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Iron is considered as attractive energy carrier in a carbon-free circular energy economy. The reduction of iron oxide is crucial for its applica-tion as a metal fuel as it determines the efficiency of the cycle. Temperature programmed reduction of α-Fe2O3 was monitored by complementary X-ray absorption spectroscopy (XAS) and diffraction (XRD) to obtain the phase composition with high time resolution. Synchrotron Mössbauer spectroscopy (SMS) was additionally employed due to its high sensitivity to the different iron species. Theoretical calculations of surface and bulk adsorption processes were performed to establish the potential reaction pathways and the corresponding energy barriers. A kinetic particle model was then developed to bridge the experimental data and theoretical calculations, which reproduced the reduction onset and behavior. The reduction process was found to be strongly dependent on the heating rate in terms of the reduction window and the observed intermediate species. We propose that a core-shell mechanism determines the reaction by forming an iron layer which subsequently hinders diffusion of water out of the porous particles leading to some unreduced FeO at high temperature. This study demonstrates the need for complementary methods for describing complex heterogeneous systems and overcoming the chemical sensitivity limitations of any single method.
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Leukemia relapse is a major cause of death after allogeneic hematopoietic cell transplantation (allo-HCT). We tested the potential of targeting T cell (Tc) immunoglobulin and mucin-containing molecule 3 (TIM-3) for improving graft-versus-leukemia (GVL) effects. We observed differential expression of TIM-3 ligands when hematopoietic stem cells overexpressed certain oncogenic-driver mutations. Anti-TIM-3 Ab treatment improved survival of mice bearing leukemia with oncogene-induced TIM-3 ligand expression. Conversely, leukemia cells with low ligand expression were anti-TIM-3 treatment resistant. In vitro, TIM-3 blockade or genetic deletion in CD8+ Tc enhanced Tc activation, proliferation, and IFN-γ production while enhancing GVL effects, preventing Tc exhaustion, and improving Tc cytotoxicity and glycolysis in vivo. Conversely, TIM-3 deletion in myeloid cells did not affect allogeneic Tc proliferation and activation in vitro, suggesting that anti-TIM-3 treatment-mediated GVL effects are Tc induced. In contrast to anti-programmed cell death protein 1 (anti-PD-1) and anti-cytotoxic T lymphocyte-associated protein 4 (anti-CTLA-4) treatment, anti-TIM-3-treatment did not enhance acute graft-versus-host disease (aGVHD). TIM-3 and its ligands were frequently expressed in acute myeloid leukemia (AML) cells of patients with post-allo-HCT relapse. We decipher the connections between oncogenic mutations found in AML and TIM-3 ligand expression and identify anti-TIM-3 treatment as a strategy for enhancing GVL effects via metabolic and transcriptional Tc reprogramming without exacerbation of aGVHD. Our findings support clinical testing of anti-TIM-3 Ab in patients with AML relapse after allo-HCT.
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Receptor 2 Celular del Virus de la Hepatitis A , Animales , Receptor 2 Celular del Virus de la Hepatitis A/genética , Receptor 2 Celular del Virus de la Hepatitis A/metabolismo , Ratones , Trasplante de Células Madre Hematopoyéticas , Efecto Injerto vs Leucemia/inmunología , Efecto Injerto vs Leucemia/genética , Humanos , Aloinjertos , Ligandos , Oncogenes , Linfocitos T CD8-positivos/inmunología , Ratones Noqueados , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/inmunología , Leucemia Mieloide Aguda/metabolismo , Leucemia Mieloide Aguda/terapia , Leucemia Mieloide Aguda/patología , Antígeno CTLA-4/genética , Antígeno CTLA-4/inmunología , Antígeno CTLA-4/metabolismo , Antígeno CTLA-4/antagonistas & inhibidores , Regulación Leucémica de la Expresión GénicaRESUMEN
Cancer immunotherapy with chimeric antigen receptor (CAR) T cells can cause immune effector cell-associated neurotoxicity syndrome (ICANS). However, the molecular mechanisms leading to ICANS are not well understood. Here we examined the role of microglia using mouse models and cohorts of individuals with ICANS. CD19-directed CAR (CAR19) T cell transfer in B cell lymphoma-bearing mice caused microglia activation and neurocognitive deficits. The TGFß-activated kinase-1 (TAK1)-NF-κB-p38 MAPK pathway was activated in microglia after CAR19 T cell transfer. Pharmacological TAK1 inhibition or genetic Tak1 deletion in microglia using Cx3cr1CreER:Tak1fl/fl mice resulted in reduced microglia activation and improved neurocognitive activity. TAK1 inhibition allowed for potent CAR19-induced antilymphoma effects. Individuals with ICANS exhibited microglia activation in vivo when studied by translocator protein positron emission tomography, and imaging mass cytometry revealed a shift from resting to activated microglia. In summary, we prove a role for microglia in ICANS pathophysiology, identify the TAK1-NF-κB-p38 MAPK axis as a pathogenic signaling pathway and provide a rationale to test TAK1 inhibition in a clinical trial for ICANS prevention after CAR19 T cell-based cancer immunotherapy.
