Identifying neuron subtype-specific metabolic network changes in single cell transcriptomics of Alzheimer’s Disease using perturb-Met

Abstract

Metabolic aberrations are a prominent feature of Alzheimer’s Disease (AD). Different neuronal subtypes have selective vulnerability in AD. Despite the recent advance of single cell and single nucleus RNA-seq of AD brains, genome-scale metabolic network changes in neuronal subtypes have not been systematically studied with detail. To bridge this knowledge gap, I developed a computational method called perturb-Met that can uncover transcriptional dysregulation centered at hundreds of metabolites in a metabolic network. perturb-Met successfully recapitulated known glycolysis, cholesterol, and other metabolic defects in APOE4-neurons and microglia, many of which are missed by current methods. Applying perturb-Met on AD snRNA-seq data, I revealed that the four neuronal subtypes in the entorhinal cortex shows subtype-specific metabolic changes, namely mitochondrial complex I metabolism, ganglioside metabolism, galactose and heparan sulfate metabolism, as well as glucose and lipid metabolism, respectively. perturb-Met also revealed significant changes in protein glycosylation in the neuron subtype specifically found in AD brains. These subtype-specific metabolic changes may potentially underlie their selective vulnerability in AD. perturb-Met is a valuable tool to discover potential metabolic network changes in many other single cell or bulk transcriptomic studies.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

“…In neurosciences, although there are few studies associated with the application, authors such as [ 141 ] have described the importance of computational methods that can infer the deregulation of key metabolites associated with neurons and microglia and that, in turn, can determine the potential basis for the development of diseases such as Alzheimer’s disease. Nevertheless, neuroscience and neuroengineering still face unique challenges due to the multiple interacting components that produce emergent behaviors [ 138 ].…”

Toward modeling metabolic state from single-cell transcriptomics

“…While pathway databases are mainly used for interpretation, the GEMs allow the prediction of metabolism on a system-wide level. However, both usually encompass the whole spectrum of possible metabolic conversions [ 27 , [38] , [39] , [40] ] that do not jointly occur in individual cells.…”

“…Pathway-level analysis, as described above, has a few shortcomings. First, the curated pathway definitions aim to capture the whole biological process under different biological contexts, disregarding complex regulatory interplay, and thus contain more genes than are involved in any specific cellular state or type [ 39 , 40 ]. Predefined pathways may be composed of genes responding to perturbation in different directions, leading to false negatives in enrichment analysis, which could be solved by defining context-specific pathways [ 45 ].…”

“…Predefined pathways may be composed of genes responding to perturbation in different directions, leading to false negatives in enrichment analysis, which could be solved by defining context-specific pathways [ 45 ]. Second, pathway enrichment does not account for biological changes spanning multiple pathways [ 40 ] and does not leverage the information about regulatory and reaction interactions. For example, the activity of the whole pathway depends on the activity of all enzymes in the chain with downregulation of intermediate enzymes potentially resulting in metabolic bottlenecks that lead to lower pathway activity even when other enzymes are highly active.…”

“…Similarly, scPPIN was developed for identification of differentially active modules in protein–protein interaction networks based on scRNA-seq data [ 67 ], but may be also applicable to metabolic networks. Another alternative is reporter metabolite analysis, such as perturb-Met, which aims to identify affected metabolites by analyzing changes in expression of metabolite-associated genes [ 40 ].…”