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      Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level

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          Abstract

          The biological processes that are disrupted in the Alzheimer’s disease (AD) brain remain incompletely understood. In this study, we analyzed the proteomes of more than 1,000 brain tissues to reveal new AD-related protein co-expression modules that were highly preserved across cohorts and brain regions. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism and a module related to the matrisome. The matrisome module was influenced by the APOE ε4 allele but was not related to the rate of cognitive decline after adjustment for neuropathology. By contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.

          Abstract

          The authors analyzed the levels of more than 8,600 proteins across more than 1,000 brain tissues to arrive at a consensus AD brain protein co-expression network that illustrates the complexity and multiple pathological processes that occur in AD, many of which are not reflected at the RNA level.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

              In 2011, the National Institute on Aging and Alzheimer’s Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer’s Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer’s Association Research Framework, Alzheimer’s disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.
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                Author and article information

                Contributors
                erik.c.b.johnson@emory.edu
                alevey@emory.edu
                nseyfri@emory.edu
                Journal
                Nat Neurosci
                Nat Neurosci
                Nature Neuroscience
                Nature Publishing Group US (New York )
                1097-6256
                1546-1726
                3 February 2022
                3 February 2022
                2022
                : 25
                : 2
                : 213-225
                Affiliations
                [1 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Goizueta Alzheimer’s Disease Research Center, , Emory University School of Medicine, ; Atlanta, GA USA
                [2 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Neurology, , Emory University School of Medicine, ; Atlanta, GA USA
                [3 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Biochemistry, , Emory University School of Medicine, ; Atlanta, GA USA
                [4 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Genetics, , Emory University School of Medicine, ; Atlanta, GA USA
                [5 ]GRID grid.414208.b, ISNI 0000 0004 0619 8759, Banner Sun Health Research Institute, ; Sun City, AZ USA
                [6 ]GRID grid.240871.8, ISNI 0000 0001 0224 711X, Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, ; Memphis, TN USA
                [7 ]GRID grid.240871.8, ISNI 0000 0001 0224 711X, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, ; Memphis, TN USA
                [8 ]GRID grid.413734.6, ISNI 0000 0000 8499 1112, Center for Translational & Computational Neuroimmunology, Department of Neurology, Taub Institute, Columbia University Irving Medical Center, New York Presbyterian Hospital, ; New York, NY USA
                [9 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [10 ]GRID grid.274295.f, ISNI 0000 0004 0420 1184, James J. Peters VA Medical Center MIRECC, ; Bronx, NY USA
                [11 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Genetics and Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [12 ]GRID grid.240684.c, ISNI 0000 0001 0705 3621, Rush Alzheimer’s Disease Center, , Rush University Medical Center, ; Chicago, IL USA
                [13 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Pathology and Laboratory Medicine, , Emory University School of Medicine, ; Atlanta, GA USA
                [14 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Psychiatry, , Emory University School of Medicine, ; Atlanta, GA USA
                [15 ]GRID grid.414026.5, ISNI 0000 0004 0419 4084, Division of Mental Health, Atlanta VA Medical Center, ; Atlanta, GA USA
                Author information
                http://orcid.org/0000-0002-0604-2944
                http://orcid.org/0000-0003-2947-7606
                http://orcid.org/0000-0002-3986-352X
                http://orcid.org/0000-0003-3480-3234
                http://orcid.org/0000-0002-4792-8952
                http://orcid.org/0000-0003-0472-7648
                http://orcid.org/0000-0002-8057-2505
                http://orcid.org/0000-0001-5860-2512
                http://orcid.org/0000-0002-7679-6282
                http://orcid.org/0000-0002-6360-6726
                http://orcid.org/0000-0002-3153-502X
                http://orcid.org/0000-0002-4507-624X
                Article
                999
                10.1038/s41593-021-00999-y
                8825285
                35115731
                9f57d0b8-e3ad-4bb5-a717-a0f3b47ec836
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 March 2021
                : 8 December 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: K08AG068604
                Award ID: P30AG19610
                Award ID: RF1AG057440
                Award ID: U01AG046170
                Award ID: R01AG057911
                Award ID: P30AG10161
                Award ID: R01AG15819
                Award ID: R01AG17917
                Award ID: U01AG61356
                Award ID: R01AG056533
                Award ID: U54AG065187
                Award ID: U01AG061357
                Award ID: RF1AG057470
                Award ID: R01AG01581
                Award ID: R01AG061800
                Award ID: R01AG053960
                Award ID: RF1AG062181
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: U24NS072026
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100007306, Arizona Department of Health Services (ADHS);
                Award ID: 211002
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100008335, ADHS | Arizona Biomedical Research Commission (ABRC);
                Award ID: 4001, 0011, 05-901 and 1001
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000738, U.S. Department of Veterans Affairs (Department of Veterans Affairs);
                Award ID: BX005219
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000864, Michael J. Fox Foundation for Parkinson’s Research (Michael J. Fox Foundation);
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                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                Neurosciences
                biochemical networks,alzheimer's disease,mass spectrometry,proteomic analysis
                Neurosciences
                biochemical networks, alzheimer's disease, mass spectrometry, proteomic analysis

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