Differential transcript usage in the Parkinson’s disease brain

English version

Autoři: Fiona Dick ^aff001; Gonzalo S. Nido ^aff001; Guido Werner Alves ^aff003; Ole-Bjørn Tysnes ^aff001; Gry Hilde Nilsen ^aff001; Christian Dölle ^aff001; Charalampos Tzoulis ^aff001
Působiště autorů: Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway ^aff001; Department of Clinical Medicine, University of Bergen, Bergen, Norway ^aff002; The Norwegian Center for Movement Disorders and Department of Neurology, Stavanger University Hospital, Stavanger, Norway ^aff003; Department of Mathematics and Natural Sciences, University of Stavanger, Stavanger, Norway ^aff004
Vyšlo v časopise: Differential transcript usage in the Parkinson’s disease brain. PLoS Genet 16(11): e1009182. doi:10.1371/journal.pgen.1009182
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009182

Souhrn

Studies of differential gene expression have identified several molecular signatures and pathways associated with Parkinson’s disease (PD). The role of isoform switches and differential transcript usage (DTU) remains, however, unexplored. Here, we report the first genome-wide study of DTU in PD. We performed RNA sequencing following ribosomal RNA depletion in prefrontal cortex samples of 49 individuals from two independent case-control cohorts. DTU was assessed using two transcript-count based approaches, implemented in the DRIMSeq and DEXSeq tools. Multiple PD-associated DTU events were detected in each cohort, of which 23 DTU events in 19 genes replicated across both patient cohorts. For several of these, including THEM5, SLC16A1 and BCHE, DTU was predicted to have substantial functional consequences, such as altered subcellular localization or switching to non-protein coding isoforms. Furthermore, genes with PD-associated DTU were enriched in functional pathways previously linked to PD, including reactive oxygen species generation and protein homeostasis. Importantly, the vast majority of genes exhibiting DTU were not differentially expressed at the gene-level and were therefore not identified by conventional differential gene expression analysis. Our findings provide the first insight into the DTU landscape of PD and identify novel disease-associated genes. Moreover, we show that DTU may have important functional consequences in the PD brain, since it is predicted to alter the functional composition of the proteome. Based on these results, we propose that DTU analysis is an essential complement to differential gene expression studies in order to provide a more accurate and complete picture of disease-associated transcriptomic alterations.

Klíčová slova:

Brain diseases – DNA-binding proteins – Gene expression – Introns – Mitochondria – Parkinson disease – RNA sequencing – Transcriptome analysis

Zdroje

1. Tysnes OB, Storstein A. Epidemiology of Parkinson’s disease. Journal of Neural Transmission. 2017;124(8):901–905. doi: 10.1007/s00702-017-1686-y 28150045

2. Borrageiro G, Haylett W, Seedat S, Kuivaniemi H, Bardien S. A review of genome-wide transcriptomics studies in Parkinson’s disease. European Journal of Neuroscience. 2018;47(1):1–16. 29068110

3. Elkon R, Ugalde AP, Agami R. Alternative cleavage and polyadenylation: extent, regulation and function. Nature Reviews Genetics. 2013;14(7):496. doi: 10.1038/nrg3482 23774734

4. Gruber AJ, Zavolan M. Alternative cleavage and polyadenylation in health and disease. Nature Reviews Genetics. 2019; p. 1. 31267064

5. Reyes A, Huber W. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. Nucleic acids research. 2017;46(2):582–592. doi: 10.1093/nar/gkx1165

6. Soneson C, Matthes KL, Nowicka M, Law CW, Robinson MD. Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage. Genome biology. 2016;17(1):12. doi: 10.1186/s13059-015-0862-3 26813113

7. Hefti MM, Farrell K, Kim S, Bowles KR, Fowkes ME, Raj T, et al. High-resolution temporal and regional mapping of MAPT expression and splicing in human brain development. PloS one. 2018;13(4):e0195771. doi: 10.1371/journal.pone.0195771 29634760

