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BAF60A governs beta cell identity to control systemic glucose homeostasis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Shuaishuai Zhu · Ziyin Zhang · Ruo-Ran Wang · Wei Chen · Qiyao Xu · Xinyuan Qiu · Xiuyu Ji · Rui Zhang · Rui Wei · Hongxing Fu · Sheng Yan · Zhuo-Xian Meng

Diabetologia. 2026 Oct 3. doi: 10.1007/s00125-026-06884-2. Online ahead of print.

ABSTRACT

AIMS/HYPOTHESIS: Chromatin remodelling is critical for maintaining pancreatic beta cell identity and function, yet the key regulatory mechanisms remain incompletely defined. This study aimed to investigate the role of the switch/sucrose non-fermentable (SWI/SNF) complex subunit BAF60A in preserving beta cell fate and glucose homeostasis.

METHODS: Pdx1-Cre-mediated BAF60A-knockout (BaBKO) and BAF60A-overexpressing (BaBOE) mice, together with tamoxifen-inducible adult beta cell-specific Smarcd1 knockout (BaBKOTM) and Isl1 knockout (Isl1BKOTM) mice, were generated to evaluate the role of BAF60A in vivo. Glucose homeostasis was assessed through glucose tolerance tests, insulin tolerance tests and glucose-stimulated insulin secretion (GSIS) assays. Multiomic analyses, including RNA-seq, ATAC-seq, Cleavage Under Targets and Tagmentation (CUT&Tag) and single-cell RNA-seq, were performed to characterise chromatin accessibility and transcriptional changes. BAF60A-interacting proteins were identified with biotin identification (BioID) and GST pull-down assays. Beta cell lineage tracing was used to assess changes in cell identity. In addition, BAF60A and the dedifferentiation marker ALDH1A3 were examined in pancreatic islets from individuals with and without type 2 diabetes.

RESULTS: BaBKO mice exhibited significant glucose intolerance, impaired GSIS and pronounced loss of beta cell identity, accompanied by the acquisition of non-beta endocrine features. Inducible deletion of Smarcd1 in adult beta cells similarly impaired beta cell maturation and promoted dedifferentiation, as confirmed by lineage tracing. BAF60A deficiency reduced enhancer accessibility and downregulated beta cell identity genes. Mechanistically, BAF60A physically interacts with the transcription factor islet-1 (ISL1) to regulate transcription of target genes. Adult beta cell-specific Isl1 deletion recapitulated key features of BAF60A deficiency and abolished the beneficial effect of BAF60A overexpression on insulin secretion. Conversely, BaBOE mice exhibited improved glucose tolerance and enhanced GSIS under high-fat diet conditions. Adeno-associated virus-mediated BAF60A overexpression markedly reduced beta cell dedifferentiation in BKS-db/db mice. In human type 2 diabetes islets, BAF60A expression was significantly reduced and inversely correlated with ALDH1A3.

CONCLUSIONS/INTERPRETATION: This work establishes BAF60A-ISL1-dependent chromatin remodelling as a key mechanism that preserves beta cell identity and function under metabolic stress, providing mechanistic insight into beta cell failure in type 2 diabetes.

PMID:42829354 | DOI:10.1007/s00125-026-06884-2

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KIFC1 engages RUNX2/TGF-β signaling to promote lung cancer bone metastasis via disrupting bone homeostasis

✇Oncogene - Issue - nature.com science feeds
By: Jinyan Feng · Qingqian Zhao · Jinwu Wang · Zeyang Li · Houzhi Yang · Yuxiang Shen · Xiaoyu Yang · Yiqin Li · Yao Xu · Xiuxin Han · Chao Zhang · Guowen Wang

Oncogene, Published online: 25 September 2026; doi:10.1038/s41388-026-03998-0

KIFC1 engages RUNX2/TGF-β signaling to promote lung cancer bone metastasis via disrupting bone homeostasis
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Skin-innervating glutamatergic neurons modulate aging

