All statistics were done within R [ 64 ] and ggplot2 [ 84 ]. The data is deposited on zenodo In order to obtain an overview of the diversity of phage defense systems in cheese-associated bacteria, we first determined which taxonomic groups are prevalent across cheese-associated communities by profiling community samples from 18 different studies Suppl.

Table 1. These included mesophilic cooked at lower temperatures and thermophilic cooked at higher temperatures cheese starter cultures as well as samples from ripened cheese selected from the FoodMicrobionet database [ 56 ].

We excluded cheese rind samples from our analysis, because they consist of highly variable microbial communities with a more complex ecology [ 86 ].

Overall, we included 16S rRNA gene amplicon sequencing and shotgun metagenomics datasets Fig. The large majority of these species were from the order Lactobacillales with Lactococcus lactis dominating mesophilic and Streptococcus thermophilus dominating thermophilic cheese samples Suppl.

Diversity of phage defense systems in the genomes of cheese-associated bacterial species. A Core genome phylogeny of the 26 predominant species found in the cheese-associated communities and their corresponding color key used in B.

B Species-level composition of cheese-associated communities starter and non-starter grouped by studies. Sample type and community profiling method 16S rRNA gene amplicon or shotgun metagenomics sequencing is indicated.

C Heatmap illustrating the fraction of genomes per species containing different innate and adaptive immunity mechanisms. The color scheme is indicated below D and E. G The number of different defense systems vs. average nucleotide identity between two genomes of the same species.

Including only the most dominant species comparisons. The statistics of the regression lines are illustrated in Suppl. We next retrieved genomes of the 26 predominant species from NCBI and the in house genomic database of Agroscope Suppl.

While the genomic data from our in-house database exclusively originates from strains isolated from cheese, the metadata associated with genomes obtained from NCBI was limited so that we cannot exclude that some strains included in our analysis may have been isolated from other environments than cheese.

The genomes were screened for the presence of homologs of 25 different phage defense systems using a hmm-search approach. On the contrary, only a few species harbored homologs of e. Abi systems Suppl. All species contained CRISPR-Cas systems with the exception of Brevibacterium aurantiacum, Brevibacterium linens, Companilactobacillus versmoldensis, Lactococcus lactis, and Leuconostoc mesenteroides Fig.

None of the defense systems were found to be specific to a given species. Moreover, species did not cluster by defense systems composition Fig. On average we found 7.

Considering that the number of defense systems reflects the extent of phage pressure in a given environment, this supports the idea that phages are prevalent in cheese-associated communities [ 78 ].

The number of shared innate immune systems decreased with increasing genomic divergence as measured by pairwise average nucleotide identity ANI Fig. No correlation was found between the presence of different innate immune systems across the analyzed genomes Suppl. As phage defense systems are costly to maintain, the loss of such genes could be the result of extensive passaging of certain strains in phage-deprived environments, especially as many of the sequenced genomes come from laboratory strains.

As expected, no spacers were shared between genomes belonging to different species or between arrays from different CRISPR-Cas subtypes. This is in line with the observed decrease in shared innate immune systems with increasing genetic distance between strains.

Although there seems to be a signature of vertical evolution over very short evolutionary timescales, the results overall suggest that most spacers are not maintained for very long but are continuously gained and lost.

The only exception concerns a subset of divergent L. High turnover of CRISPR spacers in cheese-associated bacterial genomes.

B Density plots of the number of novel CRISPR spacers acquired per generation in a microbial community of 10 7 cells subdivided into the six different CRISPR-cas subtypes.

The dashed line indicates the median spacer turn-over rate. To obtain an estimate of the CRISPR spacer turnover rate, we calculated how many novel CRISPR spacers would be acquired in each new generation in a community of defined size.

When accounting for a core genome mutation rate of 8. This suggests that the acquisition of novel CRISPR spacers is extremely rapid and that at every bacterial generation several novel spacers can be incorporated.

However, as we only considered fixed mutations, we may underestimate the time of divergence between these genomes which would result in a lower turnover rate.

Interestingly, we observed marked differences in spacer turnover rates between different CRISPR-Cas subtypes but not between different species. More specifically, the spacer turnover rates of arrays belonging to CRISPR-Cas subtypes I-E, I-G, and III-A were generally lower than the median turnover rate 2.

On the contrary, spacers turnover rate of the CRISPR-Cas subtype II-C was generally higher than the median turnover rate. Finally, the turnover rates of arrays belonging to CRISPR-Cas subtypes I-C and II-A showed a bimodal distribution with some arrays having high and others low rates of spacer turnover Fig.

Variation in spacer turnover rate has been previously observed and was suggested to reflect differences in phage pressure acting on the different strains [ 2 ].

Our data suggest that it also depends, at least partially, on intrinsic properties of the CRISPR-Cas subtype within the species Suppl. The high turnover rates of CRISPR spacers estimated from the isolate genomes suggests the presence of high levels of CRISPR spacer diversity within and across cheese-associated communities.

Based on the identification of flanking CRISPR repeats we extracted non-redundant full-length spacer sequences from the Illumina reads of the shotgun metagenomic samples presented in Fig. On average 5. This was surprising, as mesophilic cheese communities are dominated by the non-CRISPR containing species L.

lactis and Leuc. mesenteroides , and suggests that subdominant community members harbor a high number of CRISPR spacers. Metagenomic CRISPR diversity. A , B Number of CRISPR spacers present in the different metagenomic samples normalized by A the sequencing depth and B the sequencing depth and the species richness.

The human microbiome data is from [ 53 ]. C The number of spacers detected in the isolated genomes of predominant and subdominant cheese community species and in the shotgun metagenomic samples.

D The cumulative plot rarefaction curve of the CRISPR spacers detected in the metagenomic samples. We compared our dataset to a previously published analysis of CRISPR spacer diversity in human microbiomes [ 53 ] and found that the diversity of CRISPR spacers in cheese-associated communities and the human microbiomes is not significantly different from each other Fig.

However, when accounting for the higher species diversity in the human microbiome i. This is in line with previous studies, which had shown that high CRISPR-Cas diversity is associated with anaerobic growth, high temperatures and non-host environments [ 10 , 50 , 82 ], all of which are characteristics of cheese environments.

Surprisingly, only a small fraction of the spacers identified across the metagenomic datasets As no other species were detected in the analyzed metagenomes Fig.

Further, we found little overlap in CRISPR spacer diversity between metagenomes. Moreover, a rarefaction curve analysis showed that with the addition of each metagenomic sample, new spacers are being discovered Fig. Together, this indicates that the CRISPR spacer diversity within and between cheese-associated communities is extensive and that we have only detected a fraction of this diversity in our study.

If the spacers have any ecological relevance [ 12 , 29 ], one would expect to find a positive correlation between the abundances of phages and their matching spacer sequences. To quantify both spacer and target i. As spacers are usually shorter than Illumina reads, reads containing spacer and repeat sequence were considered to come from a CRISPR array hereafter referred to as spacer reads.

In contrast, reads mapping to only spacer sequences were considered to come from a target e. In each metagenome, we identified between 41 and repeat-spacer-repeat sequences, which recruited at least one spacer or protospacer read. In many cases, only protospacers For the remaining This is in line with previous results obtained for the Earth Microbiome Project [ 50 ] and supports the idea that spacers targeting highly abundant phages are under positive selection and thus dominant in the community [ 30 ].

Notably, in our previous study focusing on a single cheese starter culture, we had found the opposite pattern.

This may be explained by the fact that a single phage dominated this community, causing chronic infections and thereby overcoming CRISPR-based immunity [ 74 ].

A similar correlation has previously been described for the viral fraction outside of the cells measured by virus-microbe-ratios VMR [ 36 , 83 ]. Metagenomic CRISPR spacer and protospacer abundance. A The protospacer and spacer abundance of all metagenomic samples indicated in counts per million cpm.

The dashed blue and colored lines indicate the linear regression across all and specific CRISPR-Cas subtypes, respectively. The correlation values relate to all subtypes. B The spacer abundance in relation to the ratio of protospacer versus spacer abundance.

Further, we wanted to see if the CRISPR spacers are genetically linked and verify if the entire strain or the individual spacers are the level of selection. Therefore we looked at the spacer abundances within one metagenomic sample. The spacer abundance within a single metagenome clearly has a non-normal distribution, with few spacers being abundant, while the majority are of low frequency Suppl.

This indicates that individual spacers can sweep through the population rather than the entire strain being selected for. For a large number of the spacers identified in the isolate genomes, we did not find a corresponding protospacer sequence in the metagenomes.

However, in To determine the identity of sequences targeted by the CRISPR-Cas system, we searched the 63, CRISPR spacers identified in the genomes and metagenomes against the NCBI nucleotide database i. These proportions are in a similar range to what has previously been reported for other bacteria [ 61 ].

The fraction of spacers targeting phages varied across species. In case of S. delbrueckii or L. Protospacer diversity. A The fraction of CRISPR spacers mapping to the Viral IMG db, the bacterial NCBI database or having no hit. Each species top and metagenomic project bottom are subdivided and the number of spacers therein are indicated in the brackets.

B The rarefaction curves of vOTU for all species with more than 50 genomes and more than 85 described vOTUs.

C The fraction of IMG vOTUs targeted by all metagenomic samples green bar , one metagenomic sample dark green bar or all metagenomic samples in that project light green bar of S.

thermophilus total vOTUs. The bars indicate the standard error. To assess the range of phages targeted by a given isolate, we categorize the phages into discrete viral operational taxonomic units vOTU inferred by the viral IMG database [ 65 ].

Further, we limited this analysis to the genomes of the six bacterial species with the largest diversity of phages represented in the database i.

thermophilus, L. delbrueckii, L. helveticus, L. fermentum, L. rhamnosus , and P. This observation is in contrast to what has been observed in laboratory studies of phage-bacteria coevolution [ 13 , 29 ] or in cases of chronic phage infections in S.

thermophilus [ 74 ], where multiple spacers targeting the same vOTU were often found to be integrated into the CRISPR array. Our results seem to suggest that the spacer repertoire of a given strain is aimed at targeting a broad range of different phages rather than being specialized towards a single vOTU.

To assess whether the presence of several strains with diverse CRISPR spacers provides pan-immunity against a broad range of phages, we conducted a rarefaction analysis of the CRISPR-vOTU matches identified in the isolate genomes. For example for S. This indicates that no combination of isolates results in complete CRISPR-based immunity against all known phages of a given species.

