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Integrated Transcriptomic and Neuroimaging Brain Model Decodes Biological Mechanisms in Aging and Alzheimer's Disease

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Integrated Transcriptomic and Neuroimaging Brain Model Decodes Biological Mechanisms in Aging and Alzheimer's Disease medRxiv preprint doi: https://doi.org/10.1101/2021.02.23.21252283; this version posted March 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . INTEGRATED TRANSCRIPTOMIC AND NEUROIMAGING BRAIN MODEL DECODES BIOLOGICAL MECHANISMS IN AGING AND ALZHEIMER’S DISEASE Quadri Adewale1,2,3, Ahmed F Khan 1,2,3, Felix Carbonell4, Yasser Iturria-Medina1,2,3*, for the Alzheimer’s Disease Neuroimaging Initiative5. 1 Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill Univ., Montreal, Canada 2 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill Univ., Montreal, Canada 3 Ludmer Centre for Neuroinformatics and Mental Health, McGill Univ., Montreal, Canada 4 Biospective Inc., Montreal, Canada 5 Data used in preparation of this article were partly obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf * Correspondence to: Y I-M, 3801 University Street, room NW312, Montreal Neurological Institute, McGill University, Montreal, Canada H3A 2B4. Email: [email protected] Summary Both healthy aging and Alzheimer’s disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model which accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-β burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective geneticNOTE: This targetspreprint reports for newextending research that healthy has not been aging certified and by peer treating review and AD should progression not be used to guide. clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2021.02.23.21252283; this version posted March 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Highlights - A multiscale model integrating gene expression and neuroimaging identifies causal genes driving healthy aging and Alzheimer’s disease progression. - The specific neuroimaging modalities modulated by the causal genes are revealed. - Healthy aging and Alzheimer’s disease share specific biological pathways even though Alzheimer’s disease has more altered pathways. Introduction Innovations in healthcare and drug delivery have led to increase in human life expectancy. However, increased lifespan is accompanied by more predisposition to frailty and late-onset Alzheimer’s disease (AD) (Guerreiro et al., 2015; Singh et al., 2019). Both healthy aging and AD are complex multifactorial processes, and understanding their molecular mechanisms is crucial for extending longevity and improving quality of life (Alkadhi et al., 2011; Kowald et al., 1996). Indeed, at the microscopic scale (~10-6 m), transcriptomics and proteomics analysis of the brain have paved the way for deciphering the mechanistic underpinnings of healthy aging and AD (Dillman et al., 2017; Y. Iturria-Medina et al., 2020; E. C. B. Johnson et al., 2020; Mostafavi et al., 2018; Tanaka et al., 2018). In parallel, macroscopic (~10-2 m) imaging phenotypes from PET and MRI are facilitating the detailed characterization of brain changes, such as amyloid-β (Aβ) and tau accumulation, glucose hypometabolism, altered cerebrovascular flow and atrophy (Dukart et al., 2013; Jack et al., 2018; Rodrigue et al., 2012; N. Zhang et al., 2017). However, in both aging and disease research, most studies incorporate brain measurements at either micro (e.g. transcriptomics) or macroscopic scale (e.g. PET imaging), failing to detect the direct causal relationships between several biologically factors at multiple spatial resolutions. Although AD is characterized by the accumulation of amyloid plaques and neurofibrillary tangles, many other biological aberrations have been associated with the disease (neuroinflammation, vascular abnormalities, white matter hyperintensities), leading to changes in diagnostic criteria in recent times (DeTure et al., 2019). The complexity of AD is further compounded by the interplay between these multiple biological factors. A growing body of evidence points to the synergistic interaction 2 medRxiv preprint doi: https://doi.org/10.1101/2021.02.23.21252283; this version posted March 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . between Aβ and tau in driving neuronal loss, functional dysregulation and glucose hypometabolism in AD (Iaccarino et al., 2017; Ittner et al., 2011; Pascoal et al., 2017; Pickett et al., 2019) . Also, cerebral blood flow (CBF) promotes Aβ clearance, suggesting that vascular dysregulation could impact neuronal function and facilitate Aβ deposition (Qosa et al., 2014; Zlokovic, 2011). To account for the synergy between multiple biological factors, we previously introduced a multifactorial causal model (MCM) (Iturria-Medina et al., 2017), which uses multimodal imaging data to characterize the macroscale intra-regional interactions among any pair of biological factors (tau, Aβ, CBF) while accounting for the inter-regional spreading of the pathological alterations across axonal and/or vascular connections. However, this multifactorial model did not consider the microscopic properties of the modelled brain regions. In an initial attempt to integrate brain variables at multiple scales, a few recent studies have used the regional expression patterns of pre-selected genes as complementary information in intra-brain disease spreading models (Freeze et al., 2018; Freeze et al., 2019; Zheng et al., 2019). Applied to Parkinson’s disease (PD), improvements in the capacity to explain the regional brain atrophy patterns were observed, based on each brain region’s genetic predisposition to the disease. However, most of these studies have selected very specific genes already known for their crucial role in disease (e.g. SNCA, TMEM175, GBA), while disregarding the individual and combined roles of several other relevant gene candidates. Moreover, the analyses have focused on the influence of transcriptomics on a single biological factor at a time, without accounting for the multiplicity of biological alterations and interactions that occur at different spatial scales. As a result, we continue to lack brain generative models integrating a large set of genetic activities with multimodal brain properties. An integrated multiscale and multifactorial brain model (from genes to neuroimaging and cognition) may be critical to further our understanding of both healthy aging and neurodegeneration, and engender the development of inclusive biomarkers for personalized diagnosis and treatment. Driven by this motivation, here we combine whole-brain transcriptomics, PET and MRI in a comprehensive generative and personalized formulation which we successfully validated in healthy aging and AD progression. This novel approach concurrently accounts for the direct influence of hundreds of genes on regional macroscopic multifactorial effects, the pathological 3 medRxiv preprint doi: https://doi.org/10.1101/2021.02.23.21252283; this version posted March 1, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . spreading of the ensuing aberrations across axonal and vascular networks, and the resultant effects of these alterations on cognition. The proposed framework constitutes a promising technique for the development of effective genetic targets for preventing aging-related disorders and ameliorating existing neurodegenerative conditions. Results Capturing Gene and Macroscopic Factor Interactions in the Human Brain Genes control many biological functions, and their dysregulation can cause abnormal development, accelerated aging or disease (Kuintzle et al., 2017; Lee et al., 2013). Aiming to characterize the direct influence of genes on multiple brain processes, here we have developed a multiscale and multifactorial spatiotemporal brain model (Figs. 1A-C) linking whole-brain
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