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PubMed2025

Effects of nitrogen allocation and photosynthetic proteins response in peanut leaves on photosynthesis under conditions of water scarcity and nitrogen deficiency.

Leaf nitrogen allocation and photosynthetic proteins response can affect net photosynthetic rate (Pn), ultimately influencing crop yield under diverse environmental stresses. However, the internal relationship between Pn with leaf nitrogen allocation and photosynthetic proteins response under nitrogen or water scarcity in peanut (Arachis hypogaea L.) remains elusive. Here, comprehensive physiological property and proteomic analyses of peanut were conducted, revealing that both nitrogen and water scarcity remarkably impeded leaf growth and reduced Pn. Nitrogen deficiency significantly reduced the total nitrogen content per unit leaf area (Narea), chlorophyll content, and Pn, whereas drought stress caused a greater decline in photosynthetic nitrogen use efficiency (PNUE). The allocation of leaf nitrogen to photosynthetic components, including the carboxylation system and electron transport system in leaves, was significantly reduced when subjected to individual or combined deficiency. Proteomic analyses exhibited that several key photosynthetic proteins underwent a decrease under both single and combined water and nitrogen deficiency conditions. Thereby, Pn may decline due to the disruption of nitrogen allocation and down-regulated expression of photosynthetic proteins under these stress conditions. Our findings establish a benchmark for future research exploring the roles of leaf nitrogen allocation and photosynthetic proteins in the plant's response to nitrogen or water deficiency.

Nitrogen↗

PubMed2025

Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.

The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.

Humans↗