UC Berkeley

Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells with current technologies. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing (scRNA-Seq) and flux balance analysis. We applied Compass to associate metabolic states with Th17 functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/Treg differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzymes expression were enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells towards a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in central nervous system (CNS) autoimmunity.

The introduction highlights the motivation to this study, which stems from the conjunction of two transformative developments of recent years – the emergence of single-cell RNA sequencing technologies and the growing appreciation of cellular metabolism as key player in health and disease. Chapter 1 introduces lays the groundwork by introducing the fields of computational modeling of metabolism, immunometabolism, and single-cell genomics. Chapter 2 then introduces the Compass algorithm that serves as the computational framework to the rest of the study. Chapter 3 turns to T helper 17 (Th17) cells and emphasizes their diverse effector phenotype, which makes them an attractive system to query with computationally-informed metabolic methods. A Compass-based study reveals a parallel diversity of metabolic phenotypes within the Th17 cell type. In the next chapters we demonstrate our computational framework’s ability to discover metabolic regulators of Th17 inflammatory potential (pathogenicity). Chapters 4 and 5 complement one another; chapter 4 restricts its analysis to the well-studied central carbon metabolism pathways, whereas chapter 5 presents an unsupervised network-wide analysis that uncovers a novel metabolic regulator in the peripheral polyamine pathway. Chapter 6 follows on this discovery and shows that chemical and genetic perturbations of the polyamine pathway lead to a sizable shift in both the molecular and effector profiles of Th17 cells. It is suggested that polyamine metabolism might be a novel therapeutic target in autoimmune disorders by showing that in vivo inhibition of two different enzymes in the pathway alleviates experimental autoimmune encephalomyelitis (EAE) – a murine model for human multiple sclerosis.