CSF proteomics identifies early changes in autosomal dominant Alzheimer’s disease
Yuanyuan Shen, Jigyasha Timsina, Gyujin Heo, Aleksandra Beric, Muhammad Ali, Ciyang Wang, Chengran Yang, Yueyao Wang, Daniel Western, Menghan Liu, Priyanka Gorijala, John Budde, Anh Do, Haiyan Liu, Brian A. Gordon, Jorge J. Llibre‐Guerra, Nelly Joseph‐Mathurin, Richard J. Perrin, Darío Maschi, Tony Wyss‐Coray, Pau Pástor, Alan E. Renton, Ezequiel Surace, Erik C. B. Johnson, Allan I. Levey, Ignacio Álvarez, Johannes Levin, John M. Ringman, Ricardo Allegri, Nicholas T. Seyfried, Gregg S. Day, Qisi Wu, María Victoria Fernández, Rawan Tarawneh, Eric McDade, John C. Morris, Randall J. Bateman, Alison Goate, James M. Noble, Gregory S. Day, Neill R. Graff-Radford, Jonathan Voglein, Ricardo Allegri, Patricio Chrem Mendez, Ezequiel Surace, Sarah B. Berman, Snezana Ikonomovic, Neelesh Nadkarni, Francisco Lopera, Laura Ramirez, David Aguillon, Yudy Leon, Claudia Ramos, Diana Alzate, Ana Baena, Natalia Londono, Sonia Moreno Mathias Jucker, Christoph Laske, Elke Kuder-Buletta, Susanne Graber-Sultan, Oliver Preische, Anna Hofmann, Takeshi Ikeuchi, Kensaku Kasuga, Yoshiki Niimi, Kenji Ishii, Michio Senda, Raquel Sanchez-Valle, Pedro Rosa-Neto, Nick Fox, Dave Cash, Jae-Hong Lee, Jee Hoon Roh, Meghan Riddle, William Menard, Courtney Bodge, Mustafa Surti, Leonel Tadao Takada, Martin Farlow, Jasmeer P. Chhatwal, V.J. Sanchez-Gonzalez, Maribel Orozco-Barajas, Alison Goate, Alan Renton, Bianca Esposito, Celeste M. Karch, Jacob Marsh, Carlos Cruchaga, Carlos Cruchaga, Victoria Fernandez, Brian A. Gordon, Anne M. Fagan, Gina Jerome, Elizabeth Herries, Jorge Llibre-Guerra, Allan I. Levey, Erik C.B. Johnson, Nicholas T. Seyfried, Peter R. Schofield, William Brooks
Abstract
In this high-throughput proteomic study of autosomal dominant Alzheimer's disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers.