Skip to content
#protein folding Open access

DMT1 Facilitates Heme-iron Absorption in Rats and Mice.

Oct 2026 · Blood · 0 citations
Medicine

Abstract

Mechanisms of heme-iron (HI) absorption remain uncertain. Heme is probably absorbed by receptor-mediated endocytosis; iron is then enzymatically liberated from heme within endosomes, or in the cytosol. If heme is catabolized in endosomes, a transporter, such as DMT1, would be required to export ionic iron into the cytosol. Here, we tested the hypothesis that intestinal DMT1 functions in the HI absorption pathway. The first experimental approach utilized Belgrade (b) rats, expressing dysfunctional DMT1 protein, and the second used conditional, intestine-specific DMT1 KO mice. HI absorption experiments were carried out with radiolabeled, donor rat RBCs, containing ~98% of iron as HI and with >94% of activity in 59Fe-heme. Iron-deficient, anemic +/b (control) rats absorbed 10.8% of a peroral dose of 59Fe-heme within 24 hours, while similarly anemic b/b rats absorbed 1.4% (an 8-fold reduction). Notably, serum hepcidin levels were invariable between both groups of rats (and 10x lower than controls). Moreover, ablation of intestinal DMT1 led to iron-restricted erythropoiesis and severe anemia in adolescent mice weaned on to nonheme iron (NHI) or HI diets. DMT1 was also required to upregulate 59Fe-heme absorption in iron-deficient, anemic mice. DMT1 thus facilitates the absorption of both main forms of dietary iron, NHI and HI.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.