bioRxiv preprint doi: https://doi.org/10.1101/205807; this version posted October 19, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. A molecular network of the aging brain implicates INPPL1 and PLXNB1 in Alzheimer’s disease S. Mostafavi1*, C. Gaiteri2*, S. E. Sullivan3, C.C. White3,5, S. Tasaki2, J. Xu3,5, M. Taga3,5, H. Klein3,5, E. Patrick3,5, V. Komashko2, C. McCabe6, R. Smith3, E.B. Bradshaw3,4,5, D. Root5, A. Regev5, L. Yu2, L.B. Chibnik3,4,5, J.A. Schneider2, T. Young-Pearse3,4, D.A. Bennett2*, P.L. De Jager3,4,5* *contributed equally 1 University of British Columbia, Vancouver, British Columbia, Canada 2 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA. 3 Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women’s Hospital, Boston, Massachusetts, USA. 4 Harvard Medical School, Boston, Massachusetts, USA. 5 Broad Institute, Cambridge, Massachusetts, USA. One sentence summary: Molecular network analysis of RNA sequencing data from the aging human cortex identifies new Alzheimer’s and cognitive decline genes. 1 bioRxiv preprint doi: https://doi.org/10.1101/205807; this version posted October 19, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. Abstract The fact that only symptomatic therapies of small effect are available for Alzheimer’s disease (AD) today highlights the need for new therapeutic targets with which to prevent a major contributor to aging-related cognitive decline. Here, we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first identify the role of modules of coexpressed genes, and then confirm them in independent AD datasets. Then, we prioritize influential genes in AD-related modules and test our predictions in human model systems. We functionally validate two putative regulator genes in human astrocytes: INPPL1 and PLXNB1, whose activity in AD may be related to semaphorin signalling and type II diabetes, which have both been implicated in AD. This arc of network identification followed by statistical and experimental validation provides specific new targets for therapeutic development and illustrates a network approach to a complex disease. Introduction The incidence of late-onset Alzheimer’s disease (AD) is expected to triple by 20501, yet no therapies are available to treat or prevent the disease2. Possible reasons for the continued failure of AD trials include the biological complexity of the disease coupled with its phenotypic heterogeneity3. Recent genome-wide association studies (GWAS) have identified new potential therapeutic targets involved in endocytosis, metabolism, and inflammation4. However, it has been difficult to transition from mostly non-coding genetic variants that influence AD risk to molecular and cellular mechanisms that lead to the characteristic accumulation of β-amyloid and helical filament tau (PHFtau) pathology as well as the subsequent cognitive decline of AD. Furthermore, as large GWAS generally do not distinguish between variants related to AD neuropathology versus unknown contributors to cognitive decline, additional molecular, pathological and cognitive measures are likely necessary to determine the drivers of various aspects of AD. Here, we describe an analysis of participants from two large longitudinal cohort studies of aging (total n=478), as well as a validation cohort (n=82), which have careful assessments of both antemortem cognitive function and postmortem neuropathologic burden. We hypothesized that using RNA-sequencing (RNA-Seq) data from the dorsal lateral frontal cortex (DLPFC) enabled us to identify coherent intermediate cellular mechanisms that are associated with cognitive decline and/or neuropathological changes. Noting that dynamic genomic measurements such as RNA-Seq reflect the collective effect of upstream, downstream, and disease-correlated processes, we use a 2 bioRxiv preprint doi: https://doi.org/10.1101/205807; this version posted October 19, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. series of network-based approaches that account for known and hidden confounding factors in order to enrich our results for likely upstream associations. As cellular functions are carried out by networks of interacting molecules, uncovering these network’s structures can provide clues about cellular functions5,6. Such a network- based perspective also provides a more nuanced molecular definition of complex disease than do traditional single-gene associations7-9 because it provides a natural framework to assemble disparate single gene findings into disease mechanisms10-12. Transcriptomic data can be used to build such networks, facilitating the identification of groups of coexpressed genes or “modules” that represent cellular processes and can be related to phenotypes of interest13,14. Compared to traditional single gene or post-hoc pathway enrichment approaches, coexpression network-based analysis offers an unsupervised and tissue-specific approach that identifies cellular subnetworks related to disease phenotypes, independent of historical bias arising from research on particular genes and pathways14. Coexpression approaches have been applied to AD and have identified genes associated with a syndromic diagnosis of AD dementia15,16. However these approaches have never been applied to large cohorts with quantitative measurements of both AD, other common neuropathologies, and cognitive decline. Because neuropathology and cognition show important divergence in AD17,18, jointly modeling both aspects of the disease may better capture the relationship between molecular events and different stages of the disease process. Further, previous efforts did not distinguish between transcriptomic patterns that are indirectly associated with AD phenotypes, via a chain of intermediaries, from those that are directly associated15. Here, we build on prior work to address these limitations. Our approach, called gene module trait network analysis (MTN) (Figure 1), constructs gene expression modules and identifies those that are directly associated with cognitive decline, conditioned on neuropathology and other large-scale transcriptomic changes in the aging brain. We confirm the biological plausibility of this systems biology analysis in five other types of independent datasets. Finally, we test the identified associations in a relevant human model system to functionally characterize selected genes and prioritize candidates for further drug development. Results Data origin and phenotypes Data were derived from subjects enrolled in the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP), two prospective clinical-pathologic cohort studies of aging and dementia. All participants are non-demented at enrolment, have annual detailed clinical evaluations and have agreed to brain donation. At death, each brain undergoes a structured, quantitative neuropathologic assessment (see 3 bioRxiv preprint doi: https://doi.org/10.1101/205807; this version posted October 19, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. Supplementary methods). The two studies (collectively referred to as ROSMAP) share clinical and neuropathological standards, allowing for joint analyses of the data. Individual trajectories of cognitive decline are calculated from longitudinal cognitive measures that include up to 20 yearly evaluations19. For this study, we used data from 478 participants, of which 257 (62.5%) are women. The mean age at death was 88.7 years (95% confidence interval 88.1-89.3). Over the course of the study, some subjects experience cognitive decline, and, at the time of death, 32% remain cognitively non- impaired, 27% have mild cognitive impairment (MCI), 39% have a diagnosis of AD dementia (with or without a co-morbid condition), and 2% have another form of dementia. When brain pathology is measured in all of these persons, 58% of subjects receive a diagnosis of pathologic AD. However, of these individuals with pathologic AD, 46% are clinically non-demented (they are either cognitively non-impaired or have MCI), illustrating the well-described divergence of pathologic and clinical diagnoses of AD dementia (Table S1). Our analysis includes five phenotypic traits related to AD: two of these traits are clinical measures - a clinical diagnosis of AD dementia proximate to death (ClinAD), and a continuous measure of cognitive decline over time quantified as a per-subject slope of the cognitive decline trajectory from a linear mixed-effects model20 (“cognitive decline” abbreviated