A humoral stress response protects Drosophila tissues from antimicrobial peptides.

7An efficient immune system must provide protection against a broad range of pathogens without causing excessive collateral tissue damage. While immune effectors have been well characterized, we know less about the resilience mechanisms protecting the host from its own immune response. Antimicrobial peptides (AMPs) are small, cationic peptides that contribute to innate defenses by targeting negatively charged membranes of microbes. While protective against pathogens, AMPs can be cytotoxic to host cells. Here, we reveal that a family of stress-induced proteins, the Turandots, protect the Drosophila respiratory system from AMPs, increasing resilience to stress. Flies lacking Turandot genes are susceptible to environmental stresses due to AMP-induced tracheal apoptosis. Turandot proteins bind to host cell membranes and mask negatively charged phospholipids, protecting them from cationic pore-forming AMPs. Collectively, these data demonstrate that Turandot stress proteins mitigate AMP cytotoxicity to host tissues and therefore improve their efficacy.

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.

Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both spatial and temporal scales. Protein PTMs have also emerged as reliable biomarkers for several diseases. However, only a handful of techniques are available to accurately measure their levels, capture their complexity at a single molecule level, and characterize their multifaceted roles in health and disease. Nanopore sensing provides high sensitivity for the detection of low-abundance proteins, holding the potential to impact single-molecule proteomics and PTM detection, in particular. Here, we demonstrate the ability of a biological nanopore, the pore-forming toxin aerolysin, to detect and distinguish α-synuclein-derived peptides bearing single or multiple PTMs, namely, phosphorylation, nitration, and oxidation occurring at different positions and in various combinations. The characteristic current signatures of the α-synuclein peptide and its PTM variants could be confidently identified by using a deep learning model for signal processing. We further demonstrate that this framework can quantify α-synuclein peptides at picomolar concentrations and detect the C-terminal peptides generated by digestion of full-length α-synuclein. Collectively, our work highlights the advantage of using nanopores as a tool for simultaneous detection of multiple PTMs and facilitates their use in biomarker discovery and diagnostics.