Anthropization of Palaeolithic caves open for tourism may favour collembola invasion and result in the formation of black stains attributed to pigmented fungi. However, ecological processes underpinning black stain formation are not fully understood. Here, we tested the hypotheses that black stains from the Apse room of Lascaux Cave display a specific microbiota enriched in pigmented fungi, and that collembola thriving on the stains have the potential to consume and disseminate these black fungi. Metabarcoding showed that the microbiota of black stains and neighbouring unstained parts strongly differed, with in black stains a higher prevalence of Ochroconis and other pigmented fungi and the strong regression of Pseudomonas bacteria (whose isolates inhibited in vitro the growth of pigmented fungi). Isotopic analyses indicated that Folsomia candida collembola thriving on stains could feed on black stain in situ and assimilate the pigmented fungi they were fed with in vitro. They could carry these fungi and disseminate them when tested with complex black stains from Lascaux. This shows that black stain formation is linked to the development of pigmented fungi, which coincides with the elimination of antagonistic pseudomonads, and points towards a key role of F. candida collembola in the dynamics of pigmented fungi.

Predicting which receptor a phage binds to from genome sequence alone has remained an intractable challenge, principally because the experimental phenotypic data required to train and validate predictive models have not been available at sufficient scale. Here we address this by conducting 1,050 genome-wide genetic screens across 255 taxonomically diverse Escherichia coli dsDNA phages, assigning host receptors to 193 phages across 19 receptor classes. Comparative genomics and AlphaFold3 structural modelling resolved the sequence determinants of specificity to defined receptor-binding protein domains and individual residues. Machine learning models trained on this dataset predicted host receptor identity from phage genome sequence alone without prior annotation of receptor-binding genes, achieving perfect precision and greater than 80% recall on 49 independently validated phages, and yielding predictions for 1,060 of 1,875 E. coli phage genomes in NCBI. Domain swaps redirected receptor specificity as predicted, and a single amino acid substitution proved both necessary and sufficient to switch recognition between two distinct porins. These results demonstrate that systematic phenotyping at scale makes sequence-based prediction of molecular interaction specificity tractable, with direct implications for phage-based medicine, microbiome engineering and the broader challenge of inferring host-pathogen interaction outcomes from sequence.

Causing major health and ecological disturbances, polychlorinated biphenyls (PCBs) are persistent organic pollutants still recovered all over the world. Microbial PCB biotransformation is a promising technique for depollution, but the involved molecular mechanisms remain misunderstood. Ligninolytic enzymes are suspected to be involved in many PCB transformations, but their assessments remain scarce. To further inventory the capabilities of microbes to transform PCBs through their ligninolytic enzymes, we investigated the role of oxidase and peroxidase among a set of microorganisms isolated from a historically PCB-contaminated site. Among 29 isolated fungi and 17 bacteria, this work reports for the first time the PCB-transforming capabilities from fungi affiliated to Didymella, Dothiora, Ilyonectria, Naganishia, Rhodoturula, Solicoccozyma, Thelebolus and Truncatella genera and bacteria affiliated to Peribacillus frigotolerans, Peribacillus muralis, Bacillus mycoides, Bacillus cereus, Bacillus toyonensis, Pseudarthrobacter sp., Pseudomonas chlororaphis, Erwinia aphidicola and Chryseobacterium defluvii. In the same way, this is the first report of fungal isolates affiliated to the Dothiora maculans specie and Cladosporium genus that displayed oxidase (putatively laccase) and peroxidase activity, respectively, enhanced in the presence of PCBs (more than 4-fold and 20-fold, respectively, compared to controls). Based on these results, the observed activities are suspected to be involved in PCB transformation.

The rise of antibiotic-resistant bacterial infections has driven renewed interest in bacteriophage therapy, where viruses that specifically kill bacteria are used as targeted antimicrobials. Pseudomonas aeruginosa , a WHO critical-priority pathogen that causes severe infections in hospitalized and immunocompromised patients, presents a major challenge for phage therapy because of its extraordinary genetic diversity. Phages effective against one bacterial strain often fail against others, and existing cross-resistance-profiling approaches require iterative empirical testing of each new patient isolate. To establish a genome-based framework for rapid phage-isolate matching, we assembled a collection of 95 genomically diverse P. aeruginosa phages representing 20 genera and tested each against 99 genetically diverse clinical isolates, generating 9,405 infection outcome measurements. Bacterial O-antigen serotype emerged as the dominant determinant of strain susceptibility, while defense systems, anti-defense systems, and prophage burden contributed smaller strain-specific effects. The full curated multivariate model explained 47% of strain-susceptibility variance. Machine-learning models integrating these features and pangenome-derived gene clusters reached a per-strain AUROC of 0.86. In an in vivo proof-of-concept test against a single held-out strain, the ML-designed cocktail produced a ∼12-fold greater median CFU reduction than the expert-designed cocktail (q = 0.045), with both cocktails substantially reducing burden relative to the untreated control (∼113-fold for ML, ∼9-fold for CG; both q < 10□³). SHAP analysis of the model identified bacterial surface-architecture genes (LPS biosynthesis, outer membrane proteins, type IV pili) as the dominant predictors, with defense-system content modulating which specific phages succeed against a strain rather than uniformly damping susceptibility. Together, these results establish a genome-based framework for predicting phage susceptibility in genetically diverse clinical isolates.