MGE- Acr Clusters in Pseudomonas aeruginosa Clinical Isolates

Abstract

Background:

Mobile genetic elements (MGEs) play an important role in the pathogenicity of bacteria and are regulated by mechanisms such as CRISPR-Cas systems. In response, MGEs encode CRISPR-Cas antagonists, known as anti-CRISPR (Acr), contributing to their spread. Methods: Here, we performed genomic analysis of 128 P. aeruginosa clinical isolates to determine the presence of CRISPR-Cas systems, MGEs and Acrs.

Results:

We examined the relationship between the clinical origin of the isolates and the presence of CRISPR-Cas systems, observing a predominance of type I-F. However, acr genes were present at low frequency in the isolates, being the AcrIF type the most prevalent. These genes were mainly organized in clusters, such as AcrIE1/IF3/IF12, which were always detected in pathogenicity islands. We identified five new pathogenicity islands, which we annotated and compared, thus determining the presence of 89 conserved proteins.

Conclusion:

Our findings suggest that pathogenicity islands may play an important role in bacterial adaptation and evolution. However, more detailed analyses are required to understand the complex mechanism regulating the CRISPR-Cas/Acr interactions and their contribution to bacterial evolution and horizontal gene transfer.

Introduction

Pseudomonas aeruginosa is an opportunistic pathogen that causes important nosocomial infections. It also causes respiratory tract infections, especially in cystic fibrosis (CF) patients and those with ventilator-associated pneumonia.^1,2 It has been included as a high-priority pathogen in the WHO bacterial priority pathogens list in 2024, because of its threat to global health, which is mainly due to resistance to carbapenems.³

New virulence mechanisms of P. aeruginosa are still being discovered to this day.^4,5 Although many of the virulence genes are located in the core genome of bacteria,^6–8 horizontal gene transfer (HGT) of mobile genetic elements (MGEs) is known to be an important mechanism for acquiring new virulence factors that lead to the emergence of epidemic clones.⁹ MGEs include prophages, plasmids, and integrative and conjugative/mobilizable elements (ICEs/IMEs), among others.¹⁰ ICEs are conjugative self-transmissible elements that integrate into the host genome and replicate alongside it,¹¹ whereas IMEs do not have the capacity for self-transmission and need an additional conjugative element that mediates their horizontal transfer.^12,13 Their ability for genomic integration and replication along with the chromosome is the primary feature that distinguishes them from plasmids.¹⁴ Both are typically found in genomic areas known as regions of genomic plasticity that are usually located adjacent to tRNA genes, and flanked by direct repeats, serving as insertion sites.^15–17 ICEs and IMEs carry modules responsible for excision, maintenance, conjugative transfer, and integration in the new host genome, as well as elements that intervene in this process, such as integrase and type IV secretion system (T4SS).¹⁸ Considering these characteristics, some genomic and pathogenicity islands fall under the category of ICEs.¹⁹

ICEs contribute to the dispersal of virulence and resistance genes; among these, the genomic islands carrying virulence factors are known as pathogenicity islands. More specifically, those detected in P. aeruginosa are commonly known as P. aeruginosa genomic islands (PAGIs),²⁰ although an alternative terminology refers to PAGIs as pathogenicity genomic islands.²¹ These islands harbor several resistance and virulence genes that increase the fitness and pathogenicity of bacteria, strengthening the global threat of antimicrobial resistance.^22–24 Some of the pathogenicity islands found in P. aeruginosa have been classified as ICEs, such as PAPI-1, PAGI-2, or PAGI-3.^25,26

Clustered regularly interspaced short palindromic repeats and their associated Cas proteins (CRISPR-Cas systems) constitute the bacterial immune system against foreign nucleotide sequences. Although CRISPR-Cas systems have been widely described as phage-resistant mechanisms, they also induce protection against other types of MGEs such as plasmids, thus representing a significant barrier to HGT.^27,28 CRISPR-Cas systems have the ability to incorporate incoming DNA sequences into CRISPR loci (spacers) providing immunological memory.^29,30 Transcription and maturation of these spacers generate CRISPR-RNAs (crRNAs) that cause Cas nucleases to cleave specific regions of foreign sequences.³¹ Nowadays, there are six types of CRISPR-Cas systems grouped into two classes, described on the basis of the associated Cas proteins.³² These Cas proteins are responsible for the maturation of crRNAs, formation of the CRISPR complex (crRNAs-Cas), cleavage of the target genetic material, and acquisition of new sequences in the CRISPR array.^33,34

Despite the existence of these bacterial defense systems, PAGIs are able to integrate into the host genome due to mechanisms that inhibit the systems.³¹ More specifically, PAGIs have acquired inhibitors called anti-CRISPR (Acr) proteins in order to evade CRISPR-Cas systems.³⁵ These proteins are usually named after the targeted CRISPR system and are characterized by being small proteins of low molecular weight and high sequence variability, making them difficult to identify.³⁶ The following main mechanisms of action of Acrs have been described to date: I) prevention of foreign DNA recognition avoiding spacer acquisition, ii) competition with the Cas in the establishment of the complex, iii) inhibition of the DNA-binding activity of the crRNA-Cas complex, and iv) inhibition of Cas cleavage activity.^33,37,38 Acr proteins are frequently linked to anti-CRISPR-associated genes (aca), which are highly-conserved genes harboring predicted helix-turn-helix (HTH) DNA-binding domains²⁷ with acr transcriptional regulator functions.³⁸ In addition, these mobile elements can also encode other components that may have contributed to their dispersion and bacterial defense, for instance, CRISPR-Cas systems or antibiotic resistance genes.³⁹

In this work, we analyzed the genomes of a collection of 128 clinical isolates of P. aeruginosa. We described 4 MGE-acr clusters, 1 harbored in 5 new PAGIs, and another 3 acr clusters found in a total of 21 of the isolates, which conferred immunity against 2 different types of CRIPSR-Cas systems (type I-F and type I-E).