Analysis of the metagenomic CRISPR spacers mapping to S. thermophilus phages confirmed these results: only thermophilus phages were targeted by CRISPR spacers identified across the metagenomic datasets Fig. Phages not targeted by any spacer did not seem to be rare as the vOTU clusters were not necessarily smaller than the clusters of targeted vOTU Suppl.

Moreover, they did not contain more anti-CRISPR genes than phages that had matching CRISPR spacers in the communities Suppl. It is possible that these phages are integrated as prophages in the bacterial genomes and thereby avoid CRISPR-based immunity or that the bacteria and phages have not encountered each other due to spatial population structure or segregation into different communities that have not yet been sampled [ 79 ].

Previous studies have used shotgun metagenomics to characterize CRISPR diversity in bacteria found on the human body, in the ocean or the soil [ 50 , 53 ]. Cheese-associated communities are much simpler than these previously analyzed communities [ 20 , 28 ].

They contain much fewer species and are propagated in relatively stable environments [ 57 , 73 ]. A large amount of genomic data is available for cheese-associated communities as they are established experimental model systems to study bacteria-phage interactions. This allowed us to assess the intraspecific diversity and evolutionary dynamics of phage defense systems across a wide range of bacterial species and communities by analyzing publicly available genomes and metagenomic datasets.

We found extensive diversity in innate and adaptive immune defense mechanisms across cheese-associated bacteria, despite the overall little genomic diversity present in these communities.

Phages are known to be common in these environments and pose a risk for the cheese making process [ 40 ]. Our analysis revealed that innate immune systems were distributed in a strain-specific manner across the analyzed genomes of cheese-associated communities.

They are part of the accessory genome and are rapidly gained and lost. Likewise, CRISPR spacer repertoires varied substantially across nearly clonal isolates and the amount of CRISPR spacers present in the metagenomic datasets seemed infinite. Accordingly, our estimation of the CRISPR spacer turnover rates suggested rapid gain and loss of CRISPR spacers with important differences found between different CRISPR-Cas subtypes but not necessarily species.

The ecological relevance of the identified diversity is highlighted by the finding that metagenomic CRISPR spacers matching abundant target sequences e. Our observations align with the pan-immunity model proposed for understanding the evolutionary ecology of innate immune systems [ 6 ] and suggest that this model can be extended to CRISPR-based adaptive immunity, as the three key points of this model seem to be fulfilled.

First, we show that there is standing genetic diversity in CRISPR spacer diversity in cheese-associated bacteria and communities. Second, similar as for the horizontal mode of transfer proposed for the innate immune systems [ 33 ], the high turnover of spacers detected in our study reflects that novel defensive repertoires can be rapidly acquired and lost in the population.

Third, CRISPR spacers seem to be selected for and hence functional as their abundance correlates with phage abundance. This may hint at the combined importance of both innate and adaptive immune systems and could suggest that CRISPR is not effective against all phages.

It is possible that these phages avoid CRISPR targeting, making them interesting candidates to look for novel anti-CRISPRs mechanisms. Alternatively, the untargeted phages could be incompatible because of surface modifications, which are known to be crucial for phage resistance in the cheese-associated species Streptococcus thermophilus [ 49 , 77 ].

Overall, our results allow us not only to understand the evolutionary ecology of phage-bacteria interactions but could also be instrumental in improving the protection of cheese starter culture by developing phage-based therapy [ 23 ] as a protection against pathogens [ 22 ] or invasive strains [ 63 ].

Alessandria V, Ferrocino I, De Filippis F, Fontana M, Rantsiou K, Ercolini D, et al. Microbiota of an Italian grana-like cheese during manufacture and ripening, Unraveled by 16S rRNA-based approaches. Appl Environ Microbiol. Article CAS PubMed PubMed Central Google Scholar.

Almeida GMF, Hoikkala V, Ravantti J, Rantanen N, Sundberg L-R. Mucin induces CRISPR-Cas defence in an opportunistic pathogen. Ankenbrand MJ, Keller A.

bcgTree: automatized phylogenetic tree building from bacterial core genomes. Article CAS PubMed Google Scholar. Bernheim A, Calvo-Villamañán A, Basier C, Cui L, Rocha EPC, Touchon M, et al. Inhibition of NHEJ repair by type II-A CRISPR-Cas systems in bacteria.

Nat Commun. Article CAS Google Scholar. Bernheim A, Millman A, Ofir G, Meitav G, Avraham C, Shomar H, et al. Prokaryotic viperins produce diverse antiviral molecules. Bernheim A, Sorek R. The pan-immune system of bacteria: antiviral defence as a community resource.

Nat Rev Microbiol. Birkholz N, Jackson SA, Fagerlund RD, Fineran PC. A mobile restriction—modification system provides phage defence and resolves an epigenetic conflict with an antagonistic endonuclease. Nucleic Acids Res. Bottari B, Levante A, Bancalari E, Sforza S, Bottesini C, Prandi B, et al.

The interrelationship between microbiota and peptides during ripening as a driver for parmigiano reggiano cheese quality. Front Microbiol. Article PubMed PubMed Central Google Scholar. Bradde S, Nourmohammad A, Goyal S, Balasubramanian V.

The size of the immune repertoire of bacteria. Proc Natl Acad Sci U S A. Burstein D, Sun CL, Brown CT, Sharon I, Anantharaman K, Probst AJ, et al. Major bacterial lineages are essentially devoid of CRISPR-Cas viral defence systems.

Calasso M, Ercolini D, Mancini L, Stellato G. Relationships among house, rind and core microbiotas during manufacture of traditional Italian cheeses at the same dairy plant. Food Microbiol. Article Google Scholar. Childs LM, England WE, Young MJ, Weitz JS, Whitaker RJ. CRISPR-induced distributed immunity in microbial populations.

PLoS One. Article PubMed PubMed Central CAS Google Scholar. Common J, Morley D, Westra ER, van Houte S. CRISPR-Cas immunity leads to a coevolutionary arms race between Streptococcus thermophilus and lytic phage.

Philos Trans R Soc Lond B Biol Sci. Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins.

De Filippis F, La Storia A, Stellato G, Gatti M, Ercolini D. A selected core microbiome drives the early stages of three popular italian cheese manufactures. De Filippis F, Parente E, Ercolini D.

Metagenomics insights into food fermentations. Microb Biotechnol. Article PubMed Google Scholar. De Pasquale I, Di Cagno R, Buchin S, De Angelis M, Gobbetti M. Dimitriu T, Szczelkun MD, Westra ER.

Evolutionary ecology and interplay of prokaryotic innate and adaptive immune systems. Curr Biol. Doron S, Melamed S, Ofir G, Leavitt A, Lopatina A, Keren M, et al. Systematic discovery of anti-phage defense systems in the microbial pan-genome.

Dugat-Bony E, Garnier L, Denonfoux J, Ferreira S, Sarthou A-S, Bonnarme P, et al. Highlighting the microbial diversity of 12 French cheese varieties. Int J Food Microbiol. Duru IC, Laine P, Andreevskaya M, Paulin L, Kananen S, Tynkkynen S, et al.

Metagenomic and metatranscriptomic analysis of the microbial community in Swiss-type Maasdam cheese during ripening. El Haddad L, Roy J-P, Khalil GE, St-Gelais D, Champagne CP, Labrie S, et al.

Efficacy of two Staphylococcus aureus phage cocktails in cheese production. Article PubMed CAS Google Scholar. Diverse enzymatic activities mediate antiviral immunity in prokaryotes.

Science , — This study uses a bioinformatic prediction method to validates 29 novel defence systems. Bacterial retrons function in anti-phage defense.

e12 This study shows that retrons can function as antiphage sensors that guard the RecBCD complex and trigger toxic effectors when activated. Bobonis, J. Bacterial retrons encode phage-defending tripartite toxin—antitoxin systems. Bacteria deplete deoxynucleotides to defend against bacteriophage infection.

Kaufmann, G. Anticodon nucleases. Trends Biochem. Penner, M. Phage T4-coded Stp: double-edged effector of coupled DNA and tRNA-restriction systems. Hsueh, B. Phage defence by deaminase-mediated depletion of deoxynucleotides in bacteria.

Koga, M. Escherichia coli rnlA and rnlB compose a novel toxin—antitoxin system. Genetics , — Uzan, M. Post-transcriptional control by bacteriophage T4: mRNA decay and inhibition of translation initiation. Athukoralage, J. Cyclic nucleotide signaling in phage defense and counter-defense.

Steens, J. The diverse arsenal of type III CRISPR—Cas-associated CARF and SAVED effectors. Diversity and classification of cyclic-oligonucleotide-based anti-phage signalling systems. Whiteley, A. Bacterial cGAS-like enzymes synthesize diverse nucleotide signals.

Leavitt, A. Viruses inhibit TIR gcADPR signaling to overcome bacterial defense. The dynamic interplay of host and viral enzymes in type III CRISPR-mediated cyclic nucleotide signalling. eL ife 9 , e Google Scholar. Nussenzweig, P.

Molecular mechanisms of CRISPR—Cas immunity in bacteria. LeGault, K. A phage parasite deploys a nicking nuclease effector to inhibit viral host replication. Doron, S. Systematic discovery of antiphage defense systems in the microbial pangenome.

Science , eaar This study develops a bioinformatic prediction method and experimentally validates nine novel antiphage systems. Jaskólska, M.

Two defence systems eliminate plasmids from seventh pandemic vibrio cholerae. Lau, R. Structure and mechanism of a cyclic trinucleotide-activated bacterial endonuclease mediating bacteriophage immunity.

Cell 77 , — Cheng, R. A nucleotide-sensing endonuclease from the Gabija bacterial defense system. Davidov, E. RloC: a wobble nucleotide-excising and zinc-responsive bacterial tRNase. Williams, M. Restriction endonuclease cleavage of phage DNA enables resuscitation from Casinduced bacterial dormancy.

Bari, S. A unique mode of nucleic acid immunity performed by a multifunctional bacterial enzyme. Morehouse, B. STING cyclic dinucleotide sensing originated in bacteria. Gao, Y. Molecular basis of RADAR anti-phage supramolecular assemblies.

e20 Duncan-Lowey, B. Cryo-EM structure of the RADAR supramolecular anti-phage defense complex. e15 Bernheim, A. Prokaryotic viperins produce diverse antiviral molecules.

Johnson, A. Bacterial gasdermins reveal an ancient mechanism of cell death. This study uncovers the existence of bacterial Gasdermins that, similar to eukaryotic ones, form pores leading to immunity-related cell death.

VanderWal, A. CRISPR-Csx28 forms a Cas13b-activated membrane pore required for robust CRISPR-Cas adaptive immunity. Cheng, X. F exclusion of bacteriophage T7 occurs at the cell membrane.

