Bacteriophage strategies for overcoming host antiviral immunity

Molecular basis of bacterial DSR2 anti-phage defense and viral immune evasion

Defense-associated sirtuin 2 (DSR2) systems are widely distributed across prokaryotic genomes, providing robust protection against phage infection. DSR2 recognizes phage tail tube proteins and induces abortive infection by depleting intracellular NAD+, a process that is counteracted by another phage-encoded protein, DSR Anti Defense 1 (DSAD1). Here, we present cryo-EM structures of Bacillus subtilis DSR2 in its apo, Tube-bound, and DSAD1-bound states. DSR2 assembles into an elongated tetramer, with four NADase catalytic modules clustered in the center and the regulatory-sensing modules distributed at four distal corners. Interestingly, monomeric Tube protein, rather than its oligomeric states, docks at each corner of the DSR2 tetramer to form a 4:4 DSR2-Tube assembly, which is essential for DSR2 NADase activity. DSAD1 competes with Tube for binding to DSR2 by occupying an overlapping region, thereby inhibiting DSR2 immunity. Thus, our results provide important insights into the assembly, activation and inhibition of the DSR2 anti-phage defense system.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

Correlation of Pseudomonas aeruginosa Phage Resistance with the Numbers and Types of Antiphage Systems

Phage therapeutics offer a potentially powerful approach for combating multidrug-resistant bacterial infections. However, to be effective, phage therapy must overcome existing and developing phage resistance. While phage cocktails can reduce this risk by targeting multiple receptors in a single therapeutic, bacteria have mechanisms of resistance beyond receptor modification. A rapidly growing body of knowledge describes a broad and varied arsenal of antiphage systems encoded by bacteria to counter phage infection. We sought to understand the types and frequencies of antiphage systems present in a highly diverse panel of Pseudomonas aeruginosa clinical isolates utilized to characterize novel antibacterials. Using the web-server tool PADLOC (prokaryotic antiviral defense locator), putative antiphage systems were identified in these P. aeruginosa clinical isolates based on sequence homology to a validated and curated catalog of known defense systems. Coupling this host bacterium sequence analysis with host range data for 70 phages, we observed a correlation between existing phage resistance and the presence of higher numbers of antiphage systems in bacterial genomes. We were also able to identify antiphage systems that were more prevalent in highly phage-resistant P. aeruginosa strains, suggesting their importance in conferring resistance.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary -and largely uncharacterized- genetics of adsorption, injection, and cell take-over. Here we present a machine learning (ML) approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. The most effective ML approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, predicting phage host range with 86% mean classification accuracy while reducing the relative error in the estimated strength of the infection phenotype by 40%. Further, transparent feature selection in the predictive model revealed 18 of 176 phage λ and 6 of 18 E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. While the genetic variation studied was limited to a focal, coevolved phage-bacteria system, the method's success at recapitulating strain-level infection outcomes provides a path forward towards developing strategies for inferring interactions in non-model systems, including those of therapeutic significance.

dbAPIS: a database of anti-prokaryotic immune system genes

Anti-prokaryotic immune system (APIS) proteins, typically encoded by phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, toxin-antitoxin, CRISPR-Cas). A growing number of APIS genes have been characterized and dispersed in the literature. Here we developed dbAPIS (https://bcb.unl.edu/dbAPIS), as the first literature curated data repository for experimentally verified APIS genes and their associated protein families. The key features of dbAPIS include: (i) experimentally verified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted structures, genomic context, sequence and structural homologs from different microbiome/virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Markov models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (except for anti-CRISPRs) that inhibit a variety of prokaryotic defense systems (e.g. RM, TA, CBASS, Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins.

Architecture and infection-sensing mechanism of the bacterial PARIS defense system

Bacteria and the viruses that infect them (bacteriophages or phages) are engaged in an evolutionary arms race that has resulted in the development of hundreds of bacterial defense systems and myriad phage-encoded counterdefenses1-5. While the mechanisms of many bacterial defense systems are known1, how these systems avoid toxicity outside infection yet activate quickly upon sensing phage infection is less well understood. Here, we show that the bacterial Phage Anti-Restriction-Induced System (PARIS) operates as a toxin-antitoxin system, in which the antitoxin AriA sequesters and inactivates the toxin AriB until triggered by the T7 phage counterdefense protein Ocr. Using cryoelectron microscopy (cryoEM), we show that AriA is structurally similar to dimeric SMC-family ATPases but assembles into a distinctive homohexameric complex through two distinct oligomerization interfaces. In the absence of infection, the AriA hexamer binds up to three monomers of AriB, maintaining them in an inactive state. Ocr binding to the AriA-AriB complex triggers rearrangement of the AriA hexamer, releasing AriB and allowing it to dimerize and activate. AriB is a toprim/OLD-family nuclease whose activation arrests cell growth and inhibits phage propagation by globally inhibiting protein translation. Collectively, our findings reveal the intricate molecular mechanisms of a bacterial defense system that evolved in response to a phage counterdefense protein, and highlight how an SMC-family ATPase has been adapted as a bacterial infection sensor.