Material and Methods

Genomes of clinical isolates

The genomes of 128 clinical isolates of P. aeruginosa were obtained in three different multicenter studies conducted in Spain and Portugal^40–42 and were of different origins and sequence types (STs) (Table 1 and Supplementary Table S1). The sequencing was conducted by short-paired-read bridge amplification by Illumina HiSeq 4000 or the Illumina NovaSeq 6000 platform (Illumina, Oxford Genomics Centre, Oxford, UK). The sequences were assembled using SPAdes versions v3.11.1 or v3.15 and annotated with RAST NMPDR.⁴³ Briefly, RASTtk was used for the search with automatically fixed errors and frameshifts, enabling construction of a metabolic model and computing similarities, and a verbosity level of 0 was applied. The minimum identity for call features repeat regions was 95%, with a maximum length of 100 bp and a call features CDS glimmer with minimum training length of 2000. The prune invalid CDS feature was between 0 and 0.9, and the minimum k-mer hits required was 5. The genomes of the bacterial collection used here were assembled in contigs, which may have affected identification of some of the systems and genes.

Table 1:

Details of the strains from both Pseudomonas aeruginosa collections harboring anti-CRISPR genes (Acr)

Strain	ST	Origin	CRISPR	Acr	Acr location	Pathogenicity islands	Prophages
008-1318	NEW2	CF	CRISPR-Cas IE	AcrIE1/AcrIF3/AcrIF12/AcrIF3	Pathogenicity island/prophage	NNI/PAGI-135/PAGI-7/PAGI-9	3
008-4371	CCNEW2						4
008-5924	NEW2						4
014-4114	319	CF	CRISPR-Cas IE	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	PAGI-135/PAGI-7	3
014-4688							3
014-5818							3
016-0109	252	CF	CRISPR-Cas IF/CRISPR-Cas IE	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI	3
016-2856	252	CF	CRISPR-Cas IE	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI	3
022-5546	2101	CF	—	AcrIF24	Prophage		2
2-10	499-1LV	LRTI	—	AcrIF3/AcrIE3	Prophage/genome	—	4
3-38	348	LRTI	—	AcrIE3/AcrIF4	Prophage	NNI	3
3-41	348-1LV	LRTI	—	AcrIE3/AcrIF4	Prophage	NNI	4
4-14	313-1LV	LRTI	—	AcrIF3/AcrIE2	Prophage	PAGI-10	4
7-41	3292-1LV	LRTI	CRISPR-Cas IF	AcrIF2	Prophage	PAGI-9	2
8-12	253-1LV	LRTI	CRISPR-Cas IF	AcrIE2/AcrIF3/AcrIF5	Prophage	—	2
C11	175	UTI	—	AcrIE1/AcrIF3/AcrIF12/AcrIF5/AcrIF3/AcrIE2	Pathogenicity island/plasmid	NNI/PAGI-135	5
D4	27-1LV	IAI	—	AcrIF12/AcrIF3/AcrIE1/AcrIE3/AcrIF4	Pathogenicity island/prophage	PAGI-135/PAGI-6/PAGI-7/PAGI-8/PAGI-9/PAGI-10	2
E16	175-1LV	UTI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	1
E17	175-1LV	IAI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	1
G6	175-1LV	IAI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	4
G7	175-2LV	IAI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	3
G26	175-1LV	IAI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	6
G31	175-1LV	IAI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	4
H18	175-2LV	UTI	—	AcrIE1/AcrIF3/AcrIF12	Pathogenicity island	NNI/PAGI-135	4
H19	309-2LV	IAI	—	AcrIF1	Prophage	—	4

Red highlighting indicates high-risk clones.

CF, cystic fibrosis; LRTI, lower respiratory tract infection; UTI, urinary tract infection; IAI, intra-abdominal tract infection; NNI, no named island (AF332547).

Prophage identification

The presence of prophages in the genomes of the isolates was detected by PHASTEST.⁴⁴ This bioinformatic tool uses the ORF identification algorithm Prodigal,⁴⁵ running 54 reference genomes and Diamond BLAST for the phage protein alignment. We only considered those predictions identified as intact prophages with a score value >90 (Table 1 and Supplementary Table S1).

Determination of CRISPR and anti-CRISPR genes

As a result of genome annotation, CRISPR-Cas systems were detected by RAST NMPDR.⁴³ To corroborate these results, a second specific identification tool was employed, CRISPRCasFinder.⁴⁶ Repeat regions with high homology levels were detected using the FASTA files of the genomes of the isolates, by applying the default settings of CRISPRCasFinder. Briefly, repeat lengths were between 23 and 55 (allowing mismatches) and spacer sizes were set between 0.6 and 2.5, depending on the repeat size, with a percentage of similarity between spacers lower than 60, and allowing a percentage of mismatches between repeats of 20 and 33 for truncated repeats. Moreover, the size of flanking regions for each CRISPR array was set at 100 bp, and SubTyping was the clustering model selected for cas gene detection. In addition, type I-F CRISPR was manually determined by the identification of cys genes associated with identified CRISPR repeats, while type I-E CRISPR was determined by the presence of cse genes close to CRISPR repeats.