Virology , — Schmitt, C. Genes 1. Effector-mediated membrane disruption controls cell death in CBASS antiphage defense. Piel, D. Phage—host coevolution in natural populations. Samson, J. Revenge of the phages: defeating bacterial defences.

Labrie, S. Bacteriophage resistance mechanisms. Puigbò, P. Reconstruction of the evolution of microbial defense systems.

BMC Evol. Payne, L. Identification and classification of antiviral defence systems in bacteria and archaea with PADLOC reveals new system types.

Fillol-Salom, A. Bacteriophages benefit from mobilizing pathogenicity islands encoding immune systems against competitors. Vassallo, C. A functional selection reveals previously undetected anti-phage defence systems in the E.

coli pangenome. In this study, the authors developed a new high-throughput method for the discovery of antiphage systems through random cloning of genomic fragments of E. coli isolates, uncovering 21 novel systems. Benler, S. Cargo genes of Tn7-like transposons comprise an enormous diversity of defense systems, mobile genetic elements, and antibiotic resistance genes.

mBio 12 , e Hochhauser, D. The defense island repertoire of the Escherichia coli pan-genome. PLoS Genet. Temporal shifts in antibiotic resistance elements govern phage-pathogen conflicts. Science , eabg This study demonstrates that V.

cholerae resistance to phages is determined by the turnover of SXT integrative and conjugative elements, which often encode both antibiotic resistance genes and antiphage systems. Picton, D. The phage defence island of a multidrug resistant plasmid uses both BREX and type IV restriction for complementary protection from viruses.

Wu, Y. Defence systems provide synergistic anti-phage activity in E. Rocha, E. Microbial defenses against mobile genetic elements and viruses: who defends whom from what? PLoS Biol. Hussain, F. Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages.

This study shows that in a set of nearly clonal V. lentus strains, phage host range is largely explained by the rapid turnover of phage defence elements. The pan-immune system of bacteria: antiviral defence as a community resource.

Dedrick, R. Prophage-mediated defence against viral attack and viral counter-defence. Gentile, G. More evidence of collusion: a new prophage-mediated viral defense system encoded by mycobacteriophage Sbash. mBio 10 , e Montgomery, M. Yet more evidence of collusion: a new viral defense system encoded by Gordonia Phage CarolAnn.

mBio 10 , e—e Owen, S. Prophages encode phage-defense systems with cognate self-immunity. Cell Host Microbe 29 , — e8 Ali, Y. Temperate Streptococcus thermophilus phages expressing superinfection exclusion proteins of the Ltp type. Yu, Y.

Translation elongation factor Tu cleaved by a phage-exclusion system. USA 91 , — Koonin, E. Evolutionary entanglement of mobile genetic elements and host defence systems: guns for hire.

Anantharaman, V. Comprehensive analysis of the HEPN superfamily: identification of novel roles in intra-genomic conflicts, defense, pathogenesis and RNA processing. Direct 8 , 15 Lowey, B. Cell , 38— e17 Francois, R. A conserved family of immune effectors cleaves cellular ATP upon viral infection.

Aravind, L. Discovering biological conflict systems through genome analysis: evolutionary principles and biochemical novelty. Data Sci. Stern, A. The phage-host arms race: shaping the evolution of microbes.

Bioessays 33 , 43—51 Dupuis, M. CRISPR—Cas and restriction—modification systems are compatible and increase phage resistance. Costa, A. Accumulation of defense systems drives panphage resistance in Pseudomonas aeruginosa.

Srikant, S. The evolution of a counter-defense mechanism in a virus constrains its host range. eLife 11 , e Maguin, P. Cleavage of viral DNA by restriction endonucleases stimulates the type II CRISPR—Cas immune response.

Zheng, Z. The CRISPR—Cas systems were selectively inactivated during evolution of Bacillus cereus group for adaptation to diverse environments. ISME J. Vasu, K. Diverse functions of restriction—modification systems in addition to cellular defense.

Varble, A. Recombination between phages and CRISPR—Cas loci facilitates horizontal gene transfer in staphylococci. Watson, B. CRISPR-Cas-mediated phage resistance enhances horizontal gene transfer by transduction. mBio 9 , e—e Hsu, B. Dynamic modulation of the gut microbiota and metabolome by bacteriophages in a mouse model.

Cell Host Microbe 25 , — Braga, L. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome 8 , 52 Kellner, M.

SHERLOCK: nucleic acid detection with CRISPR nucleases. Loenen, W. Highlights of the DNA cutters: a short history of the restriction enzymes. Lopez, S. Precise genome editing across kingdoms of life using retron-derived DNA.

Pennisi, E. The CRISPR craze. Schubert, M. High-throughput functional variant screens via in vivo production of single-stranded DNA. The abundance and diversity of this biological arsenal are clear. Lactic acid production is a defining trait of lactic acid bacteria. Bacteriocins are found in almost every bacterial species examined to date, and within a species, tens or even hundreds of different kinds of bacteriocins are produced.

Halobacteria universally produce their own version of bacteriocins, the halocins.

Picton, D. The phage defence island of a multidrug resistant plasmid uses both BREX and type IV restriction for complementary protection from viruses. Wu, Y. Defence systems provide synergistic anti-phage activity in E.

Rocha, E. Microbial defenses against mobile genetic elements and viruses: who defends whom from what? PLoS Biol. Hussain, F. Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages. This study shows that in a set of nearly clonal V. lentus strains, phage host range is largely explained by the rapid turnover of phage defence elements.

The pan-immune system of bacteria: antiviral defence as a community resource. Dedrick, R. Prophage-mediated defence against viral attack and viral counter-defence.

Gentile, G. More evidence of collusion: a new prophage-mediated viral defense system encoded by mycobacteriophage Sbash.

mBio 10 , e Montgomery, M. Yet more evidence of collusion: a new viral defense system encoded by Gordonia Phage CarolAnn. mBio 10 , e—e Owen, S. Prophages encode phage-defense systems with cognate self-immunity. Cell Host Microbe 29 , — e8 Ali, Y.

Temperate Streptococcus thermophilus phages expressing superinfection exclusion proteins of the Ltp type. Yu, Y. Translation elongation factor Tu cleaved by a phage-exclusion system.

USA 91 , — Koonin, E. Evolutionary entanglement of mobile genetic elements and host defence systems: guns for hire. Anantharaman, V.

Comprehensive analysis of the HEPN superfamily: identification of novel roles in intra-genomic conflicts, defense, pathogenesis and RNA processing. Direct 8 , 15 Lowey, B. Cell , 38— e17 Francois, R. A conserved family of immune effectors cleaves cellular ATP upon viral infection.

Aravind, L. Discovering biological conflict systems through genome analysis: evolutionary principles and biochemical novelty.

Data Sci. Stern, A. The phage-host arms race: shaping the evolution of microbes. Bioessays 33 , 43—51 Dupuis, M. CRISPR—Cas and restriction—modification systems are compatible and increase phage resistance.

Costa, A. Accumulation of defense systems drives panphage resistance in Pseudomonas aeruginosa. Srikant, S. The evolution of a counter-defense mechanism in a virus constrains its host range. eLife 11 , e Maguin, P. Cleavage of viral DNA by restriction endonucleases stimulates the type II CRISPR—Cas immune response.

Zheng, Z. The CRISPR—Cas systems were selectively inactivated during evolution of Bacillus cereus group for adaptation to diverse environments. ISME J. Vasu, K. Diverse functions of restriction—modification systems in addition to cellular defense. Varble, A. Recombination between phages and CRISPR—Cas loci facilitates horizontal gene transfer in staphylococci.

Watson, B. CRISPR-Cas-mediated phage resistance enhances horizontal gene transfer by transduction. mBio 9 , e—e Hsu, B. Dynamic modulation of the gut microbiota and metabolome by bacteriophages in a mouse model.

Cell Host Microbe 25 , — Braga, L. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome 8 , 52 Kellner, M. SHERLOCK: nucleic acid detection with CRISPR nucleases.

Loenen, W. Highlights of the DNA cutters: a short history of the restriction enzymes. Lopez, S. Precise genome editing across kingdoms of life using retron-derived DNA.

Pennisi, E. The CRISPR craze. Schubert, M. High-throughput functional variant screens via in vivo production of single-stranded DNA.

USA , e Alseth, E. Bacterial biodiversity drives the evolution of CRISPR-based phage resistance. Shaer Tamar, E. Multistep diversification in spatiotemporal bacterial-phage coevolution. Jacob, F. Evolution and tinkering.

Kibby, E. Bacterial NLR-related proteins protect against phage. e18 Wein, T. Bacterial origins of human cell-autonomous innate immune mechanisms. Cury, J. Conservation of antiviral systems across domains of life reveals novel immune mechanisms in humans. Article Google Scholar. Warren, R.

Modified bases in bacteriophage DNAs. Liu, Y. Covalent modifications of the bacteriophage genome confer a degree of resistance to bacterial CRISPR systems. Hobbs, S. Phage anti-CBASS and anti-PYCSAR nucleases subvert bacterial immunity. Borges, A. Bacteriophage cooperation suppresses CRISPR-Cas3 and Cas9 immunity.

e10 Landsberger, M. Anti-CRISPR phages cooperate to overcome CRISPR-Cas immunity. Blower, T. Viral evasion of a bacterial suicide system by RNA-based molecular mimicry enables infectious altruism.

Makarova, K. Defense islands in bacterial and archaeal genomes and prediction of novel defense systems. Bondy-Denomy, J. Prophages mediate defense against phage infection through diverse mechanisms.

Scholl, D. Ohshima, Y. The role of capsule as a barrier to bacteriophage adsorption in an encapsulated Staphylococcus simulans strain. Scanlan, P. Co-evolution with lytic phage selects for the mucoid phenotype of Pseudomonas fluorescens SBW Seed, K.

Evolutionary consequences of intra-patient phage predation on microbial populations. eLife 3 , e Harvey, H. Pseudomonas aeruginosa defends against phages through type IV pilus glycosylation.

Tzipilevich, E. Bacteria elicit a phage tolerance response subsequent to infection of their neighbors. Wohlfarth, J. l -form conversion in Gram-positive bacteria enables escape from phage infection. PubMed PubMed Central CAS Google Scholar.

Ongenae, V. Reversible bacteriophage resistance by shedding the bacterial cell wall. Dillingham, M. RecBCD enzyme and the repair of double-stranded DNA breaks.

