bims-traimu 2025-11-09 papers

Issue of 2025–11–09
six papers selected by
Yantong Wan, Southern Medical University

Elife. 2025 Nov 05. pii: e106029. [Epub ahead of print]14

Evolving our understanding of trained immunity.

Eva Kaufmann, Yahya Sohrabi, Jorge Domínguez-Andrés, Boris Novakovic, Mihai G Netea, Jos W M van der Meer.

The articles in this focus issue discuss progress towards a more complete understanding of memory in the innate immune system, and efforts to exploit "trained immunity" for the development of new vaccines and therapeutics.

Keywords: epigenetic changes; immunology; immunotherapy; infectious disease; inflammation; innate immune cells; microbiology; trained immunity

DOI: https://doi.org/10.7554/eLife.106029

Trends Immunol. 2025 Nov 06. pii: S1471-4906(25)00248-0. [Epub ahead of print]

DNA methylation and histone modifications drive the trained immunity duration.

Mohua Liu, Xihui Shen, Lei Xu.

Trained immunity (TRIM) is a de facto form of innate immune memory. While histone modifications contribute to TRIM, their reversible nature and susceptibility to dilution during cell division cannot fully account for its long-term persistence. Here, we propose that DNA methylation patterns, particularly hypomethylation at proinflammatory gene loci, could serve as a key epigenetic mechanism contributing to long-term TRIM. Mechanistically, these hypomethylated states are biochemically stable and faithfully inherited through cell division, acting as a permissive scaffold that enables the rapid accumulation of activating histone marks upon restimulation. This DNA-methylation-mediated process could underpin the durability of TRIM across multiple contexts, including hematopoietic stem cell self-renewal, differentiation from central to peripheral compartments, and autonomy of tissue-resident cells.

Keywords: DNA methylation; epigenetic dynamics; histone modification; innate immune response; long-term trained immunity; trained immunity

DOI: https://doi.org/10.1016/j.it.2025.10.004

Cell Rep. 2025 Nov 04. pii: S2211-1247(25)01289-6. [Epub ahead of print]44(11): 116518

Trained immunity in myocardial infarction: From mechanisms to translational potential.

Shihan Xu, Yanfei Liu, Mengmeng Zhu, Fengqin Xu, Yue Liu.

Myocardial infarction (MI) ranks among the leading causes of death globally, with its prognosis closely linked to inflammatory responses. Both excessive early inflammation and persistent residual inflammation lead to adverse cardiac remodeling and heart failure. In recent years, the role of trained immunity in cardiovascular risk factors and MI-associated inflammatory responses has garnered increasing attention. Cardiovascular risk factors can induce trained immunity, placing the body in a "preactivated" innate immune state prior to MI occurrence. This state is further amplified upon myocardial necrosis, triggering excessive inflammation. Additionally, MI itself can induce trained immunity, leading to long-term inflammatory memory and residual inflammation risk. Targeting metabolic and epigenetic pathways of trained immunity offers approaches for post-MI anti-inflammatory interventions. A comprehensive understanding of trained immunity's mechanisms in MI holds promise for establishing theoretical foundations and translational directions for precision anti-inflammatory therapies and improving long-term patient outcomes.

Keywords: CP: immunology; inflammation; myocardial infarction; risk factors; trained immunity

DOI: https://doi.org/10.1016/j.celrep.2025.116518

Signal Transduct Target Ther. 2025 Nov 05. 10(1): 362

A novel long-acting C5a-blocking cyclic peptide prevents sepsis-induced organ dysfunction via effective blockade of the inflammatory cascade.

Zimiao Luo, Pengfei Luo, Haoyu Gu, Xiaoyan Hu, Shichu Xiao, Weiyue Lu, Zhaofan Xia.

Sepsis is a life-threatening syndrome characterized by dysregulated host responses to infection, leading to severe organ dysfunction and a high mortality rate. Reducing the incidence of sepsis is of paramount importance. Given that sepsis-associated drugs largely fail in clinical trials, in this project, we devised and validated a novel long-acting C5a-blocking cyclic peptide drug (Cp1) via phage screening technology to block the upstream "bottleneck molecule" C5a-mediated amplification cascade of the inflammatory response. In the early stage of infection, we utilized the efficient neutralization of Cp1 against C5a to effectively curb the "waterfall effect" of inflammatory factors and mitigate the progression to dysregulated systemic inflammation, thereby providing effective prevention and therapeutic intervention for sepsis. First, in vitro and in vivo studies collectively demonstrated the optimal binding affinity and blocking selectivity of Cp1. The excellent plasma stability of Cp1 further endows it with antibody-like systemic circulation. In the CLP-induced sepsis model, Cp1 significantly suppressed the expression of inflammatory factors and chemokines in both plasma and peritoneal lavage fluid (PLF). Additionally, Cp1 potently inhibited innate immune injury. Ultimately, after a single administration of Cp1, the CLP-induced septic mice presented a significant reduction in bacterial burden, evident amelioration of organ dysfunction, and notable prolongation of survival time. Overall, the novel cyclic peptide drug Cp1 developed in this study is a highly promising and cost-competitive therapeutic option for sepsis prophylaxis and therapy.

DOI: https://doi.org/10.1038/s41392-025-02457-8

Cell Syst. 2025 Nov 05. pii: S2405-4712(25)00276-5. [Epub ahead of print] 101443

scCausalVI disentangles single-cell perturbation responses with causality-aware generative model.

Shaokun An, Jae-Won Cho, Kai Cao, Jiankang Xiong, Martin Hemberg, Lin Wan.

Single-cell RNA sequencing provides detailed insights into cellular heterogeneity and responses to external stimuli. However, distinguishing inherent cellular variation from extrinsic effects induced by external stimuli remains a major analytical challenge. Here, we present scCausalVI, a causality-aware generative model designed to disentangle these sources of variation. scCausalVI decouples intrinsic cellular states from treatment effects through a deep structural causal network that explicitly models the causal mechanisms governing cell-state-specific responses to external perturbations while accounting for technical variations. Our model integrates structural causal modeling with cross-condition in silico prediction to infer gene expression profiles under hypothetical scenarios. Comprehensive benchmarking demonstrates that scCausalVI outperforms existing methods in disentangling causal relationships, quantifying treatment effects, generalizing to unseen cell types, and separating biological signals from technical variation in multi-source data integration. Applied to COVID-19 datasets, scCausalVI effectively identifies treatment-responsive populations and delineates molecular signatures of cellular susceptibility.

Keywords: causal disentanglement; cell-state-specific treatment effect; deep structural causal model; in silico perturbation; multi-source data integration; out-of-distribution prediction; perturbational analysis; responsive cell identification

DOI: https://doi.org/10.1016/j.cels.2025.101443