We used AcrDB⁴⁷ to search for acr regions, based on the principle of guilt by association, sequence homology search and self-targeting approaches. We focused our search on MGE in bacteria, with an aca maximum E-value of 0.01, minimum of 30% identity, and a minimum coverage of 0.8. In addition, the maximum amino acid length of the protein was set at 200 aa, the maximum intergenic distance between genes at 150 pb, and the operon up/down-stream range for MGE/prophage search at 10 genes.

PAGI search and molecular characterization

In those genomes harboring nonprophage acr genes, the genomic environments of the acr were analyzed to identify any possible PAGIs by using PAI DB,⁴⁸ a BLAST search compared the identified sequences against those of genomic islands (PAGI) included in the NCBI database,⁴⁹ identifying five PAGIs not previously described in the literature. To obtain the complete PAGI sequence, those contigs with high homology with PAGI were assembled by employing the CSAR-web⁵⁰ and Vector NTI (version 11).

PAGI sequences were annotated using RAST NMPDR.⁴³ A more detailed analysis of the unidentified proteins was conducted using BLASTP,⁵¹ HMMER v.3.3,⁵² and HHpred PDB_mmCIF70_16_Aug database.⁵³ The proteins were then classified according to their functional category: I) metabolism; ii) defense, iii) genetic mobility; iv) bacterial mobility, and v) unknown function (Table 2). The five new PAGIs described in this work were named after the isolates in which they were identified (PAGI-08_1318, PAGI-14_4114, PAGI-16_0109, PAGI-D4, and PAGI-C11). Finally, we proceed to categorize these PAGIs into ICE or IME using the ICEFinder tool v.1.0.⁵⁴ The analysis was executed for Gram-negative organisms using the default settings.

TABLE 2:

Proteins common to all of the new pathogenicity islands described in this article

Known function	System	Category
MobH family relaxase	DNA metabolism	Metabolism
Putative DNA helicase
Coupling protein VirD4, ATPase required for T-DNA transfer
DEAD/DEAH box helicase
DNA topoisomerase I (EC 5.99.1.2)
STY4528 family pathogenicity island replication protein
Arc family DNA-binding protein
Nucleoid-associated protein NdpA
Replicative DNA helicase (DnaB) (EC 3.6.4.12)
ParA-like protein
Single-stranded DNA-binding protein
Protein with ParB-like nuclease domain
Protein-disulfide isomerase	Protein metabolism
Aconitase B	RNA metabolism
Rha family transcriptional regulator	RNA metabolism
SOS response-associated peptidase family protein	Stress response	Defense
AcrIE1	Anti-CRISPR cluster
AcrIF3
TrfB-related DNA-binding protein
vWA domain-containing protein
Hypothetical protein
AcrIF12
Type IV secretory pathway, VirB4 components	Secretion system
Candidate type III effector Hop protein
Probable exported protein STY4558
Type II secretion system protein F
dTDP-D-glucose 4,6-dehydratase	Structure
Putative membrane protein
Putative membrane protein
Putative lipoprotein
Lytic transglycosylase
ABC transporter substrate-binding protein
Periplasmic protein TonB
Integrase	Mobile elements	Genetic motility
Conjugal transfer protein
TIGR03756 family integrating conjugative element protein
TIGR03757 family integrating conjugative element protein
Conjugal transfer protein
Conjugal transfer protein
PFL_4703 family integrating conjugative element protein
Conjugal transfer protein
Conjugal transfer protein
Conjugal transfer protein
Integrating conjugative element protein
Integrase regulator R
Conjugal transfer protein
Methyl-accepting chemotaxis protein	Bacterial communication	Bacterial motility
IncI1 plasmid conjugative transfer ATPase PilQ	Pili
Type 4b pilus protein PilO2
Pilus assembly protein PilL
Conjugative transfer ATPase PilQ
Conjugative transfer protein PilM
Pilus assembly protein PilV
Conjugative transfer ATPase PilU
Conjugative transfer protein PilS
Conjugative transfer protein PilP
Conjugative transfer protein PilN

There were 32 proteins with unknown functions. Blue highlighting indicates Acr cluster.

Phylogenetic tree/comparative analysis

We compared the homology of the five PAGIs detected and PAGI-135 by using BV-BCR⁵⁵ and a protein comparison tree with alignment parameters 0, RAXML algorithm and using a LG model. Easyfig 2.2.5⁵⁶ was used for linear comparison of the PAGIs. We also constructed a phylogenetic tree with the 25 stains containing acr using the sourmash tool.⁵⁷ This tool employs a Rust/Python library to conduct a metagenomic analysis and genome comparison using k-mers.

Sequence accession

All the sequences of the PAGIs described in this work have been uploaded to the NCBI database under the following accession numbers: PAGI_08-1318 (PQ868299), PAGI_14-4114 (PQ868300), PAGI_16-0109 (PQ868301), PAGI_C11 (PQ868302), and PAGI_D4 (PQ868303). These sequences are available at GenBank Bioproject PRJNA1210109.

