liu, X. | Search bioRxiv

bioRxiv2023

Palmitoylation of gasdermin D directs its membrane translocation and pore formation in pyroptosis

Gasdermin D (GSDMD)-mediated macrophage pyroptosis plays a critical role in inflammation and host defense. Plasma membrane perforation elicited by caspase-cleaved GSDMD N-terminal domain (GSDMD-NT) triggers membrane rupture and subsequent pyroptotic cell death, resulting in release of pro-inflammatory IL-1{beta} and IL-18. However, the biological processes leading to its membrane translocation and pore formation are not fully understood. Here, using a proteomics approach, we identified fatty acid synthase (FASN) as a GSDMD-binding partner and demonstrated that post-translational palmitoylation of GSDMD at Cys191/Cys192 (human/mouse) led to membrane translocation of GSDMD-NT but not full-length GSDMD. GSDMD lipidation, mediated by palmitoyl acyltransferases ZDHHC5/9 and facilitated by LPS-induced reactive oxygen species (ROS), was essential for GSDMD pore-forming activity and pyroptosis. Inhibition of GSDMD palmitoylation with palmitate analog 2-bromopalmitate or a cell permeable GSDMD-specific competing peptide suppressed pyroptosis and IL-1{beta} release in macrophages, mitigated organ damage, and extended the survival of septic mice. Collectively, we establish GSDMD-NT palmitoylation as a key regulatory mechanism controlling GSDMD membrane localization and activation, providing a novel target for modulating immune activity in infectious and inflammatory diseases. One Sentence SummaryLPS-induced palmitoylation at Cys191/Cys192 is required for GSDMD membrane translocation and its pore-forming activity in macrophages.

immunology↗

bioRxiv2022

Identifying Lymph Node Metastasis-related Factors in Breast Cancer using Differential Modular and Mutational Structural Analysis

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Network theory provides useful tools to study the underlying laws governing complex diseases. Within this framework, comparisons of network topologies, including the node, edge, and community, between different disease states can highlight key factors within these dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors including functional protein-protein interactions, pathways, and drive mutations are predicted by the topological-functional connection score and structural modeling. We applied the approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis. Author summaryMetastasis is the hallmark of cancer that is responsible for the greatest number of cancer-related deaths. However, it remains poorly understood. PPI networks not only provide a static picture of cellular function and biological processes, but also have emerged as new paradigms in the study of the dynamic process of disease progression, including cancer metastasis. Herein, a network-based strategy was proposed based on the integration of expression profiles with protein interactions, by filtering with "date hubs" and "inter-modular edges", demonstrating that different network modules may provide robust predictors to represent the dynamic mechanisms involved in metastasis formation. Furthermore, the mapping of protein structure and mutation data on the network module level provides insight into signaling mechanisms; helps understand the mechanism of disease-related mutations; and helps in drug discovery. The application of our method to study the LNM in breast cancer highlights network modules defining protein communities that respond to therapeutics, and the implications of detailed structural and mechanistic insight into oncogenic activation and how it can advance allosteric precision oncology.

systems biology↗