Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson's disease.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 03 2022
31 03 2022
Historique:
received:
15
10
2021
accepted:
24
03
2022
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
5
4
2022
Statut:
epublish
Résumé
Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder.
Identifiants
pubmed: 35361840
doi: 10.1038/s41598-022-09506-0
pii: 10.1038/s41598-022-09506-0
pmc: PMC8971452
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5483Informations de copyright
© 2022. The Author(s).
Références
Poewe, W. et al. Parkinson disease. Nat. Rev. Dis. Primers 3(1), 17013 (2017).
pubmed: 28332488
doi: 10.1038/nrdp.2017.13
Smith, C. R. et al. Cognitive impairment in Parkinson’s disease is multifactorial: A neuropsychological study. Acta Neurol. Scand. 141(6), 500–508 (2020).
pubmed: 32002988
doi: 10.1111/ane.13226
Wellstead, P. & Cloutier, M. An energy systems approach to Parkinson’s disease. WIREs Syst. Biol. Med. 3(1), 1–6 (2011).
doi: 10.1002/wsbm.107
Ballarini, T. et al. Regional gray matter changes and age predict individual treatment response in Parkinson’s disease. NeuroImage Clin. 21, 101636 (2019).
pubmed: 30558868
doi: 10.1016/j.nicl.2018.101636
Horn, A. et al. Connectivity predicts deep brain stimulation outcome in Parkinson disease. Ann. Neurol. 82(1), 67–78 (2017).
pubmed: 28586141
pmcid: 5880678
doi: 10.1002/ana.24974
Hartman, R. E. et al. A biomarker for predicting responsiveness to stem cell therapy based on mechanism-of-action: Evidence from cerebral injury. Cell Rep. 31(6), 107622 (2020).
pubmed: 32402283
doi: 10.1016/j.celrep.2020.107622
Iturria-Medina, Y., Carbonell, F. M. & Evans, A. C. Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration. Neuroimage 179, 40–50 (2018).
pubmed: 29894824
doi: 10.1016/j.neuroimage.2018.06.028
Fleming, S. M. Mechanisms of gene-environment interactions in Parkinson’s disease. Curr. Environ. Health Rep. 4(2), 192–199 (2017).
pubmed: 28417442
doi: 10.1007/s40572-017-0143-2
He, M. & Allen, A. Testing gene-treatment interactions in pharmacogenetic studies. J. Biopharm. Stat. 20(2), 301–314 (2010).
pubmed: 20309760
pmcid: 3706096
doi: 10.1080/10543400903572761
Ko, T.-M. et al. Use of HLA-B* 58: 01 genotyping to prevent allopurinol induced severe cutaneous adverse reactions in Taiwan: National prospective cohort study. BMJ 351, h4848 (2015).
pubmed: 26399967
pmcid: 4579807
doi: 10.1136/bmj.h4848
Droździk, M., Białecka, M. & Kurzawski, M. Pharmacogenetics of Parkinson’s disease—Through mechanisms of drug actions. Curr. Genomics 14(8), 568–577 (2013).
pubmed: 24532988
pmcid: 3924251
doi: 10.2174/1389202914666131210212521
Guin, D. et al. A systematic review and integrative approach to decode the common molecular link between levodopa response and Parkinson’s disease. BMC Med. Genomics 10(1), 56 (2017).
pubmed: 28927418
pmcid: 5606117
doi: 10.1186/s12920-017-0291-0
Pearlson, G., Calhoun, V. & Liu, J. An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders. Front. Genet. 6, 276 (2015).
pubmed: 26442095
pmcid: 4561364
doi: 10.3389/fgene.2015.00276
Worsley, K. J. et al. Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos. Trans. R. Soc. B: Biol. Sci. 360(1457), 913–920 (2005).
doi: 10.1098/rstb.2005.1637
Friston, K. et al. Functional connectivity: The principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13(1), 5–14 (1993).
pubmed: 8417010
doi: 10.1038/jcbfm.1993.4
McIntosh, A. R. & Lobaugh, N. J. Partial least squares analysis of neuroimaging data: Applications and advances. Neuroimage 23, S250–S263 (2004).
pubmed: 15501095
doi: 10.1016/j.neuroimage.2004.07.020
Turner, C. A. et al. Dysregulated fibroblast growth factor (FGF) signaling in neurological and psychiatric disorders. Semin. Cell Dev. Biol. 53, 136–143 (2016).
pubmed: 26454097
doi: 10.1016/j.semcdb.2015.10.003
Martínez-Moreno, C. G. et al. Growth hormone (GH) and gonadotropin-releasing hormone (GnRH) in the central nervous system: A potential neurological combinatory therapy?. Int. J. Mol. Sci. 19(2), 375 (2018).
pmcid: 5855597
doi: 10.3390/ijms19020375
Xie, A. et al. Shared mechanisms of neurodegeneration in Alzheimer’s disease and Parkinson’s disease. BioMed Res. Int. 2014, 648740 (2014).
