Publications by authors named "Alexandre Amlie-Wolf"

17 Publications

  • Page 1 of 1

Using INFERNO to Infer the Molecular Mechanisms Underlying Noncoding Genetic Associations.

Methods Mol Biol 2021 ;2254:73-91

Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

The INFERNO method provides an integrative computational framework for characterizing the causal variants, tissue contexts, affected regulatory mechanisms, and target genes underlying noncoding genetic variants associated with any phenotype or disease of interest. Here we describe the computational steps required to run the full INFERNO pipeline on any dataset of interest.
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http://dx.doi.org/10.1007/978-1-0716-1158-6_6DOI Listing
March 2021

Author Correction: An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer's disease.

Nat Genet 2020 Nov;52(11):1266

Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41588-020-00733-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145746PMC
November 2020

An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer's disease.

Nat Genet 2020 10 28;52(10):1024-1035. Epub 2020 Sep 28.

Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Protein aggregation is the hallmark of neurodegeneration, but the molecular mechanisms underlying late-onset Alzheimer's disease (AD) are unclear. Here we integrated transcriptomic, proteomic and epigenomic analyses of postmortem human brains to identify molecular pathways involved in AD. RNA sequencing analysis revealed upregulation of transcription- and chromatin-related genes, including the histone acetyltransferases for H3K27ac and H3K9ac. An unbiased proteomic screening singled out H3K27ac and H3K9ac as the main enrichments specific to AD. In turn, epigenomic profiling revealed gains in the histone H3 modifications H3K27ac and H3K9ac linked to transcription, chromatin and disease pathways in AD. Increasing genome-wide H3K27ac and H3K9ac in a fly model of AD exacerbated amyloid-β42-driven neurodegeneration. Together, these findings suggest that AD involves a reconfiguration of the epigenome, wherein H3K27ac and H3K9ac affect disease pathways by dysregulating transcription- and chromatin-gene feedback loops. The identification of this process highlights potential epigenetic strategies for early-stage disease treatment.
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http://dx.doi.org/10.1038/s41588-020-0696-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8098004PMC
October 2020

SparkINFERNO: a scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants.

Bioinformatics 2020 06;36(12):3879-3881

Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center.

Summary: We report Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants (SparkINFERNO), a scalable bioinformatics pipeline characterizing non-coding genome-wide association study (GWAS) association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWASs and show that SparkINFERNO is more than 60 times efficient and scales with data size and amount of computational resources.

Availability And Implementation: SparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno.

Contact: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa246DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320617PMC
June 2020

HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions.

NAR Genom Bioinform 2020 Jun 31;2(2):lqaa022. Epub 2020 Mar 31.

Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.

Most regulatory chromatin interactions are mediated by various transcription factors (TFs) and involve physically interacting elements such as enhancers, insulators or promoters. To map these elements and interactions at a fine scale, we developed HIPPIE2 that analyzes raw reads from high-throughput chromosome conformation (Hi-C) experiments to identify precise loci of DNA physically interacting regions (PIRs). Unlike standard genome binning approaches (e.g. 10-kb to 1-Mb bins), HIPPIE2 dynamically infers the physical locations of PIRs using the distribution of restriction sites to increase analysis precision and resolution. We applied HIPPIE2 to Hi-C datasets across six human cell lines (GM12878, IMR90, K562, HMEC, HUVEC, NHEK) with matched ENCODE/Roadmap functional genomic data. HIPPIE2 detected 1042 738 distinct PIRs, with high resolution (average PIR length of 1006 bp) and high reproducibility (92.3% in GM12878). PIRs are enriched for epigenetic marks (H3K27ac, H3K4me1) and open chromatin, suggesting active regulatory roles. HIPPIE2 identified 2.8 million significant PIR-PIR interactions, 27.2% of which were enriched for TF binding sites. 50 608 interactions were enhancer-promoter interactions and were enriched for 33 TFs, including known DNA looping/long-range mediators. These findings demonstrate that the novel dynamic approach of HIPPIE2 (https://bitbucket.com/wanglab-upenn/HIPPIE2) enables the characterization of chromatin and regulatory interactions with high resolution and reproducibility.
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http://dx.doi.org/10.1093/nargab/lqaa022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106622PMC
June 2020

Activity of the poly(A) binding protein MSUT2 determines susceptibility to pathological tau in the mammalian brain.

Sci Transl Med 2019 12;11(523)

Geriatric Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA 98108, USA.

