Inter-organ communication shapes human metabolic tissue states and resolves anti-diabetic drug response modes in a...

Systemic glucose regulation depends on coordinated signaling among metabolically specialized tissues, yet most human in vitro models capture only limited portions of this network. Here, we developed and benchmarked a perfused human six-tissue MPS by combining AnthroHive, a recirculating perfusion platform, with MOTIVE-6, a six-compartment Multiorgan Tissue Interaction Vessel, to culture human gut epithelium, pancreatic islets, liver organoids, adipocytes, skeletal muscle, and midbrain-patterned brain organoids in a microphysiological system. Shared perfusion redirected engineered tissue states toward tissue-aligned metabolic, endocrine, absorptive, contractile, and neural-associated programs while reducing selected isolation-associated stress and remodeling signatures. Under High nutrient conditions, however, multi-tissue interaction shifted liver and islet responses toward inflammatory and nutrient-stress-associated gene expression, indicating context-dependent effects of cross-compartment signaling. Graded nutrient exposure resolved a staged circuit trajectory: Low nutrient conditions supported maintenance-associated programs, Mid nutrient exposure induced compensatory endocrine and anabolic remodeling with declining net glucose depletion, and High nutrient exposure shifted the system toward stress-associated metabolic dysfunction. Under High conditions, metformin and semaglutide produced distinct response modes. Metformin preserved circuit-level glucose handling without increasing insulin or C-peptide accumulation, while semaglutide remodeled gut, brain organoid, islet, and liver organoid transcriptional programs linked to nutrient sensing, epithelial maintenance, endocrine signaling, and neurometabolic state. Together, this study establishes a benchmarked human six-tissue MPS resource, paired with tissue-resolved transcriptomic, shared-media metabolomic, functional, endocrine, and inflammatory datasets, for investigating how tissue interaction, nutrient availability, and metabolic therapies reshape glucose-regulatory networks.