Results

Description of clinical isolates

We analyzed 128 genomes of P. aeruginosa clinical isolates in this study: 58.59% were associated with CF or with nosocomial origin, including 11.72% from lower respiratory tract infections (LRTIs), 13.28% from urinary tract infections (UTIs), and 16.41% from intra-abdominal infections (IAIs). The isolates belonged to different STs, 37 of which corresponded to high-risk clones (Table 1 and Supplementary Table S1).⁵⁸

CRISPR-Cas and acr identification

CRISPR-Cas analysis of the 128 genomes revealed 60 isolates (46.87%) without a CRISPR-Cas system and 68 (53.13%) with at least one CRISPR-Cas system. Of the isolates carrying CRISPR-Cas systems, 79.42% originated from CF, 7.35% from IAI, 8.82% from LRTI, and 4.41% from UTI samples (Supplementary Table S1).

Of the 68 P. aeruginosa isolates with CRISPR-Cas, 65 had only one CRISPR-Cas system identified, of which 53 (81.54%) belonged to type I-F and 12 (18.46%) to type I-E. The three remaining isolates contained two types of CRISPR-Cas, two of the strains had types I-E and I-C and the other one had types I-F and I-E (Supplementary Table S1).

Identification of acr genes was pursued, and the analysis revealed the presence of acr genes in 25 (19.53%) of the 128 genomes (Table 1). Four of them had only individual acr (three isolates contained only one gene, and the other one had two separate acr genes), and three isolates coexisted one cluster and an individual gene. The remaining 18 isolates only had clusters; only one cluster was identified in 16 of the isolates, and the other two harbored two clusters.

Of the 25 isolates with Acrs (Table 1), 28% harbored isolated acr genes, of which three were identified in isolates in which no CRISPR-Cas system was detected, three had CRISPR-Cas type I-E, and one of them had CRISPR-Cas type I-F. In addition, 87.5% of these individual acr genes belonged to type I-F and 12.5% to type I-E. All of these acrIF were located in prophages, although the only acrIE found alone was located in the bacterial chromosome, outside any identifiable features; this strain also contained an individual acrIF.

The acr of the 21 isolates (72%) were organized in clusters (Fig. 1). Four different Acr clusters were found: I) AcrIE1/IF3/IF12, which represented 80.95% of the Acr clusters and was always located in pathogenicity islands, ii) AcrIE3/IF4 (14.29%), which was located in prophages, iii) AcrIE2/IF3 (4.76%), which was detected in a prophage, and iv) AcrIE2/IF3/IF5 (9.52%), which was located in a plasmid identified as plasmid pPA-H120act (CP1424441). Among the isolates carrying an Acr cluster, 57.15% did not have any CRISPR-Cas system but the others had CRISPR-Cas type IF (4.76%), type IE (33.33%), or both systems (4.76%) (Table 1 and Supplementary Table S1).

FIG. 1.

(A) Location of the Acrs: 55% in pathogenicity islands, 39% in prophages, 3% in genome, and 3% in a plasmid. (B) Frequency of the different Acrs. Frequency of the different acr genes found in the studied strains: 75% of them are associated in clusters and the remaining 25% are individual genes. The distribution of the clusters among the strains is shown in Table 1. (C) Representation of the clusters I) cluster of aca2/acrIF3/acrIE2 from the strain 4-14; II) cluster of acrIE2/acrIF3/acrIF5/aca2 from strains 8-12 and C11; III) cluster of acrIE3/acrIF4/aca2 from prophages from the strains 3-38, 3-41, and D4; IV) cluster of acrIE1/acrIF3/aca/acrIF12 from PAGI from the strains 008-1318, 008-4371, 08-5924, 014-4114, 014-4688, 014-5818, 016-0109, 016-2856, C11, D4, E16, E17, G6, G7, G26, G31, and H18.

The aca genes were identified by HHpred,⁵³ and identification of the genes was validated when the probability was higher than 90%. In the case of the clusters located in PAGIs, the possible aca was predicted as the trfB gene transcriptional repressor with an HTH DNA-binding domain protein. Although aca genes are usually located downstream of acr clusters,^35,59 we found a predicted aca gene in the middle of the AcrIE1/IF3/IF12 cluster, accompanied by a vWA-domain containing protein and a hypothetical protein.

Phylogenetic analysis of the 25 clinical isolates of P. aeruginosa acr-carriers revealed five clusters (Fig. 2). Two of these (B and D) are composed solely of isolates from the same patient, whereas the other three (A, C, and E) included nonrelated isolates.

FIG. 2.

Phylogenetic tree of the 25 strains with Acrs. On the left is the phylogenetic tree representation of the 25 strains containing acr genes. On the right is the heat map clustering the different strains. Construction of the heat map allowed us to visualize the relationship: Darker colors represent closer connections.

Mobile genetic elements

To further analyze the genomes of the 128 clinical isolates and obtain a better understanding of the position of the acr genes, the presence of prophages and PAGIs was examined. The pursuit of intact prophages by PHASTEST⁴⁴ resulted in a range of prophages from 0 to 7 without any clear pattern among the different origins.

