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Received today — 7 October 2026 ⏭

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
3 October 2026 at 18:00

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

Received — 15 September 2026 ⏭

MMSDH facilitates ACSL4 propionylation to counteract ferroptosis upon hypoxia and impairs PDAC chemotherapy efficacy

✇Nature Cancer
By: Peixiang Zheng · Yanni Lin · Meiyin Zhang · Zhiyuan Tang · Wenxing Qin · Min Xiao · Xue Sun · Yuli Shen · Zhiqiang Hu · Shuo Ru · Yuran Duan · Bofei Dong · Ting Wen · Mengdie Li · Hong Wu · Yueru Hou · Guimei Ji · Zheng Wang · Xiaohong Wu · Qimin Zhou · Xiaoguang Liu · Gaopeng Li · Yuhao Wang · Shengzhong Duan · Yang Luo · Bo Chu · Xu Qian · Zhimin Lu · Yuan Ding · Chuan Xu · Tong Liu · Daqian Xu
11 September 2026 at 08:00

Nature Cancer, Published online: 11 September 2026; doi:10.1038/s43018-026-01236-w

Zheng et al. describe how hypoxia-induced methylmalonate semialdehyde dehydrogenase lactylation promotes acyl-CoA synthetase long-chain family member 4 propionylation and degradation, thereby suppressing ferroptosis induced by chemotherapy, and develop a blocking peptide that increased chemotherapy efficacy in pancreatic ductal adenocarcinoma.
Received — 27 May 2026 ⏭

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongyi He · Bin Jiang · Kecheng Feng · Luyin Zhang · Ling Liu · Yuxuan Li · Yun Zhao · He Yan
26 May 2026 at 12:00
arXiv:2603.03354v3 Announce Type: replace Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet
Received — 13 April 2026 ⏭

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

✇Omics In Lung
By: Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo
9 April 2026 at 18:00

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo
9 April 2026 at 18:00

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

Received — 5 March 2026 ⏭

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongyi He · Bin Jiang · Kecheng Feng · Luyin Zhang · Ling Liu · Yuxuan Li · Yun Zhao · He Yan
5 March 2026 at 13:00
arXiv:2603.03354v1 Announce Type: new Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet
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