GLUD1

Nitrogen (N) metabolism-related key genes and conserved amino acid sites in key enzymes play a crucial role in improving N use efficiency (NUE) under N stress. However, it is not clearly known about the molecular mechanism of N deficiency-induced improvement of NUE in the N-sensitive rhizomatous medicinal plant Panax notoginseng (Burk.) F. H. Chen. To explore the potential regulatory mechanism, the transcriptome and proteome were analyzed and the three-dimensional (3D) information and molecular docking models of key genes were compared in the roots of P. notoginseng grown under N regimes.

Total N uptake and the proportion of N distribution to roots were significantly reduced, but the NUE, N use efficiency in biomass production (NUEb), the recovery of N fertilizer (RNF) and the proportion of N distribution to shoot were increased in the N₀-treated (without N addition) plants. The expression of N uptake- and transport-related genes NPF1.2, NRT2.4, NPF8.1, NPF4.6, AVP, proteins AMT and NRT2 were obviously up-regulated in the N₀-grown plants. Meanwhile, the expression of CIPK23, PLC2, NLP6, TCP20, and BT1 related to the nitrate signal-sensing and transduction were up-regulated under the N₀ condition. Glutamine synthetase (GS) activity was decreased in the N-deficient plants, while the activity of glutamate dehydrogenase (GDH) increased. The expression of genes GS1-1 and GDH1, and proteins GDH1 and GDH2 were up-regulated in the N₀-grown plants, there was a significantly positive correlation between the expression of protein GDH1 and of gene GDH1. Glu192, Glu199 and Glu400 in PnGS1 and PnGDH1were the key amino acid residues that affect the NUE and lead to the differences in GDH enzyme activity. The 3D structure, docking model, and residues of Solanum tuberosum and P. notoginseng was similar.

N deficiency might promote the expression of key genes for N uptake (genes NPF8.1, NPF4.6, AMT, AVP and NRT2), transport (NPF1.2 and NRT2.4), assimilation (proteins GS1 and GDH1), signaling and transduction (genes CIPK23, PLC2, NLP6, TCP20, and BT1) to enhance NUE in the rhizomatous species. N deficiency might induce Glu192, Glu199 and Glu400 to improve the biological activity of GS1 and GDH, this has been hypothesized to be the main reason for the enhanced ability of N assimilation in N-deficient rhizomatous species. The key genes and residues involved in improving NUE provide excellent candidates for the breeding of medicinal plants.

Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.

We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.

In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).

This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.