8. Vitting-Seerup K, Sandelin A. The landscape of isoform switches in human cancers. Molecular Cancer Research. 2017;15(9):1206–1220. doi: 10.1158/1541-7786.MCR-16-0459 28584021

9. Lin L, Park JW, Ramachandran S, Zhang Y, Tseng YT, Shen S, et al. Transcriptome sequencing reveals aberrant alternative splicing in Huntington’s disease. Human molecular genetics. 2016;25(16):3454–3466. doi: 10.1093/hmg/ddw187 27378699

10. Rhinn H, Qiang L, Yamashita T, Rhee D, Zolin A, Vanti W, et al. Alternative α-synuclein transcript usage as a convergent mechanism in Parkinson’s disease pathology. Nature communications. 2012;3:1084. doi: 10.1038/ncomms2032 23011138

11. La Cognata V, D’Agata V, Cavalcanti F, Cavallaro S. Splicing: is there an alternative contribution to Parkinson’s disease? Neurogenetics. 2015;16(4):245–263. doi: 10.1007/s10048-015-0449-x 25980689

12. Beyer K, Domingo-Sàbat M, Humbert J, Carrato C, Ferrer I, Ariza A. Differential expression of alpha-synuclein, parkin, and synphilin-1 isoforms in Lewy body disease. Neurogenetics. 2008;9(3):163–172. doi: 10.1007/s10048-008-0124-6 18335262

13. Humbert J, Beyer K, Carrato C, Mate JL, Ferrer I, Ariza A. Parkin and synphilin-1 isoform expression changes in Lewy body diseases. Neurobiology of disease. 2007;26(3):681–687. doi: 10.1016/j.nbd.2007.03.007 17467279

14. Lin X, Cook TJ, Zabetian CP, Leverenz JB, Peskind ER, Hu SC, et al. DJ-1 isoforms in whole blood as potential biomarkers of Parkinson disease. Scientific reports. 2012;2:954. doi: 10.1038/srep00954 23233873

15. Alves G, Müller B, Herlofson K, HogenEsch I, Telstad W, Aarsland D, et al. Incidence of Parkinson’s disease in Norway: the Norwegian ParkWest study. Journal of Neurology, Neurosurgery & Psychiatry. 2009;80(8):851–857. doi: 10.1136/jnnp.2008.168211

16. Nowicka M, Robinson MD. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. F1000Research. 2016;5. doi: 10.12688/f1000research.8900.1 28105305

17. Reyes A, Anders S, Huber W. Inferring differential exon usage in RNA-Seq data with the DEXSeq package; 2013.

18. Nido GS, Dick F, Toker L, Petersen K, Alves G, Tysnes OB, et al. Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition. Acta Neuropathologica Communications. 2020;8(1):55. doi: 10.1186/s40478-020-00932-7 32317022

19. Zhuravleva E, Gut H, Hynx D, Marcellin D, Bleck CK, Genoud C, et al. Acyl coenzyme A thioesterase Them5/Acot15 is involved in cardiolipin remodeling and fatty liver development. Molecular and cellular biology. 2012;32(14):2685–2697. doi: 10.1128/MCB.00312-12 22586271

20. Paradies G, Paradies V, De Benedictis V, Ruggiero FM, Petrosillo G. Functional role of cardiolipin in mitochondrial bioenergetics. Biochimica et Biophysica Acta (BBA)-Bioenergetics. 2014;1837(4):408–417. doi: 10.1016/j.bbabio.2013.10.006 24183692

21. Burté F, Houghton D, Lowes H, Pyle A, Nesbitt S, Yarnall A, et al. Metabolic profiling of Parkinson’s disease and mild cognitive impairment. Movement Disorders. 2017;32(6):927–932. 28394042

22. Kaji S, Maki T, Kinoshita H, Uemura N, Ayaki T, Kawamoto Y, et al. Pathological endogenous α-synuclein accumulation in oligodendrocyte precursor cells potentially induces inclusions in multiple system atrophy. Stem cell reports. 2018;10(2):356–365. doi: 10.1016/j.stemcr.2017.12.001 29337114