Brain lesions composed of pathological tau help to drive neurodegeneration in Alzheimer's disease (AD) and related tauopathies. Here, we identified the mammalian suppressor of tauopathy 2 () gene as a modifier of susceptibility to tau toxicity in two mouse models of tauopathy. Transgenic PS19 mice overexpressing tau, a model of AD, and lacking the gene exhibited decreased learning and memory deficits, reduced neurodegeneration, and reduced accumulation of pathological tau compared to PS19 tau transgenic mice expressing Conversely, overexpression in 4RTauTg2652 tau transgenic mice increased pathological tau deposition and promoted the neuroinflammatory response to pathological tau. MSUT2 is a poly(A) RNA binding protein that antagonizes the canonical nuclear poly(A) binding protein PABPN1. In individuals with AD, MSUT2 abundance in postmortem brain tissue predicted an earlier age of disease onset. Postmortem AD brain tissue samples with normal amounts of MSUT2 showed elevated neuroinflammation associated with tau pathology. We observed co-depletion of MSUT2 and PABPN1 in postmortem brain samples from a subset of AD cases with higher tau burden and increased neuronal loss. This suggested that MSUT2 and PABPN1 may act together in a macromolecular complex bound to poly(A) RNA. Although MSUT2 and PABPN1 had opposing effects on both tau aggregation and poly(A) RNA tail length, we found that increased poly(A) tail length did not ameliorate tauopathy, implicating other functions of the MSUT2/PABPN1 complex in tau proteostasis. Our findings implicate poly(A) RNA binding proteins both as modulators of pathological tau toxicity in AD and as potential molecular targets for interventions to slow neurodegeneration in tauopathies.
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http://dx.doi.org/10.1126/scitranslmed.aao6545DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7311111PMC
December 2019

Inferring the Molecular Mechanisms of Noncoding Alzheimer's Disease-Associated Genetic Variants.

J Alzheimers Dis 2019 ;72(1):301-318

Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Most of the loci identified by genome-wide association studies (GWAS) for late-onset Alzheimer's disease (LOAD) are in strong linkage disequilibrium (LD) with nearby variants all of which could be the actual functional variants, often in non-protein-coding regions and implicating underlying gene regulatory mechanisms. We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying these associations. We applied our INFERNO algorithm to the top 19 non-APOE loci from the IGAP GWAS study. INFERNO annotated all LD-expanded variants at each locus with tissue-specific regulatory activity. Bayesian co-localization analysis of summary statistics and eQTL data was performed to identify tissue-specific target genes. INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). In several cases, we identified dysregulation of long noncoding RNA (lncRNA) transcripts and applied the lncRNA target identification algorithm from INFERNO to characterize their downstream biological effects. We also validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. By integrating functional genomics with GWAS signals, our analysis yielded insights into the regulatory mechanisms, tissue contexts, genes, and biological processes affected by noncoding genetic variation associated with LOAD risk.
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http://dx.doi.org/10.3233/JAD-190568DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7316086PMC
November 2020

Loss of Nuclear TDP-43 Is Associated with Decondensation of LINE Retrotransposons.

Cell Rep 2019 04;27(5):1409-1421.e6

Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address:

Loss of the nuclear RNA binding protein TAR DNA binding protein-43 (TDP-43) into cytoplasmic aggregates is the strongest correlate to neurodegeneration in amyotrophic lateral sclerosis and frontotemporal degeneration. The molecular changes associated with the loss of nuclear TDP-43 in human tissues are not entirely known. Using subcellular fractionation and fluorescent-activated cell sorting to enrich for diseased neuronal nuclei without TDP-43 from post-mortem frontotemporal degeneration-amyotrophic lateral sclerosis (FTD-ALS) human brain, we characterized the effects of TDP-43 loss on the transcriptome and chromatin accessibility. Nuclear TDP-43 loss is associated with gene expression changes that affect RNA processing, nucleocytoplasmic transport, histone processing, and DNA damage. Loss of nuclear TDP-43 is also associated with chromatin decondensation around long interspersed nuclear elements (LINEs) and increased LINE1 DNA content. Moreover, loss of TDP-43 leads to increased retrotransposition that can be inhibited with antiretroviral drugs, suggesting that TDP-43 neuropathology is associated with altered chromatin structure including decondensation of LINEs.
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http://dx.doi.org/10.1016/j.celrep.2019.04.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6508629PMC
April 2019

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.

Nat Genet 2019 03 28;51(3):414-430. Epub 2019 Feb 28.

Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades-Universitat Internacional de Catalunya, Barcelona, Spain.

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.
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http://dx.doi.org/10.1038/s41588-019-0358-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6463297PMC
March 2019

DASHR 2.0: integrated database of human small non-coding RNA genes and mature products.

Bioinformatics 2019 03;35(6):1033-1039

Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine.

Motivation: Small non-coding RNAs (sncRNAs, <100 nts) are highly abundant RNAs that regulate diverse and often tissue-specific cellular processes by associating with transcription factor complexes or binding to mRNAs. While thousands of sncRNA genes exist in the human genome, no single resource provides searchable, unified annotation, expression and processing information for full sncRNA transcripts and mature RNA products derived from these larger RNAs.