Guo, L. A bacterial dynamin-like protein confers a novel phage resistance strategy on the population level in Bacillus subtilis. Stokar-Avihail, A. Discovery of phage determinants that confer sensitivity to bacterial immune systems.

This study systematically uncovers triggers of antiphage defence systems and major trends of sensing mechanisms of antiphage immunity. An anti-CRISPR viral ring nuclease subverts type III CRISPR immunity. Huiting, E. Bacteriophages inhibit and evade cGAS-like immune function in bacteria.

e21 Pawluk, A. Anti-CRISPR: discovery, mechanism and function. Download references. The authors are grateful to A. Millman, G. Ofir and F. Rousset for their very useful feedback on early versions of this manuscript. The authors also thank all members of the Molecular Diversity of Microbes Lab for their comments and suggestions during the writing process.

and A. are supported by the ERC Starting Grant PECAN To promote gender equality and inclusivity in research, we are convinced of the importance of acknowledging gender bias in research article citation. Molecular Diversity of Microbes Lab, Institut Pasteur, Université Paris Cité, INSERM, Paris, France.

You can also search for this author in PubMed Google Scholar. Correspondence to Aude Bernheim. Nature Reviews Microbiology thanks Eugene Koonin, John van der Oost and Lennart Randau for their contribution to the peer review of this work.

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Buy or subscribe. Change institution. Learn more. References Ofir, G. Article PubMed CAS Google Scholar Suttle, C. Article PubMed CAS Google Scholar Kever, L. Article PubMed Google Scholar Kronheim, S. Article PubMed CAS Google Scholar Cohen, D.

Article PubMed CAS Google Scholar Ofir, G. Article PubMed CAS Google Scholar Tal, N. Article PubMed PubMed Central CAS Google Scholar Gao, L.

Article PubMed PubMed Central CAS Google Scholar Chopin, M. Article PubMed CAS Google Scholar Hampton, H. Article PubMed CAS Google Scholar LeRoux, M. Article PubMed Google Scholar Tesson, F.

Article PubMed PubMed Central CAS Google Scholar Millman, A. Article PubMed CAS Google Scholar Lopatina, A. Article PubMed CAS Google Scholar Tock, M. Article PubMed CAS Google Scholar Goldfarb, T. Article PubMed CAS Google Scholar Gordeeva, J. Article PubMed CAS Google Scholar Wang, L.

Article PubMed CAS Google Scholar Xiong, L. Article PubMed PubMed Central Google Scholar Wang, S. Article PubMed PubMed Central CAS Google Scholar Xiong, X. Article PubMed CAS Google Scholar Thiaville, J. Article PubMed PubMed Central CAS Google Scholar Hille, F. Article PubMed CAS Google Scholar Garb, J.

Article PubMed Google Scholar Zaremba, M. Article PubMed CAS Google Scholar Zeng, Z. Article PubMed CAS Google Scholar Deep, A.

Article PubMed CAS Google Scholar Dalton, V. Article PubMed PubMed Central Google Scholar Depardieu, F. Article PubMed CAS Google Scholar Zhang, T. Article PubMed PubMed Central CAS Google Scholar Rousset, F.

Article PubMed PubMed Central CAS Google Scholar Parma, D. Article PubMed CAS Google Scholar Durmaz, E. Article PubMed PubMed Central CAS Google Scholar Guegler, C. Article PubMed CAS Google Scholar Bobonis, J. Article PubMed CAS Google Scholar Kaufmann, G.

Article PubMed CAS Google Scholar Penner, M. Article PubMed CAS Google Scholar Hsueh, B. Article PubMed PubMed Central CAS Google Scholar Koga, M. Article PubMed PubMed Central CAS Google Scholar Uzan, M. Article PubMed PubMed Central CAS Google Scholar Athukoralage, J.

Article PubMed Google Scholar Steens, J. Article PubMed PubMed Central CAS Google Scholar Whiteley, A. Article PubMed PubMed Central CAS Google Scholar Leavitt, A. Article PubMed CAS Google Scholar Athukoralage, J.

Google Scholar Nussenzweig, P. Article PubMed CAS Google Scholar LeGault, K. Article PubMed PubMed Central Google Scholar Doron, S. Article PubMed PubMed Central Google Scholar Jaskólska, M.

Article PubMed PubMed Central Google Scholar Lau, R. Article PubMed PubMed Central CAS Google Scholar Cheng, R. Article PubMed PubMed Central CAS Google Scholar Davidov, E. Article PubMed PubMed Central CAS Google Scholar Williams, M.

Article PubMed PubMed Central CAS Google Scholar Bari, S. Article PubMed CAS Google Scholar Morehouse, B. Article PubMed PubMed Central CAS Google Scholar Gao, Y.

Article PubMed CAS Google Scholar Duncan-Lowey, B. Article PubMed PubMed Central CAS Google Scholar Bernheim, A. Article PubMed CAS Google Scholar Johnson, A. Article PubMed PubMed Central CAS Google Scholar VanderWal, A. Article PubMed CAS Google Scholar Schmitt, C. Article PubMed PubMed Central CAS Google Scholar Duncan-Lowey, B.

Article PubMed CAS Google Scholar Piel, D. Article PubMed CAS Google Scholar Samson, J. Article PubMed CAS Google Scholar Labrie, S. Article PubMed CAS Google Scholar Puigbò, P.

Article PubMed PubMed Central Google Scholar Payne, L. Article PubMed PubMed Central CAS Google Scholar Fillol-Salom, A. Article PubMed CAS Google Scholar Vassallo, C. Article PubMed PubMed Central CAS Google Scholar Benler, S.

Article PubMed Google Scholar Hochhauser, D. Article PubMed PubMed Central CAS Google Scholar LeGault, K. Article PubMed PubMed Central CAS Google Scholar Picton, D. Article PubMed PubMed Central CAS Google Scholar Wu, Y.

Article PubMed PubMed Central Google Scholar Rocha, E. Article PubMed PubMed Central CAS Google Scholar Hussain, F. Article PubMed CAS Google Scholar Bernheim, A. Article PubMed CAS Google Scholar Dedrick, R.

Article PubMed PubMed Central CAS Google Scholar Gentile, G. Article PubMed PubMed Central Google Scholar Montgomery, M. Article PubMed PubMed Central CAS Google Scholar Owen, S. Article PubMed PubMed Central CAS Google Scholar Ali, Y. Article PubMed PubMed Central Google Scholar Yu, Y.

Article PubMed PubMed Central CAS Google Scholar Koonin, E. Article PubMed CAS Google Scholar Anantharaman, V. Article PubMed PubMed Central CAS Google Scholar Lowey, B. Article PubMed PubMed Central CAS Google Scholar Francois, R.

Article PubMed PubMed Central Google Scholar Aravind, L. Article PubMed CAS Google Scholar Stern, A. Article PubMed PubMed Central CAS Google Scholar Dupuis, M.

Article PubMed Google Scholar Costa, A. Article PubMed PubMed Central Google Scholar Srikant, S. Article PubMed PubMed Central CAS Google Scholar Maguin, P. Article PubMed PubMed Central CAS Google Scholar Zheng, Z. Article PubMed PubMed Central Google Scholar Vasu, K.

Article PubMed PubMed Central CAS Google Scholar Varble, A. This evolutionary process generates a great diversity, which implies different gene gain and loss dynamics. Of these two genome dynamic events, gene loss is common in bacterial genomes and, on the contrary, gene gain is an incidental event Wolf and Koonin, The most common mechanism for gene gain is horizontal gene transfer HGT which results in genome expansion and acquisition of new functions Ochman et al.

Alternatively, gene duplication also generates greater availability of diverse genes that are useful to face new challenges usually imposed by the diversified phage attack Zhang, The rate of gene gain and loss also differs mostly depending on the biological activities of the genes.

The most stable are the genes that are devoted to basic or essential cell processes such as translation during protein synthesis Puigbò et al.

In the case of defense systems, they generally show 3 times more gene loss than gain and an order of magnitude more common than the duplication of gene families Puigbò et al.

Ralstonia solanacearum species complex RSSC is a diverse group of bacterial pathogens that infect and cause diseases in dozens of plant families. Members of this complex are the causal agent of bacterial wilt mainly in Solanaceae family of plants, Moko disease of banana and brown rot of potato Peeters et al.

It is considered a major pathogen since it heavily affects agricultural production worldwide Mansfield et al. The diversity of RSSC allowed classification of four groups called phylotypes, of which, the phylotype II was subdivided into two subgroups IIA and IIB Fegan and Prior, However, the current taxonomic classification of RSSC comprises three different species: R.

pseudosolanacearum which includes phylotypes I and III , R. solanacearum phylotype II and R. syzygii phylotype IV, the original R. syzygii and the blood disease bacterium Safni et al.

Ralstonia solanacearum species complex is mostly a soil-borne pathogen although insect vectors also transmit some particular subgroups to host plants. RSSC first invades plant roots through wounds or natural openings, colonizes the root intercellular spaces and then invades xylem vessels eventually leading the plant host to death Hikichi et al.

This dual lifestyle soil-plant or insect-plant has placed RSSC at a high risk of phage attack. Certainly, over the years many researchers have been reporting a large number of phages infecting RSSC and these spans a considerable large range of genetic diversity Table 1.

Three are the main viral families that attack RSSC: Inoviridae, Myoviridae, and Podoviridae, however a member of a fourth family has recently been found: phage ϕRS that belongs to family Siphoviridae Van Truong Thi et al.

Depending on the family to which phages belong, they contain single- or double-stranded DNA genomes and are filamentous or showing a head-tail structure. Most interesting, many phages are lytic which opens the possibility to use them in phage therapy to control different strains of RSSC.

The RSSC-phage relationship implies that this bacterial group has had to evolve to acquire and update a repertoire of defense systems while phages adapt to overcome these mechanisms.

This competitive interaction has created an evolutionary arms race that has driven the production of the extraordinary diversity of bacterial defense mechanisms in RSSC to hinder phage aggressions. However, the relative abundance, diversity, and evolution of the defense systems that RSSC possesses are unknown, with the exception of the CRISPR system reported by da Silva, Xavier et al.

Therefore, in this study, we present a detailed study of the arsenal of defense systems that RSSC harbors and their evolutionary dynamics.

The study of defense systems in RSSC takes greater relevance in the context of biological control against bacterial wilt. It is urgent to apply effective control strategies, which may include the use and application of phages. Lytic phages are of greater interest since they proliferate and destroy the host bacterial cell.

