SUMMARY We present a consensus atlas of the human brain transcriptome in Alzheimer’s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington’s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

52Alzheimer's disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related 53 biological processes. This complexity contributes, in part, to existing difficulties in the identification of 54 successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to 55 characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular 56 models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-57 analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression 58 methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research 59 studies. From this analysis, five consensus clusters were identified that described the major sources of 60 AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. 61AD genetic associations, previously studied AD-related biological processes, and AD targets under active 62 investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated 63 strong heterogeneity between males and females in AD-related expression that was consistently observed 64 across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams 65 were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, 66 which was specific for genes that showed the strongest association with changes in AD-related gene 67 expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules 68 that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been 69 previously identified and was enriched for genes related to proteostasis. This study provides an atlas to 70 map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery 71 efforts.

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL.