Big Spider, Big Genome: Chromosome-level genome of a North American tarantula (Aphonopelma marxi) and comparative genomics across 300 million years of spider evolution

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  Image Credit: WikiCommons Big Spider, Big Genome: Chromosome-level genome of a North American tarantula (Aphonopelma marxi) and comparative genomics across 300 million years of spider evolution Abstract The comparison of chromosome-level genomes allows biologists to investigate new axes of organismal evolution. Spiders comprise a significant proportion of known arachnid diversity, with many complex morphologies and unique natural histories, yet comparative genomics in spiders has been limited due to the number of available genomes. We present a de novo chromosomal reference genome of a mature male tarantula, Aphonopelma marxi, and comparatively examine spider genome evolution across the Order Araneae. Using PacBio HiFi and Hi-C sequencing, the final 6.5 Gb assembly consists of 17 autosomes, 1 X chromosome, and 127 unplaced scaffolds, with an N50 of 370 Mb and Arachnida (odb10; 2934 genes) BUSCO of 96.7%. By comparing 20 additional spider genomes from 15 families, we find mygalomo...

Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence

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Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence

Abstract

The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discovery has significantly stagnated. Venoms form an immense and largely untapped reservoir of bioactive molecules with antimicrobial potential. In this study, we mined global venomics datasets to identify new antimicrobial candidates. Using deep learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides. From these, we identified 386 candidates that are structurally and functionally distinct from known antimicrobial peptides. They display high net charge and elevated hydrophobicity, characteristics conducive to bacterial-membrane disruption. Structural studies revealed that many of these peptides adopt flexible conformations that transition to α-helical conformations in membrane-mimicking environments, supporting their antimicrobial potential. Of the 58 peptides selected for experimental validation, 53 display potent antimicrobial activity. Mechanistic assays indicated that they primarily exert their effects through bacterial-membrane depolarization, mirroring AMP-like mechanisms. In a murine model of Acinetobacter baumannii infection, lead peptides significantly reduced bacterial burden without observable toxicity. Our findings demonstrate that venoms are a rich source of previously hidden antimicrobial scaffolds, and that integrating large-scale computational mining with experimental validation can accelerate the discovery of urgently needed antibiotics.

Guan, C., Torres, M.D.T., Li, S. et al. Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence. Nat Commun 16, 6446 (2025). https://doi.org/10.1038/s41467-025-60051-6