Thus, phage therapy is a promising strategy against bacterial wilt since there are already some reported successful cases in the control of this serious disease using phages Fujiwara et al. et al. Due to the scarcity of available genomic sequences of phylotype III only 3 , we rather work with an even number of sequences 3 sequences for each phylotype including phylotypes IIA and IIB , totaling the 15 genome sequences see Supplementary Table 1 for strains and genomic accession numbers.

We searched protein sequence homology in whole RSSC genomes using the HMMER online tool 2 Finn et al. This search allowed us to find Pfam El-Gebali et al.

Similarly, for Clusters of Orthologous Groups COGs , we used the online server Batch CD-Search tool 3 which is useful for both a conserved domain search on multiple protein sequences and for COG designation. A list of Pfam accessions involved in bacterial defense systems was constructed see Table 2 using the information of different articles that report experimental results.

We complemented this data with a list of COG accessions related to antiphage defense, if available. Defense proteins in RSSC genomes were identified based on the list of known Pfams and COGs involved on defense using the complete set of Pfam obtained from RSSC genomes using the HMMER online tool as said above.

For some defense systems, we used additional tools: to detect CRISPR genes, we visited CRISPRCasFinder online service 4 and the CRISPI Interactive database 5 ; to identify TA genes, we reviewed genome annotations gb files; to detect RM genes, we searched the REBASE 6 , a database of restriction enzymes and related proteins.

The rate estimations were calculated using the gain-loss-duplication model with the Poisson distribution, and searching for increasing complexity by three discrete categories for the gamma distribution.

When used 4 gamma categories we obtained similar results than with 3 categories. For optimization, rounds were executed to reach the convergence criteria with a likelihood threshold of 0. The species tree for evolutionary analysis was estimated under the Bayesian framework using the software BEAST v1.

Initial data was obtained from concatenating sequence proteins using the BPGA v1. This strategy produced an aligned sequence of The best model selection for protein substitution across sites was estimated using the online tool SMS 7 Lefort et al.

The Bayesian phylogenetic inference was set to the strict clock model with a constant growth for the tree prior. The shape α parameter of the gamma distribution and the proportion of invariant sites pInv were set up to lognormal distribution with initial value and mu μ equal to 0.

The analysis was run for 25 million generations, sampling every 2, generations. The convergence of the MCMC chains was assessed by evaluating the Effective Sample Size ESS of all parameters using Tracer v1.

We summarized the posterior sample of trees generated by BEAST to produce the maximum clade credibility tree using TreeAnnotator v1. To search for HGT events in the defense system genes, we employed the software Notung v2. To detect HGT events, Notung requires rooted trees.

For this, we employed the maximum clade credibility tree obtained previously see above using BEAST, which corresponds to the species tree. Then, we reconstructed gene trees in BEAST using similar strategies and settings than for species tree.

Briefly, protein datasets were created for each Pfam, based on Table 2. Not enough Druantia homologous proteins were found to construct a robust gene tree, therefore there are no results about HGT in this defense system.

The protein datasets were aligned using the MAFFT aligner Katoh et al. The aligned protein sequences of each Pfam were tested for the best model selection for protein substitution using SMS software Lefort et al.

The phylogenetic reconstruction was set up to JTT as the model of amino acid substitution that best fit in all datasets with gamma distribution and invariant sites. MCMC was run for 20 million generations to ensure stationary and convergence of parameters was assessed by calculating the ESS using Tracer v1.

Like above, the maximum clade credibility MCC trees were summarized using TreeAnnotator v1. Horizontal gene transfer events inferred by Notung for each defense system were displayed and visualized as a donor-recipient network using Gephi v0. The graph type was set as undirected i.

We downloaded protein sequences of basal metabolism enzymes, effectors T3E and cell-wall-degrading enzymes CWDE from protein databank 9. We confirmed that all basal metabolism enzymes selected in this study are present in all RSSC strains analyzed here using BLASTp.

The basal metabolism enzymes, T3E and CWDE protein sequences were useful to find their respective Pfam accessions using the HMMER web server, as explained above. A binary matrix was created of defense systems and basal metabolism enzymes, using Pfam accessions for both groups.

A similar approach was performed to develop a matrix for defense systems and T3E and CWDE. In general, we followed the methodology described in Press et al. Briefly, we analyzed discrete traits evolution under independent or dependent assumptions. We used the ML approach and repeated the analysis by calling the ML algorithm times, which produces more stable results.

To establish whether the dependent or independent model of evolution fits better the data, we employed the likelihood ratio test LRT. A Chi squared test significance that equals 9.

The likelihood ratios less than this critical value were considered independent. We aligned all available DNA sequences for each Pfam of defense systems Table 2 using the MAFFT online server 10 Katoh et al. We excluded from the analysis, the Pfams with an insufficient number of sequences less than 5 sequences to calculate the recombination and mutation parameters.

The recombination rate, rho ρ per site and the mutation rate, theta θ per site were calculated for each aligned set of sequences using the RDP v. Throughout evolution, bacteria have developed an ample arsenal of mechanisms to defend themselves from the attack of phages and other mobile elements such as plasmids.

RSSC is not the exception. By matching the Pfam and COG entries of well-known defense components in bacteria with the respective entries of RSSC genomes, we confirmed the presence of certain protein families and domains with a potential role in defense.

In this way, we identified numerous defense systems in RSSC phylotypes Table 2. We have focused our search on bacterial systems with greater distribution in all bacteria, but not in systems that are specific to particular groups such as the Dpd cluster in Salmonella enterica Thiaville et al.

We also did not pay attention to defense systems that are virus-mediated such as sie superinfection exclusion, Broecker and Moelling, As seen in Table 2 , different RSSC phylotypes harbor diverse systems.

In general, we found nine different systems devoted to defense from phage attack and one against plasmid transformation. Among the antiphage systems, the toxin-antitoxin system comprises the largest group of defense systems on average Additionally, a pan-genomic analysis of the fifteen RSSC genomes revealed that the defense systems are composed of a core of 43 protein families Pfams and, 10 families are unique among different phylotypes, being the phylotype III the one that harbors more unique protein families Figure 2.

Each defense system in RSSC is described below. Figure 1. Distribution of the relative abundance of defense systems in the RSSC.

Figure 2. Venn diagram of the RSSC defense system pan-genome. Numbers correspond to Pfam entries in phylotypes. Twenty-eight family proteins Pfam accessions make up modules of two contiguous genes that encode the toxin and its cognate antitoxin, representing the TA system in RSSC.

Of these families, five are involved in the abortive infection Abi process that protects bacteria from phage infection through an altruistic suicide mode Table 2. Abi systems work causing the death of the infected cells as a sacrifice to protect the surrounding cells from future predation Seed, The RM system shows eighteen protein families in RSSC.

Just like the TA system, all phylotypes harbor RM systems. Two main proteins form this system: a restriction endonuclease, and an enzyme for methylation.

Eventually, a third member that adds specificity participates in this system. This diversity of proteins allows the organization of this system in four main types as is found in most of the bacterial species.

We found that all four types of RM systems are present in different strains of RSSC, although the most abundant is Type II that is composed of the methyltransferase and the endonuclease encoded as two separate and independent proteins Supplementary Table 2.

The complete set of protein families that correspond to the CRISPR associated proteins Cas is present in the strains studied in this work that belongs to phylotypes IIA, IIB and III strains CFBP, CIP, IBSBF, Po82, and CFBP A genomic analysis of the palindromic repeats and spacer content indicates that these strains have the Type I-E CRISPR-Cas system Supplementary Table 3.

Besides the mentioned strains, other strains from phylotypes I and IV not included in Table 2 show the presence of CRISPR-Cas system as da Silva, Xavier et al. This result indicates that the CRISPR-Cas system is present in all phylotypes although it is not widely distributed across strains.

The gajAB genes are arranged in an operon and they are present in two copies in the strain PSI07 and one copy in CIP, and KACC The other strains HA, IBSBF, CMR15, CFBP, and UW that contain the genes of this system are dispersed in the genome highlighted with an asterisk in Table 2 ; therefore, it is very likely that they do not fulfill a biological function as a defense system.

This system is also composed of two genes hamAB that encodes a protein with unknown function HamA, Pfam accession: and a helicase HamB. Only one strain KACC from phylotype IV shows this system complete and both genes are organized in an operon.

This system is characterized by gene, thsB , encoding a protein with a TIR domain. This gene is typically preceded by thsA , a gene that encodes a protein containing a NAD-binding domain that is commonly annotated as SIR2-like domain.

In RSSC, this system is found in phylotype I strain GMI and phylotype IIB strain IBSBF Since both Thoeris genes are essential for the normal functioning, these strains most likely have the system inactive. This system comprises five genes in RSSC: druA , encodes a large protein with a domain with unknown function DUF , druBCD with unknown function and no protein family designation and druE that encodes also a large protein with DUF domain as well as a helicase signature and ATP-binding motif.

This arrangement of genes corresponds to the so-called Type I Druantia system and it is not found in any of the strains studied in this work but in other strains of RSSC.

Certainly, strain UW Phylotype IIB harbors the complete set of genes which indicates that this system would be fully functional in this strain. Other strains contain the incomplete system T82, Grenada , T, UW, SL, SL, P, SL, UA, BBAC-C1, UW, and T Two contiguous genes that encode a nuclease with PIWI acronym of the P -element Induced Wimpy Testis domain and a protein of SIR2 family characterize this system.

No strains of our set of strains contain this system, however, it is present in strains: OE, EP1, PSS, and VT phylotype I.

Incomplete versions of this Argonaute system are shown in different strains highlighted with an asterisk in Table 2 but we note that they probably have no biological function as a defense system.

This is the only defense system dedicated to reducing the incidence of plasmid transformation in bacteria. It consists of four genes, jetABCD , with unknown function except for jetC that encodes a protein with an N -terminal domain that is found in the system that performs structural maintenance of chromosomes and harbors an ATP-binding motif.

To unravel the evolutionary process of the defense systems in RSSC, we focused to study gene content evolution. For this, we used the COUNT software that applies maximum likelihood and the birth-death model that can take into account the effects of different evolutionary mechanisms of gene gain and loss when analyzing gene family data.

It is feasible to model the evolution of protein families because losses and gains seem to occur independently between the members of multigene families Nei and Rooney, Therefore, the most general process of the gene family evolution is gain most common through HGT , loss and duplication.

We calculated the rate of gene family gain, loss, and duplication for defense systems as well as for all genes encoding proteins in the genomes of RSSC. Results indicate that, contrary to what is observed in other bacterial systems in which gene loss has dominated the evolution of defense systems Puigbò et al.

