Multi-omics integration identifies APOE as a metabolic regulator of macrophage-fibroblast crosstalk in idiopathic pulmonary fibrosis

By: Bing Bai · Le Chang · Zhi Cheng · Yingjie Wang · Chenfeng Hua · Yonghai Feng · Wenfei Zhao

2 September 2026 at 18:00

Front Immunol. 2026 Aug 18;17:1904638. doi: 10.3389/fimmu.2026.1904638. eCollection 2026.

ABSTRACT

BACKGROUND: Aberrant tissue repair and relentless fibroblast activation are hallmark features of idiopathic pulmonary fibrosis (IPF). Although IPF and Alzheimer's disease (AD) share underlying aging-related pathologies, including immune and metabolic dysregulation, the putative genetic mechanisms linking AD susceptibility to pathogenic macrophage remodeling in the fibrotic niche are not fully established.

METHODS: We performed a two-sample Mendelian randomization (MR) analysis to assess the genetic association and potential causal relationship between AD and IPF. Shared hub genes were identified via protein interaction networks. To characterize macrophage heterogeneity and intercellular crosstalk within the IPF microenvironment, we interrogated scRNA-seq data (GSE122960) utilizing Monocle 3 and CellChat algorithms. The functional essentiality of APOE was evaluated bridging computational virtual knockout (scTenifoldKnk) with laboratory in vitro assays. Specifically, downstream transcriptomic shifts and fibroblast activation capacities were validated using APOE-silenced THP-1 macrophages and a Transwell co-culture model with MRC-5 cells.

RESULTS: MR estimates indicated that genetic liability to AD is associated with a lower risk of developing IPF. Integrated profiling identified the lipid-metabolism gene APOE as a central hub, specifically enriched in lung macrophages. Pseudotime modeling captured a pathogenic bifurcation in IPF, where macrophages evolve toward a terminal state marked by profound oxidative phosphorylation defects and massive SPP1 secretion. These SPP1+ macrophages primarily activate fibroblasts via CD44 and integrin signaling axes. Furthermore, both virtual simulations and in vitro THP-1 experiments demonstrated that loss of APOE function triggers the hyperactivation of complement (C1QA) and antigen-presentation (HLA-DR) pathways. Co-culture assays ultimately confirmed that APOE ablation in macrophages strongly exacerbates myofibroblast differentiation (elevated α-SMA and collagen I) in adjacent MRC-5 cells.

CONCLUSION: APOE functions as a vital metabolic barrier against pro-fibrotic macrophage polarization in the lung. Disruption of this specific lipid metabolic network is strongly associated with SPP1-driven fibroblast activation and local immune imbalance, providing a theoretical framework that strictly warrants future in vivo investigation to determine its clinical relevance.

PMID:42682427 | PMC:PMC13529525 | DOI:10.3389/fimmu.2026.1904638

Disrupted molecular glue complex drives RAS inhibitor resistance

Cell

By: Ben Sang · Ling Feng Ye · Zheng Fu · Yasin Pourfarjam · Antonio Cuevas-Navarro · Shijie Fan · Feng Hu · Aaliyah Washington · Diego J. Rodriguez · Alberto Vides · Sumit Kar · Ethan Ahler · Kevin K. Lin · Aparna Hegde · Jacqueline A.M. Smith · Brian M. Wolpin · Salman R. Punekar · Alexander I. Spira · Ignacio Garrido-Laguna · David S. Hong · Arvin C. Dar · Rona Yaeger · Kathryn C. Arbour · Piro Lito

5 May 2026 at 08:00

Cancers evade RAS-targeting molecular glues through distinct alterations that converge on disrupting synthetic complex formation, exposing strategies for improved drug design and rational combination therapy.

Genetic mutation and dysfunction of AT2 cells drive B(a)P/LPS-induced inflammation-related lung tumorigenesis: evidence and mechanism of autophagy

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Zhihao Tang · Xianchao Zhou · Pingping Shang · Quanping Yan · Ping Lu · Yanting Lei · Chenfeng Hua · Xiaoshan Zhou · Qiao Zhang · Wei Wang · Huaiyong Chen · Jian He · Feifei Feng

9 April 2026 at 18:00

Acta Biochim Biophys Sin (Shanghai). 2026 Mar 25. doi: 10.3724/abbs.2025238. Online ahead of print.