Results: Our goal is to establish a complete catalog of annotation, expression, processing, conservation, tissue-specificity and other biological features for all human sncRNA genes and mature products derived from all major RNA classes. DASHR (Database of small human non-coding RNAs) v2.0 database is the first that integrates human sncRNA gene and mature products profiles obtained from multiple RNA-seq protocols. Altogether, 185 tissues/cell types and sncRNA annotations and >800 curated experiments from ENCODE and GEO/SRA across multiple RNA-seq protocols for both GRCh38/hg38 and GRCh37/hg19 assemblies are integrated in DASHR. Moreover, DASHR is the first to contain both known and novel, previously un-annotated sncRNA loci identified by unsupervised segmentation (13 times more loci with 1 678 800 total). Additionally, DASHR v2.0 adds >3 200 000 annotations for non-small RNA genes and other genomic features (long-noncoding RNAs, mRNAs, promoters, repeats). Furthermore, DASHR v2.0 introduces an enhanced user interface, interactive experiment-by-locus table view, sncRNA locus sorting and filtering by biological features. All annotation and expression information directly downloadable and accessible as UCSC genome browser tracks.

Availability And Implementation: DASHR v2.0 is freely available at https://lisanwanglab.org/DASHRv2.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty709DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419920PMC
March 2019

INFERNO: inferring the molecular mechanisms of noncoding genetic variants.

Nucleic Acids Res 2018 09;46(17):8740-8753

Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, affecting regulatory elements including transcriptional enhancers. However, characterizing their effects requires the integration of GWAS results with context-specific regulatory activity and linkage disequilibrium annotations to identify causal variants underlying noncoding association signals and the regulatory elements, tissue contexts, and target genes they affect. We propose INFERNO, a novel method which integrates hundreds of functional genomics datasets spanning enhancer activity, transcription factor binding sites, and expression quantitative trait loci with GWAS summary statistics. INFERNO includes novel statistical methods to quantify empirical enrichments of tissue-specific enhancer overlap and to identify co-regulatory networks of dysregulated long noncoding RNAs (lncRNAs). We applied INFERNO to two large GWAS studies. For schizophrenia (36,989 cases, 113,075 controls), INFERNO identified putatively causal variants affecting brain enhancers for known schizophrenia-related genes. For inflammatory bowel disease (IBD) (12,882 cases, 21,770 controls), INFERNO found enrichments of immune and digestive enhancers and lncRNAs involved in regulation of the adaptive immune response. In summary, INFERNO comprehensively infers the molecular mechanisms of causal noncoding variants, providing a sensitive hypothesis generation method for post-GWAS analysis. The software is available as an open source pipeline and a web server.
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http://dx.doi.org/10.1093/nar/gky686DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158604PMC
September 2018

SPAR: small RNA-seq portal for analysis of sequencing experiments.

Nucleic Acids Res 2018 07;46(W1):W36-W42

Penn Neurodegeneration Genomics Center, University of Pennsylvania, Philadelphia, PA 19104, USA.

The introduction of new high-throughput small RNA sequencing protocols that generate large-scale genomics datasets along with increasing evidence of the significant regulatory roles of small non-coding RNAs (sncRNAs) have highlighted the urgent need for tools to analyze and interpret large amounts of small RNA sequencing data. However, it remains challenging to systematically and comprehensively discover and characterize sncRNA genes and specifically-processed sncRNA products from these datasets. To fill this gap, we present Small RNA-seq Portal for Analysis of sequencing expeRiments (SPAR), a user-friendly web server for interactive processing, analysis, annotation and visualization of small RNA sequencing data. SPAR supports sequencing data generated from various experimental protocols, including smRNA-seq, short total RNA sequencing, microRNA-seq, and single-cell small RNA-seq. Additionally, SPAR includes publicly available reference sncRNA datasets from our DASHR database and from ENCODE across 185 human tissues and cell types to produce highly informative small RNA annotations across all major small RNA types and other features such as co-localization with various genomic features, precursor transcript cleavage patterns, and conservation. SPAR allows the user to compare the input experiment against reference ENCODE/DASHR datasets. SPAR currently supports analyses of human (hg19, hg38) and mouse (mm10) sequencing data. SPAR is freely available at https://www.lisanwanglab.org/SPAR.
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http://dx.doi.org/10.1093/nar/gky330DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030839PMC
July 2018

Publisher Correction: Dysregulation of the epigenetic landscape of normal aging in Alzheimer's disease.

