A multi-omics profile reveals porcine developmental nonlinearity organizes intestinal maturation and long-term metabolic phenotypes

Intestinal development is a highly orchestrated dynamic process for metabolic homeostasis and long-term physiological health. Developmental nonlinearity offers new insights on developmental dynamics. However, how nonlinear developmental dynamics coordinate intestinal maturation and contribute to metabolic phenotypes remains unclear. We construct an intestinal multi-omics framework of 1,153,328 biological molecules, integrating transcriptomics, metabolomics, and single-cell dataset across developmental stages from 136 pigs. We performed trajectory clustering and functional identification on molecules. Multi-tissue and cross-species analyses were performed to characterize nonlinear regulatory factors and conserved molecular trajectories at the cellular level. By integrating phenotypes, we identified nonlinear molecules within developmental transient windows. Functional experiments were conducted to validate the role of nonlinear molecules. Our analysis revealed widespread nonlinear molecular trajectories and biological pathways during intestinal development. At cellular resolution, age-related molecules exhibited collaborative dynamics within cell populations and tissue ecosystems, while cross-species analyses revealed conserved molecular trajectories in both porcine and human intestinal systems. We identified a critical developmental transition window at 60–90 days, characterized by unique cellular niches, linking nonlinear dynamics to metabolic and fat-related phenotypes. We further identified nonlinear molecules associated with obesity susceptibility, such as PCK1 , highlighting the pivotal role of multi-level collaborative dynamic systems in developmental programming. Our study offers new insights into intestinal developmental patterns and demonstrates how coordinated molecular dynamics across cells, tissues, and species shape physiological phenotypes. The findings establish a valuable framework for understanding developmental origins of metabolic traits, and identify potential temporal windows and molecular targets for metabolic intervention.