Microglia specific deletion of miR-155 in Alzheimer's disease mouse models reduces amyloid-β pathology but causes hyperexcitability and seizures. J Neuroinflammation 2023; 20:60. [PMID: 36879321 PMCID: PMC9990295 DOI: 10.1186/s12974-023-02745-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
Alzheimer's Disease (AD) is characterized by the accumulation of extracellular amyloid-β (Aβ) as well as CNS and systemic inflammation. Microglia, the myeloid cells resident in the CNS, use microRNAs to rapidly respond to inflammatory signals. MicroRNAs (miRNAs) modulate inflammatory responses in microglia, and miRNA profiles are altered in Alzheimer's disease (AD) patients. Expression of the pro-inflammatory miRNA, miR-155, is increased in the AD brain. However, the role of miR-155 in AD pathogenesis is not well-understood. We hypothesized that miR-155 participates in AD pathophysiology by regulating microglia internalization and degradation of Aβ. We used CX3CR1CreER/+ to drive-inducible, microglia-specific deletion of floxed miR-155 alleles in two AD mouse models. Microglia-specific inducible deletion of miR-155 in microglia increased anti-inflammatory gene expression while reducing insoluble Aβ1-42 and plaque area. Yet, microglia-specific miR-155 deletion led to early-onset hyperexcitability, recurring spontaneous seizures, and seizure-related mortality. The mechanism behind hyperexcitability involved microglia-mediated synaptic pruning as miR-155 deletion altered microglia internalization of synaptic material. These data identify miR-155 as a novel modulator of microglia Aβ internalization and synaptic pruning, influencing synaptic homeostasis in the setting of AD pathology.
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Affiliation(s)
- Macarena S Aloi
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, 98195, USA
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Katherine E Prater
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | | | - Asad Beck
- Department of Biology, University of Washington, Seattle, WA, 98109, USA
| | - Jasmine L Pathan
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Stephanie Davidson
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Angela Wilson
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, 98195, USA
| | | | - Suman Jayadev
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Gwenn A Garden
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, 98195, USA.
- Department of Neurology, University of North Carolina at Chapel Hill, 170 Manning Drive, Chapel Hill, NC, 27517, USA.
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20
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Kang X, Zhang K, Wang Y, Zhao Y, Lu Y. Single-cell RNA sequencing analysis of human chondrocytes reveals cell-cell communication alterations mediated by interactive signaling pathways in osteoarthritis. Front Cell Dev Biol 2023; 11:1099287. [PMID: 37082621 PMCID: PMC10112522 DOI: 10.3389/fcell.2023.1099287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/27/2023] [Indexed: 04/22/2023] Open
Abstract
Objective: Osteoarthritis (OA) is a common joint disorder characterized by degenerative articular cartilage, subchondral bone remodeling, and inflammation. Increasing evidence suggests that the substantial crosstalk between cartilage and synovium is closely related to Osteoarthritis development, but the events that cause this degeneration remain unknown. This study aimed to explore the alterations in intercellular communication involved in the pathogenesis of Osteoarthritis using bioinformatics analysis. Methods: Single-cell transcriptome sequencing (scRNA-seq) profiles derived from articular cartilage tissue of patients with Osteoarthritis were downloaded from a public database. Chondrocyte heterogeneity was assessed using computational analysis, and cell type identification and clustering analysis were performed using the "FindClusters" function in the Seurat package. Intercellular communication networks, including major signaling inputs and outputs for cells, were predicted, and analyzed using CellChat. Results: Seven molecularly defined chondrocytes clusters (homeostatic chondrocytes, hypertrophic chondrocyte (HTC), pre-HTC, regulatory chondrocytes, fibro-chondrocytes (FC), pre-FC, and reparative chondrocyte) with different compositions were identified in the damaged cartilage. Compared to those in the intact cartilage, the overall cell-cell communication frequency and communication strength were remarkably increased in the damaged cartilage. The cellular communication among chondrocyte subtypes mediated by signaling pathways, such as PTN, VISFATIN, SPP1, and TGF-β, was selectively altered in Osteoarthritis. Moreover, we verified that SPP1 pathway enrichment scores increased, but VISFATIN pathway enrichment scores decreased based on the bulk rna-seq datasets in Osteoarthritis. Conclusion: Our results revealed alterations in cell-cell communication among OA-related chondrocyte subtypes that were mediated by specific signaling pathways, which might be a crucial underlying mechanism associated with Osteoarthritis progression.
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Affiliation(s)
- Xin Kang
- Department of Orthopaedic Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi'an, Shaanxi, China
| | - Kailiang Zhang
- Department of Orthopedics, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong, China
| | - Yakang Wang
- Department of Orthopaedic Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi'an, Shaanxi, China
| | - Yang Zhao
- Department of Orthopaedic Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi'an, Shaanxi, China
- *Correspondence: Yao Lu, ; Yang Zhao,
| | - Yao Lu
- Department of Orthopaedic Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi'an, Shaanxi, China
- *Correspondence: Yao Lu, ; Yang Zhao,
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21
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Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O’Donnell K, Petyuk VA, Gauthier S, De Jager PL, Breitner J, Bennett DA. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity. SCIENCE ADVANCES 2022; 8:eabo6764. [PMID: 36399579 PMCID: PMC9674284 DOI: 10.1126/sciadv.abo6764] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.
