The present study is undertaken to assess the alleviating effects of antimicrobial peptide cecropin A on inflammatory bowel disease (IBD) in C57BL/6 mice and changes in the gut microbiota, compared to an antibiotic gentamicin. Different doses of cecropin A were intraperitoneally injected into C57BL/6 mice for 5 days to determine the safe doses. The injection doses at ≤ 15 mg/kg showed no negative impact on the liver, heart, spleen, and kidney. The severe and moderate IBD mice model was successfully established via supplementation of 4 or 2.5% dextran sulfate sodium (DSS) in drinking water for 5 days. The severe IBD model was used to ensure the optimal therapeutic dose of cecropin A. Survival rate, body weight and disease activity index (DAI) scores were measured. Administration of 15 mg/kg, not 5 mg/kg cecropin A, for 5 days increased survival rate and decreased body weight loss of mice. The moderate IBD model was applied to investigate the mechanisms for cecropin A to alleviate inflammation in comparison to gentamicin. The mice were treated with 15 mg/kg cecropin A or 5 mg/kg gentamicin for 3 days. The levels of cytokines and related proteins in the colon were detected by ELISA and Western blotting. The microbiota in cecum contents were analyzed using 16S rRNA gene sequencing. The results showed that cecropin A and gentamicin relieved body weight loss, DAI, and gut mucosa disruption, while decreasing tumor necrosis factor-α (TNF-α), interlukin-1β (IL-1β), and interlukin-6 (IL-6) induced by DSS. In addition, cecropin A and gentamicin showed different effects on the gut microbiota structure. Both cecropin A and gentamicin decreased DSS-induced enrichment of Bacteroidaceae and Enterobacteriaceae . However, cecropin A showed a selective enrichment of Lactobacillus in contrast to gentamicin, which demonstrated a selective effect on Desulfovibrionaceae and Ruminococcaceae . Cecropin A alleviates IBD through decreasing harmful gut microflora and specifically enhancing beneficial gut microflora. The mechanism of this effect is different from gentamicin.

Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q2) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

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