TNF upregulates peptidoglycan recognition protein 1 in esophageal cancer cells to clear the path to its signaling: Making the “enemy” a friend

The Peptidoglycan Recognition Protein 1 confers immune evasive properties on pancreatic cancer stem cells

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) has limited therapeutic options, particularly with immune checkpoint inhibitors. Highly chemoresistant 'stem-like' cells, known as cancer stem cells (CSCs), are implicated in PDAC aggressiveness. Thus, comprehending how this subset of cells evades the immune system is crucial for advancing novel therapies.

DESIGN

We used the KPC mouse model (LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx-1-Cre) and primary tumour cell lines to investigate putative CSC populations. Transcriptomic analyses were conducted to pinpoint new genes involved in immune evasion. Overexpressing and knockout cell lines were established with lentiviral vectors. Subsequent in vitro coculture assays, in vivo mouse and zebrafish tumorigenesis studies, and in silico database approaches were performed.

RESULTS

Using the KPC mouse model, we functionally confirmed a population of cells marked by EpCAM, Sca-1 and CD133 as authentic CSCs and investigated their transcriptional profile. Immune evasion signatures/genes, notably the gene peptidoglycan recognition protein 1 (PGLYRP1), were significantly overexpressed in these CSCs. Modulating PGLYRP1 impacted CSC immune evasion, affecting their resistance to macrophage-mediated and T-cell-mediated killing and their tumourigenesis in immunocompetent mice. Mechanistically, tumour necrosis factor alpha (TNFα)-regulated PGLYRP1 expression interferes with the immune tumour microenvironment (TME) landscape, promoting myeloid cell-derived immunosuppression and activated T-cell death. Importantly, these findings were not only replicated in human models, but clinically, secreted PGLYRP1 levels were significantly elevated in patients with PDAC.

CONCLUSIONS

This study establishes PGLYRP1 as a novel CSC-associated marker crucial for immune evasion, particularly against macrophage phagocytosis and T-cell killing, presenting it as a promising target for PDAC immunotherapy.

The microglial innate immune protein PGLYRP1 mediates neuroinflammation and consequent behavioral changes

Peptidoglycan recognition protein 1 (PGLYRP1) is a pattern-recognition protein that mediates antibacterial actions and innate immune responses. Its expression and role in neuroinflammatory conditions remain unclear. We observed the upregulation of PGLYRP1 in inflamed human and mouse spinal cord and brain, with microglia being the primary cellular source. Experiments using a recombinant PGLYRP1 protein show that PGLYRP1 potentiates reactive gliosis, neuroinflammation, and consequent behavioral changes in multiple animal models of neuroinflammation. Furthermore, shRNA-mediated knockdown of Pglyrp1 gene expression attenuates this inflammatory response. In addition, we identify triggering receptor expressed on myeloid cell-1 (TREM1) as an interaction partner of PGLYRP1 and demonstrate that PGLYRP1 promotes neuroinflammation through the TREM1-Syk-Erk1/2-Stat3 axis in cultured glial cells. Taken together, our results reveal a role for microglial PGLYRP1 as a neuroinflammation mediator. Finally, we propose that PGLYRP1 is a potential biomarker and therapeutic target in various neuroinflammatory diseases.

Investigating the evolution process of lung adenocarcinoma via random walk and dynamic network analysis

Lung adenocarcinoma (LUAD) is a typical disease regarded as having multi-stage progression. However, many existing methods often ignore the critical differences among these stages, thereby limiting their effectiveness for discovering key biological molecules and biological functions as signals at each stage. In this study, we propose a method to discover the evolution between biological molecules and biological functions by investigating the multi-stage biological molecules of LUAD. The method is based on the random walk algorithm and the Monte Carlo method to generate clusters as the modules, which were used as subgraphs of the differentiated biological molecules network in each stage. The connection between modules of adjacent stages is based on the measurement of the Jaccard coefficient. The online gene set enrichment analysis tool (DAVID) was used to obtain biological functions corresponding to the individual important modules. The core evolution network was constructed by combining the aforementioned two networks. Since the networks here are all dynamic, we also propose a strategy to visualize the dynamic information together in one network. Eventually, 12 core modules and 11 core biological functions were found through such evolutionary analyses. Among the core biological functions that we obtained, six functions are related to the disease, the biological function of neutrophil chemotaxis is not directly associated with LUAD but can serve as a predictor, two functions may serve as a predictive signal, and two functions need to be verified through more biological evidence. Compared with two alternative design methods, the method proposed in this study performed more efficiently.