MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms

• Published in: Nature communications Vol. 14; no. 1; p. 3991
• Main Authors: Erdem, Cemal, Gross, Sean M., Heiser, Laura M., Birtwistle, Marc R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.07.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2401; 631/553; 631/80/304; Adaptability; Availability; Binary integration; Biological analysis; Bone morphogenetic protein 2; Breast cancer; Cell size; Cellular communication; Clustering; Context; Datasets; Growth factors; Humanities and Social Sciences; Interferon; Laminin; multidisciplinary; Networks; Nominations; PD-L1 protein; Phenotypes; Regulatory mechanisms (biology); Science; Science (multidisciplinary); Transforming growth factor-b1; γ-Interferon
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-39729-2

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2 , CLIC2 , FAM83D , ACSL5 , and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways. A problem in network biology is identification of context-specific networks. Here the authors report Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) to nominate molecular features associated with cellular phenotypes and pathways, and use this to assess interferon-γ regulated PD-L1 expression.