Nat Neurosci 2018 Jul;21(7):1018

Epigenetics Program, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

In the version of this article initially published online, the fifth author's name was given as Alexander Amlie-Wolf. The correct name is Alexandre Amlie-Wolf. The error has been corrected in the print, PDF and HTML versions of this article.
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http://dx.doi.org/10.1038/s41593-018-0124-2DOI Listing
July 2018

Dysregulation of the epigenetic landscape of normal aging in Alzheimer's disease.

Nat Neurosci 2018 04 5;21(4):497-505. Epub 2018 Mar 5.

Epigenetics Program, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Aging is the strongest risk factor for Alzheimer's disease (AD), although the underlying mechanisms remain unclear. The chromatin state, in particular through the mark H4K16ac, has been implicated in aging and thus may play a pivotal role in age-associated neurodegeneration. Here we compare the genome-wide enrichment of H4K16ac in the lateral temporal lobe of AD individuals against both younger and elderly cognitively normal controls. We found that while normal aging leads to H4K16ac enrichment, AD entails dramatic losses of H4K16ac in the proximity of genes linked to aging and AD. Our analysis highlights the presence of three classes of AD-related changes with distinctive functional roles. Furthermore, we discovered an association between the genomic locations of significant H4K16ac changes with genetic variants identified in prior AD genome-wide association studies and with expression quantitative trait loci. Our results establish the basis for an epigenetic link between aging and AD.
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http://dx.doi.org/10.1038/s41593-018-0101-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124498PMC
April 2018

DASHR: database of small human noncoding RNAs.

Nucleic Acids Res 2016 Jan 8;44(D1):D216-22. Epub 2015 Nov 8.

Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA Institute on Aging, University of Pennsylvania, Philadelphia, PA 19104, USA

Small non-coding RNAs (sncRNAs) are highly abundant RNAs, typically <100 nucleotides long, that act as key regulators of diverse cellular processes. Although thousands of sncRNA genes are known to exist in the human genome, no single database provides searchable, unified annotation, and expression information for full sncRNA transcripts and mature RNA products derived from these larger RNAs. Here, we present the Database of small human noncoding RNAs (DASHR). DASHR contains the most comprehensive information to date on human sncRNA genes and mature sncRNA products. DASHR provides a simple user interface for researchers to view sequence and secondary structure, compare expression levels, and evidence of specific processing across all sncRNA genes and mature sncRNA products in various human tissues. DASHR annotation and expression data covers all major classes of sncRNAs including microRNAs (miRNAs), Piwi-interacting (piRNAs), small nuclear, nucleolar, cytoplasmic (sn-, sno-, scRNAs, respectively), transfer (tRNAs), and ribosomal RNAs (rRNAs). Currently, DASHR (v1.0) integrates 187 smRNA high-throughput sequencing (smRNA-seq) datasets with over 2.5 billion reads and annotation data from multiple public sources. DASHR contains annotations for ∼ 48,000 human sncRNA genes and mature sncRNA products, 82% of which are expressed in one or more of the curated tissues. DASHR is available at http://lisanwanglab.org/DASHR.
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http://dx.doi.org/10.1093/nar/gkv1188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702848PMC
January 2016

Transcriptomic Changes Due to Cytoplasmic TDP-43 Expression Reveal Dysregulation of Histone Transcripts and Nuclear Chromatin.

PLoS One 2015 28;10(10):e0141836. Epub 2015 Oct 28.

Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.

TAR DNA-binding protein 43 (TDP-43) is normally a nuclear RNA-binding protein that exhibits a range of functions including regulation of alternative splicing, RNA trafficking, and RNA stability. However, in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP), TDP-43 is abnormally phosphorylated, ubiquitinated, and cleaved, and is mislocalized to the cytoplasm where it forms distinctive aggregates. We previously developed a mouse model expressing human TDP-43 with a mutation in its nuclear localization signal (ΔNLS-hTDP-43) so that the protein preferentially localizes to the cytoplasm. These mice did not exhibit a significant number of cytoplasmic aggregates, but did display dramatic changes in gene expression as measured by microarray, suggesting that cytoplasmic TDP-43 may be associated with a toxic gain-of-function. Here, we analyze new RNA-sequencing data from the ΔNLS-hTDP-43 mouse model, together with published RNA-sequencing data obtained previously from TDP-43 antisense oligonucleotide (ASO) knockdown mice to investigate further the dysregulation of gene expression in the ΔNLS model. This analysis reveals that the transcriptomic effects of the overexpression of the ΔNLS-hTDP-43 transgene are likely due to a gain of cytoplasmic function. Moreover, cytoplasmic TDP-43 expression alters transcripts that regulate chromatin assembly, the nucleolus, lysosomal function, and histone 3' untranslated region (UTR) processing. These transcriptomic alterations correlate with observed histologic abnormalities in heterochromatin structure and nuclear size in transgenic mouse and human brains.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141836PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4624943PMC
June 2016
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