In fact, the average rate of gene gain versus loss is 5-fold higher and the duplication rate being approximately 1. Our results indicate that, although in many defense systems, several genes have been lost in terminal edges as is the case of Gabija, Abi, Thoeris, other systems have experienced an early surge in gene content, which gave rise to a wide range of orthologous genes in the current strains.

This is the case of CRISPR that was probably acquired early when RSSC was divided into the phylotypes that we observe today. This fact is evidenced by observing a gene gain occurrence close to the root of the tree Supplementary Figure 1.

When comparing the rate of gene gain and loss of defense systems with all genes in the RSSC genomes, we observe an inclination toward a net loss of all gene families in the RSSC genomes Table 3.

Horizontal gene transfer is probably the main mechanism through which defense genes have been gained in RSSC genomes. We tested to confirm and measure the extent of HGT in the defense systems using a well-known approach aimed at reconciling the gene tree with the reference species tree.

Donor-recipient networks summarizing HGT events are shown in Supplementary Figure 2 for most of the defense systems analyzed in this work. Only one optimal solution tree was considered for each Pfam and all Pfams for a defense system is briefed in Supplementary Figure 2.

Multiple optimal solutions occur when transfer events are a dominant process, according to Notung. It should also be pointed out that we were unable to track HGT events for all Pfams mainly due to the low number of sequences available for some Pfams or the impossibility of Notung to calculate a temporally feasible solution of possible HGT events.

This happens when a transfer HGT event occurs and both, recipient and donor had to co-exist in the same time interval Stolzer et al. As it is possible to observe in Supplementary Figure 2 , the HGT events were profuse between strains of RSSC. Pfams of the Argonaute, Gabija, Hachiman, RM, TA-Abi, and Wadjet have undergone several transfer and loss events across their evolutionary history.

Conversely, few events of HGT were detected in CRISPR-Cas system and Thoeris, which have been restricted mainly to the tips of the trees reconciled trees not shown and only present in a few strains. In the case of CRISPR-Cas proteins, they are highly conserved; this may be due, precisely, to the low horizontal transfer rate observed in this system.

The results of the previous analysis bring to light that the genes encoding the defense systems may follow a different and independent regime of evolution than the rest of the genes in the genomes.

To confirm this presumption, we set out to correlate defense systems evolution with other cellular systems devoted to essential functions or pathogenicity. We selected a group of genes encoding enzymes of the basal metabolism to examine whether there is an evolutionary association between defense systems and essential housekeeping enzymes.

We used a novel method that performs analyses of trait evolution among groups, genes or systems for which phylogeny is available. This method uses a continuous-time Markov process to evaluate different models of evolution and estimates each of these rate parameters by maximum-likelihood.

Then, the best evolutionary model for the particular data under analysis is selected by computing the likelihood ratio test LRT using likelihood scores.

To analyze RSSC data, we created a binary matrix to compare two groups of genes: defense system vs basal metabolism housekeeping enzymes or defense system vs pathogenicity T3E or CWDE within a phylogenetic tree and determined if changes in the two groups have evolved independently or dependently.

In this way, we estimated the extent of association or participation of defense systems in basal metabolism or pathogenicity. Results indicate that all pairwise comparisons between systems generate values of the LRT below the critical value demonstrating that there is no significant association between evolution of defense systems and basal metabolism and neither between defense systems and pathogenicity see Supplementary Table 4.

This implies that defense systems in RSSC must have evolved independently from other systems at least, independently from basal metabolism and pathogenicity determinants.

The analysis performed above suggests that defense systems follow an independent evolution, unlinked to other cellular systems i. This implies that a similar discrepancy between the evolutionary rate of defense systems and other systems must be observed. Therefore, we wonder what the rate of molecular evolution of defense systems is compared to the general rate of RSSC genomes.

For this estimation, we used two different estimators, recombination rate ρ and mutation rate θ as proxies of the rate of molecular evolution. Both estimators provide population-scaled data so they are useful for getting an idea about the molecular evolution rate of the defense systems in the RSSC population.

We calculated ρ and, θ for each aligned sequence corresponding to the Pfams of the defense systems. However, some defense systems are rare in RSSC, namely, they are present in only a few strains such as Argonaute , therefore, it was not possible to include in the analysis the systems that lacked the minimum critical number of sequences to perform the calculations.

The average values of ρ and θ calculated for 48 Pfams are 0. These values are 1. This result indicates that the relative contribution of recombination and mutation to the evolution of defense systems is higher than the rest of the genome in RSSC.

We set out to describe the diversity of defense systems that are present in the phylotypes of RSSC. We found nine protein families of different systems devoted to defense from phage attack and one linked to reducing plasmid transformation.

The density of defense systems in RSSC genomes varies over broad range: some defense systems are widely extended in all phylotypes i. Although the number of defense systems in RSSC is significant, we do not rule out that computer and experimental means might identify other cryptic systems.

In this work, we did not perform in vivo experiments to determine the antiphage or anti-plasmid efficacy of the systems; however, we based our analysis on the results of different colleagues who experimentally validated all of the protein families for the defense capacity in many other bacteria and archaea groups.

Besides, the defense systems are widely distributed in bacteria so it is not rare to find them in RSSC. The RM and TA systems are thought to be ubiquitous in bacteria Makarova et al. The mechanism of action to abolish phage attack is known for some systems e.

Some defense systems in RSSC are particularly interesting to describe. It has several possible cellular functions: it can participate in the regulation of the transcriptional expression of host genes, it might act as a suicide system similar to abortive infection systems that kill a bacterial host under stress conditions and it works as defense against foreign genetic elements such as transposons, phages, and plasmids Lisitskaya et al.

In Betaproteobacteria the class where RSSC is taxonomically located most Argonaute proteins are short-type with only MID and PIWI domains Ryazansky et al. Although this system is poorly distributed in RSSC phylotypes, it seems to be complete in a few strains of phylotype I, although we do not rule out that it might be present in strains of other phylotypes.

The CRISPR-Cas system in RSSC was first described by da Silva, Xavier et al. Similarly, our results of gene content analysis and HGT indicate that CRISPR-Cas system is ancient and that would have been present in RSSC before the split in phylotypes, agreeing with da Silva, Xavier et al.

Thoeris system works to reduce or control the entry of plasmids into the bacterial cell Doron et al. We found this system in a few strains of our set of strains analyzed here; however, we do not rule out that other strains can also harbor this system.

Conversely, it is well-known the competency of RSSC to natural transformation, so that many strains can exchange DNA fragments up to 90 Kb Coupat et al. How can we accommodate these two seemingly contradictory functions?

Most likely, a dynamic equilibrium of both functions occurs in parallel inside the cell, which guarantees the genetic diversification without the burden of taking useless DNA fragments.

We have used different methods to study the evolutionary dynamics of defense systems in RSSC, which offer concordant and complementary results.

All the evidence collected in this work on the evolution of defense systems in RSSC indicates that they have been principally gained as opposed to the rest of genes present on the RSSC genomes that are preferably lost Table 3.

This result is consistent with that reported by Lefeuvre et al. We have also found some traces of gene duplication in a few defense systems mostly at the base of trees or ancestral nodes.

Thereby, gene gain and duplication are the main forces that have driven the expansion of the defense gene content in RSSC. Contrary, it has been demonstrated that the dominant mode of evolution of defense systems in other bacterial groups is gene loss Puigbò et al.

Undoubtedly, the main mechanism of gene gain is HGT, which has played a significant role in shaping defense systems in RSSC. Results of tree reconciliation to detect HGT events Supplementary Figure 2 show a profuse transference of genes between RSSC strains and phylotypes.

This abundant transference of genes in RSSC is not surprising since other studies reported multiple DNA acquisitions along the genome through HGT events Guidot et al.

We tested the evolutionary association of defense systems with other non-defense systems such as essential housekeeping and pathogenicity T3E or the CWDE functions. Results provided by the BayesTraits program suggest that the defense systems of RSSC follow an independent evolutionary pattern than other cellular systems.

In other words, the evolution of these systems is not correlated among them, suggesting that defense systems follow an independent evolutionary regime than the other functions. Maybe this is because the defense systems are subject to different selective pressures, which forces different evolutionary rates than the rest of the cellular functions.

Indeed, we found different evolutionary rates in the defense systems than the rest of the genome, when we calculated the rates of recombination and mutation Supplementary Table 4. The abundance and diversity of defense systems in RSSC implies that they play an important role as a major line of innate defense against a great diversity of phages see Table 1 that reside in the different natural environments where RSSC strains live.

The continuous process of defense and counter-defense mechanisms must constantly evolve to maintain the fitness of both interacting partners.

This coevolutionary process generates an enormous phage diversity, which in turn have triggered an adaptive race for increasing resistance in RSSC. Although much work remains to be done, especially at the experimental level, this study opens the door for further research focused on understanding the dynamic world of RSSC and its parasites.

Our study is also useful for designing better phage therapy strategies. An important problem in phage therapy is that bacteria may evolve resistance to phages, thus making the use of phages fruitless.

The knowledge of the defense systems present in particular strains of RSSC can help select more carefully the appropriate phages to avoid possible resistance. Likewise, studies on the evolutionary dynamics of RSSC-phage interaction could provide useful information about evolutionary parameters such as the fitness cost to maintain resistance to phage types.

Alternatively, it would be possible to design experimental evolution assays as is the case of Pseudomonas syringae and four related phages, Koskella et al. The genomic data analyzed in this study can be found in the NCBI database, see Supplementary Table 1 in Supplementary Data Sheet 1 for details.

HS-M and SM performed the phylogenetic and HGT analyses. KS analyzed evolutionary association. JC conceived and designed the study, analyzed the genomic data, calculated evolutionary rates and wrote the manuscript. All co-authors contributed to the manuscript revision, read, and approved the submitted version.

This research was partially supported by the Vice Chancellery of Research and Innovation, Yachay Tech University, Ecuador. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

We are grateful to Dr. Florent Lasalle for his critical reading and helpful suggestions to improve the first draft of the manuscript. We wish also to thanks Ms. Addy, H. Molecular and biological characterization of Ralstonia phage RsoM1USA, a new species of P2virus, isolated in the United States.

doi: CrossRef Full Text Google Scholar. Host range and molecular characterization of a lytic pradovirus-like Ralstonia phage RsoP1IDN isolated from Indonesia.

Ahmad, A. Sequencing, genome analysis and host range of a novel Ralstonia phage, RsoP1EGY, isolated in Egypt. Molecular and biological characterization of φRs, a filamentous bacteriophage isolated from a race 3 biovar 2 strain of Ralstonia solanacearum.

PLoS ONE e Álvarez, B. Biocontrol of the major plant pathogen Ralstonia solanacearum in irrigation water and host plants by novel waterborne lytic bacteriophages.

