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Precise hepatic base editing of ASGR1 enables robust and durable LDLR-independent lipid lowering in vivo

✇Molecular Therapy
By: Yaofeng Hou · Yaxin Luo · Jiabei Chen · Xindi Shang · Yan Chang · Xianfang Zhang · Yan Ding · Wenwen Zhao · Chengfeng Ding · Xuan Feng · Jia Chen · Jianfeng Li · Bei Yang
Yang and colleagues demonstrate that lipid nanoparticle-mediated precise hepatic ASGR1 base editing safely produces robust and durable lipid lowering in an LDLR-deficient mouse model of familial hypercholesterolemia. Their work further benchmarks the lipid-lowering effects of ASGR1 and ANGPTL3 editing and supports combined ASGR1/ANGPTL3 targeting for enhanced cholesterol lowering.
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Agrimol B induces autophagic death in TP53-mutant pancreatic cancer by targeting the S100A6-HDAC2-mutant p53 acetylation axis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Rui Xu · Liuyuan Xia · Wei Zhou · Xin Chen · Yixin Jiang · Yue Yan · Qingyi Xiong · Dong Lu · Hongzhuan Chen · Tong Wu · Jinmei Jin · Weidong Zhang · Xin Luan

Phytomedicine. 2026 Aug 26;161:158760. doi: 10.1016/j.phymed.2026.158760. Online ahead of print.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) harbors TP53 mutations at high frequency, yet therapeutic strategies that specifically target mutant p53 remain limited.

PURPOSE: This study aimed to identify S100A6, a calcium-binding protein frequently upregulated in TP53-mutant PDAC, as a critical regulator of mutant p53 stability and tumor progression, and to explore potential S100A6-targeting agents for therapeutic intervention.

METHODS: We integrated computer-assisted drug screening with transcriptomics, acetylation omics, and molecular biology techniques to identify Agrimol B (AgrB), a bioactive compound derived from the traditional Chinese herb Agrimonia pilosa Ledeb., as a potential S100A6-targeting agent.

RESULTS: High S100A6 expression was closely associated with poor prognosis in patients with TP53-mutant PDAC, whereas S100A6 depletion markedly suppressed PDAC cell growth and metastatic potential. Mechanistically, AgrB enhanced the interaction between S100A6 and the deacetylase HDAC2, leading to reduced acetylation of mutant p53 at lysine 382. This disruption activated autophagy-dependent cell death and thereby inhibited PDAC progression.

CONCLUSION: Our findings reveal an S100A6-HDAC2-mutant p53 acetylation axis that regulates TP53-mutant pancreatic tumorigenesis, providing mechanistic evidence supporting S100A6 as a therapeutic vulnerability and highlighting AgrB as a promising natural-product-derived candidate for further development against this aggressive malignancy.

PMID:42700714 | DOI:10.1016/j.phymed.2026.158760

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From the invasive front to organotropic pre-metastatic niches: spatial immune regulatory networks governing cholangiocarcinoma dissemination and metastasis-intercepting immunotherapy

✇Omics In Lung
By: Longhao Zhang · Kai Zhang · Zhihong Chen · Xin Lu

Front Immunol. 2026 Aug 20;17:1919864. doi: 10.3389/fimmu.2026.1919864. eCollection 2026.

ABSTRACT

Cholangiocarcinoma is an aggressive biliary tract malignancy in which metastatic relapse and primary or acquired resistance to immunotherapy remain major causes of mortality. Although immune checkpoint inhibitors have improved first-line treatment for advanced biliary tract cancer, most patients do not achieve durable benefit, indicating that immune failure is not explained by a single checkpoint pathway. In this Review, we propose a spatial immune-regulatory continuum for cholangiocarcinoma dissemination. Most direct single-cell and spatial evidence currently derives from intrahepatic cholangiocarcinoma, and its applicability to perihilar and distal disease remains to be established. This continuum begins in the tumor core and invasive front, where malignant cells, cancer-associated fibroblasts, tumor-associated macrophages, endothelial and lymphatic cells, regulatory T cells, immature neutrophils and excluded or dysfunctional cytotoxic T cells form a pro-invasive ecosystem. It then extends through extracellular vesicles, soluble mediators and lymphovascular routes that may educate organotropic pre-metastatic niches. Finally, lymph node, lung, liver, peritoneal and bone microenvironments provide organ-specific extracellular matrix, myeloid and stromal programs that enable immune evasion and metastatic colonization. By integrating clinical evidence, multi-omics studies, single-cell and spatial transcriptomics, extracellular vesicle biology, pre-metastatic niche concepts and emerging therapeutic strategies, we argue that cholangiocarcinoma metastasis should be targeted before overt dissemination whenever possible. In this Review, "metastasis-intercepting immunotherapy" is used as an author-defined conceptual framework for strategies intended to prevent or disrupt the immune-stromal conditions that enable dissemination and colonization, rather than merely shrink established metastatic lesions. Metastasis-intercepting immunotherapy will likely require rational combinations that reprogram the invasive front, restore dendritic-cell-mediated antigen presentation, block tumor-stroma-myeloid circuits, disrupt EV-mediated communication that may contribute to niche formation and select patients using spatial biomarkers rather than bulk immune markers alone.