✇Cell
By: Zhikai Wang · Xinping Jin · Yue Wu · Wenxiang Ding · Fengjiao Zhang · Yang Sun · Fangzhou Lou · Jing Bai · Xiuli Yang · Hong Zhou · Jihuan Liang · Xiaojie Cai · Yan Li · Xichen Zheng · Jiajia Han · Fang Tong · Qing Yang · Zhixiao Fang · Liuqi Zhao · Qingqing Shen · Zhenyao Xu · Siyu Deng · Li Fan · Jiajia Tong · Linhui Tong · Junjie Wang · Min Gao · Zilong Fang · Qun Li · Rui Chen · Lina Huang · Zhen Li · Yuqin Yang · Jing Zou · Ying Wu · Xiwen Zhang · Honglin Wang
Within the skin, glutamatergic neurons expressing neurofilament heavy chain (Nefh) play a role in aging. Loss of Nefh during aging drives skin fibroblast senescence and collagen loss, whereas glutamate supplementation improves skin aging phenotypes.
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No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tiankai Yang · Jiate Li · Yi Nian · Shen Dong · Ruiyao Xu · Ryan Rossi · Kaize Ding · Yue Zhao
arXiv:2604.01350v1 Announce Type: cross Abstract: LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serves multiple users within a team or organization, reusing a shared knowledge layer across user identities. This shared persistence expands the failure surface: information that is locally valid for one user can silently degrade another user's outcome when the agent reapplies it without regard for scope. We refer to this failure mode as unintentional cross-user contamination (UCC). Unlike adversarial memory poisoning, UCC requires no attacker; it arises from benign interactions whose scope-bound artifacts persist and are later misapplied. We formalize UCC through a controlled evaluation protocol, introduce a taxonomy of three contamination types, and evaluate the problem in two shared-state mechanisms. Under raw shared state, benign interactions alone produce contamination rates of 57--71%. A write-time sanitization is effective when shared state is conversational, but leaves substantial residual risk when shared state includes executable artifacts, with contamination often manifesting as silent wrong answers. These results indicate that shared-state agents need artifact-level defenses beyond text-level sanitization to prevent silent cross-user failures.
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SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qingsong Zou · Zhi Yan · Zhiyao Xu · Kuofeng Gao · Jingyu Xiao · Yong Jiang
arXiv:2603.06636v1 Announce Type: cross Abstract: Due to the strong context-awareness capabilities demonstrated by large language models (LLMs), recent research has begun exploring their integration into smart home assistants to help users manage and adjust their living environments. While LLMs have been shown to effectively understand user needs and provide appropriate responses, most existing studies primarily focus on interpreting and executing user behaviors or instructions. However, a critical function of smart home assistants is the ability to detect when the home environment is in an anomalous state. This involves two key requirements: the LLM must accurately determine whether an anomalous condition is present, and provide either a clear explanation or actionable suggestions. To enhance the anomaly detection capabilities of next-generation LLM-based smart home assistants, we introduce SmartBench, which is the first smart home dataset designed for LLMs, containing both normal and anomalous device states as well as normal and anomalous device state transition contexts. We evaluate 13 mainstream LLMs on this benchmark. The experimental results show that most state-of-the-art models cannot achieve good anomaly detection performance. For example, Claude-Sonnet-4.5 achieves only 66.1% detection accuracy on context-independent anomaly categories, and performs even worse on context-dependent anomalies, with an accuracy of only 57.8%. More experimental results suggest that next-generation LLM-based smart home assistants are still far from being able to effectively detect and handle anomalous conditions in the smart home environment. Our dataset is publicly available at https://github.com/horizonsinzqs/SmartBench.
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aCAPTCHA: Verifying That an Entity Is a Capable Agent via Asymmetric Hardness

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zuyao Xu · Xiang Li · Fubin Wu · Yuqi Qiu · Lu Sun · FaSheng Miao
arXiv:2603.07116v1 Announce Type: cross Abstract: As autonomous AI agents increasingly populate the Internet, a novel security challenge arises: "Is this entity an AI agent?" It is a new entity-type verification problem with no established solution. We formalize the problem through a three-class entity taxonomy (Human, Script, Agent) based on a verifiable agentic capability vector (action, reasoning, and memory). A timing threshold t exploits the asymmetric hardness between human cognition and AI processing to separate the three classes. We define the Agentic Capability Verification Problem (ACVP) through three necessity primitives, each testing one capability dimension. Building on this foundation, we introduce aCAPTCHA (Agent CAPTCHA), a time-constrained security game for agent admission whose security rests on ACVP hardness under t. We instantiate aCAPTCHA through time-bounded natural-language understanding as a multi-round HTTP verification protocol, and evaluate it with preliminary agent trials that validate the protocol's soundness and completeness. aCAPTCHA provides a composable, infrastructure-free admission gate for any service where entity-type verification is required.
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