Metabolic compartmentalization along the stem axis of Striga and Alectra reflects distinct zones of carbohydrate...

Parasitic plants such as witchweeds transition from complete host dependence below ground to partial autotrophy after emergence, yet the mechanisms coordinating this metabolic shift remain poorly understood. Here, we combine fluorescent dye tracing, metabolite profiling, three-dimensional vascular imaging, tissue-specific transcriptomics, and targeted metabolomics to examine metabolic compartmentalization in Striga hermonthica and Alectra vogelii. We identify a consistent interruption of host-derived transport at a stem-localized 'sink equilibrium zone' (SEqZ) positioned below the first green leaves. This zone coincides with vascular reorganization, the onset of photosynthesis, and a pronounced reconfiguration of carbohydrate gradients along the parasite axis. Using newly designed metabolite indices, we show that tissues below the SEqZ accumulate raffinose-family oligosaccharides and starch near the haustorium, consistent with strong sink activity and carbon storage, whereas tissues above the SEqZ are enriched in monosaccharides and metabolites associated with photosynthetic activity and growth. Transcriptomic analyses reveal that below-ground tissues preferentially express genes involved in sugar unloading, apoplastic barrier formation, Casparian strip development, and carbohydrate storage, while above-ground tissues activate photosynthesis, circadian regulation, and sugar redistribution pathways. Notably, spatially restricted expression of circadian regulators suggests a localized establishment of temporal control following emergence. Together, these findings support a model in which the SEqZ represents a developmentally defined transition zone where transport, unloading, and metabolic priorities shift to coordinate host-derived and self-fixed carbon through combined anatomical and molecular mechanisms. This framework provides mechanistic insight into trophic mode switching in Striga and Alectra and identifies metabolic features that may be exploited for improved parasitic weed control.