Abstract
BACKGROUND: The symbiotic nitrogen-fixing system formed between alfalfa (Medicago sativa L.) and rhizobia requires precise regulation of carbohydrate and lipid metabolism to sustain their high-energy-demand system. However, metabolic divergence between roots and nodules remains poorly characterized. RESULTS: Using comparative transcriptomics, we analyzed gene expression profiles in pink nodules (PN), white nodules (WN), Pink nodule roots (PNR), white nodule roots (WNR), non-nodule roots (NNR) and control roots (CKR) from rhizobia-inoculated plants at 35 days post-inoculation. Key findings revealed metabolic specialization between tissues: PN exhibited elevated expression of lipid catabolism genes (MsECHIA, MsACX) and key genes of the TCA cycle regulators, driving direct energy supply for nitrogenase activity. PNR, WNR preferentially expressed glycolysis (MsPKP2) and pentose phosphate pathway (MsG6PD5) genes to convert photoassimilates into dicarboxylic acids via a directional transport system to nodules. WN showed enriched fatty acid elongation genes (MsKCR1, MsHACD2), suggesting compensatory synthesis of structural lipid to maintain symbiotic interfaces under carbon limitation. NNR, CKR retained starch metabolism dominance. Weighted geneco-expression network analysis revealed that symbiotic signaling synchronizes nodule lipid degradation with root carbon repartitioning to prioritize photoassimilate allocation to nodules. Nodulated roots may supplement nodule energetics through lipid precursor synthesis or storage lipid hydrolysis, thereby forming a "root-nodule metabolic relay" mechanism. Our results demonstrate that the alfalfa-rhizobia symbiosis establishes a hierarchical energy distribution network through tissue-specific regulation of metabolic genes, coordinating nitrogen fixation efficiency with energy supply homeostasis. CONCLUSIONS: This study elucidates metabolic coordination mechanisms underlying legume-rhizobial symbiosis, providing a theoretical framework for optimizing symbiotic energy economics through targeted gene editing approaches.