Viromes vs. mixed community metagenomes: choice of method dictates interpretation of viral community ecology

Background

Viruses, the majority of which are uncultivated, are among the most abundant biological entities on Earth. From altering microbial physiology to driving community dynamics, viruses are fundamental members of microbiomes. While the number of studies leveraging viral metagenomics (viromics) for studying uncultivated viruses is growing, standards for viromics research are lacking. Viromics can utilize computational discovery of viruses from total metagenomes of all community members (hereafter metagenomes) or use physical separation of virus-specific fractions (hereafter viromes). However, differences in the recovery and interpretation of viruses from metagenomes and viromes obtained from the same samples remain understudied.

Results

Here, we compare viral communities from paired viromes and metagenomes obtained from 60 diverse samples across human gut, soil, freshwater, and marine ecosystems. Overall, viral communities obtained from viromes were more abundant and species rich than those obtained from metagenomes, although there were some exceptions. Despite this, metagenomes still contained many viral genomes not detected in viromes. We also found notable differences in the predicted lytic state of viruses detected in viromes vs metagenomes at the time of sequencing. Other forms of variation observed include genome presence/absence, genome quality, and encoded protein content between viromes and metagenomes, but the magnitude of these differences varied by environment.

Conclusions

Overall, our results show that the choice of method can lead to differing interpretations of viral community ecology. We suggest that the choice of whether to target a metagenome or virome to study viral communities should be dependent on the environmental context and ecological questions being asked. However, our overall recommendation to researchers investigating viral ecology and evolution is to pair both approaches to maximize their respective benefits.

Single phage proteins sequester TIR- and cGAS-generated signaling molecules

Prokaryotic anti-phage immune systems use TIR (toll/interleukin-1 receptor) and cGAS (cyclic GMP-AMP synthase) enzymes to produce 1"-3'/1"-2' glycocyclic ADPR (gcADPR) and cyclid di-/trinucleotides (CDNs and CTNs) signaling molecules that limit phage replication, respectively 1-3. However, how phages neutralize these common systems is largely unknown. Here, we show that Thoeris anti-defense proteins Tad1 4 and Tad2 5 both have anti-CBASS activity by simultaneously sequestering CBASS cyclic oligonucleotides. Strikingly, apart from binding Thoeris signals 1"-3' and 1"-2' gcADPR, Tad1 also binds numerous CBASS CDNs/CTNs with high affinity, inhibiting CBASS systems using these molecules in vivo and in vitro. The hexameric Tad1 has six binding sites for CDNs or gcADPR, which are independent from two high affinity binding sites for CTNs. Tad2 also sequesters various CDNs in addition to gcADPR molecules, inhibiting CBASS systems using these CDNs. However, the binding pockets for CDNs and gcADPR are different in Tad2, whereby a tetramer can bind two CDNs and two gcADPR molecules simultaneously. Taken together, Tad1 and Tad2 are both two-pronged inhibitors that, alongside anti-CBASS protein 2, establish a paradigm of phage proteins that flexibly sequester a remarkable breadth of cyclic nucleotides involved in TIR- and cGAS-based anti-phage immunity.

OLD family nuclease function across diverse anti-phage defense systems

Bacteriophages constitute a ubiquitous threat to bacteria, and bacteria have evolved numerous anti-phage defense systems to protect themselves. These systems include well-studied phenomena such as restriction endonucleases and CRISPR, while emerging studies have identified many new anti-phage defense systems whose mechanisms are unknown or poorly understood. Some of these systems involve overcoming lysogenization defect (OLD) nucleases, a family of proteins comprising an ABC ATPase domain linked to a Toprim nuclease domain. Despite being discovered over 50 years ago, OLD nuclease function remained mysterious until recent biochemical, structural, and bioinformatic studies revealed that OLD nucleases protect bacteria by functioning in diverse anti-phage defense systems including the Gabija system and retrons. In this review we will highlight recent discoveries in OLD protein function and their involvement in multiple discrete anti-phage defense systems.