Defective iron homeostasis and hematological abnormalities in Niemann-Pick disease type C1.
Oscar C W ChenStephan SiebelAlexandria ColacoElena-Raluca NicoliNick PlattDawn ShepherdStephanie NewmanAndrew E ArmitageNicole Y FarhatGeorge SeligmannClaire SmithDavid A SmithAlaa Abdul-SadaMylvaganam JeyakumarHal DrakesmithForbes D PorterFrances Mary PlattPublished in: Wellcome open research (2022)
Background : Niemann-Pick disease type C1 (NPC1) is a neurodegenerative lysosomal storage disorder characterized by the accumulation of multiple lipids in the late endosome/lysosomal system and reduced acidic store calcium. The lysosomal system regulates key aspects of iron homeostasis, which prompted us to investigate whether there are hematological abnormalities and iron metabolism defects in NPC1. Methods : Iron-related hematological parameters, systemic and tissue metal ion and relevant hormonal and proteins levels, expression of specific pro-inflammatory mediators and erythrophagocytosis were evaluated in an authentic mouse model and in a large cohort of NPC patients. Results : Significant changes in mean corpuscular volume and corpuscular hemoglobin were detected in Npc1 -/- mice from an early age. Hematocrit, red cell distribution width and hemoglobin changes were observed in late-stage disease animals. Systemic iron deficiency, increased circulating hepcidin, decreased ferritin and abnormal pro-inflammatory cytokine levels were also found. Furthermore, there is evidence of defective erythrophagocytosis in Npc1 -/- mice and in an in vitro NPC1 cellular model. Comparable hematological changes, including low normal serum iron and transferrin saturation and low cerebrospinal fluid ferritin were confirmed in NPC1 patients. Conclusions : These data suggest loss of iron homeostasis and hematological abnormalities in NPC1 may contribute to the pathophysiology of this disease.
Keyphrases
- iron deficiency
- end stage renal disease
- newly diagnosed
- mouse model
- ejection fraction
- peritoneal dialysis
- chronic kidney disease
- stem cells
- prognostic factors
- type diabetes
- cerebrospinal fluid
- adipose tissue
- insulin resistance
- skeletal muscle
- patient reported outcomes
- big data
- fatty acid
- deep learning
- long non coding rna
- bone marrow
- cell therapy
- drug induced
- machine learning
- polycystic ovary syndrome
- artificial intelligence