{"title":"Biomaterial Physical Cues as Gene Regulators for Cellular Agriculture","authors":"Jianuo Liu, Ye Liu","doi":"10.1002/ggn2.202500052","DOIUrl":"10.1002/ggn2.202500052","url":null,"abstract":"<p>Cultured meat relies on biomaterial scaffolds that not only sustain cell proliferation but also regulate lineage commitment and tissue maturation. Increasing evidence shows that the biophysical properties of biomaterials—such as stiffness, incorporated force fields, wettability, surface charge, and geometry—profoundly influence gene expression through mechanotransduction and epigenetic remodeling pathways. This review outlines how these material-driven cues modulate myogenic and adipogenic programs relevant to meat engineering, and how their integration with synthetic biology enables programmable control of cell fate. Emerging strategies including 4D-adaptive scaffolds, high-throughput material–gene mapping, and AI-guided scaffold design are discussed as future directions. Collectively, these advances establish the concept of a “material genome”, providing a rational framework to engineer structured, nutritious, and scalable cultured meat.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haonan Zhang, Ge Zhang, Chaoyang Yu, Ruhao Wu, Shiqian Zhang, Xufeng Huang, Yingxue Yuan, Yaxin Chen, Shaotong Pei, Ge Zhang
{"title":"From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks","authors":"Haonan Zhang, Ge Zhang, Chaoyang Yu, Ruhao Wu, Shiqian Zhang, Xufeng Huang, Yingxue Yuan, Yaxin Chen, Shaotong Pei, Ge Zhang","doi":"10.1002/ggn2.202500053","DOIUrl":"https://doi.org/10.1002/ggn2.202500053","url":null,"abstract":"<p>Artificial intelligence (AI) has advanced predictive modeling in medicine, yet many models remain disease-specific and difficult to generalize across clinical settings. Key challenges include the trade-off between interpretability and accuracy, reliance on single algorithms, limited external validation, and biased feature importance estimation. In this Perspective, we discuss how methodological advances in computational sciences, including automated machine learning (AutoML) and neural architecture search (NAS), reveal a gap between automated hybrid systems and current clinical modeling practices. To address these challenges, we outline a principled artificial intelligence hybrid ensemble framework based on three design principles: integration of diverse learners, consensus-driven validation across independent cohorts, and transparent feature attribution using Shapley Additive exPlanations (SHAP). This framework emphasizes methodological robustness, interpretability, and cross-disease applicability to support the translation of artificial intelligence models into clinical practice.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haonan Zhang, Ge Zhang, Chaoyang Yu, Ruhao Wu, Shiqian Zhang, Xufeng Huang, Yingxue Yuan, Yaxin Chen, Shaotong Pei, Ge Zhang
{"title":"From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks","authors":"Haonan Zhang, Ge Zhang, Chaoyang Yu, Ruhao Wu, Shiqian Zhang, Xufeng Huang, Yingxue Yuan, Yaxin Chen, Shaotong Pei, Ge Zhang","doi":"10.1002/ggn2.202500053","DOIUrl":"10.1002/ggn2.202500053","url":null,"abstract":"<p>Artificial intelligence (AI) has advanced predictive modeling in medicine, yet many models remain disease-specific and difficult to generalize across clinical settings. Key challenges include the trade-off between interpretability and accuracy, reliance on single algorithms, limited external validation, and biased feature importance estimation. In this Perspective, we discuss how methodological advances in computational sciences, including automated machine learning (AutoML) and neural architecture search (NAS), reveal a gap between automated hybrid systems and current clinical modeling practices. To address these challenges, we outline a principled artificial intelligence hybrid ensemble framework based on three design principles: integration of diverse learners, consensus-driven validation across independent cohorts, and transparent feature attribution using Shapley Additive exPlanations (SHAP). This framework emphasizes methodological robustness, interpretability, and cross-disease applicability to support the translation of artificial intelligence models into clinical practice.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weiwei Guo, Ying Shi, Shanshan Wu, Hong Yang, Anqi Lin, Peng Luo
{"title":"PDMSA: A Web-Based Tool for Pan-Cancer Survival Analysis Using DNA Methylation Levels as Biomarkers","authors":"Weiwei Guo, Ying Shi, Shanshan Wu, Hong Yang, Anqi Lin, Peng Luo","doi":"10.1002/ggn2.202500069","DOIUrl":"10.1002/ggn2.202500069","url":null,"abstract":"<p>DNA methylation levels are intimately associated with tumor development, progression, and therapeutic outcomes. Accurate analysis of the relationship between DNA methylation levels and tumor prognosis facilitates comprehensive investigation of tumor development mechanisms, enabling optimization of clinical decision-making and subsequent enhancement of cancer patient survival rates. However, current web-based tools for analyzing tumor methylation levels and survival prognosis exhibit significant limitations. We have developed a web-based tool called Pan-cancer DNA Methylation Survival Analysis (PDMSA) implemented in Shiny, which integrates DNA methylation data and clinical information from large public databases (TCGA and GEO). PDMSA currently encompasses tumor DNA methylation data from 30 TCGA datasets and 15 GEO datasets, consisting of 16 205 211 records that span 39 cancer types, 45 datasets, 19 909 genes, and 8369 samples. The tool executes prognostic Kaplan–Meier survival analysis and Cox regression analysis utilizing two distinct cutoff value grouping methods, offering customizable visualization options for the results. As a user-friendly analytical platform, PDMSA serves as a comprehensive tool for biomedical researchers to investigate the relationship between methylation levels at specific gene loci and tumor survival outcomes, thereby facilitating the advancement of precision medicine in oncology. Access PDMSA at robinl-lab.com/PDMSA.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Antioxidation is a Common Defense Strategy for Different Species Under Mechanical Stress","authors":"Feixiang Zhu, Kai Huang","doi":"10.1002/ggn2.202500043","DOIUrl":"10.1002/ggn2.202500043","url":null,"abstract":"<p>Mechanical stress, a ubiquitous physical stimulus, triggers oxidative stress through mechanotransduction in diverse organisms, driving the evolution of antioxidant defense systems. This perspective deciphers the molecular evolutionary principles governing these antioxidant strategies, uncovering both conserved and lineage-specific adaptations across biological kingdoms. We demonstrate that redox regulation—an ancient and evolutionarily conserved mechanism—serves as a core defense against mechanical stress-induced oxidative damage, albeit with kingdom-specific variations. Notably, mechanosensors such as Piezo channels are conserved in animals and plants, initiating antioxidant responses via Ca<sup>2</sup><sup>+</sup> signaling. While animals rely on the Nrf2 regulatory axis to upregulate antioxidant enzymes, plants deploy intricate networks involving SOD, CAT, and non-enzymatic antioxidants (e.g., ascorbic acid). In contrast, extremophilic microorganisms employ streamlined yet highly efficient strategies, including multifunctional molecules like DMSP, to mitigate oxidative stress. Our comparative analysis highlights the evolutionary significance of these adaptations, shaped by ecological pressures and physiological constraints. Beyond elucidating life's evolutionary trajectory, these insights hold translational potential—from enhancing crop stress tolerance to informing biomaterial innovations and therapeutic interventions for oxidative disorders.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}