PMID:42694469 | PMC:PMC13539491 | DOI:10.3389/fimmu.2026.1919864

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Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jihyun Janice Ahn · Ryo Kamoi · Berk Atil · Renze Lou · WonWoo Kang · Heehyun Park · Sarkar Snigdha Sarathi Das · Zhuoyang Zou · Xiaoxin Lu · Yusen Zhang · Asfahan Shah · Ridwanul Hasan Tanvir · Lingxiao Zhao · Hongxi Huang · Vignesh Venkatesh · Dianjun Lin · Hamid Shah · Wentao Wang · Zhanpeng Song · Joshua Reed Bassin · Dax Patel · Ishan Appareddy Agrahar · Sahil Pardasani · Xin Dong · Fatemeh Rahbari · Benjamin David Rishel · Soochan Andrew Lee · Yuv Boghani · Ali B. AlNaseeb · Pranav Suby · Seokhyeon Bae · Shreya Buddharaju · Damien Kula · Soumyadeep Das · Hanyang Frank Liu · Faye Mo · Wenpeng Yin
arXiv:2603.22619v1 Announce Type: new Abstract: LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify such issues, yet fail to reflect this in standard generative responses. This reveals a fundamental know-act gap between discriminative recognition and generative behavior. Prior work largely characterizes this issue in narrow settings, such as math word problems or question answering, with limited focus on how to integrate these two modes. In this work, we present a comprehensive analysis using FaultyScience, a newly constructed large-scale, cross-disciplinary benchmark of faulty scientific questions. We show that the gap is pervasive and stems from token-level autoregression, which entangles task selection (validate vs. answer) with content generation, preventing discriminative knowledge from being utilized. To address this, we propose DeIllusionLLM, a task-level autoregressive framework that explicitly models this decision. Through self-distillation, the model unifies discriminative judgment and generative reasoning within a single backbone. Empirically, DeIllusionLLM substantially reduces answer-despite-error failures under natural prompting while maintaining general reasoning performance, demonstrating that self-distillation is an effective and scalable solution for bridging the discriminative-generative know-act gap
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Dataset Distillation via Committee Voting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiacheng Cui · Zhaoyi Li · Xiaochen Ma · Xinyue Bi · Yaxin Luo · Zhiqiang Shen
arXiv:2501.07575v2 Announce Type: replace-cross Abstract: Dataset distillation aims to synthesize a compact yet representative dataset that preserves the essential characteristics of the original data for efficient model training. Existing methods mainly focus on improving data-synthetic alignment or scaling distillation to large datasets. In this work, we propose $\textbf{C}$ommittee $\textbf{V}$oting for $\textbf{D}$ataset $\textbf{D}$istillation ($\textbf{CV-DD}$), an orthogonal approach that leverages the collective knowledge of multiple models to produce higher-quality distilled data. We first establish a strong baseline that achieves state-of-the-art performance through modern architectural and optimization choices. By integrating distributions and predictions from multiple models and generating high-quality soft labels, our method captures a broader range of data characteristics, reduces model-specific bias and the impact of distribution shifts, and significantly improves generalization. This voting-based strategy enhances diversity and robustness, alleviates overfitting, and improves post-evaluation performance. Extensive experiments across multiple datasets and IPC settings demonstrate that CV-DD consistently outperforms single- and multi-model distillation methods and generalizes well to non-training-based frameworks and challenging synthetic-to-real transfer tasks. Code is available at: https://github.com/Jiacheng8/CV-DD.
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