Biomedical subjects

Di Huang

Publications and source records attributed to Di Huang.

3 recordsLinked to original sources

PubMed2026

Ubiquitin ligase HcPUB30 targets HcWRKY1 to regulate monoterpenoids synthesis in Hedychium coronarium.

Hedychium coronarium, a perennial herb belonging to the genus Hedychium Koenig within the family Zingiberaceae, is renowned for its pleasant fragrance. The volatile compounds of flowers are primarily terpenoids, which are catalyzed by terpenoid synthase (TPS). Earlier studies have shown that HcWRKY1 transcription factor can bind to the promoter of HcTPS1, regulating the metabolism of terpenoids. To further investigate the upstream molecular mechanisms that regulate the release of volatile compounds in Hedychium, we focused on a crucial U-box type of E3 ubiquitin ligase involved in regulating transcription factors. This study utilized genomic data to identify HcPUB gene family. In combination with transcriptome data, seven candidate HcPUB genes were identified and cloned with subsequent functional analysis. Yeast two-hybrid assay demonstrated that HcPUB30 was the sole interactor of HcWRKY1 among the seven HcPUB candidates. In vivo and in vitro ubiquitination assays demonstrated that HcPUB30 ubiquitinates and promotes the degradation of HcWRKY1 via the 26S proteasome pathway. Multi-alignment analysis revealed that HcPUB30 possesses a conserved U-box domain and ARM motifs, which are implicated in plant growth and development. Subcellular localization indicated that HcPUB30 is localized in both the nucleus and cytoplasm. Quantitative real-time PCR analysis revealed that HcPUB30 exhibited the highest expression in petal tissues, and its expression peaked during floral senescence stage. Virus-induced gene silencing of HcPUB30 in Hedychium petals resulted in a significant decrease in monoterpenoid content, accompanied by a significant reduction in the relative expression levels of HcWRKY1 and HcTPS1. These findings indicate that HcPUB30 participates in the regulation of monoterpenoid biosynthesis by mediating HcWRKY1 in Hedychium petals.

Plant Proteins↗

PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗

PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