Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis are pressing health concerns in modern societies for which effective therapies are still lacking. Recent high-throughput genomic technologies have enabled genome-scale, multidimensional investigations to facilitate a better understanding of the underlying mechanisms and the identification of novel targets. Here we review the molecular insights gained through such studies, and compare the similarities and differences between neurodegenerative diseases revealed by systems genomics and gene network modelling approaches. We focus specifically on the shared mechanisms at multiple molecular scales ranging from genetic factors to gene expression to network-level features of gene regulation, and whenever possible also point out mechanisms that distinguish one disease from another. Our review sets the stage for similar genomewide inspection in the future on shared/distinct features of neurodegenerative diseases at the levels of cellular, proteomic or epigenomic signatures, and how these features may interact to determine the progression and treatment response of different diseases afflicting the same individual.

Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10(-12)), while Dnmt3a KO signature does not (P = 0.017).