The Firmicutes are a phylum of bacteria that dominate numerous polymicrobial habitats of importance to human health and industry. Although these communities are often densely colonized, a broadly distributed contact-dependent mechanism of interbacterial antagonism utilized by Firmicutes has not been elucidated. Here we show that proteins belonging to the LXG polymorphic toxin family present in Streptococcus intermedius mediate cell contact- and Esx secretion pathway-dependent growth inhibition of diverse Firmicute species. The structure of one such toxin revealed a previously unobserved protein fold that we demonstrate directs the degradation of a uniquely bacterial molecule required for cell wall biosynthesis, lipid II. Consistent with our functional data linking LXG toxins to interbacterial interactions in S. intermedius, we show that LXG genes are prevalent in the human gut microbiome, a polymicrobial community dominated by Firmicutes. We speculate that interbacterial antagonism mediated by LXG toxins plays a critical role in shaping Firmicute-rich bacterial communities.
- John C Whitney
- Adrian J Verster
- Waldemar Vollmer
- Young Ah Goo
- Joseph D Mougous
- Joseph D Mougous
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Michael T Laub, Massachusetts Institute of Technology, United States
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
HIV establishes a persistent infection in heterogeneous cell reservoirs, which can be maintained by different mechanisms including cellular proliferation, and represent the main obstacle to curing the infection. The expression of the Fcγ receptor CD32 has been identified as a marker of the active cell reservoirs in people on antiretroviral therapy, but if its expression has any role in conferring advantage for viral persistence is unknown. Here, we report that HIV-infected cells expressing CD32 have reduced susceptibility to natural killer (NK) antibody-dependent cell cytotoxicity (ADCC) by a mechanism compatible with the suboptimal binding of HIV-specific antibodies. Infected CD32 cells have increased proliferative capacity in the presence of immune complexes, and are more resistant to strategies directed to potentiate NK function. Remarkably, reactivation of the latent reservoir from antiretroviral-treated people living with HIV increases the pool of infected CD32 cells, which are largely resistant to the ADCC immune mechanism. Thus, we report the existence of reservoir cells that evade part of the NK immune response through the expression of CD32.
Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40–60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71 M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.