A Randomized Pilot Trial Comparing Cyclosporine and Azathioprine for Maintenance Therapy in Diffuse Lupus Nephritis over Four Years

TLDR

High‑throughput cost reductions enable repeated sampling across multiple omics and timepoints, yet integrating heterogeneous data remains challenging and interpreting resulting models is essential to understand biological systems. The study aims to combine longitudinal multi‑omics data to detect temporal relationships and inter‑omics interactions by proposing a generic analytic framework of kinetic clustering and multi‑layer network analysis. The framework applies multi‑omics kinetic clustering and multi‑layer network propagation to two case studies—transcriptomic/proteomic cell‑cycle changes in HeLa cells and transcriptomic/metabolomic maize responses to aphid feeding—to identify regulatory mechanisms. Application of the framework uncovered novel multi‑layer interactions and regulatory mechanisms in key biological functions that single‑omics analyses missed, demonstrating its effectiveness across diverse experimental designs.

Abstract

<h3>Abstract</h3> Cost reduction of high-throughput technologies has enabled the monitoring of the same biological sample across multiple omics studies and multiple timepoints. The goal is to combine longitudinal multi-omics data to detect temporal relationships between molecules and interactions between omics layers. This can finally lead to uncover new regulation mechanisms and interactions that could be responsible for causing complex phenotype or disease. However multi-omics integration of diverse omics data is still challenging due to heterogeneous data and designs. Moreover, interpretation of multi-omics models is the key to understand biological systems. We propose a generic analytic and integration framework for multi-omics longitudinal datasets that consists of multi-omics kinetic clustering and multi-layer network-based analysis. This frame-work was successfully applied to two case studies with different experimental designs and omics data collected. The first case studied transcriptomic and proteomic changes during cell cycle in human HeLa cells, while the second focused on maize transcriptomic and metabolomic response to aphid feeding. Propagation analysis on multi-layer networks identifies regulatory mechanisms and function prediction for both case studies. Our framework has led to the identification of new multi-layer interactions involved in key biological functions that cannot be revealed with single omics analysis and interplay in the kinetics that could help identify novel biological mechanisms.