Transketolase-like 1 potentiates PD-1 blockade in hepatocellular carcinoma by glycolysis to prime dendritic cell lactylation

By: Hengsong Cao · Tian Huang · Yue Chai · Zhengqing Lu · Shipeng Dai · Danyang Xu · Yanshu He · Na Yu · Yuliang Wang · Ziyuan Liu · Yidong Xia · Jie Zhao · Hongyu Wang · Dawei Rong · Yuchen Xie · Chongyu Wang · Pengyu Chen · Juyue Zhou · Li Liu · Zhuozheng Li · Hanyuan Liu · Xiao Li · Xuehao Wang · Weiwei Tang · Yongxiang Xia

27 September 2026 at 18:00

Signal Transduct Target Ther. 2026 Sep 28;11(1):418. doi: 10.1038/s41392-026-02875-2.

ABSTRACT

Hepatocellular carcinoma (HCC) exhibits a suboptimal response to immune checkpoint blockade (ICB) therapy; to overcome this resistance, we aimed to delineate key immune resistance factors via multi-omics analysis, develop strategies to block their immunosuppressive axes, and engineer a targeted nanosystem to enhance immunotherapy efficacy against PD-1 resistance in HCC. Using transcriptomic and proteomic data from anti-PD-1-treated HCC patients, along with functional validation in murine models and mechanistic molecular and cell biology studies, we identified transketolase-like 1 (TKTL1) as a dual-nature biomarker where overexpression predicted poor baseline prognosis yet enhanced response to ICB. Mechanistically, TKTL1 diverts glucose flux into glycolysis rather than pentose phosphate pathway (PPP), recruiting USP9X to deubiquitinate and stabilize HIF-1α, which upregulates HK2 to amplify glycolytic output and lactate accumulation. This metabolic rewiring orchestrates dual immunosuppressive circuits through HIF-1α-driven CCL4 secretion recruiting PD-L1^high dendritic cells (DCs), coupled with lactate-induced TRIM28^K408 lactylation that stabilizes PD-L1 by blocking ubiquitin-mediated degradation. We engineered a hepatoma-membrane-coated MnO₂ nanosystem (CQLH) co-delivering a TKTL1 inhibitor and lactate oxidase, which disrupted the TKTL1-HIF-1α-HK2 axis, depleted lactate, and reprogrammed the tumor microenvironment, thereby enhanced anti-PD-1 therapy to suppress tumor growth, especially in TKTL1^high tumors. These findings define a critical "TKTL1-glycolysis-lactate-DC" axis driving anti-PD-1 sensitivity in HCC, position TKTL1 as both a potential biomarker for ICB response and a tractable therapeutic target, and demonstrate that the targeted CQLH nanosystem overcomes resistance and enhances anti-PD-1 efficacy, offering a precision immunotherapeutic strategy for TKTL1^high HCC.

PMID:42802226 | PMC:PMC13616917 | DOI:10.1038/s41392-026-02875-2

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

cs.AI, q-bio.NC updates on arXiv.org

By: Xirui Liu · Sihang Zhou · Yanning Hou · Rong Zhou · Haoyuan Chen · Maolin He · Siwei Wang · Hao Chen · Jian Huang

26 May 2026 at 12:00

arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily on page-specific contexts. Existing methods store experiences uniformly. This creates a dilemma: abstract representations lose executability on concrete pages, while concrete representations fail to generalize across domains. This entanglement limits capability accumulation: on new websites, agents either fail to recognize reusable task logic due to surface-level differences or attempt infeasible actions from outdated page structures. To disentangle them, we propose DRIVE, a dual-level skill modeling framework separating historical experience into natural language reasoning skills, which capture transferable task logic, and programmatic interaction skills, grounding abstract actions to executable operations. A scene-aware coordination mechanism adaptively retrieves and invokes these dual-level skills based on task semantics. DRIVE also uses skill-level reflection to identify hierarchy-specific failure modes, enabling targeted skill library expansion and refinement. Experiments across five WebArena domains show DRIVE attains an average task success rate of 52.8%, exceeding the skill-free baseline by 7.3 percentage points. Further ablations show reasoning and interaction skills provide distinct, complementary benefits, supporting separation of transferable task logic from executable page-level operations.

CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test

cs.AI, q-bio.NC updates on arXiv.org

By: Zhangyi Hu · Chenhui Liu · Tian Huang · Jindong Li · Yang Yang · Jiemin Wu · Zining Zhong · Menglin Yang · Yutao Yue

26 May 2026 at 12:00

arXiv:2605.23491v2 Announce Type: replace-cross Abstract: Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground-Truth Unit Tests (GT UTs) remain a bottleneck: SOTA RLVR methods require them for costly training, while existing TTS methods lose competitiveness without them. This motivates GT-free TTS, where existing methods directly use self-generated UTs to refine and select code candidates. Yet such UTs are often noisy or spuriously coupled with wrong code, and UT quality in turn cannot be validated without reliable code. The key challenge is therefore to jointly improve both. To this end, we present CoSPlay, a GT-free, training-free framework that jointly improves codes and UTs through cooperative self-play. It first explores diverse solution ideas and identifies their potential failure modes to produce discriminative UT ideas. It then uses bidirectional pass-count signals from the Code-UT execution matrix to iteratively prune or fix weak codes and refresh or replace unreliable UTs, letting the two pools co-evolve. Finally, when multiple codes remain tied at the highest pass count, it picks the final code from the largest output-consensus cluster, since correct codes agree on the same inputs while wrong codes diverge. Experiments on four challenging benchmarks show that CoSPlay on Qwen2.5-7B-Instruct improves average BoN from 22.1% to 33.2% and UT accuracy from 14.6% to 78.3%, matching or surpassing the RLVR model CURE-7B. When applied to CURE-7B, it further improves BoN by 5.7%. CoSPlay also generalizes across diverse backbones and outperforms GT-free TTS baselines under comparable token budgets, with continued gains as the budget scales up. These results suggest a scalable inference strategy for competitive code generation without any GT data.

PRXL2B facilitates the progression of hepatocellular carcinoma and the therapeutic efficacy of oncolytic adenovirus H101 through the PI3K/AKT/PD-L1 axis

Omics in Hepatocellular

By: Ziqi Zhang · Hongli Chen · Yangming Liu · Hao Wang · You Yan · Yanxi Yu · Zuotian Huang · Yanyao Liu · Wei Li · Zhongjun Wu · Meiqing Kang · Xiaoyong Luo · Ping Che · Rui Liao

20 May 2026 at 18:00

Biosci Trends. 2026 May 21. doi: 10.5582/bst.2026.01000. Online ahead of print.

ABSTRACT

Oncolytic adenovirus H101 has shown antitumor activity in hepatocellular carcinoma (HCC), but the molecular determinants of treatment response remain unclear. In this study, a Hepa1-6 subcutaneous tumor model was established in C57BL/6 mice and treated with intratumoral H101, followed by integrated transcriptomic and proteomic analyses to identify candidate genes associated with H101 response. PRXL2B was selected for further investigation using public multi-omics datasets, tissue microarray-based immunohistochemistry, in vitro functional assays, mechanistic analyses, and in vivo validation experiments. Integrated multi-omics analyses identified PRXL2B as a candidate gene downregulated after H101 treatment. Public datasets and tissue-based validation further showed that PRXL2B was upregulated in HCC tissues. In MHCC97H and HCCLM3 cells, PRXL2B knockdown inhibited proliferation, migration, and invasion, promoted apoptosis and cell-cycle arrest, and enhanced the antitumor effect of H101. Mechanistically, PRXL2B silencing reduced AKT phosphorylation and PD-L1 expression. In vivo, PRXL2B knockdown suppressed tumor growth, and the combination of PRXL2B knockdown and H101 produced the strongest antitumor effect. These findings indicate that PRXL2B promotes malignant phenotypes in HCC and may modulate H101 efficacy through the PI3K/AKT/PD-L1 axis. Targeting PRXL2B may therefore represent a potential strategy to enhance the therapeutic efficacy of oncolytic virus therapy in HCC.

PMID:42161529 | DOI:10.5582/bst.2026.01000

General scales unlock AI evaluation with explanatory and predictive power

1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10303-2

A fully automated methodology based on rubrics capturing a broad range of cognitive and intellectual demands is illustrated using LLMs and tasks, demonstrating a new way to evaluate the capabilities of AI systems and anticipate their performance.