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Affiliation(s)
- Yasser Iturria-Medina
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Quadri Adewale
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Ahmed F. Khan
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada
| | - Pedro Rosa-Neto
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, Canada
| | - Kieran O’Donnell
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
- Yale School of Medicine, New Haven, CT 06519, USA
| | - Vladislav A. Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, Canada
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - John Breitner
- Centre for Studies on Prevention of Alzheimer’s Disease (StoP-AD), Douglas Research Centre, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - 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
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22
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Toh K, Saunders D, Verd B, Steventon B. Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo. iScience 2022; 25:105216. [PMID: 36274939 PMCID: PMC9579027 DOI: 10.1016/j.isci.2022.105216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/05/2022] [Accepted: 09/22/2022] [Indexed: 11/30/2022] Open
Abstract
The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.
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Affiliation(s)
- Kane Toh
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Dillan Saunders
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Berta Verd
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
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23
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Pun FW, Leung GHD, Leung HW, Liu BHM, Long X, Ozerov IV, Wang J, Ren F, Aliper A, Izumchenko E, Moskalev A, de Magalhães JP, Zhavoronkov A. Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine. Aging (Albany NY) 2022; 14:2475-2506. [PMID: 35347083 PMCID: PMC9004567 DOI: 10.18632/aging.203960] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/06/2022] [Indexed: 11/25/2022]
Abstract
Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.
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Affiliation(s)
- Frank W. Pun
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Geoffrey Ho Duen Leung
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Hoi Wing Leung
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Bonnie Hei Man Liu
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Xi Long
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Ivan V. Ozerov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Ju Wang
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Feng Ren
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Alexander Aliper
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Evgeny Izumchenko
- Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL 60637, USA
| | - Alexey Moskalev
- School of Systems Biology, George Mason University (GMU), Fairfax, VA 22030, USA
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
- Buck Institute for Research on Aging, Novato, CA 94945, USA
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24
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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25
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Redefining microglia states: Lessons and limits of human and mouse models to study microglia states in neurodegenerative diseases. Semin Immunol 2022; 60:101651. [PMID: 36155944 DOI: 10.1016/j.smim.2022.101651] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/03/2022] [Indexed: 01/15/2023]
Abstract
Microglia are resident macrophages of the brain parenchyma and play an essential role in various aspects of brain development, plasticity, and homeostasis. With recent advances in single-cell RNA-sequencing, heterogeneous microglia transcriptional states have been identified in both animal models of neurodegenerative disorders and patients. However, the functional roles of these microglia states remain unclear; specifically, the question of whether individual states or combinations of states are protective or detrimental (or both) in the context of disease progression. To attempt to answer this, the field has largely relied on studies employing mouse models, human in vitro and chimeric models, and human post-mortem tissue, all of which have their caveats, but used in combination can enable new biological insight and validation of candidate disease pathways and mechanisms. In this review, we summarize our current understanding of disease-associated microglia states and phenotypes in neurodegenerative disorders, discuss important considerations when comparing mouse and human microglia states and functions, and identify areas of microglia biology where species differences might limit our understanding of microglia state.
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26
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Wang Q, Chen K, Su Y, Reiman EM, Dudley JT, Readhead B. Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease. Brain Commun 2022; 4:fcab293. [PMID: 34993477 PMCID: PMC8728025 DOI: 10.1093/braincomms/fcab293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/05/2021] [Accepted: 11/09/2021] [Indexed: 01/20/2023] Open
Abstract
Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer's disease neuropathology biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68, P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer's disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer's disease.
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Affiliation(s)
- Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Eric M Reiman
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA.,Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Joel T Dudley
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA.,Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Benjamin Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
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27
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Baloni P, Funk CC, Readhead B, Price ND. Systems modeling of metabolic dysregulation in neurodegenerative diseases. Curr Opin Pharmacol 2021; 60:59-65. [PMID: 34352486 PMCID: PMC8511060 DOI: 10.1016/j.coph.2021.06.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 06/28/2021] [Indexed: 02/07/2023]
Abstract
Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's diseases negatively impact both length and quality of life, due to lack of effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches, and integration with multiomics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multiomics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and predisease states.
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Affiliation(s)
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA, USA
| | - Ben Readhead
- Onegevity, a Division of Thorne HealthTech, New York, NY, USA; Arizona State University-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA; Onegevity, a Division of Thorne HealthTech, New York, NY, USA.
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28
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Brabec JL, Lara MK, Tyler AL, Mahoney JM. System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration. Front Genet 2021; 12:625246. [PMID: 33889174 PMCID: PMC8056044 DOI: 10.3389/fgene.2021.625246] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/22/2021] [Indexed: 12/11/2022] Open
Abstract
Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.
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Affiliation(s)
- Jeffrey L Brabec
- Department of Neurological Sciences, University of Vermont, Burlington, VT, United States
| | - Montana Kay Lara
- Department of Neurological Sciences, University of Vermont, Burlington, VT, United States
| | - Anna L Tyler
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - J Matthew Mahoney
- Department of Neurological Sciences, University of Vermont, Burlington, VT, United States.,The Jackson Laboratory, Bar Harbor, ME, United States
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