The spacer Bacyerial within a single metagenome clearly has a Bacterial defense systems cefense, with few spacers being Defebse, while edfense majority Next-generation weight loss supplements of Bacterial defense systems frequency Suppl. 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. Since both Thoeris genes are essential for the normal functioning, these strains most likely have the system inactive. Defense islands in bacterial and archaeal genomes and prediction of novel defense systems.

Bacterial defense systems -

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.

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Evolutionary emergence of infectious diseases in heterogeneous host populations. While these characteristics align with the pan-immunity model, the detected CRISPR spacers only covered a subset of the phages previously identified in cheese, providing evidence that CRISPR does not enable complete immunity against all phages, and that the innate immune mechanisms may have complementary roles.

Our findings show that the evolution of bacterial defense mechanisms is a highly dynamic process and highlight that experimentally tractable, low complexity communities such as those found in cheese, can help to understand ecological and molecular processes underlying phage-defense system relationships.

These findings can have implications for the design of robust synthetic communities used in biotechnology and the food industry. Bacteria have evolved diverse defense systems to cope with the parasitic lifestyle of phages [ 19 , 45 ].

Classical examples of innate immunity are restriction-modification [ 80 ] or abortive infection systems [ 39 ]. However, many additional innate immune mechanisms have recently been discovered highlighting the strong selective pressure imposed by phages on microbial communities [ 19 , 38 ].

It is based on the incorporation of short DNA sequences of phages or other genetic elements so-called spacers into dedicated CRISPR arrays encoded in the bacterial genome. Upon a phage encounter, the transcribed spacers bind to the phage DNA and target it for degradation via the Cas proteins [ 37 ].

Phage defense systems are prevalent across bacteria and most bacteria harbor several systems in their genome [ 19 , 52 ]. However, their distribution varies across bacteria. Moreover, phage defense systems are often strain-specific [ 78 ], i. Various factors have been discussed to influence the distribution of defense systems across bacteria [ 60 ].

Notably, the presence of CRISPR-Cas has been associated with environmental factors such as temperature, oxygen levels or phage abundance [ 10 , 50 , 82 ]. Also, genetic incompatibilities of defense systems with other cellular functions, including other defense systems, have been reported [ 4 , 7 ].

Innate immune systems often cluster in genomic islands and are associated with mobile genetic elements [ 41 , 46 , 48 ] suggesting an important role of horizontal gene transfer HGT for defense system evolution.

The ecological relevance of such genomic plasticity was demonstrated by a recent study which showed that nearly clonal isolates of Vibrio spp.

are resistant to diverse phages due to the presence of distinctive defense islands acquired via HGT [ 33 ]. Based on the observation that phage defense mechanisms show a high extent of genetic turnover, the pan-immunity hypothesis has been proposed, which states that the effective immune system of a bacterial species is not the one encoded in a single genome, but in the pan-genome of the entire population [ 6 ].

In other words, while a single strain cannot carry all possible defense systems, the presence of nearly clonal strains with different defense systems increases the available arsenal of defensive mechanisms via HGT and thus increases the resistance of the entire population pan-immunity.

Comparative genomics of closely related isolates combined with shotgun metagenomics provide excellent opportunities to assess the diversity of phage defense systems in natural bacterial communities and can help to advance our understanding of their evolutionary ecology [ 69 ].

Two recent shotgun metagenomic studies have looked at CRISPR spacer diversity in microbial communities, one focusing on environmental samples from the Earth Microbiome Project [ 50 ] and another one focusing on diverse samples from the human microbiome [ 53 ]. Meaden et al.

identified a positive association between CRISPR spacer and the abundance of the corresponding phage target sequences protospacer , suggesting that there is a direct link between phage pressure and the maintenance of corresponding spacer sequences.

Münch et al. identified differences in the prevalence of CRISPR spacers between different human body sites suggesting the existence of niche-specific phage populations.

However, none of the two studies looked at the diversity of innate immune systems. Moreover, in both studies highly diverse communities from heterogeneous environments were compared providing limited insights into the intraspecific variation of phage defense systems and their evolutionary turnover in bacterial populations.

Here, we focused on cheese-associated bacterial communities. These communities harbor only a few bacterial species, have been propagated in relatively stable environments i. This makes them tractable systems to study the evolution of phage defense systems in microbial communities at the strain-level [ 25 , 75 ].

Indeed, our previous study of a single Swiss cheese starter culture has shown that extensive intraspecific CRISPR spacer diversity exists in these otherwise nearly clonal populations of bacteria [ 74 ]. Here, we expanded this analysis to all publicly available genomic datasets from cheese-associated communities excluding cheese rind comprising 26 bacterial species, genomes, and metagenomes.

We determined the distribution of both innate and adaptive immune systems across these datasets and quantified the diversity, abundance, turnover rate, and viral targets of all CRISPR spacers.

We find that i cheese-associated bacteria contain an unprecedented high degree of diversity in phage resistance mechanisms across nearly identical strains, ii there is a strong correlation between CRISPR spacer and phage abundance, and iii CRISPR spacers only provide immunity to a subset of the phages identified in cheese.

These results indicate highly dynamic bacteria-phage interactions driving genomic plasticity in cheese-associated environments and are compatible with the pan-immunity model of the evolutionary ecology of defense systems in microbial communities.

In order to analyze species and CRISPR diversity in cheese related samples we gathered shotgun metagenomic samples and 16S rRNA amplicon sequencing samples from overall 18 studies from NCBI see Suppl.

We included mesophilic cooked at lower temperatures and thermophilic cooked at higher temperatures cheese starter cultures as well as samples from ripened cheese as based on the FoodMicrobionet database 8. We excluded cheese rind samples from this analysis, because they consist of highly variable microbial communities with a more complex ecology [ 86 ].

To determine the species composition of the metagenomic samples, we used MetaPhlAn v3. For the 16S rRNA gene amplicon sequencing datasets we used the extensive FoodMicrobionet 8.

While 16S rRNA community analysis samples were only used for species profiling, the metagenomic samples were also used for spacer and protospacer analysis see further down. Overall, species were identified.

From those species we randomly selected one genome and created a species tree with BCGtree v1. All genomes or max. The Dialact database includes genomes from 9 species isolated from a wide range of samples obtained from Swiss cheese and Swiss cheese starter cultures Suppl.

Due to limited metadata information associated with many of the genomes obtained from NCBI, we cannot exclude that some of the strains included in our analysis may have not been isolated from cheese.

The genome assemblies were annotated with CRISPRCasFinder v4. The raw JSON outputs were parsed and quality filtered with custom Python and R script see script section. CRISPR-Cas subtypes were assigned with CRISPRCasTyper v1. Further, the remaining defense mechanisms were annotated with defense mechanisms specific HMM files.

For the R-M we used previously described HMMs [ 54 ]. The search was done with hmm-search v3. Average nucleotide identity ANI was calculated with fastANI v1.

The proportion of shared spacers was calculated as being the proportion of identical spacer clusters between two strains, divided by all spacers. Additionally, the number of common spacers, the nucleotide diversity and the turnover rate were computed for all strain pairs as well as for each array.

Further, the CRISPR acquisition rate in microbial communities per generation i. When accounting for a mutation rate of 8.

In order to identify CRISPR in the metagenomic samples, the raw metagenomic reads were processed using CRASS v0. CRISPR spacers, repeats and flanking sequences were then extracted. Each spacer was automatically annotated with coverage information as well as the spacer count per million reads for the whole sample.

Spacers with a coverage of 1 were removed as well as spurious spacers of length smaller than 15 bp. Repeats, often referred to as consensus repeats or direct repeats, are not necessarily identical within an array [ 68 ] as well as spacers, which do not need a perfect match with their target sequence to be cleaved [ 71 ].

Thus, repeat and spacer sequences were clustered using CD-HIT-EST v4. Eighty percent identity clusters were added in the database separately for genomic repeats, genomic spacers, metagenomic repeats, and metagenomic spacers.

Venn diagram representations Fig. The observed number of spacers and repeats was calculated by dividing the absolute number of spacers and repeats by the total number of reads and later by the total number of bacterial species richness.

Here we also included the data previously described for the human microbiome [ 53 ]. In order to quantify the proportion of spacers and protospacers in the metagenomes, we created for every spacer a repeat-spacer-repeat sequence and mapped all metagenomic samples individually against this reference.

Reads mapping only to the ~ 40 bp spacer area were assigned to protospacers, whereas reads mapping to the repeat and spacer area were assigned to the CRISPR array.

The sum of these counts was normalized by the reads count for each sample. BLASTn megablast was performed using an e-value cutoff of 0. To rule out putative prophage and annotate gene targets, we further blasted to the nr non-redundant protein and screened for phage genes and annotated with eggnog [ 31 ].

Further CRISPR mappings were ruled out by scanning the 2 kb up and downstream of the target site for CRISPR repeat annotation in the corresponding genbank files.

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.

Researchers built a detailed 3-D picture of the BrxU bacterial defense system to better understand how it protects from bacteriophages with modified DNA. Scientists from Durham University, in collaboration with the University of Liverpool, Northumbria University, and New England Biolabs, plan to exploit newly characterized defense systems in bacteria to compare changes to the human genome.

Undergraduates at Durham University have also been working on this research to demonstrate the complex workings of bacterial innate immunity. Bacteria have evolved a multitude of defense systems to protect themselves bacteriophages.

Many of these systems have already been developed into useful biotechnological tools, such as for gene editing, where small changes are made to the target DNA. Using a suite of thirty environmentally-isolated coliphages, we demonstrate multi-layered and robust phage protection provided by a plasmid-encoded defence island that expresses both a type I BREX system and the novel GmrSD-family type IV DNA modification-dependent restriction enzyme, BrxU.

Additionally, BrxU undergoes a multi-step reaction cycle instigated by an unexpected ATP-dependent shift from an intertwined dimer to monomers. The researchers demonstrated that two defense systems worked in a complementary manner to protect the bacteria from bacteriophages.

One system protected the bacteria from bacteriophages that did not have any modifications to their DNA.

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