Metabolomic and molecular insights into sickle cell disease and innovative therapies

Abstract

Sickle cell disease (SCD) is an autosomal-recessive hemolytic disorder with high morbidity and mortality. The pathophysiology of SCD is characterized by the polymerization of deoxygenated intracellular sickle hemoglobin, which causes the sickling of erythrocytes. The recent development of metabolomics, the newest member of the "omics" family, has provided a powerful new research strategy to accurately measure functional phenotypes that are the net result of genomic, transcriptomic, and proteomic changes. Metabolomics changes respond faster to external stimuli than any other "ome" and are especially appropriate for surveilling the metabolic profile of erythrocytes. In this review, we summarize recent pioneering research that exploited cutting-edge metabolomics and state-of-the-art isotopically labeled nutrient flux analysis to monitor and trace intracellular metabolism in SCD mice and humans. Genetic, structural, biochemical, and molecular studies in mice and humans demonstrate unrecognized intracellular signaling pathways, including purinergic and sphingolipid signaling networks that promote hypoxic metabolic reprogramming by channeling glucose metabolism to glycolysis via the pentose phosphate pathway. In turn, this hypoxic metabolic reprogramming induces 2,3-bisphosphoglycerate production, deoxygenation of sickle hemoglobin, polymerization, and sickling. Additionally, we review the detrimental role of an impaired Lands' cycle, which contributes to sickling, inflammation, and disease progression. Thus, metabolomic profiling allows us to identify the pathological role of adenosine signaling and S1P-mediated erythrocyte hypoxic metabolic reprogramming and hypoxia-induced impaired Lands' cycle in SCD.These findings further reveal that the inhibition of adenosine and S1P signaling cascade and the restoration of an imbalanced Lands' cycle have potent preclinical efficacy in counteracting sickling, inflammation, and disease progression.

In silico study of peculiarities of metabolism of erythrocytes with glucosephosphate isomerase deficiency

“…It is known that extracellular enzymes such as еcto-5'-nucleotidase (CD73) and ATPase/CD39 are part of the regulatory control of adenine nucleotide in erythrocytes. The extracellular AMP may also be dephosphorylated to adenosine by CD73 (Sun, et al, 2017;Adebiyi et al, 2019). Adenosine is transmitted through nucleoside transporters, such as ENT1/2, and rapidly catabolized by intracellular adenosine deaminase (ADA).…”

“…To achieve this aim, a stoichiometric model has been developed that includes the main metabolic systems of the erythrocyte: glycolytic and pentose phosphate pathways (PPP), adenylate metabolism, processes of synthesis and transformation of glutathione in the cell. Due to the fact that PGI directly affects the process of glucose uptake in the metabolic pathway, we took into account the possibility of using additional metabolites as energy substrates such as S-adenosylmethionine and adenosine (Komarova et al, 1999;Schuster & Kenanov, 2005;Sun et al, 2017;Adebiyi et al, 2019). The potential of the presented structural modeling tools for revealing possible sites of metabolic control is analyzed.…”

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

“…The metabolome is directly linked to perturbations in cell metabolism caused by environmental stimuli as well as disease, thus metabolomics has been successfully used to measure phenotypic outcomes in vitro and in vivo [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][40][41][42][43][44][45][46][47][48][49]. Examples include observations of oncometabolites, such as 2-hyroxybutyrate, sarcosine, choline, succinate, lactate, fumarate, and glucose, associated with tumor growth and metastasis that may also function as biomarkers for leukemia, renal carcinoma, breast, brain, or prostate cancer [26][27][28][29][30][31][32].…”

“…These technologies harvest substantial volumes of data at multiple biological levels and sift the resulting large data sets with informatics tools including artificial intelligence (AI). This intriguing approach has accelerated biomarker discovery and routinely incorporated in preclinical study workflows, including for the search for potential novel therapeutic targets and candidate drugs [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. The capability of metabolites to illustrate varied biological processes, such as cell differentiation and maturation, insulin signaling, T-cell survival, and macrophage immune responses is reported and extensively reviewed [20,21].…”

Sickle Cell Disease: Metabolomic Profiles of Vaso-Occlusive Crisis in Plasma and Erythrocytes

“…More than 100 biomarkers have been reported in sickle cell disease, such as the disruption of the arginine and nitric oxide (NO) metabolism at the origin of sickle cell vasculopathy [2,3]. Despite these profound biological changes accompanying the crisis, relatively few metabolomics studies, which were recently reviewed [4], have been conducted in sickle cell patients, and to our knowledge, none of them have specifically reported the biological fingerprint of the crisis, in comparison to the steady state of the disease.…”