pubmed: 24900975
pmcid: 4037122
doi: 10.1155/2014/648740
Gagne, J. J. & Power, M. C. Anti-inflammatory drugs and risk of Parkinson disease. Neurology 74(12), 995 (2010).
pubmed: 20308684
pmcid: 2848103
doi: 10.1212/WNL.0b013e3181d5a4a3
Brauer, R. et al. Diabetes medications and risk of Parkinson’s disease: A cohort study of patients with diabetes. Brain 143(10), 3067–3076 (2020).
pubmed: 33011770
pmcid: 7794498
doi: 10.1093/brain/awaa262
Bykov, K. et al. Confounding of the association between statins and Parkinson disease: Systematic review and meta-analysis. Pharmacoepidemiol. Drug Saf. 26(3), 294–300 (2017).
pubmed: 27527987
doi: 10.1002/pds.4079
Yan, J. et al. Effect of statins on Parkinson’s disease: A systematic review and meta-analysis. Medicine 98(12), e14852–e14852 (2019).
pubmed: 30896628
pmcid: 6709163
doi: 10.1097/MD.0000000000014852
Blauwendraat, C., Nalls, M. A. & Singleton, A. B. The genetic architecture of Parkinson’s disease. Lancet Neurol. 19(2), 170–178 (2020).
pubmed: 31521533
doi: 10.1016/S1474-4422(19)30287-X
Gibson, G. The environmental contribution to gene expression profiles. Nat. Rev. Genet. 9(8), 575–581 (2008).
pubmed: 18574472
doi: 10.1038/nrg2383
Elizabeth Qian, Y. H. Subtyping of Parkinson’s disease—Where are we up to?. Aging Dis. 10(5), 1130–1139 (2019).
pubmed: 31595207
pmcid: 6764738
doi: 10.14336/AD.2019.0112
Thenganatt, M. A. & Jankovic, J. Parkinson disease subtypes. JAMA Neurol. 71(4), 499–504 (2014).
pubmed: 24514863
doi: 10.1001/jamaneurol.2013.6233
Zhang, X. et al. Data-driven subtyping of Parkinson’s disease using longitudinal clinical records: A cohort study. Sci. Rep. 9(1), 797 (2019).
pubmed: 30692568
pmcid: 6349906
doi: 10.1038/s41598-018-37545-z
Evers, L. J. W. et al. Measuring Parkinson’s disease over time: The real-world within-subject reliability of the MDS-UPDRS. Mov. Disord. 34(10), 1480–1487 (2019).
pubmed: 31291488
pmcid: 6851993
doi: 10.1002/mds.27790
Pieterman, M., Adams, S. & Jog, M. Method of levodopa response calculation determines strength of association with clinical factors in Parkinson disease. Front. Neurol. 9, 260–260 (2018).
pubmed: 29867708
pmcid: 5966537
doi: 10.3389/fneur.2018.00260
Lorenzi, M. et al. Susceptibility of brain atrophy to TRIB3 in Alzheimer’s disease, evidence from functional prioritization in imaging genetics. Proc. Natl. Acad. Sci. 115(12), 3162 (2018).
pubmed: 29511103
pmcid: 5866534
doi: 10.1073/pnas.1706100115
Goswami, P., Joshi, N. & Singh, S. Neurodegenerative signaling factors and mechanisms in Parkinson’s pathology. Toxicol. In Vitro 43, 104–112 (2017).
pubmed: 28627426
doi: 10.1016/j.tiv.2017.06.008
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51(9), 1339–1348 (2019).
pubmed: 31427789
doi: 10.1038/s41588-019-0481-0
Pagano, G., Niccolini, F. & Politis, M. Imaging in Parkinson’s disease. Clin. Med. (Lond.) 16(4), 371–375 (2016).
doi: 10.7861/clinmedicine.16-4-371
Horsager, J. et al. Brain-first versus body-first Parkinson’s disease: A multimodal imaging case–control study. Brain 143, 3077–3088 (2020).
pubmed: 32830221
doi: 10.1093/brain/awaa238
Cilia, R. et al. Natural history of motor symptoms in Parkinson’s disease and the long-duration response to levodopa. Brain 143(8), 2490–2501 (2020).
pubmed: 32844196
pmcid: 7566883
doi: 10.1093/brain/awaa181
Parmar, M., Grealish, S. & Henchcliffe, C. The future of stem cell therapies for Parkinson disease. Nat. Rev. Neurosci. 21, 103–115 (2020).
pubmed: 31907406
doi: 10.1038/s41583-019-0257-7
Armstrong, M. J. & Okun, M. S. Choosing a Parkinson disease treatment. JAMA 323(14), 1420–1420 (2020).
pubmed: 32286645
doi: 10.1001/jama.2020.1224
Marek, K. et al. The Parkinson’s progression markers initiative (PPMI)—Establishing a PD biomarker cohort. Ann. Clin. Transl. Neurol. 5(12), 1460–1477 (2018).
pubmed: 30564614
pmcid: 6292383
doi: 10.1002/acn3.644
Baumgarten, N. et al. EpiRegio: Analysis and retrieval of regulatory elements linked to genes. Nucleic Acids Res. 48(W1), W193–W199 (2020).
pubmed: 32459338
pmcid: 7319550
doi: 10.1093/nar/gkaa382
Mi, H., Muruganujan, A. & Thomas, P. D. PANTHER in 2013: Modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41(Database issue), D377-86 (2013).
pubmed: 23193289
Li, X. et al. Patterns of grey matter loss associated with motor subscores in early Parkinson’s disease. NeuroImage Clin. 17, 498–504 (2018).
pubmed: 29201638
doi: 10.1016/j.nicl.2017.11.009
Tomlinson, C. L. et al. Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov. Disord. 25(15), 2649–2653 (2010).
doi: 10.1002/mds.23429
Oertel, W. & Dodel, R. International guide to drugs for Parkinson’s disease. Mov. Disord. 10(2), 121–131 (1995).
pubmed: 7753054
doi: 10.1002/mds.870100202
Ferreira, J. J. et al. Effect of opicapone on levodopa pharmacokinetics, catechol-O-methyltransferase activity and motor fluctuations in patients with Parkinson’s disease. Eur. J. Neurol. 22(5), 815-e56 (2015).
pubmed: 25649051
doi: 10.1111/ene.12666
Avila, A. et al. Rasagiline and safinamide as a dopamine-sparing therapy for Parkinson’s disease. Acta Neurol. Scand. 140(1), 23–31 (2019).
pubmed: 30963543
doi: 10.1111/ane.13096
Cervantes-Arriaga, A. et al. Cálculo de unidades de equivalencia de levodopa en enfermedad de Parkinson. Archivos de Neurociencias 14(2), 116–119 (2009).
Weaver, F. M. et al. Bilateral deep brain stimulation vs best medical therapy for patients with advanced Parkinson disease: A randomized controlled trial. JAMA 301(1), 63–73 (2009).
pubmed: 19126811
pmcid: 2814800
doi: 10.1001/jama.2008.929
Sled, J. G., Zijdenbos, A. P. & Evans, A. C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998).
pubmed: 9617910
doi: 10.1109/42.668698
Evans, A. C. et al. An MRI-based probabilistic atlas of neuroanatomy. In Magnetic Resonance Scanning and Epilepsy (eds Shorvon, S. D. et al.) 263–274 (Springer, 1994).
doi: 10.1007/978-1-4615-2546-2_48
Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95–113 (2007).
pubmed: 17761438
doi: 10.1016/j.neuroimage.2007.07.007
Horn, A. & Kühn, A. A. Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations. Neuroimage 107, 127–135 (2015).
pubmed: 25498389
doi: 10.1016/j.neuroimage.2014.12.002
Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26(1), 288–303 (2016).
pubmed: 25316338
doi: 10.1093/cercor/bhu239
Yan, C. & Zang, Y. DPARSF: A MATLAB toolbox for" pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 13 (2010).
Zou, Q.-H. et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods 172, 137–141 (2008).
pubmed: 18501969
pmcid: 3902859
doi: 10.1016/j.jneumeth.2008.04.012
PPMI, P.s.P.M.I. November 1, 2018 September 3, 2020. https://www.ppmi-info.org/ .
Yeh, F.-C. et al. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178, 57–68 (2018).
pubmed: 29758339
doi: 10.1016/j.neuroimage.2018.05.027
Yeh, F.-C. & Tseng, W.-Y.I. NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage 58(1), 91–99 (2011).
pubmed: 21704171
doi: 10.1016/j.neuroimage.2011.06.021
Yeh, F.-C., Wedeen, V. J. & Tseng, W.-Y.I. Generalized ${q} $-sampling imaging. IEEE Trans. Med. Imaging 29(9), 1626–1635 (2010).
pubmed: 20304721
doi: 10.1109/TMI.2010.2045126
Iturria-Medina, Y. et al. Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Commun. Biol. 4(1), 614 (2021).
pubmed: 34021244
pmcid: 8140107
doi: 10.1038/s42003-021-02133-x
Krishnan, A. et al. Partial least squares (PLS) methods for neuroimaging: A tutorial and review. Neuroimage 56(2), 455–475 (2011).
pubmed: 20656037
doi: 10.1016/j.neuroimage.2010.07.034
Carbonell, F. et al. Spatially distributed amyloid-β reduces glucose metabolism in mild cognitive impairment. J. Alzheimers Dis. 73, 543–557 (2020).
pubmed: 31796668
pmcid: 7029335
doi: 10.3233/JAD-190560