23. Lee Y, Morrison BM, Li Y, Lengacher S, Farah MH, Hoffman PN, et al. Oligodendroglia metabolically support axons and contribute to neurodegeneration. Nature. 2012;487(7408):443. doi: 10.1038/nature11314 22801498

24. Ramanan VK, Risacher SL, Nho K, Kim S, Swaminathan S, Shen L, et al. APOE and BCHE as modulators of cerebral amyloid deposition: a florbetapir PET genome-wide association study. Molecular psychiatry. 2014;19(3):351–357. doi: 10.1038/mp.2013.19 23419831

25. Lockridge O, Masson P. Pesticides and susceptible populations: people with butyrylcholinesterase genetic variants may be at risk. Neurotoxicology. 2000;21(1-2):113–126. 10794391

26. Rösler TW, Salama M, Shalash AS, Khedr EM, El-Tantawy A, Fawi G, et al. K-variant BCHE and pesticide exposure: Gene-environment interactions in a case–control study of Parkinson’s disease in Egypt. Scientific reports. 2018;8(1):16525. doi: 10.1038/s41598-018-35003-4

27. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

28. Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S. FastQC; 2012. Babraham Institute.

29. Patro R, Duggal G, Kingsford C. Salmon: accurate, versatile and ultrafast quantification from RNA-seq data using lightweight-alignment. BioRxiv. 2015; p. 021592.

30. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2015;4. doi: 10.12688/f1000research.7563.1 26925227

31. Love MI, Soneson C, Patro R. Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification. F1000Research. 2018;7. doi: 10.12688/f1000research.15398.1 30356428

32. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome research. 2012;22(10):2008–2017. doi: 10.1101/gr.133744.111 22722343

33. Mancarci BO, Toker L, Tripathy SJ, Li B, Rocco B, Sibille E, et al. Cross-laboratory analysis of brain cell type transcriptomes with applications to interpretation of bulk tissue data. Eneuro. 2017;4(6). doi: 10.1523/ENEURO.0212-17.2017 29204516

34. Toker L, Mancarci BO, Tripathy S, Pavlidis P. Transcriptomic evidence for alterations in astrocytes and parvalbumin interneurons in subjects with bipolar disorder and schizophrenia. Biological psychiatry. 2018;84(11):787–796. doi: 10.1016/j.biopsych.2018.07.010 30177255

35. Van den Berge K, Soneson C, Robinson MD, Clement L. stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage. Genome biology. 2017;18(1):151. doi: 10.1186/s13059-017-1277-0 28784146

36. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research. 2019;47(D1):D607–D613. doi: 10.1093/nar/gky1131 30476243

37. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nature genetics. 2000;25(1):25–29. doi: 10.1038/75556 10802651

38. Consortium GO. The gene ontology resource: 20 years and still GOing strong. Nucleic acids research. 2019;47(D1):D330–D338. doi: 10.1093/nar/gky1055

39. Almagro Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O. DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics. 2017;33(21):3387–3395. doi: 10.1093/bioinformatics/btx431 29036616

Článek Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data

Článek Genetic engineering of sex chromosomes for batch cultivation of non-transgenic, sex-sorted males

Článek Opposing functions of Fng1 and the Rpd3 HDAC complex in H4 acetylation in Fusarium graminearum

Článek Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation

Článek TOR Complex 2- independent mutations in the regulatory PIF pocket of Gad8AKT1/SGK1 define separate branches of the stress response mechanisms in fission yeast

Článek NIGT1 family proteins exhibit dual mode DNA recognition to regulate nutrient response-associated genes in Arabidopsis

Článek Oxidative stress antagonizes fluoroquinolone drug sensitivity via the SoxR-SUF Fe-S cluster homeostatic axis

Článek A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population

Článek Mms19 promotes spindle microtubule assembly in Drosophila neural stem cells

Článek Mosquito genomes are frequently invaded by transposable elements through horizontal transfer

Článek Genotype imputation using the Positional Burrows Wheeler Transform

Článek Formal commentary