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      TREM2 brain transcript-specific studies in AD and TREM2 mutation carriers

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          Abstract

          Background

          Low frequency coding variants in TREM2 are associated with Alzheimer disease (AD) risk and cerebrospinal fluid (CSF) TREM2 protein levels are different between AD cases and controls. Similarly, TREM2 risk variant carriers also exhibit differential CSF TREM2 levels. TREM2 has three different alternative transcripts, but most of the functional studies only model the longest transcript. No studies have analyzed TREM2 expression levels or alternative splicing in brains from AD and cognitively normal individuals. We wanted to determine whether there was differential expression of TREM2 in sporadic-AD cases versus AD- TREM2 carriers vs sex- and aged-matched normal controls; and if this differential expression was due to a particular TREM2 transcript.

          Methods

          We analyzed RNA-Seq data from parietal lobe brain tissue from AD cases with TREM2 variants ( n = 33), AD cases ( n = 195) and healthy controls ( n = 118), from three independent datasets using Kallisto and the R package tximport to determine the read count for each transcript and quantified transcript abundance as transcripts per million.

          Results

          The three TREM2 transcripts were expressed in brain cortex in the three datasets. We demonstrate for the first time that the transcript that lacks the transmembrane domain and encodes a soluble form of TREM2 (sTREM2) has an expression level around 60% of the canonical transcript, suggesting that around 25% of the sTREM2 protein levels could be explained by this transcript. We did not observe a difference in the overall TREM2 expression level between cases and controls. However, the isoform which lacks the 5′ exon, but includes the transmembrane domain, was significantly lower in TREM2- p.R62H carriers than in AD cases ( p = 0.007).

          Conclusion

          Using bulk RNA-Seq data from three different cohorts, we were able to quantify the expression level of the three TREM2 transcripts, demonstrating: (1) all three transcripts of them are highly expressed in the human cortex, (2) that up to 25% of the sTREM2 may be due to the expression of a specific isoform and not TREM2 cleavage; and (3) that TREM2 risk variants do not affect expression levels, suggesting that the effect of the TREM2 variants on CSF levels occurs at post-transcriptional level.

          Electronic supplementary material

          The online version of this article (10.1186/s13024-019-0319-3) contains supplementary material, which is available to authorized users.

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          Most cited references65

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          Near-optimal probabilistic RNA-seq quantification.

          We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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            Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

            High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
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              TREM2 variants in Alzheimer's disease.

              Homozygous loss-of-function mutations in TREM2, encoding the triggering receptor expressed on myeloid cells 2 protein, have previously been associated with an autosomal recessive form of early-onset dementia. We used genome, exome, and Sanger sequencing to analyze the genetic variability in TREM2 in a series of 1092 patients with Alzheimer's disease and 1107 controls (the discovery set). We then performed a meta-analysis on imputed data for the TREM2 variant rs75932628 (predicted to cause a R47H substitution) from three genomewide association studies of Alzheimer's disease and tested for the association of the variant with disease. We genotyped the R47H variant in an additional 1887 cases and 4061 controls. We then assayed the expression of TREM2 across different regions of the human brain and identified genes that are differentially expressed in a mouse model of Alzheimer's disease and in control mice. We found significantly more variants in exon 2 of TREM2 in patients with Alzheimer's disease than in controls in the discovery set (P=0.02). There were 22 variant alleles in 1092 patients with Alzheimer's disease and 5 variant alleles in 1107 controls (P<0.001). The most commonly associated variant, rs75932628 (encoding R47H), showed highly significant association with Alzheimer's disease (P<0.001). Meta-analysis of rs75932628 genotypes imputed from genomewide association studies confirmed this association (P=0.002), as did direct genotyping of an additional series of 1887 patients with Alzheimer's disease and 4061 controls (P<0.001). Trem2 expression differed between control mice and a mouse model of Alzheimer's disease. Heterozygous rare variants in TREM2 are associated with a significant increase in the risk of Alzheimer's disease. (Funded by Alzheimer's Research UK and others.).
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                Author and article information

                Contributors
                del-aguila@wustl.edu
                babenitez@wustl.edu
                zeranli@wustl.edu
                udube@wustl.edu
                mihindu@wustl.edu
                budde@wustl.edu
                ffarias@wustl.edu
                fernandezv@wustl.edu
                ibanezl@wustl.edu
                jiangshan@wustl.edu
                rperrin@wustl.edu
                nigelcairns@hotmail.com
                jcmorris@wustl.edu
                harario@wustl.edu
                314-286-0546 , cruchagac@wustl.edu , ccruchaga@wustl.edu
                Journal
                Mol Neurodegener
                Mol Neurodegener
                Molecular Neurodegeneration
                BioMed Central (London )
                1750-1326
                8 May 2019
                8 May 2019
                2019
                : 14
                : 18
                Affiliations
                [1 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Department of Psychiatry, , Washington University School of Medicine, ; St. Louis, MO USA
                [2 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, NeuroGenomics and Informatics, , Washington University School of Medicine, ; St. Louis, MO USA
                [3 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Hope Center for Neurological Disorders, , Washington University School of Medicine, ; St. Louis, MO USA
                [4 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Knight Alzheimer’s Disease Research Center, , Washington University School of Medicine, ; St. Louis, MO USA
                [5 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Department of Neurology, , Washington University School of Medicine, ; St. Louis, MO USA
                [6 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Department of Pathology and Immunology, , Washington University School of Medicine, ; St. Louis, MO USA
                Author information
                http://orcid.org/0000-0002-0276-2899
                Article
                319
                10.1186/s13024-019-0319-3
                6505298
                31068200
                aa317743-c430-4c25-b01c-e7cc3c0fb0a8
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 November 2018
                : 26 April 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG044546
                Award ID: RF1AG053303
                Award ID: R01AG058501
                Award ID: U01AG058922
                Award ID: RF1AG058501
                Award ID: R01AG057777
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Neurosciences
                trem2,rnaseq,soluble trem2,r47h,alzheimer’s disease,brain transcripts,risk variants
                Neurosciences
                trem2, rnaseq, soluble trem2, r47h, alzheimer’s disease, brain transcripts, risk variants

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