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Transcriptome Analysis of Distinct Mouse Strains Reveals Kinesin Light Chain-1 Splicing As an Amyloid-Β Accumulation Modifier

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Transcriptome Analysis of Distinct Mouse Strains Reveals Kinesin Light Chain-1 Splicing As an Amyloid-Β Accumulation Modifier Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-β accumulation modifier Takashi Moriharaa,1,2, Noriyuki Hayashia,b,1, Mikiko Yokokojia,1, Hiroyasu Akatsuc, Michael A. Silvermana,d, Nobuyuki Kimurae, Masahiro Satoa, Yuhki Saitof, Toshiharu Suzukif, Kanta Yanagidaa, Takashi S. Kodamaa, Toshihisa Tanakaa, Masayasu Okochia, Shinji Tagamia, Hiroaki Kazuia, Takashi Kudoa, Ryota Hashimotoa,g, Naohiro Itoha, Kouhei Nishitomia, Yumi Yamaguchi-Kabatah, Tatsuhiko Tsunodai, Hironori Takamuraj, Taiichi Katayamaj, Ryo Kimuraa,k, Kouzin Kaminoa,l, Yoshio Hashizumec, and Masatoshi Takedaa aDepartment of Psychiatry, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan; bDepartment of Complementary and Alternative Medicine, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan; cChoju Medical Institute, Fukushimura Hospital, Aichi 441-8124, Japan; dDepartment of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada, V5A 1S6; eSection of Cell Biology and Pathology, Department of Alzheimer’s Disease Research, Center for Development of Advanced Medicine for Dementia, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan; fLaboratory of Neuroscience, Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan; gMolecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and Fukui University, Osaka 565-0871, Japan; hDepartment of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Miyagi 980-8575, Japan; iLaboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan; jDepartment of Child Development & Molecular Brain Science, United Graduate School of Child Development, Osaka University, Osaka 565-0871, Japan; kDepartment of Anatomy and Developmental Biology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan; and lNational Hospital Organization, Yamato Mental Medical Center, Nara 639-1042, Japan Edited by Robert W. Mahley, The J. David Gladstone Institutes, San Francisco, CA, and approved December 13, 2013 (received for review May 1, 2013) Alzheimer’s disease (AD) is characterized by the accumulation of These limitations can be resolved by using mice. Mice with amyloid-β (Aβ). The genes that govern this process, however, have a mixed genetic background prepared from inbred mouse strains remained elusive. To this end, we combined distinct mouse strains have simple genetic backgrounds, which drastically increase the with transcriptomics to directly identify disease-relevant genes. statistical power for the identification of disease-related genes We show that AD model mice (APP-Tg) with DBA/2 genetic back- (14). AD is a complex disease not only genetically but also, neu- grounds have significantly lower levels of Aβ accumulation compared ropathologically and symptomatically (11), with its clinical diagnosis with SJL and C57BL/6 mice. We then applied brain transcriptomics to often ambiguous. Although increased Aβ levels in the brain are reveal the genes in DBA/2 that suppress Aβ accumulation. To avoid central to the pathology of AD, Aβ levels are difficult to measure in β detecting secondarily affected genes by A , we used non-Tg mice humans. In contrast, Aβ levels can be directly measured in mice. β in the absence of A pathology and selected candidate genes dif- Furthermore, in human studies, although aging is the strongest risk ferently expressed in DBA/2 mice. Additional transcriptome analy- sis of APP-Tg mice with mixed genetic backgrounds revealed Significance kinesin light chain-1 (Klc1)asanAβ modifier, indicating a role for intracellular trafficking in Aβ accumulation. Aβ levels correlated with the expression levels of Klc1 splice variant E and the genotype Genetic studies of common complex human diseases, including ’ of Klc1 in these APP-Tg mice. In humans, the expression levels of Alzheimer s disease (AD), are extremely resource-intensive and KLC1 variant E in brain and lymphocyte were significantly higher in have struggled to identify genes that are causal in disease. AD patients compared with unaffected individuals. Finally, functional Combined with the costs of studies and the inability to identify analysis using neuroblastoma cells showed that overexpression or the missing heritability, particularly in AD, alternate strategies knockdown of KLC1 variant E increases or decreases the production warrant consideration. We devised a unique strategy that of Aβ, respectively. The identification of KLC1 variant E suggests that combines distinct mouse strains that vary naturally in amyloid- β the dysfunction of intracellular trafficking is a causative factor of Aβ production with transcriptomics to identify kinesin light (Klc1 β pathology. This unique combination of distinct mouse strains and chain-1 ) splice variant E as a modifier of amyloid- accu- model mice with transcriptomics is expected to be useful for the mulation, a causative factor of AD. In AD patients, the ex- KLC1 study of genetic mechanisms of other complex diseases. pression levels of variant E in brain were significantly higher compared with levels in unaffected individuals. The KLC1 mouse-to-human translation | alternative splicing identification of variant E suggests that dysfunction of intracellular trafficking is causative in AD. lzheimer’s disease (AD) is a common cause of dementia β β Author contributions: T.M. and M.T. designed research; T.M., N.H., M.Y., H.A., N.K., M.S., K.Y., Athat is characterized by the accumulation of amyloid- (A ) T.S.K., T. Tanaka, S.T., H.K., T. Kudo, R.H., H.T., T. Katayama, and Y.H. performed research; peptide. Its causes (especially of sporadic AD, which comprises T.M., N.H., M.Y., N.K., M.S., Y.S., T.S., K.Y., T.S.K., T. Tanaka, N.I., K.N., H.T., T. Katayama, R.K., the majority of AD cases), however, are still largely unknown, and and K.K. contributed new reagents/analytic tools; T.M., N.H., M.Y., H.A., N.K., M.S., Y.Y.-K., and T. Tsunoda analyzed data; and T.M., M.A.S., and M.O. wrote the paper. no efficient treatment exists. Since the first AD risk gene, apolio- The authors declare no conflict of interest. protein E (APOE), was identified, over 1,300 genetic studies have This article is a PNAS Direct Submission. been done (www.alzgene.org)(1),and∼10,000 human genomic samples have identified AD risk genes (2–8). Regardless, these Freely available online through the PNAS open access option. – Data deposition: The transcriptome datasets reported in this paper have been deposited genes cannot account for the estimated 60 80% hereditary risk of in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession AD (9). Also, they do not reveal their role in the cause of AD (10), no. GSE40330). because complex diseases, including AD, are often explained 1T.M., N.H., and M.Y. contributed equally to this work. by the heterogeneity of diseases, uncontrollable environmental 2To whom correspondence should be addressed. E-mail: [email protected]. factors, and the complexity of human genome variation, which This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. complicate conclusions from genome studies (11–13). 1073/pnas.1307345111/-/DCSupplemental. 2638–2643 | PNAS | February 18, 2014 | vol. 111 | no. 7 www.pnas.org/cgi/doi/10.1073/pnas.1307345111 Downloaded by guest on October 2, 2021 factor for AD, it is not practical to collect same age samples or shows that the central pathology of AD is modified by the control for environmental factors. Mice, however, can be aged in splicing of KLC1 and suggests that the combination of animal equally controlled environments and analyzed at exactly the same models and transcriptomics is an efficient approach to identify- age. Despite these significant advantages, most of the rodent ge- ing key genes in common complex diseases. nomic studies addressing human diseases, including AD, have not identified targets at the molecular level (15, 16). Thus, we applied Results transcriptomics: a straightforward approach to identify genes DBA/2 Genetic Backgrounds Suppress Aβ Levels in AD Model Mice. To compared with conventional genome studies based on linkage examine the impact on Aβ accumulation by genetic background, disequilibrium between markers (17). we prepared amyloid precursor protein (APP)-Tg mice with We first generated mice with different genetic backgrounds mixed genetic backgrounds by crossing the Tg2576 mice with the that accumulated varying amounts of Aβ. Then, instead of using phenotypically distinct strains C57BL/6 (B6), SJL, and DBA/2 standard genetic approaches, we performed genome-wide tran- (DBA). We obtained six groups of APP-Tg mice, and each group scriptome analysis on the mice. We identified a specific splice contained different mixture ratios of the three strains in their form of kinesin light chain-1 (Klc1), variant E, as a modifier of genetic background (Fig. 1A). We analyzed these APP-Tg mice the Aβ accumulation. Notably, the transcript levels of KLC1 at 12 mo of age to assess the effects on Aβ accumulation by variant E were significantly higher in pathologically diagnosed genetic background (n = 59). The levels of Aβ40 and Aβ42 in AD patients with confirmed levels of excessive Aβ compared a 1% Triton-X (Fig. 1 B–D) and 6 M guanidine HCl (GuHCl) with controls. A functional role for KLC1 variant E was shown by (Fig. 1 E–G) fraction from brain were measured by ELISA. The manipulating its expression levels in neuroblastoma cells and levels of Aβ ranged more than 10-fold, and the mice carrying showing that this variant can modulate Aβ production. This study DBA alleles (dark blue and light blue) had lower amounts of Aβ A **** * BC4.0 4.4 4.2 * 4.0 3.5 3.8 (Aβ40) 10 3.6 3.4 Log 3.2 3.0 fracon Triton-X 0% 50% 75% (Aβ42) DBA Fig. 1. Effects of the genetic background on Aβ 10 D 2.5 accumulation in APP-Tg mouse brain.
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