Conserved cell-type specific signature of resilience to Alzheimer's disease nominates role for excitatory cortical neurons
Telpoukhovskaia, M. A.; Hadad, N.; Gurdon, B.; Dai, Y.; Ouellette, A. R.; Neuner, S. M.; Dunn, A. R.; Willcox, J. A. L.; Wu, Y.; Dumitrescu, L.; Bellur, O.; Zhang, J.-G.; O'Connell, K. M. S.; Dammer, E. B.; Seyfried, N. T.; Muzumdar, S.; Gillis, J.; Robson, P.; Arnold, M.; Hohman, T. J.; Philip, V.; Menon, V.; Kaczorowski, C. C.
Show abstract
Alzheimers disease (AD), the leading cause of dementia, affects millions of people worldwide. With no disease-modifying medication currently available, the human toll and economic costs are rising rapidly. Under current standards, a patient is diagnosed with AD when both cognitive decline and pathology (amyloid plaques and neurofibrillary tangles) are present. Remarkably, some individuals who have AD pathology remain cognitively normal. Uncovering factors that lead to "cognitive resilience" to AD is a promising path to create new targets for therapies. However, technical challenges discovering novel human resilience factors limit testing, validation, and nomination of novel drugs for AD. In this study, we use single-nucleus transcriptional profiles of postmortem cortex from human individuals with high AD pathology who were either cognitively normal (resilient) or cognitively impaired (susceptible) at time of death, as well as mouse strains that parallel these differences in cognition with high amyloid load. Our cross-species discovery approach highlights a novel role for excitatory layer 4/5 cortical neurons in promoting cognitive resilience to AD, and nominates several resilience genes that include ATP1A1, GRIA3, KCNMA1, and STXBP1. This putative cell type has been implicated in resilience in previous studies on bulk RNA-seq tissue, but our single-nucleus and cross-species approach identifies particular resilience-associated gene signatures in these cells. These novel resilience candidate genes were tested for replication in orthogonal data sets and confirmed to be correlated with cognitive resilience. Based on these gene signatures, we identified several potential mechanisms of resilience, including regulation of synaptic plasticity, axonal and dendritic development, and neurite vesicle transport along microtubules that are potentially targetable by available therapeutics. Because our discovery of resilience-associated genes in layer 4/5 cortical neurons originates from an integrated human and mouse transcriptomic space from susceptible and resilient individuals, we are positioned to test causality and perform mechanistic, validation, and pre-clinical studies in our human-relevant AD-BXD mouse panel.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Downregulation of Dickkopf-3, a Wnt antagonist elevated in Alzheimer's disease, restores synapse integrity and memory in a disease mouse model 96%
- Cystatin F (Cst7) drives sex-dependent changes in microglia in an amyloid-driven model of Alzheimer's Disease 96%
- Microglia and border-associated mouse macrophages maintain their embryonic origin during Alzheimers disease 95%
Similar papers in this journal
Similar papers in this journal
- Genome-wide consensus transcriptional signatures identify synaptic pruning linking Alzheimer's disease and epilepsy 96%
- Integrative Brain Transcriptome Analysis Links Complement Component 4 and HSPA2 to the APOE ε2 Protective Effect in Alzheimer Disease 95%
- Alzheimer's genetic risk factor FERMT2 (Kindlin-2) controls axonal growth and synaptic plasticity in an APP-dependent manner. 95%
Similar papers in this journal
- ZCCHC17 modulates neuronal RNA splicing and supports cognitive resilience in Alzheimer's disease 98%
- Endothelial Cells are Heterogeneous in Different Brain Regions and are Dramatically Altered in Alzheimer's Disease 98%
- Amelioration of symptomatic Alzheimer's Disease after selective impairment of p75NTR function in adult forebrainexcitatory neurons 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.