For example, the surface exclusion system of plasmid F prevents infection by similar plasmids thanks to the production of thousands of copies of an outer membrane protein that accounts for a large part of the plasmid carrier cost [ 80 ].

An even more extreme case concerns phages encoding defense or antidefense systems against their satellites. These are engaging in an interaction with their parasites in a way that resembles their own interaction with the cell but with their own position reversed as they are now the ones being exploited [ 47 ].

Such phage-encoded defense systems could be highly deleterious to the cell because they remove a protective satellite and favor a phage that will eventually kill the host. The misalignment of interests between MGEs and the host is particularly striking when it concerns abortive infection systems, because these are extremely costly to the cell [ 27 ].

The traditional view is that such strategies can only be selected in very particular cases favoring cooperation between individuals, e.

A recent investigation of abortive infection provided by retron elements suggests that retron-encoding bacteria lose in competition with bacteria lacking the retron when challenged by a phage even in a structured environment [ 82 ].

Yet, genomic data suggest that abortive infection systems are very frequent [ 35 ], which requires an explanation. The presence of abortive infection systems on MGEs could facilitate the control of epidemics of competitive elements and would justify their abundance in the host.

Such systems could be deleterious to the host if they drive cell death upon infection by elements with little negative impact on its fitness. But in other circumstances, the presence of these systems in MGEs could benefit the host by enforcing cooperation [ 83 ], since the transfer of the MGEs to sensitive hosts spreads the abortive system and therefore favors the cooperative process.

To understand the fitness impact of defense systems, it is thus important to know if they are encoded in MGEs. The identification of functional MGEs is difficult both computationally and experimentally, since many MGEs are poorly known and many of the others are defective [ 74 ].

It is often even more difficult to predict which genetic elements are being targeted by the defense system. That many systems are effective against virulent phages may be in part the result of ascertainment biases, since virulent phages are often used to identify defense systems. One might also argue that virulent phages are going to be targeted by hosts and most MGEs because they kill the host and its MGEs.

However, many systems, among which all those using epigenetic markers like R—M, target generic exogenous DNA independently of it being part of a phage genome. This makes it particularly hard to know who they were selected to target. The analysis of the spacer content can thus inform on the selection pressure that maintain CRISPR immunity.

These results suggest that systems encoded in MGEs may be targeting other competing MGE that are not costly to the cell. They may even be targeting elements that are adaptive to the cell or targeting the cell itself e. Knowing which genetic elements are being targeted in nature will require a better mechanistic understanding of the defense systems and the ecological contexts where they are selected for.

Acquisition of defense systems requires HGT, but defense systems are expected to decrease the rates of transfer of MGEs, and thus decrease HGT. Gene flow, including allelic recombination and acquisition of novel genes by HGT, is a key driver of bacterial evolution, and there is an evolutionary cost to restricting it.

For example, epidemic Vibrio cholerae strains depend on a prophage for a key virulence factor the cholera toxin. When they are infected by SXT-like conjugative elements carrying defense systems, they are hampered in their ability to acquire the toxin [ 13 ].

More generally, a computational analysis of approximately 80 species showed that gene flow is decreased between strains with incompatible R—M systems [ 85 ]. As a result, defense systems have the potential to fragment gene flow within bacterial populations.

When a population has a single R—M system left , HGT between cells is not affected by restriction. As the diversity of systems increases phylogenetic tree at the center , the subpopulations of individuals with similar R—M systems exchange genes at higher rates high flow than those with different R—M systems low gene flow, right top , leading to fragmentation of gene flow in populations right bottom.

HGT, horizontal gene transfer; R—M, restriction—modification. The presence of mechanisms of defense may impact gene flow in diverse ways. The negative impact of defense systems on gene flow has been regarded as a costly by-product of selection for protection of the cell.

But MGE defense systems may be selected exactly because they block HGT to prevent the cell from acquiring competitor MGEs. The resulting sexual barriers are advantageous for the MGE but can be deleterious to the cell.

Yet, these barriers are not unbreakable. The presence of multiple MGEs in genomes is in itself an indication of this. Accordingly, R—M systems only provide transient protection from phages [ 88 ], because one single successful infection is enough to result in correctly methylated phages that can pass the restriction barrier and then propagate across the population.

Further work is needed to quantify the impact of different defense systems in gene flow, to identify the types of MGEs that are most affected, and to understand how defenses affect host evolvability.

The effect of defense systems on gene flow is not always negative. In this case, the defense system facilitates gene flow. While many systems have been called defensive relative to their ability to defend bacteria or MGEs from other MGEs, they may be addictive or attack systems when part of MGEs.

A striking example is provided by phage—satellite interactions. The reproduction of virulent phages of the ICP1 family in V. cholerae is abolished by phage-inducible chromosomal island-like elements PLEs [ 18 ].

In this context, they could be regarded as attack systems from the point of view of the bacterium, because their success results in cell death. They could also be regarded as phage counter-defenses, if satellites are considered as a bacterial defense system.

There is thus some ambiguity between functions of defense, counter-defense, and attack, depending on the perspective of the observer. Some systems may have multiple roles specifically when encoded in MGEs. R—M systems contribute to the stabilization of plasmids in the cell by acting as poison—antidote addictive systems [ 91 ].

In such cases, loss of the plasmid and its R—M system prevents further expression of the latter. Since endonucleases have longer half-lives than methylases, this eventually results in genomes that are restricted because they are insufficiently methylated.

R—Ms are thus part of the attack arsenal of plasmids. Yet, these R—M systems can also protect the consortium cell and plasmid from infection by other MGEs, thereby acting as cell defense systems.

Plasmids also frequently encode toxin—antitoxin systems that behave as addiction systems [ 92 ], some of which are implicated in phage defense.

Homologues of cell defense systems encoded in MGEs can thus be addiction tools with positive side effects in cellular defense. It is possible that such systems have started as genetic elements that propagate selfishly in genomes because of their addictive properties and have later been co-opted to become defense systems although the inverse scenario cannot be excluded at this stage.

It was observed a decade ago that defense systems are often clustered in a few loci in microbial chromosomes [ 93 ]. This characteristic was leveraged into a systematic method to discover novel systems by colocalization with known ones [ 23 ]. The clustering of these systems could result from selection for the coregulation of their expression, but there is very little evidence of that.

The presence of defense systems in MGEs provides a simple explanation for the colocalization of defense and counter-defense systems in a few locations of the bacterial chromosome Fig 4. Genes acquired by HGT, and MGEs in particular, tend to integrate at a small number of chromosome hotspots [ 95 — 98 ], and some of these were found to have defense systems over a decade ago [ 51 ].

These MGEs may degenerate by the accumulation of mutations, deletions, and insertions. Chromosome hotspots are thus littered with remnants of previous events of transfer.

As MGEs are integrated and eventually degrade in the hotspot, some genes may remain functional because they are adaptive for the cell [ 74 ]. Since MGEs often carry defense and antidefense systems, their rapid turnover in hotspots may be accompanied by selection for the conservation of some of their defense systems.

Ultimately, this could result in their co-option by the host cell. MGEs tend to integrate the chromosome at a few hotspots and may subsequently be inactivated by mutations resulting in the loss of genes that are not adaptive to the host. The clustering of defense systems may facilitate the evolution of functional interactions between them or the coregulation of their expression.

These systems are often colocalized [ 53 ]. Even if advantages of their colocalization in the genome are yet unclear, it may facilitate cotranscription or coevolution of the systems. The clustering of these systems in islands could also facilitate their subsequent transfer as a block by HGT to other cells.

This could occur by mechanisms able to transfer large genetic loci such as conjugation starting a conjugative element integrated in the chromosome or lateral transduction starting from a neighboring phage [ 47 ]. MGEs of bacteria and archaea encode accessory functions of adaptive value for the host.

That many of these accessory functions concern systems facilitating host infection or MGE protection from other elements testifies to the importance of such interactions for the fitness of MGEs.

These defense systems may also be adaptive to the host, but this should not be taken for granted. Having this conceptual framework in mind can aid the field move forward along the following lines.

Their study will shed novel light on the function, evolution, and ecology of microbes. The authors thank Aude Bernheim, Frédérique Le Roux, and Maria Pilar Garcillan Barcia for comments and suggestions and Marie Touchon for discussions and graphical elements for the figures.

Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Prokaryotes have numerous mobile genetic elements MGEs that mediate horizontal gene transfer HGT between cells.

Introduction: Mobile genetic elements drive gene flow at a sometimes hefty cost Horizontal gene transfer HGT allows bacteria and archaea to rapidly match novel ecological challenges and opportunities.

Box 1. Defense islands : chromosomal loci with high density of defense systems. Phages : bacterial viruses. Download: PPT. Why are there so many defense systems in each genome?

Why are defense systems very diverse within species? How is immunity gained? Defending whom from what? How do defense systems affect gene flow? Fig 3. Diversification of R—M systems changes gene flow within species. Is it defense, counter-defense, addiction, or something else?

Fig 4. MGE turnover at hotspots may result in defense islands. Outlook MGEs of bacteria and archaea encode accessory functions of adaptive value for the host. Many defense systems are poorly known and probably many more remain to be uncovered. The recent expansion in the number and type of defense systems occurred because researchers searched for novel systems colocalizing with previously known ones.

Many novel systems may be awaiting discovery among the countless MGEs present across microbial genomes. Since these genes are often found at specific locations in MGEs, e. Most of the studies on the mechanisms of defense systems use virulent phages as targets.

Yet, systems encoded by MGEs may target different elements and having this information may result in the discovery of novel molecular mechanisms, especially among systems targeting specific MGE functions. Recent works have revealed defense systems targeting specific molecular mechanisms of phages [ 73 , ].

Maybe other defense systems target mechanisms of conjugative elements or other MGEs. Counter-defense mechanisms are now being identified for the best-known mechanisms of defense. Integrating the knowledge of the existence of mechanism of defense in an element, its molecular mechanism, and the elements being targeted could provide important clues on where to find novel antidefense systems from known or novel defense systems.

As defense systems provide multiple layers of defense against MGEs, it is important to understand what these layers are and how they interact. Ultimately, immune systems of bacteria might rely on complex networks of functional and genetic interactions between defense systems that provide a robust and thorough response to most parasites.

These networks may resemble those of the eukaryotic immune system. These evolutionary mechanisms may also share similarities across the tree of life, since some regulatory elements or components of the immune system of vertebrates and plants also derive from co-options of MGEs [ , ].

