bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.08.14.607960

Genome-wide screen overexpressing mycobacteriophage Amelie genes identifies multiple inhibitors of mycobacterial growth

Abstract

The genome sequences of thousands of bacteriophages have been determined and functions for many of the encoded genes have been assigned based on homology to characterized sequences. However, functions have not been assigned to more than two-thirds of the identified phage genes as they have no recognizable sequence features. Recent genome-wide overexpression screens have begun to identify bacteriophage genes that encode proteins that reduce or inhibit bacterial growth. This study describes the construction of a plasmid-based overexpression library of 76 genes encoded by Cluster K1 mycobacteriophage Amelie, which is genetically similar to Cluster K phages Waterfoul and Hammy recently described in similar screens and closely related to phages that infect clinically important mycobacteria. 26 out of the 76 genes evaluated in our screen, encompassing 34% of the genome, reduced growth of the host bacterium Mycobacterium smegmatis to various degrees. More than one-third of these 26 toxic genes have no known function, and 10 of the 26 genes almost completely abolished host growth upon overexpression. Notably, while several of the toxic genes identified in Amelie shared homologs with other Cluster K phages recently screened, this study uncovered eight previously unknown gene families that exhibit cytotoxic properties, thereby broadening the repertoire of known phage-encoded growth inhibitors. This work, carried out under the HHMI-supported SEA-GENES project (Science Education Alliance Gene-function Exploration by a Network of Emerging Scientists), underscores the importance of comprehensive overexpression screens in elucidating genome-wide patterns of phage gene function and novel interactions between phages and their hosts.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tafoya, C., Ching, B., Garcia, E. P., Lee, A., Acevedo, M., Bass, K., Chau, E., Lin, H., Mamora, K., Reeves, M., Vaca, M., van Iderstein, W., Velasco, L., Williams, V., Yonemoto, G., Yonemoto, T., Heller, D. M., Diaz, A.. 2024-08-14. Genome-wide screen overexpressing mycobacteriophage Amelie genes identifies multiple inhibitors of mycobacterial growth. https://doi.org/10.1101/2024.08.14.607960

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A population-scale landscape of the subgingival microbiome reveals divergent routes to periodontal dysbiosis

Periodontitis is an archetypical mucosal inflammatory disease in which microbiome dysbiosis at the tooth-epithelial interface interacts with host genetic and behavioral risk factors to drive immune-mediated tissue destruction. Although subgingival microbiome compositional shifts are thought to parallel disease severity, microbiome variation at the population-level and its relationship to periodontal clinical phenotypes and disease-modifying factors remain poorly defined. Here, we use unsupervised manifold learning to map the compositional landscape of the subgingival microbiome in 1,355 adults spanning periodontal health to severe periodontitis. We identified eight latent microbiome states organized along a branching continuum from eubiosis to dysbiosis. An intermediate microbial configuration marked ecological destabilization and bifurcation into two distinct periodontitis-associated dysbiotic trajectories, distinguished by links to gingival inflammation and smoking. Although the microbiome trajectories broadly tracked periodontal destruction, a minority of individuals showed discordant microbiome-clinical phenotypes, with some individuals with periodontitis retaining otherwise eubiotic microbiomes enriched for low-abundance pathobionts, while some cases of health or mild disease had highly dysbiotic communities, suggesting distinct host susceptibility. Together, these findings define a population-scale ecological landscape of the subgingival microbiome, reveal divergent trajectories to periodontal dysbiosis, and highlight heterogeneity in the relationship between microbial community structure and clinical disease expression.

microbiology↗

Beta-lactam enhancement against methicillin-resistant Staphylococcus aureus by cell wall blockade is autolysis-dependent: a butyrolactone derivative as case in point

Methicillin-resistant Staphylococcus aureus (MRSA) is non-susceptible to beta-lactams. Blockade of cell wall biosynthesis is a potential target for beta-lactam enhancement but requires further investigation. A butyrolactone derivative enhanced beta-lactams against MRSA strains by reducing the availability of D-Ala-D-Ala. Unlike D-cycloserine, it did not inhibit D-Ala-D-Ala ligase (Ddl). Nor did it show an additive or synergistic effect when combined with cycloserine, indicating a unique mechanism for blocking cell wall precursor production that does not involve the traditional Lipid II pathway. Notably, beta-lactam potentiation by our chemical or D-cycloserine was highly dependent on the intrinsic autolytic ability of the tested MRSA strains. Strains that resisted lysis upon Triton X-100 exposure showed a minimal increase in beta-lactam susceptibility, whereas highly autolytic strains showed significant changes in their beta-lactam MICs. We have thus identified autolytic ability as the Achilles Heel in the strategy of targeting cell wall biosynthesis for beta-lactam potentiation.

microbiology↗

Rapid and largely reversible shifts in the canine fecal metabolome during dietary change

Diet can rapidly change the fecal metabolome, but less is known about recovery after the original diet is restored. We used untargeted UPLC-MS metabolomics to analyze 72 fecal samples from nine Pumi dogs during an owner-managed switch from dry food to raw food and back to dry food. Diet phase accounted for a large proportion of variation in both ionization modes. More than 13,000 LC-MS features changed at the first sampling point after the switch to raw food, with a similarly large response after return to dry food. Among features significant in both comparisons, more than 99% changed in opposite directions. At the final sampling point, no positive-mode (ESI+) features and only 13 negative-mode (ESI-) features differed from the second dry-food baseline under the same threshold. BARF-associated patterns persisted in analyses excluding individual dogs and in pedigree-adjusted candidate models, although individual feature effects depended on normalization. Putative metabolites from several biochemical classes differed in their response and recovery. The fecal metabolome therefore changed rapidly and returned largely toward baseline, with differences among dogs.

microbiology↗