ABSTRACT

The environmental pollutant benzo(a)pyrene (B(a)P), a representative polycyclic aromatic hydrocarbon (PAH), is a recognized carcinogen, and chronic pulmonary inflammation is closely associated with lung carcinogenesis. Although alveolar type 2 (AT2) cells are the origin of lung adenocarcinoma, the genetic and functional changes in AT2 cells and the mechanisms involved in inflammation-related lung tumorigenesis have not been elucidated. Here, C57BL/6J mice are exposed to B(a)P and the inflammatory irritant lipopolysaccharide (LPS) to establish a model of inflammation-related lung tumorigenesis. Single-cell RNA sequencing is performed on lung tissues. DNA mutations in AT2 cells are analyzed via whole-exome sequencing. The protein expression of AT2 cells in lung cancer tissue is determined by immunofluorescence staining. The results reveal that LPS promotes B(a)P-induced lung tumorigenesis; in the whole lungs of B(a)P/LPS, a decreased proportion, altered differentiation trajectory, and increased gene mutation number in AT2 cells are observed. Additionally, in B(a)P/LPS-treated lung cancer tissue, the levels of γ-H2AX DNA damage and the proliferation marker Ki67 in AT2 cells are increased, whereas the levels of differentiation markers are decreased. Single-cell RNA transcriptomics reveals that the autophagy-related genes Foxo3 and Ppp2r5, which are enriched in the PI3K-Akt pathway, and the autophagy-related genes in AT2 cells in lung cancer are decreased in the B(a)P/LPS group. Thus, chronic inflammation promotes DNA damage, gene mutation and dysfunction in AT2 cells, and decreased autophagy in AT2 cells may be an important mechanism for inflammation-related lung tumorigenesis.

PMID:41952558 | DOI:10.3724/abbs.2025238

SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition

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

By: Jingxiao Yang · DaLin He · Miao Pan · Ge Su · Wenqi Zhang · Yifeng Hu · Tangwei Li · Yuke Li · Xuhong Zhang

25 March 2026 at 12:00

arXiv:2603.17729v2 Announce Type: replace-cross Abstract: Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains challenging due to the inherent visual ambiguity of subordinate-level categories. Existing methods predominantly adopt either retrieval-oriented or reasoning-oriented paradigms to tackle this challenge, but both are constrained by two fundamental limitations:(1) They apply the same inference pipeline to all samples without accounting for uneven recognition difficulty, thereby leading to suboptimal accuracy and efficiency; (2) The lack of mechanisms to consolidate and reuse error-specific experience causes repeated failures on similar challenging cases. To address these limitations, we propose SARE, a Sample-wise Adaptive textbfREasoning framework for training-free FGVR. Specifically, SARE adopts a cascaded design that combines fast candidate retrieval with fine-grained reasoning, invoking the latter only when necessary. In the reasoning process, SARE incorporates a self-reflective experience mechanism that leverages past failures to provide transferable discriminative guidance during inference, without any parameter updates. Extensive experiments across 14 datasets substantiate that SARE achieves state-of-the-art performance while substantially reducing computational overhead.

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

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

By: Yuang Ai · Jiaming Han · Shaobin Zhuang · Weijia Mao · Xuefeng Hu · Ziyan Yang · Zhenheng Yang · Yali Wang · Huaibo Huang · Xiangyu Yue · Hao Chen

16 March 2026 at 12:00

arXiv:2602.14041v2 Announce Type: replace-cross Abstract: We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $2^{256}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text-to-image generation, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.

Q-BERT4Rec: Quantized Semantic-ID Representation Learning for Multimodal Recommendation

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

By: Haofeng Huang · Ling Gai

4 March 2026 at 13:00

arXiv:2512.02474v2 Announce Type: replace-cross Abstract: Sequential recommendation plays a critical role in modern online platforms such as e-commerce, advertising, and content streaming, where accurately predicting users' next interactions is essential for personalization. Recent Transformer-based methods like BERT4Rec have shown strong modeling capability, yet they still rely on discrete item IDs that lack semantic meaning and ignore rich multimodal information (e.g., text and image). This leads to weak generalization and limited interpretability. To address these challenges, we propose Q-Bert4Rec, a multimodal sequential recommendation framework that unifies semantic representation and quantized modeling. Specifically, Q-Bert4Rec consists of three stages: (1) cross-modal semantic injection, which enriches randomly initialized ID embeddings through a dynamic transformer that fuses textual, visual, and structural features; (2) semantic quantization, which discretizes fused representations into meaningful tokens via residual vector quantization; and (3) multi-mask pretraining and fine-tuning, which leverage diverse masking strategies -- span, tail, and multi-region -- to improve sequential understanding. We validate our model on public Amazon benchmarks and demonstrate that Q-Bert4Rec significantly outperforms many strong existing methods, confirming the effectiveness of semantic tokenization for multimodal sequential recommendation. Our source code will be publicly available on GitHub after publishing.

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

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

By: Yuang Ai · Jiaming Han · Shaobin Zhuang · Weijia Mao · Xuefeng Hu · Ziyan Yang · Zhenheng Yang · Huaibo Huang · Xiangyu Yue · Hao Chen

17 February 2026 at 13:00

arXiv:2602.14041v1 Announce Type: cross Abstract: We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $2^{256}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text-to-image generation, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.

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