Salmonella Impacts Tumor-Induced Macrophage Polarization, and Inhibits SNAI1-Mediated Metastasis in Melanoma.
Christian Ronquillo PangilinanLi-Hsien WuChe-Hsin LeePublished in: Cancers (2021)
Targeting metastasis is a vital strategy to improve the clinical outcome of cancer patients, specifically in cases with high-grade malignancies. Here, we employed a Salmonella-based treatment to address metastasis. The potential of Salmonella as an anticancer agent has been extensively studied; however, the mechanism through which it affects metastasis remains unclear. This study found that the epithelial-to-mesenchymal transition (EMT) inducer SNAI1 was markedly reduced in Salmonella-treated melanoma cells, as revealed by immunoblotting. Furthermore, wound healing and transwell assays showed a reduced in vitro cell migration following Salmonella treatment. Transfection experiments confirmed that Salmonella acted against metastasis by suppressing protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling, which in turn inhibited SNAI1 expression. Since it is known that metastasis is also influenced by inflammation, we partly characterized the immune infiltrates in melanoma as affected by Salmonella treatment. We found through tumor-macrophage co-culture that Salmonella treatment increased high mobility group box 1 (HMGB1) secretion in tumors to coax the polarization of macrophages in favor of an M1-like phenotype, as shown by increased inducible nitric oxide synthase (iNOS) expression and Interleukin 1 Beta (IL-1β) secretion. Data from our animal study corroborated the in vitro findings, wherein the Salmonella-treated group obtained the lowest lung metastases, longer survival, and increased iNOS-expressing immune infiltrates.
Keyphrases
- escherichia coli
- listeria monocytogenes
- nitric oxide synthase
- high grade
- cell proliferation
- poor prognosis
- oxidative stress
- cell migration
- nitric oxide
- adipose tissue
- epithelial mesenchymal transition
- binding protein
- wound healing
- combination therapy
- transcription factor
- machine learning
- long non coding rna
- single molecule
- high glucose
- endothelial cells
- data analysis