Balancing selection seems to explain the evolutionary patterns of defense systems in bacteria, plants and animals [ 60 ]. Yet, one must keep in mind that a lot of the variation in the bacterial immune response is associated with rapid gain and loss of defense systems, many of which in MGEs, which is different from the processes driving the diversification of immune systems of vertebrates.

Knowing the mechanisms of defense systems carried by a specific MGE can hint at their possible targets and therefore reveal the MGEs or host affected by the element. This can be leveraged to map antagonistic interactions between MGEs.

Virulence factors and antimicrobial resistance genes are frequently carried by MGEs. A better understanding of the defense, addiction, or attack systems that these elements employ to ensure their propagation might lead to the identification of novel strategies to counteract the spread of these costly elements, for instance, by favoring competing harmless MGEs.

The presence of antiphage systems on MGEs could also promote the rapid evolution of resistance to phage therapies, and conversely, the identification of counter-defenses deployed by phages and other MGEs might provide solutions for the selection or engineering of more potent therapeutic phages.

Acknowledgments The authors thank Aude Bernheim, Frédérique Le Roux, and Maria Pilar Garcillan Barcia for comments and suggestions and Marie Touchon for discussions and graphical elements for the figures.

References 1. Taylor VL, Fitzpatrick AD, Islam Z, Maxwell KL. The diverse impacts of phage morons on bacterial fitness and virulence.

Adv Virus Res. Bennett P. Plasmid encoded antibiotic resistance: acquisition and transfer of antibiotic resistance genes in bacteria. Br J Pharmacol. Baltrus DA. Exploring the costs of horizontal gene transfer.

Trends Ecol Evol. Croucher NJ, Mostowy R, Wymant C, Turner P, Bentley SD, Fraser C. Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict.

PLoS Biol. Touchon M, Rocha EP. Causes of insertion sequences abundance in prokaryotic genomes. Mol Biol Evol. De Toro M, Garcillán-Barcia MP, De La Cruz F. Plasmid diversity and adaptation analyzed by massive sequencing of Escherichia coli plasmids. Microbiol Spectr. Bobay LM, Touchon M, Rocha EPC.

Pervasive domestication of defective prophages by bacteria. Proc Natl Acad Sci U S A. Hussain FA, Dubert J, Elsherbini J, Murphy M, VanInsberghe D, Arevalo P, et al.

Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages. Penadés JR, Christie GE. The phage-inducible chromosomal islands: a family of highly evolved molecular parasites.

AnnuRev Virol. Smillie C, Pilar Garcillan-Barcia M, Victoria Francia M, Rocha EPC, de la Cruz F. Mobility of Plasmids. Microbiol Mol Biol Rev. Gama JA, Zilhão R, Dionisio F. Harb L, Chamakura K, Khara P, Christie PJ, Young R, Zeng L. ssRNA phage penetration triggers detachment of the F-pilus.

LeGault K, Hays SG, Angermeyer A, McKitterick AC, Johura F-t, Sultana M, et al. Temporal shifts in antibiotic resistance elements govern virus-pathogen conflicts.

Baharoglu Z, Bikard D, Mazel D. Conjugative DNA transfer induces the bacterial SOS response and promotes antibiotic resistance development through integron activation.

PLoS Genet. He S, Chandler M, Varani AM, Hickman AB, Dekker JP, Dyda F. Mechanisms of evolution in high-consequence drug resistance plasmids. Wagner PL, Waldor MK. Bacteriophage control of bacterial virulence. Infect Immun.

Paul JH. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. Seed KD, Lazinski DW, Calderwood SB, Camilli A.

Touchon M, Bernheim A, Rocha EP. Genetic and life-history traits associated with the distribution of prophages in bacteria. Wigington CH, Sonderegger D, Brussaard CPD, Buchan A, Finke JF, Fuhrman JA, et al. Re-examination of the relationship between marine virus and microbial cell abundances.

Nat Microbiol. van Houte S, Buckling A, Westra ER. Evolutionary ecology of prokaryotic immune mechanisms. Hampton HG, Watson BN, Fineran PC. The arms race between bacteria and their phage foes. Doron S, Melamed S, Ofir G, Leavitt A, Lopatina A, Keren M, et al. Systematic discovery of antiphage defense systems in the microbial pangenome.

Gao L, Altae-Tran H, Böhning F, Makarova KS, Segel M, Schmid-Burgk JL, et al. Diverse enzymatic activities mediate antiviral immunity in prokaryotes. González-Delgado A, Mestre MR, Martínez-Abarca F, Toro N. Prokaryotic reverse transcriptases: from retroelements to specialized defense systems.

FEMS Microbiol Rev. Bernheim A, Sorek R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat Rev Microbiol. Lopatina A, Tal N, Sorek R. Abortive infection: bacterial suicide as an antiviral immune strategy. Annu Rev Virol. Nussenzweig PM, Marraffini LA. Molecular mechanisms of CRISPR-Cas immunity in bacteria.

Annu Rev Genet. Cheng K, Wilkinson M, Chaban Y, Wigley DB. A conformational switch in response to Chi converts RecBCD from phage destruction to DNA repair. Nat Struct Mol Biol. Levy A, Goren MG, Yosef I, Auster O, Manor M, Amitai G, et al. CRISPR adaptation biases explain preference for acquisition of foreign DNA.

Kuzmenko A, Oguienko A, Esyunina D, Yudin D, Petrova M, Kudinova A, et al. DNA targeting and interference by a bacterial Argonaute nuclease. Bobay L-M, Touchon M, Rocha EP. Manipulating or superseding host recombination functions: a dilemma that shapes phage evolvability.

Millman A, Bernheim A, Stokar-Avihail A, Fedorenko T, Voichek M, Leavitt A, et al. Bacterial retrons function in anti-phage defense. Oliveira PH, Touchon M, Rocha EP. The interplay of restriction-modification systems with mobile genetic elements and their prokaryotic hosts. Nucleic Acids Res.

Systematic and quantitative view of the antiviral arsenal of prokaryotes. View Article Google Scholar Isaev A, Drobiazko A, Sierro N, Gordeeva J, Yosef I, Qimron U, et al. Phage T7 DNA mimic protein Ocr is a potent inhibitor of BREX defence. Krüger DH, Bickle TA, Bacteriophage survival. Multiple mechanisms for avoiding the deoxyribonucleic acid restriction systems of their hosts.

Microbiol Rev. Rousset F, Dowding J, Bernheim A, Rocha E, Bikard D. Prophage-encoded hotspots of bacterial immune systems. Fukuda E, Kaminska KH, Bujnicki JM, Kobayashi I. Cell death upon epigenetic genome methylation: a novel function of methyl-specific deoxyribonucleases. Genome Biol. Loenen WA, Raleigh EA.

The other face of restriction: modification-dependent enzymes. Penner M, Morad I, Snyder L, Kaufmann G. Phage T4-coded Stp: double-edged effector of coupled DNA and tRNA-restriction systems.

J Mol Biol. Bondy-Denomy J, Qian J, Westra ER, Buckling A, Guttman DS, Davidson AR, et al. Prophages mediate defense against phage infection through diverse mechanisms. Dedrick RM, Jacobs-Sera D, Bustamante CA, Garlena RA, Mavrich TN, Pope WH, et al.

Prophage-mediated defence against viral attack and viral counter-defence. Piel D, Bruto M, Labreuche Y, Blanquart F, Chenivesse S, Lepanse S, et al. Genetic determinism of phage-bacteria coevolution in natural populations. Owen SV, Wenner N, Dulberger CL, Rodwell EV, Bowers-Barnard A, Quinones-Olvera N, et al.

Prophages encode phage-defense systems with cognate self-immunity. Cell Host Microbe. McKitterick AC, Seed KD. Anti-phage islands force their target phage to directly mediate island excision and spread.

Nat Commun. Fillol-Salom A, Miguel-Romero L, Marina A, Chen J, Penadés JR. Beyond the CRISPR-Cas safeguard: PICI-encoded innate immune systems protect bacteria from bacteriophage predation. Curr Opin Microbiol.

Klaenhammer TR. Plasmid-directed mechanisms for bacteriophage defense in lactic streptococci. Pinilla-Redondo R, Mayo-Muñoz D, Russel J, Garrett RA, Randau L, Sørensen SJ, et al.

Type IV CRISPR—Cas systems are highly diverse and involved in competition between plasmids. León LM, Park AE, Borges AL, Zhang JY, Bondy-Denomy J. Mobile element warfare via CRISPR and anti-CRISPR in Pseudomonas aeruginosa. Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D.

Genomic island variability facilitates Prochlorococcus-virus coexistence. Lin LF, Posfai J, Roberts RJ, Kong H. Comparative genomics of the restriction-modification systems in Helicobacter pylori.

Bernheim A, Bikard D, Touchon M, Rocha EP. Atypical organizations and epistatic interactions of CRISPRs and cas clusters in genomes and their mobile genetic elements. Bondy-Denomy J, Davidson AR. To acquire or resist: the complex biological effects of CRISPR-Cas systems. Trends Microbiol.

Vale PF, Lafforgue G, Gatchitch F, Gardan R, Moineau S, Gandon S. Costs of CRISPR-Cas-mediated resistance in Streptococcus thermophilus.

Proc R Soc B Biol Sci. Seidel R, Bloom JG, Dekker C, Szczelkun MD. Motor step size and ATP coupling efficiency of the dsDNA translocase EcoRI. EMBO J. Bernheim A, Calvo-Villamanan A, Basier C, Cui L, Rocha EPC, Touchon M, et al.

Inhibition of NHEJ repair by type II-A CRISPR-Cas systems in bacteria. Rollie C, Chevallereau A, Watson BN, Chyou T-y, Fradet O, McLeod I, et al. Targeting of temperate phages drives loss of type I CRISPR—Cas systems.

Picton DM, Luyten YA, Morgan RD, Nelson A, Smith DL, Dryden DT, et al. The phage defence island of a multidrug resistant plasmid uses both BREX and type IV restriction for complementary protection from viruses. Fijarczyk A, Babik W. Detecting balancing selection in genomes: limits and prospects.

Mol Ecol. Open for submission from 18 August Submission deadline 31 May Microbial evolution is driven by a dynamic interaction between bacteria and viruses bacteriophages. To avoid cell death or genomic invasion, bacteria have developed several sophisticated defense strategies, like preventing cell entry e.

via receptor masking or variation and infection e. restriction-modification systems and adaptive mechanisms e. the CRISPR-Cas systems. Conversely, bacteriophages have evolved strategies to evade or counteract many of these defense systems, e. anti-CRISPR and antirestriction proteins.