Intraperitoneal BSN-37 activates peritoneal macrophages in Balb/c mice and reshapes the circRNA–mRNA transcriptome

Our preliminary work indicates that the antimicrobial peptide BSN-37 exhibits immunostimulatory activity; however, its in vivo molecular mechanisms remain insufficiently defined. Here, using male Balb/c mice, we profiled mRNA and circRNA expression in peritoneal macrophages under optimized dosing and timing conditions. By integrating these in vivo datasets with our in vitro RAW264.7 results, we aimed to identify key circRNAs involved in BSN-37–mediated immune enhancement and to construct putative circRNA–miRNA–mRNA regulatory networks.Male Balb/c mice (6–8 weeks old) were randomly assigned to a control group or a BSN-37–treated group. To enrich peritoneal macrophages, mice received an intraperitoneal injection of 3% thioglycollate broth three days prior to sampling. The control group was then injected intraperitoneally with PBS, whereas the treatment group received BSN-37 (48 µg/mL, 0.2 mL per mouse). Twelve hours later, peritoneal lavage fluid was collected for macrophage isolation. Total RNA was extracted and subjected to quality control. CircRNA and mRNA transcriptomes were obtained using the Mouse circular RNA Array V2.0 and the Arraystar Mouse LncRNA Expression Microarray V3.0, respectively. Differentially expressed molecules were identified using the criteria of P < 0.05 and fold change > 2, followed by hierarchical clustering, GO/KEGG enrichment analyses, and ceRNA network construction. To reduce false positives, multiple prediction algorithms were applied and only intersecting targets were retained. Network visualization was performed using Cytoscape. Compared with the control group, a total of 149 differentially expressed mRNAs were identified in peritoneal macrophages from the BSN-37–treated group (104 upregulated and 45 downregulated), together with 20 differentially expressed circRNAs (6 upregulated and 14 downregulated). Volcano plots and hierarchical clustering heatmaps demonstrated a clear separation between the two groups at the transcriptomic level. Functional enrichment analyses indicated that these differentially expressed molecules were involved in multiple biological processes and signaling pathways related to immune regulation. By further integrating in vivo and in vitro datasets, we constructed a circRNA–miRNA–mRNA tripartite regulatory network comprising 5 circRNAs, 6 miRNAs, and 50 mRNAs, providing an omics-based foundation for prioritizing key candidate circRNAs and subsequent mechanistic validation. BSN-37 markedly reshapes the mRNA/circRNA expression landscape of peritoneal macrophages in vivo and establishes immune regulation–associated molecular networks. The candidate circRNAs and ceRNA network identified here offer important clues for elucidating the immunostimulatory mechanisms of BSN-37 and for screening critical regulatory targets. Overall design: Experimental design 1) Animals and grouping Animals: 36 healthy male Balb/c mice (6–8 weeks old; 25 ± 2 g). Grouping: Randomly assigned to two groups: Control (PBS) and BSN-37; 18 mice per group. Biological replicates: Every 6 mice were pooled as one biological replicate; 3 biological replicates per group (n = 3/group). Housing conditions: One-week acclimation; 12 h light/12 h dark cycle, with ad libitum access to food and water. 2) Peritoneal macrophage collection and treatment workflow Macrophage elicitation: Three days before sampling, mice were intraperitoneally injected with 2 mL of 3% thioglycollate broth at a fixed time each day (injections may be split to reduce irritation). In vivo treatment: Control group: Intraperitoneal injection of 0.2 mL PBS per mouse. BSN-37 group: Intraperitoneal injection of BSN-37 solution (48 µg/mL), 0.2 mL per mouse. Sampling time point: Mice were euthanized 12 h post-injection. Peritoneal lavage and cell collection: The peritoneal cavity was lavaged in multiple rounds with ice-cold PBS and lavage fluid was collected. Cells were pelleted by centrifugation at 4?, 1,000 rpm for 10 min, followed by three PBS washes. Adherence purification: Cells were resuspended in RPMI-1640 containing 10% FBS, plated in six-well plates, and allowed to adhere for 4 h. Medium was then replaced to further enrich macrophages. RNA sample preparation: After PBS washing, cells were lysed with TRIzol (500 µL–1 mL per well). Lysates were collected into RNase-free tubes and stored at -80?. Key control points: During intraperitoneal injection and lavage, avoid puncturing the intestine or organs to prevent contamination; if substantial erythrocyte contamination is present, apply red blood cell lysis buffer as needed. 3) RNA extraction and microarray profiling RNA extraction: Total RNA was extracted using the TRIzol method. Quality control: RNA concentration and purity were assessed (e.g., A260/280). Only qualified samples proceeded to microarray analysis. Platforms: circRNA: Mouse circular RNA Array V2.0 mRNA: Arraystar Mouse LncRNA Expression Microarray V3.0 (used to obtain the mRNA expression profile) Differential-expression threshold: P < 0.05 and mean fold change > 2. 4) Bioinformatics and network analysis Visualization: Volcano plots for differentially expressed mRNAs and circRNAs; hierarchical clustering and heatmap visualization of the two groups. Functional enrichment: GO and KEGG enrichment analyses were performed separately for host genes of differential circRNAs and for differential mRNAs. Integrated in vivo–in vitro screening: In vivo transcriptomic data from peritoneal macrophages were integrated with in vitro datasets from RAW264.7 cells to identify candidates showing overlap or consistent directional changes. ceRNA network construction: Multiple algorithms were used to predict circRNA–miRNA interactions and miRNA–mRNA targeting. Only intersecting predictions were retained to reduce false positives. A circRNA–miRNA–mRNA tripartite network was constructed and visualized in Cytoscape (example output: 5 circRNAs, 6 miRNAs, and 50 mRNAs). 5) Statistical analysis Appropriate software was used for differential-expression and enrichment analyses. Statistical significance was set at P < 0.05. The specific statistical tests and multiple-testing correction procedures followed standard microarray analysis workflows.