Tuning the sensitivity of mechanosensory receptors through histidine scanning

Cell

By: Yuanhao Wang · Yuhan Wang · Wenjie Yuan · Mingyu Fan · Xiaojing Wang · Anhui Wang · Yanling Bao · Yajing Zhang · Jia Chi Tan · Jianglai Wang · Junshuang Liu · Tianqi Huang · Zixuan Han · Biling Pei · Lijuan Chen · Zhengxu Ren · Xueqing Wang · Lanxin Hu · Siqi Wu · Mengke Pang · Sifan Wang · Zihuan Yang · Jiaze Li · Delian Huang · Shuai Shao · Huairui Yuan · Ling Wu · Yinnian Feng · Penghui Zhou · Guohui Li · Bo Sun · Chenqi Xu · Nicholas R.J. Gascoigne · Xiang Zhao

18 February 2026 at 08:00

Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.

SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling

cs.AI, q-bio.NC updates on arXiv.org

By: Elisabetta Fedele · Francis Engelmann · Ian Huang · Or Litany · Marc Pollefeys · Leonidas Guibas

16 March 2026 at 12:00

arXiv:2512.05343v2 Announce Type: replace-cross Abstract: Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts, which often fall short in geometric specificity: language can be ambiguous, and images are difficult to manipulate. In this work, we introduce SpaceControl, a training-free test-time method for explicit spatial control of 3D asset generation. Our approach accepts a wide range of geometric inputs, from coarse primitives to detailed meshes, and integrates seamlessly with modern generative models without requiring any additional training. A control parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SpaceControl outperforms both training-based and optimization-based baselines in geometric faithfulness while preserving high visual quality. Finally, we present an interactive interface for real-time superquadric editing and direct 3D asset generation, enabling seamless use in creative workflows. Project page: https://spacecontrol3d.github.io/.

SuperSkillsStack: Agency, Domain Knowledge, Imagination, and Taste in Human-AI Design Education

cs.AI, q-bio.NC updates on arXiv.org

By: Qian Huang · King Wang Poon

10 March 2026 at 12:00

arXiv:2603.07016v1 Announce Type: cross Abstract: This study examines how students integrate generative artificial intelligence (AI) into design projects through the lens of the SuperSkillsStack framework, which identifies four key human competencies for effective human-AI collaboration: Agency, Domain Knowledge, Imagination, and Taste. As generative AI increasingly transforms creative practice, design education must consider how human capabilities are cultivated alongside technological tools. Using qualitative thematic analysis, this study analyzes reflective writings from 80 student design teams participating in a human-centered design course. The findings show that students primarily used AI during the early stages of the design process, including brainstorming, information synthesis, and problem framing. However, students consistently relied on human judgment to interpret contextual information, validate AI-generated outputs, and refine design solutions. Domain knowledge derived from field observations enabled students to detect inaccuracies in AI suggestions, while taste played a critical role in evaluating and selecting meaningful ideas. The results suggest that generative AI functions primarily as a cognitive accelerator rather than a replacement for human creativity. The study highlights the importance of cultivating higher-order human capabilities to support effective human-AI collaboration in design education.

Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

cs.AI, q-bio.NC updates on arXiv.org

By: Zuhong Lin · Daoyuan Ren · Kai Ran · Jing Sun · Songlin Yu · Xuefeng Bai · Xiaotian Huang · Haiyang He · Pengxu Pan · Ying Fang · Zhanglin Li · Haipu Li · Jingjing Yao

24 February 2026 at 13:00

arXiv:2504.18880v4 Announce Type: replace Abstract: Accurately identifying the synthesis conditions of metal-organic frameworks (MOFs) is essential for guiding experimental design, yet remains challenging because relevant information in the literature is often scattered, inconsistent, and difficult to interpret. We present MOFh6, a large language model driven system that reads raw articles or crystal codes and converts them into standardized synthesis tables. It links related descriptions across paragraphs, unifies ligand abbreviations with full names, and outputs structured parameters ready for use. MOFh6 achieved 99% extraction accuracy, resolved 94.1% of abbreviation cases across five major publishers, and maintained a precision of 0.93 +/- 0.01. Processing a full text takes 9.6 s, locating synthesis descriptions 36 s, with 100 papers processed for USD 4.24. By replacing static database lookups with real-time extraction, MOFh6 reshapes MOF synthesis research, accelerating the conversion of literature knowledge into practical synthesis protocols and enabling scalable, data-driven materials discovery.

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