Transcriptomic profiling of Rana [Lithobates] catesbeiana back skin during natural and thyroid hormone-induced metamorphosis under different temperature regimes with particular emphasis on innate immune system components.

As amphibians undergo thyroid hormone (TH)-dependent metamorphosis from an aquatic tadpole to the terrestrial frog, their innate immune system must adapt to the new environment. Skin is a primary line of defense, yet this organ undergoes extensive remodelling during metamorphosis and how it responds to TH is poorly understood. Temperature modulation, which regulates metamorphic timing, is a unique way to uncover early TH-induced transcriptomic events. Metamorphosis of premetamorphic tadpoles is induced by exogenous TH administration at 24 °C but is paused at 5 °C. However, at 5 °C a "molecular memory" of TH exposure is retained that results in an accelerated metamorphosis upon shifting to 24 °C. We used RNA-sequencing to identify changes in Rana (Lithobates) catesbeiana back skin gene expression during natural and TH-induced metamorphosis. During natural metamorphosis, significant differential expression (DE) was observed in >6500 transcripts including classic TH-responsive transcripts (thrb and thibz), heat shock proteins, and innate immune system components: keratins, mucins, and antimicrobial peptides (AMPs). Premetamorphic tadpoles maintained at 5 °C showed 83 DE transcripts within 48 h after TH administration, including thibz which has previously been identified as a molecular memory component in other tissues. Over 3600 DE transcripts were detected in TH-treated tadpoles at 24 °C or when tadpoles held at 5 °C were shifted to 24 °C. Gene ontology (GO) terms related to transcription, RNA metabolic processes, and translation were enriched in both datasets and immune related GO terms were observed in the temperature-modulated experiment. Our findings have implications on survival as climate change affects amphibia worldwide.

Structure-aware deep learning model for peptide toxicity prediction.

Antimicrobial resistance is a critical public health concern, necessitating the exploration of alternative treatments. While antimicrobial peptides (AMPs) show promise, assessing their toxicity using traditional wet lab methods is both time-consuming and costly. We introduce tAMPer, a novel multi-modal deep learning model designed to predict peptide toxicity by integrating the underlying amino acid sequence composition and the three-dimensional structure of peptides. tAMPer adopts a graph-based representation for peptides, encoding ColabFold-predicted structures, where nodes represent amino acids and edges represent spatial interactions. Structural features are extracted using graph neural networks, and recurrent neural networks capture sequential dependencies. tAMPer's performance was assessed on a publicly available protein toxicity benchmark and an AMP hemolysis data we generated. On the latter, tAMPer achieves an F1-score of 68.7%, outperforming the second-best method by 23.4%. On the protein benchmark, tAMPer exhibited an improvement of over 3.0% in the F1-score compared to current state-of-the-art methods. We anticipate tAMPer to accelerate AMP discovery and development by reducing the reliance on laborious toxicity screening experiments.

Associating Biological Activity and Predicted Structure of Antimicrobial Peptides from Amphibians and Insects.

Antimicrobial peptides (AMPs) are a diverse class of short, often cationic biological molecules that present promising opportunities in the development of new therapeutics to combat antimicrobial resistance. Newly developed in silico methods offer the ability to rapidly discover numerous novel AMPs with a variety of physiochemical properties. Herein, using the rAMPage AMP discovery pipeline, we bioinformatically identified 51 AMP candidates from amphibia and insect RNA-seq data and present their in-depth characterization. The studied AMPs demonstrate activity against a panel of bacterial pathogens and have undetected or low toxicity to red blood cells and human cultured cells. Amino acid sequence analysis revealed that 30 of these bioactive peptides belong to either the Brevinin-1, Brevinin-2, Nigrocin-2, or Apidaecin AMP families. Prediction of three-dimensional structures using ColabFold indicated an association between peptides predicted to adopt a helical structure and broad-spectrum antibacterial activity against the Gram-negative and Gram-positive species tested in our panel. These findings highlight the utility of associating the diverse sequences of novel AMPs with their estimated peptide structures in categorizing AMPs and predicting their antimicrobial activity.