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Cadmium (Cd) is an environmental pollutant that damages various tissues. Cd may cause a depletion of iron stores and subsequently an iron deficiency state in the liver. However, the molecular mechanism of decreased iron accumulation in the liver induced by long-term exposure to Cd is unknown. In this study, we investigated the hepatic accumulation of iron and the proximal duodenal expression of the genes involved in iron transport using mice chronically exposed to Cd. Five-week-old female C57BL/6J mice were fed a diet containing 300 ppm Cd for 12, 15, 19 and 21 months. The iron concentration in the liver was markedly decreased by Cd. Among iron-transport-related genes in the proximal duodenum, the gene expression of HCP1 and Cybrd1 was significantly decreased by Cd. HCP1 is an influx transporter of heme iron. Cybrd1 is a reductase that allows non-heme iron to enter cells. The expression of iron-transport-related genes on the duodenal basolateral membrane side was hardly altered by Cd. These results suggest that long-term exposure to Cd suppresses the expression of HCP1 and Cybrd1 in the proximal duodenum, resulting in reduced iron absorption and iron accumulation in the liver. [ABSTRACT FROM AUTHOR]

To elucidate the dynamic evolution of cancer cell characteristics within the tumor microenvironment (TME), we developed an integrative approach combining single-cell tracking, cell fate simulation, and three-dimensional (3D) TME modeling. We began our investigation by analyzing the spatiotemporal behavior of individual cancer cells in cultured pancreatic (MiaPaCa2) and cervical (HeLa) cancer cell lines, with a focus on the α2-6 sialic acid (α2-6Sia) modification on glycans, which is associated with cell stemness. Our findings revealed that MiaPaCa2 cells exhibited significantly higher levels of α2-6Sia modification, correlating with enhanced reproductive capabilities, whereas HeLa cells showed less prevalence of this modification. To accommodate the in vivo variability of α2-6Sia levels, we employed a cell fate simulation algorithm that digitally generates cell populations based on our observed data while varying the level of sialylation, thereby simulating cell growth patterns. Subsequently, we performed a 3D TME simulation with these deduced cell populations, considering the microenvironment that could impact cancer cell growth. Immune cell landscape information derived from 193 cervical and 172 pancreatic cancer cases was used to estimate the degree of the positive or negative impact. Our analysis suggests that the deduced cells generated based on the characteristics of MiaPaCa2 cells are less influenced by the immune cell landscape within the TME compared to those of HeLa cells, highlighting that the fate of cancer cells is shaped by both the surrounding immune landscape and the intrinsic characteristics of the cancer cells., Competing Interests: Conflict of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)