MOESM3 of Antiviral activity of interleukin-11 as a response to porcine epidemic diarrhea virus infection

Additional file 3. IL-11 knockdown efficiency was verified by ELISA

MOESM5 of Antiviral activity of interleukin-11 as a response to porcine epidemic diarrhea virus infection

Additional file 5. Cell viability assay after different inhibitor treatments. Cell viability was determined by a CCK-8 assay after treatment of the Vero E6 cells with different inhibitor concentrations including S3I-201 for 24 h (A), LY294002 and MK-2206 2HCl for 2 h (B)

MOESM1 of Antiviral activity of interleukin-11 as a response to porcine epidemic diarrhea virus infection

Additional file 1. shRNA targeting sequences against IL-11

MOESM2 of Antiviral activity of interleukin-11 as a response to porcine epidemic diarrhea virus infection

Additional file 2. Standard curve for IL-11 (A) and PEDV M gene (B)

MOESM4 of Antiviral activity of interleukin-11 as a response to porcine epidemic diarrhea virus infection

Additional file 4. pIL-11 treatment and knockdown did not affect cell viability. (A) Cell viability was determined by CCK-8 assay after treatment of the Vero E6 cells with different concentrations of pIL-11 for 18 h. (B) NC and IL-11 KD Vero E6 cells were plated and culture to 70% confluent monolayers for the CCK-8 assay

Role of intestinal extracellular matrix-related signaling in porcine epidemic diarrhea virus infection

Porcine epidemic diarrhea virus (PEDV) is emerging as a major threat to the global swine industry. Clinical PEDV infection is associated with severe intestinal lesions, resulting in absorptive dysfunction and high mortality rates in suckling piglets. The extracellular matrix (ECM) is an important component of intestinal tissue, providing a structural framework and conveying tissue-specific signals to nearby enterocytes. In this study, we investigated the extensive ECM remodeling observed in intestinal epithelial cells infected with PEDV and elucidated the associated activated ECM receptor-related pathways. Protein-protein interaction network analysis revealed two significantly differentially expressed genes (cluster of differentiation 44 [CD44] and serpin family E member 1 [SERPINE1]) associated with the ECM. At the transcriptional level, both genes exhibited significant positive correlation with the extent of PEDV replication. Similarly, the expression of CD44 and PAI-1 (encoded by SERPINE1) was also increased in the intestines of piglets during viral infection. Furthermore, CD44 exhibited antiviral activity by enhancing the expression of antiviral cytokines (e.g., interleukin [IL]-6, IL-18, IL-11, and antimicrobial peptide beta-defensin 1) by activating nuclear factor-κB signaling. Conversely, PAI-1 was found to promote the release of progeny virions during PEDV infection, despite a decreased intracellular viral load. Nevertheless, the underlying mechanisms are still unclear. Taken together, our results highlighted the biological roles of specific ECM-regulated genes, i.e., CD44 and SERPINE1 in suppressing and promoting PEDV infection, thereby providing a theoretical foundation for the role of the ECM in intestinal infections and identifying potential therapeutic targets for PEDV.</p

Comparison of Different Neural Network Architectures for Plasmonic Inverse Design

The merge between nanophotonics and a deep neural network has shown unprecedented capability of efficient forward modeling and accurate inverse design if an appropriate network architecture and training method are selected. Commonly, an iterative neural network and a tandem neural network can both be used in the inverse design process, where the latter is well known for tackling the nonuniqueness problem at the expense of more complex architecture. However, we are curious to compare these two networks’ performance when they are both applicable. Here, we successfully trained both networks to inverse design the far-field spectrum of plasmonic nanoantenna, and the results provide some guidelines for choosing an appropriate, sufficiently accurate, and efficient neural network architecture