The Emerging Facets of Non-Cancerous Warburg Effect

Abstract

The Warburg effect (WE), or aerobic glycolysis, is commonly recognized as a hallmark of cancer and has been extensively studied for potential anti-cancer therapeutics development. Beyond cancer, the WE plays an important role in many other cell types involved in immunity, angiogenesis, pluripotency, and infection by pathogens (e.g., malaria). Here, we review the WE in non-cancerous context as a "hallmark of rapid proliferation." We observe that the WE operates in rapidly dividing cells in normal and pathological states that are triggered by internal and external cues. Aerobic glycolysis is also the preferred metabolic program in the cases when robust transient responses are needed. We aim to draw attention to the potential of computational modeling approaches in systematic characterization of common metabolic features beyond the WE across physiological and pathological conditions. Identification of metabolic commonalities across various diseases may lead to successful repurposing of drugs and biomarkers.

Keywords: Warburg effect; angiogenesis; cancer; constraint-based metabolic modeling; immune cells; malaria; pluripotency; rapid proliferation.

Figure 1

Warburg effect (WE) in cancerous and non-cancerous cells. (A) The frequency of publications on the WE in cancer cells has not witnessed a parallel surge in studies investigating the same phenomenon in non-cancerous context. (B) WE has been observed across a plethora of rapidly proliferating cells in both physiological and pathological contexts. T-cells shape is adopted from Ref. (9), the malaria life cycle stages image is adopted from Ref. (10), and the blood vessel icon is adopted from Ref. (11). ES, embryonic stem cells; iPS, induced pluripotent stem cells.

Figure 2

Proposed computational metabolic modeling approach to systematically identify common metabolic features within pathological conditions as well as across normal and disease states. Starting with a curated human metabolic network, high-throughput data specific for each normal tissue (e.g., proliferating endothelial cells and effector T lymphocytes) or disease (e.g., cancer, autoimmune diseases, and inflammation) will be used to develop the corresponding context-specific metabolic network which is amenable to simulations under the constraint-based modeling framework being subject to different levels of constraints. By assigning an appropriate objective function (e.g., biomass production), it is possible to enumerate metabolic processes that are tightly coupled to growth and proliferation. Eventually, since all context-specific models are developed under a uniform integrative framework, it is legitimate to cross compare metabolic networks potentially identifying common metabolic features (e.g., aerobic glycolysis). NEAAs, non-essential amino acids metabolism.