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Acute graft-versus-host disease (aGVHD) is a life-threatening complication of allogeneic hematopoietic cell transplantation (allo-HCT), for which therapeutic options are limited. Strategies to promote intestinal tissue tolerance during aGVHD may improve patient outcomes. Using single-cell RNA sequencing, we identified a lipocalin-2 (LCN2)-expressing neutrophil population in mice with intestinal aGVHD. Transfer of LCN2-overexpressing neutrophils or treatment with recombinant LCN2 reduced aGVHD severity, whereas the lack of epithelial or hematopoietic LCN2 enhanced aGVHD severity and caused microbiome alterations. Mechanistically, LCN2 induced insulin-like growth factor 1 receptor (IGF-1R) signaling in macrophages through the LCN2 receptor SLC22A17, which increased interleukin-10 (IL-10) production and reduced major histocompatibility complex class II (MHCII) expression. Transfer of LCN2-pretreated macrophages reduced aGVHD severity but did not reduce graft-versus-leukemia effects. Furthermore, LCN2 expression correlated with IL-10 expression in intestinal biopsies in multiple cohorts of patients with aGVHD, and LCN2 induced IGF-1R signaling in human macrophages. Collectively, we identified a LCN2-expressing intestinal neutrophil population that reduced aGVHD severity by decreasing MHCII expression and increasing IL-10 production in macrophages. This work provides the foundation for administration of LCN2 as a therapeutic approach for aGVHD.
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Enfermedad Injerto contra Huésped , Trasplante de Células Madre Hematopoyéticas , Humanos , Animales , Ratones , Neutrófilos/patología , Interleucina-10 , Lipocalina 2/genética , Enfermedad Injerto contra Huésped/genética , Macrófagos/patología , Enfermedad AgudaRESUMEN
Transglutaminase 2 (TG2) plays a vital role in stabilizing extracellular matrix (ECM) proteins through enzymatic crosslinking during tissue growth, repair, and inflammation. TG2 also binds non-covalently to fibronectin (FN), an essential component of the ECM, facilitating cell adhesion, migration, proliferation, and survival. However, the interaction between TG2 and fibrillar FN remains poorly understood, as most studies have focused on soluble or surface-adsorbed FN or FN fragments, which differ in their conformations from insoluble FN fibers. Using a well-established in vitro FN fiber stretch assay, we discovered that the binding of a crosslinking enzyme to ECM fibers is mechano-regulated. TG2 binding to FN is tuned by the mechanical tension of FN fibers, whereby TG2 predominantly co-localizes to low-tension FN fibers, while fiber stretching reduces their affinity for TG2. This mechano-regulated binding relies on the proximity between the N-terminal ß-sandwich and C-terminal ß-barrels of TG2. Crosslinking mass spectrometry (XL-MS) revealed a novel TG2-FN synergy site within TG2's C-terminal ß-barrels that interacts with FN regions located outside of the canonical gelatin binding domain, specifically FNI2 and FNIII14-15. Combining XL-MS distance restraints with molecular docking revealed the mechano-regulated binding mechanism between TG2 and modules FNI7-9 by which mechanical forces regulate TG2-FN interactions. This highlights a previously unrecognized role of TG2 as a tension sensor for FN fibers. This novel interaction mechanism has significant implications in physiology and mechanobiology, including how forces regulate cell adhesion, spreading, migration, phenotype modulation, depending on the tensional state of ECM fibers. Data are available via ProteomeXchange with identifier PXD043976.
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Fibronectinas , Proteína Glutamina Gamma Glutamiltransferasa 2 , Fibronectinas/metabolismo , Transglutaminasas/genética , Transglutaminasas/química , Transglutaminasas/metabolismo , Simulación del Acoplamiento Molecular , Proteínas de Unión al GTP/genética , Proteínas de Unión al GTP/química , Proteínas de Unión al GTP/metabolismo , Proteínas de la Matriz Extracelular/metabolismoRESUMEN
Learning in deep neural networks is known to depend critically on the knowledge embedded in the initial network weights. However, few theoretical results have precisely linked prior knowledge to learning dynamics. Here we derive exact solutions to the dynamics of learning with rich prior knowledge in deep linear networks by generalising Fukumizu's matrix Riccati solution (Fukumizu 1998 Gen 1 1E-03). We obtain explicit expressions for the evolving network function, hidden representational similarity, and neural tangent kernel over training for a broad class of initialisations and tasks. The expressions reveal a class of task-independent initialisations that radically alter learning dynamics from slow non-linear dynamics to fast exponential trajectories while converging to a global optimum with identical representational similarity, dissociating learning trajectories from the structure of initial internal representations. We characterise how network weights dynamically align with task structure, rigorously justifying why previous solutions successfully described learning from small initial weights without incorporating their fine-scale structure. Finally, we discuss the implications of these findings for continual learning, reversal learning and learning of structured knowledge. Taken together, our results provide a mathematical toolkit for understanding the impact of prior knowledge on deep learning.