GEO 数据集

主题由标题与摘要自动归类；样本量为数据集标注之和，SuperSeries 可能与其 SubSeries 重复计算。

• Time series analysis of transcriptomic responses to iron and zinc stress in Poplar
  GSE344088Populus trichocarpa173 个样本2026-10-30

  Extending our understanding of iron (Fe) and zinc (Zn) homeostasis beyond annual model plants is essential for improving plant productivity, nutritional quality, and food safety. However, the mechanisms coordinating metal acquisition, systemic signaling, and detoxification remain poorly understood in woody perennials. We generated a 21-d time-series multi-omics resource integrating transcriptomic, ionomic, and physiological measurements from roots and leaves of black cottonwood (Populus trichocarpa) exposed to Fe or Zn deficiency or excess. Temporal analyses revealed tissue-specific metal partitioning, extensive nutrient crosstalk, and restriction of Zn accumulation during prolonged Zn excess. Gene regulatory network inference identified bHLH38/39.2 as a hub associated with root Fe uptake and leaf Fe signaling. Fe deficiency rapidly induced five C-TERMINALLY ENCODED PEPTIDE (CEP) genes in roots. A fluorescently labeled CEP peptide supplied to roots accumulated in leaves, where CEP treatment induced IRON MAN (IMA) genes. Receptor-dependent protoplast assays further demonstrated CEP-mediated activation of IMA promoters. These findings support a model in which root-derived CEP peptides convey Fe status to leaf IMA signaling. Under Zn excess, declining Zn accumulation coincided with increased root suberization and induction of suberin-biosynthetic genes. NAC058.1 and NAC058.2 emerged as regulatory hubs and activated most tested suberin gene promoters. These findings suggest that NAC-regulated suberization restricts Zn entry during prolonged exposure. Our study identifies systemic and barrier-based mechanisms of micronutrient homeostasis in a woody perennial.

  多组学
• Gene Expression Data of Neuroendocrine Tumors from Different Primary Sites
  GSE305904Homo sapiens194 个样本2026-10-26

  Neuroendocrine tumors (NETs) represent a unique group of tumors with diverse anatomical origins but share common clinical and molecular characteristics. Despite their low mutational burden and limited number of recurrent mutations, epigenetic mechanisms play a crucial role in their biology. This study aims to establish a universal molecular classification of NETs, independent of their site of origin, by integrating gene expression and methylation data. The objective is to uncover shared vulnerabilities to support the development of improved diagnostic, prognostic, and predictive biomarkers, as well as innovative treatment strategies to enhance patient outcomes. Transcriptomic and methylomic profiles were examined from paraffin-embedded tumor samples of 194 patients diagnosed with G1-G3 neuroendocrine tumors (NETs) originating in the gastrointestinal-pancreatic (GEP) tract and lungs. Using these integrated datasets, a Multi-Omics Factor Analysis (MOFA) model comprising eight factors was developed. This model facilitated the application of unsupervised clustering algorithms, resulting in a consensus molecular classification for neuroendocrine tumors.

  多组学计算与 AI