Summary Elevated risk of developing Alzheimer’s disease (AD) is associated with hypomorphic variants of TREM2, a surface receptor required for microglial responses to neurodegeneration, including proliferation, survival, clustering and phagocytosis. How TREM2 promotes such diverse responses is unknown. Here, we find that microglia in AD patients carrying TREM2 risk variants and TREM2-deficient mice with AD-like pathology have abundant autophagic vesicles, as do TREM2-deficient macrophages under growth factor limitation or ER stress. Combined metabolomics and RNA-seq linked this anomalous autophagy to defective mTOR signaling, which affects ATP levels and biosynthetic pathways. Metabolic derailment and autophagy were offset in vitro through Dectin-1, a receptor that elicits TREM2-like intracellular signals, and cyclocreatine, a creatine analog that can supply ATP. Dietary cyclocreatine tempered autophagy, restored microglial clustering around plaques, and decreased plaque-adjacent neuronal dystrophy in TREM2-deficient mice with amyloid-β pathology. Thus, TREM2 enables microglial responses during AD by sustaining cellular energetic and biosynthetic metabolism.

Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM (‘genes and metabolites’): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/

Abstract. Network enrichment analysis methods allow to identify active modules without being biased towards a priori defined pathways. One of mathematical formulations of such analysis is a reduction to a maximum-weight connected subgraph problem. In particular, in analysis of metabolic networks a generalized maximum-weight connected subgraph (GMWCS) problem, where both nodes and edges are scored, naturally arises. Here we present the first to our knowledge practical exact GMWCS solver. We have tested it on real-world instances and compared to similar solvers. First, the results show that on node-weighted instances GMWCS solver has a similar performance to the best solver for that problem. Second, GMWCS solver is faster compared to the closest analogue when run on GMWCS instances with edge weights.

It is unclear how the 22q11.2 deletion predisposes to psychiatric disease. To study this, we generated induced pluripotent stem cells from deletion carriers and controls and utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Here, we show that upon differentiation into neural progenitor cells, the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including autism. In excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common and rare variants. To understand how the deletion contributed to these changes, we defined the minimal protein-protein interaction network that best explains gene expression alterations. We found that many genes in 22q11.2 interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways. Our findings suggest that the 22q11.2 deletion impacts genes that may converge with psychiatric risk loci to influence disease manifestation in each deletion carrier.