Analysis of the AcrIE1/IF3/IF12 cluster neighboring genes showed that the cluster was contained in a pathogenicity island in all of these isolates, identifying five new PAGIs related to the previously described PAGI-135 (MT074672.1) (Fig. 3). Each of these five PAGIs was named after the strain in which it was located. We noted that these PAGIs had a coverage of between 83% and 94%, with an ID between 97.06% and 99.76% relative to PAGI-135. PAGI-16_0109 was the least similar, and PAGI-08_1318 and PAGI-14_4114, which were most similar to the reference PAGI-135. The ends of the islands were identified by the attR sequences, a direct repeat that was previously used to search for new PAGIs.²¹ The presence of these sequences, as well as integrase, relaxase, and T4SS, suggest that our PAGIs can be included in a wider classification as ICEs, which was confirmed as “Putative ICE without identified DR” by the ICEFinder tool.

FIG. 3.

Comparative analysis of the different pathogenicity islands described in this article. Phylogenetic tree with branch length representing the homology between the PAGI-08_13181318, PAGI-1414_4114, PAGI-1616_0109, PAGI-C11, PAGI-D4, and PAGI-135. Genes are represented in light blue, and homology regions in gray. The anti-CRISPR cluster is highlighted in red. The minimum length of homology was established at 100 on Easyfig to make the figure easy to read.

With the aim of characterizing the function of these islands and their possible influence in the pathogenicity of the isolates, the proteins of the new PAGIs were characterized and categorized (Supplementary Tables S2, S3, S4, S5, Table and S6) Remarkably, there were 89 prevalent proteins, which we included in the following functional categories: I) unclassified (35.96%); ii) defense (20.22%); iii) metabolism (16.85%); iv) genetic mobility (14.61%); and bacterial mobility (12.36%).

ST analysis and phylogenetic distribution of CRISPR-Cas and acr

Of the collection of 128 P. aeruginosa genomes, 122 belong to a varied collection of STs, leaving 6 unclassified (Supplementary Table S1). The most widespread ST was the high-risk clone ST235 (15 isolates), which had no CRISPR-Cas systems. The most frequent type of CRISPR-Cas system was I-F, being in 45% of the STs. Only two of the STs included in the group of high-risk clones (ST244 and ST1233) carried CRISPR-Cas systems, particularly from type I-F. It should be noted that type I-C was only found in ST554 and in all these cases coexisted with type I-E. CRISPR-Cas type I-E was found in 17.5% of the STs, on occasions alone (i.e., ST1123) and in others in conjunction with other types (i.e., in ST252 appears with type I-F). The majority of STs in the collection exhibit consistency in the presence or absence of CRISPR-Cas systems and their type, except for ST312, ST274, and ST252.

Regarding the association between ST and the presence of acr, only 25% of the STs harbored acr in their genomes. As in the previous case, most of the STs are consistent in the presence or absence of acr, with few exceptions (i.e., ST175 or ST253). None of the high-risk clones carried acr within its genomes, aside from ST175, which had a pathogenicity island with the AcrIE1/IF3/IF12 cluster, except for two isolates.

Discussion

Initially, a distinction was made between ICEs and genomic islands, the latter being defined as “chromosomal regions that were horizontally acquired but that are no longer or were never self- transmissible.”¹¹ Nevertheless, more recent classification has included some of the genomic and pathogenicity islands, and therefore PAGIs, within the ICE group.^12,16,60,61 The PAGIs described in this work were classified as ICEs, as they comply with the typical characteristics of ICEs previously described.^15–17 Although the ICEFinder was not able to detect direct repeats (att), these were manually located and found on both sides of the PAGIs.

Among others, the CRISPR-Cas system is an adaptative bacterial immune system with activity against exogenous DNA of MGEs. Phages and other MGEs have evolved to counteract CRISPR-Cas activity by acquisition of anti-CRISPR (acr) proteins.²⁷ In this study, we analyzed the acr genes harbored in a clinical collection of P. aeruginosa, finding both type I-F and type I-E clustering in loci with an aca regulator inside of PAGIs.

Prior authors have described the presence of acr in MGEs, specifically in P. aeruginosa were found mainly in ICEs/IMEs.⁶² ICE/IME backbones undergo evolutionary change, due to the frequent presence of elements (i.e., recombinases and insertion sequences) that promote the integration of other genes into their structure, contributing to the genome plasticity and bacteria adaptation.¹¹ HGT is known to be the main source of new gene acquisition contributing to the evolution and heterogenicity of P. aeruginosa, and the role of genomic islands is particularly important.^20,63 Although further research is needed to understand the regulation process, it is possible that MGEs incorporated these acr genes as a solution to the selective pressure exerted by CRISPR-Cas systems.³¹ This suggests that acr genes may protect mobile DNA elements from the nuclease activity of CRISPR-Cas systems.⁶⁴ Carrying more than one acr gene may facilitate escape of the MGEs against the continuous mutation of Cas proteins. Other authors have demonstrated that acr loci are located within “antidefence islands” of MGE in combination with other antidefence genes in different hosts.^35,65 The presence of Acr clusters in MGE contributes to a better understanding of how other defense systems are dispersed in bacteria and of the complex interactions between mobile elements such as PAGIs and their bacterial hosts.³⁵

Many PAGIs and prophages carry acr in order to deceive the bacterial immune system.³⁵ The involvement of CRISPR-Cas systems in bacterial gene regulation has recently been described,⁴¹ and the researchers proposed that CRISPR-Cas systems are capable of controlling gene expression in bacteria. However, self-targeting can also be regulated by Acr proteins.^35,64 Detection of an Acr protein codified in the genome of one isolate is consistent with these observations, and the involvement of this protein in self-targeting autoregulation is hypothetically possible.²⁷ The action of acr against type I-F and type I-E CRISPR-Cas has been widely described,^27,64 with the AcrIF type being the most widely studied and broadly distributed among the bacterial kingdom.³⁶ This field of study is developing rapidly with the discovery of new nonhomologes of Acr proteins.⁶⁶ Our findings are consistent with previous reports of most of the acrIE genes being found in clusters together with acrIF genes both in prophages and other MGEs.⁶⁴

The presence of Acr proteins in an isolate harboring the target CRISPR-Cas system has previously been described.⁶⁷ The action of Acr against the host CRISPR-Cas system may indicate a function in regulating the bacterial immune system, possibly preventing autoimmunity.⁶⁸ However, we identified isolates containing acr genes but without any CRISPR-Cas system. We also detected isolates harboring clusters with a type of acr that does not match the type of CRISPR-Cas system. This led us to believe that the high level of gene movement between bacteria may have been facilitated by the acr genes.^35,64

In addition to its immunological function, the type I-F CRISPR-Cas system has been described as regulating endogenous gene expression and post-transcriptional regulation of virulence genes in P. aeruginosa.^69,70 This may explain why most CF isolates contain this CRISPR-Cas type in their genome, increasing their pathogenicity. This regulation is based on the acquisition of spacers that target host genome, also called self-targeting or autoimmunity.⁶⁸ Furthermore, integrated MGEs benefit from this system and use it for the inter-MGE competition dynamics.⁷¹ Nevertheless, multiple defense mechanisms in bacteria and mobile elements influence the MGE spread, and this approach should be taken in consideration to understand these processes.⁶²

Other authors have suggested that CRISPR-Cas systems are capable of controlling gene expression in bacteria; this self-targeting process can also be regulated by Acr proteins.^35,59 The direct interaction between CRISPR-Cas and Acr could conform a complex regulation system, as acr may be used to regulate intercellular concentrations of Cas proteins and, in turn, the overexpression of aca genes could regulate this acr.²⁷

In this work, we have seen that there is a correspondence between the ST and the CRISPR-Cas type, with some exceptions that varied in the type of CRISPR-Cas (ST312 and ST252) or the presence and absence in ST274. As was previously observed in other studies, ST235 is one of the most predominant clonal linage.⁷² The variability of CRISPR-Cas type systems in STs of P. aeruginosa has been previously observed,⁷³ commonly being the type I-C the less frequent while type I-F is the most frequent.^74,75

CRISPR-Cas technology as a gene editing tool is increasingly used in various fields, including human clinical medicine and reproduction.^32,76,77 Investing in the creation of new tools to modulate the engineering and understanding the complex interaction between CRISPR and acr are therefore necessary. Acr proteins could be used to minimize the undesirable effects of CRISPR-Cas therapeutic technology in combination with improvements in the specification of Cas proteins.^27,78 Likewise, acr-carrying phages could be combined with phage therapy and antibiotics in a promising approach to combating multidrug-resistant isolates.⁷⁹

As compiled above, HGT is considered to be one of the significant events in bacterial evolution.⁶² Even though network-based methods were applied to understand the population structures of plasmids,⁸⁰ this approach has been barely applied to explore HGT involving other MGEs, such as prophages or ICEs.⁶² The presence of these ICEs across a wide range of hosts demonstrates their propagating success, likely driven by the selective advantages they confer.^10,81 However, these sequences are not found in all possible hosts, probably due to mechanisms that prevent their spread, such as CRISPR-Cas systems.¹³ In the same way, ICEs and PAGIs harbored counter-defense genes, as acr, to overcome these mechanisms and integrate into the host genome.^35,82 It has been observed that isolates of P. aeruginosa with an active CRISPR-Cas had a smaller genome as a consequence of constraining gene acquisition, in contrast to those with both CRISPR-Cas and acr genes whose genomes were larger. This subscribed to the hypothesis that CRISPR-Cas restricts HGT and that acr benefits the incorporation of MGEs.⁷³ The observed presence of acr genes in all the strains of the same ST may enable PAGI transmission and could imply a competitive advantage for these strains.³⁵ Further research in the CRISPR-Cas/acr regulation and its involvement in HGT is necessary to understand the mechanisms of transmission of MGEs, such as PAGIs, which affect the evolution and virulence of high-interest pathogens, such as P. aeruginosa.

Conclusions

Acr proteins are wildly scattered among bacteria and are found in prophages and MGEs, particularly pathogenicity islands, suggesting that they play an important role in bacterial adaptation and evolution. However, the mechanisms of action remain unknown, and detailed analysis is required to improve understanding of the function of acr clusters in PAGIs.

Footnotes

Acknowledgments

The authors thank to GAIN the funding to L. Fernandez-Garcia and A. Barrio-Pujante were supported by Xunta de Galicia Postdoctoral Grant IN606C-2024/004 and by Xunta de Galicia Predoctoral Grant IN606A-2023/017 respectively.

Authors’ Contributions

L.A. conducted the experiments and wrote the original manuscript; L.F.-G., C.L.-C., and L.B. collaborated in conducting the experiments; I.B., C.O.-C., C.I., and A.B.-P. examined the cited studies and collaborated in writing the article; R.C. and A.O., revised the article; and M.T. supervised all results, validated the work, and obtained funding for the research.

Author Disclosure Statement

We declare that there are no conflicts of interest.

Funding Information

This study was funded by grants PI19/00878 and PI22/00323 awarded to M.T. and PI awarded to R.C. PI19/01043 within the State Plan for R+D+I 2013-2016 (National Plan for Scientific Research, Technological Development and Innovation 2008–2011) and co-financed by the ISCIII-Deputy General Directorate for Evaluation and Promotion of Research — European Regional Development Fund “A Way of Making Europe” and Instituto de Salud Carlos III FEDER, CIBER de Enfermedades Infecciosas (CIBERINFEC) (CIBER CB21/13/00012, CB21/13/00084 and CB21/13/00095), Instituto de Salud Carlos III FEDER and by a grant from the Instituto de Salud Carlos III (MePRAM Project, PMP22/00092), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, funded by NextGeneration European Union funds that support the actions of the Resilience and Recovery Facility.

Supplementary Materials

Abbreviations

References

Rossitto

, Fox

, Vrenna

, et al. The challenging life of mutators: How Pseudomonas aeruginosa survives between persistence and evolution in cystic fibrosis lung. Microorganisms., 2024; 12(10): 2051; DOI: 10.3390/microorganisms12102051

Koumans

CIM

, Tandar

, Liakopoulos

, et al. Interspecies interactions alter the antibiotic sensitivity of Pseudomonas aeruginosa. Microbiol Spectr., 2024; 12(0): E0201224. DOI: 10.1128/spectrum.02012-24

Sati

, Tacconelli

, Carrara

, et al. WHO Bacterial Priority Pathogens List, 2024. World Health Organization; 2024.

Liao

, Huang

, Wang

, et al. Virulence factors of Pseudomonas aeruginosa and antivirulence strategies to combat its drug resistance. Front Cell Infect Microbiol., 2022; 12: 926758. DOI: 10.3389/fcimb.2022.926758

Qin

, Xiao

, Zhou

, et al. Pseudomonas aeruginosa: Pathogenesis, virulence factors, antibiotic resistance, interaction with host, technology advances and emerging therapeutics. Signal Transduct Target Ther., 2022; 7(1): 199. DOI: 10.1038/s41392-022-01056-1

Wolfgang

, Kulasekara

, Liang

, et al. Conservation of genome content and virulence determinants among clinical and environmental isolates of Pseudomonas aeruginosa. Proc Natl Acad Sci U S A., 2003; 100(14): 8484–8489. DOI: 10.1073/pnas.0832438100

Valot

, Guyeux

, Rolland

, et al. What it takes to be a Pseudomonas aeruginosa? The core genome of the opportunistic pathogen updated. PLoS One., 2015; 10(5): E 0126468. DOI: 10.1371/journal.pone.0126468

Botelho

, Tüffers

, Fuss

, et al. Phylogroup-specific variation shapes the clustering of antimicrobial resistance genes and defence systems across regions of genome plasticity in Pseudomonas aeruginosa. EBioMedicine., 2023; 90: 104532. DOI: 10.1016/j.ebiom.2023.104532

Weimann

, Dinan

, Ruis

, et al. Evolution and host-specific adaptation of Pseudomonas aeruginosa. Science., 2024; 385(6704): Eadi0908. DOI: 10.1126/science.adi0908

10.

Forster

, Liu

, Kumar

, et al. Strain-level characterization of broad host range mobile genetic elements transferring antibiotic resistance from the human microbiome. Nat Commun., 2022; 13(1): 1445. DOI: 10.1038/s41467-022-29096-9

11.

Burrus

, Waldor

. Shaping bacterial genomes with integrative and conjugative elements. Res Microbiol., 2004; 155(5): 376–386. DOI: 10.1016/j.resmic.2004.01.012

12.

Benigno

, Carraro

, Sarton-Lohéac

, et al. Diversity and evolution of an abundant ICEclc family of integrative and conjugative elements in Pseudomonas aeruginosa. mSphere., 2023; 8(6): E0051723. DOI: 10.1128/msphere.00517-23

13.

Delavat

, Miyazaki

, Carraro

, et al. The hidden life of integrative and conjugative elements. FEMS Microbiol Rev., 2017; 41(4): 512–537. DOI: 10.1093/femsre/fux008

14.

Burrus

. Mechanisms of stabilization of integrative and conjugative elements. Curr Opin Microbiol., 2017; 38: 44–50. DOI: 10.1016/j.mib.2017.03.014

15.

Mathee

, Narasimhan

, Valdes

, et al. Dynamics of Pseudomonas aeruginosa genome evolution. Proc Natl Acad Sci U S A., 2008; 105(8): 3100–3105. DOI: 10.1073/pnas.0711982105

16.

Gaillard

, Vallaeys

, Vorhölter

, et al. The clc element of Pseudomonas sp. strain B13, a genomic island with various catabolic properties. J Bacteriol., 2006; 188(5): 1999–2013. DOI: 10.1128/jb.188.5.1999-2013.2006

17.

Bellanger

, Payot

, Leblond-Bourget

, et al. Conjugative and mobilizable genomic islands in bacteria: evolution and diversity. FEMS Microbiol Rev., 2014; 38(4): 720–760. DOI: 10.1111/1574-6976.12058

18.

Botelho

, Schulenburg

. The role of integrative and conjugative elements in antibiotic resistance evolution. Trends Microbiol., 2021; 29(1): 8–18. DOI: 10.1016/j.tim.2020.05.011

19.

Johnson

, Grossman

. Integrative and conjugative elements (ICEs): what they do and how they work. Annu Rev Genet., 2015; 49: 577–601. DOI: 10.1146/annurev-genet-112414-055018

20.

Battle

, Meyer

, Rello

, et al. Hybrid pathogenicity island PAGI-5 contributes to the highly virulent phenotype of a Pseudomonas aeruginosa isolate in mammals. J Bacteriol., 2008; 190(21): 7130–7140. DOI: 10.1128/jb.00785-08

21.

Ortiz de la Rosa

, Nordmann

, Poirel

. Pathogenicity genomic island-associated CrpP-like fluoroquinolone-modifying enzymes among Pseudomonas aeruginosa clinical isolates in Europe. Antimicrob Agents Chemother., 2020; 64(7). DOI: 10.1128/aac.00489-20

22.

Censini

, Lange

, Xiang

, et al. Cag, a pathogenicity island of Helicobacter pylori, encodes type I-specific and disease-associated virulence factors. Proc Natl Acad Sci U S A., 1996; 93(25): 14648–14653. DOI: 10.1073/pnas.93.25.14648

23.

, Baldini

, Déziel

, et al. The broad host range pathogen Pseudomonas aeruginosa strain PA14 carries two pathogenicity islands harboring plant and animal virulence genes. Proc Natl Acad Sci U S A., 2004; 101(8): 2530–2535. DOI: 10.1073/pnas.0304622101

24.

Singh

, Srinivasan

, Cheng

, et al. Rearrangement of a large novel Pseudomonas aeruginosa gene island in strains isolated from a patient developing ventilator-associated pneumonia. J Clin Microbiol. 2014; 52(7): 2430–2438. DOI: 10.1128/jcm.01626-13

25.

Carter

, Chen

, Lory

. The Pseudomonas aeruginosa pathogenicity island PAPI-1 is transferred via a novel type IV pilus. J Bacteriol., 2010; 192(13): 3249–3258. DOI: 10.1128/JB.00041-10

26.

Kung

, Ozer

, Hauser

. The accessory genome of Pseudomonas aeruginosa. Microbiol Mol Biol Rev., 2010; 74(4): 621–641. DOI: 10.1128/MMBR.00027-10

27.

Davidson

, Lu

, Stanley

, et al. Anti-CRISPRs: Protein inhibitors of CRISPR-Cas systems. Annu Rev Biochem., 2020; 89: 309–332. DOI: 10.1146/annurev-biochem-011420-111224

28.

Kogay

, Wolf

, Koonin

. Defence systems and horizontal gene transfer in bacteria. Environ Microbiol., 2024; 26(4): e16630. DOI: 10.1111/1462-2920.16630

29.

Alseth

, Custodio

, Sundius

, et al. The impact of phage and phage resistance on microbial community dynamics. PLoS Biol., 2024; 22(4): e3002346. DOI: 10.1371/journal.pbio.3002346

30.

McGinn

, Marraffini

. Molecular mechanisms of CRISPR-Cas spacer acquisition. Nat Rev Microbiol., 2019; 17(1): 7–12. DOI: 10.1038/s41579-018-0071-7

31.

Pawluk

, Staals

, Taylor

, et al. Inactivation of CRISPR-Cas systems by anti-CRISPR proteins in diverse bacterial species. Nat Microbiol., 2016; 1(8): 16085. DOI: 10.1038/nmicrobiol.2016.85

32.

González de Aledo

, González-Bardanca

, Blasco

, et al. CRISPR-Cas, a revolution in the treatment and study of ESKAPE infections: Pre-clinical studies. Antibiotics (Basel)., 2021; 10(7)DOI: 10.3390/antibiotics10070756

33.

Zakrzewska

, Burmistrz

. Mechanisms regulating the CRISPR-Cas systems. Front Microbiol., 2023; 14: 1060337. DOI: 10.3389/fmicb.2023.1060337

34.

Koonin

, Makarova

. Origins and evolution of CRISPR-Cas systems. Philos Trans R Soc Lond B Biol Sci., 2019; 374(1772): 20180087. DOI: 10.1098/rstb.2018.0087

35.

Pinilla-Redondo

, Shehreen

, Marino

, et al. Discovery of multiple anti-CRISPRs highlights anti-defense gene clustering in mobile genetic elements. Nat Commun., 2020; 11(1): 5652. DOI: 10.1038/s41467-020-19415-3

36.

Pons

, van Houte

, Westra

, et al. Ecology and evolution of phages encoding anti-CRISPR proteins. J Mol Biol., 2023; 435(7): 167974. DOI: 10.1016/j.jmb.2023.167974

37.

Borges

, Davidson

, Bondy-Denomy

. The discovery, mechanisms, and evolutionary impact of anti-CRISPRs. Annu Rev Virol., 2017; 4(1): 37–59. DOI: 10.1146/annurev-virology-101416-041616

38.

, Bondy-Denomy

. Anti-CRISPRs go viral: The infection biology of CRISPR-Cas inhibitors. Cell Host Microbe., 2021; 29(5): 704–714. DOI: 10.1016/j.chom.2020.12.007

39.

Silveira

, Rocha-de-Souza

, Albano

, et al. Exploring the success of Brazilian endemic clone Pseudomonas aeruginosa ST277 and its association with the CRISPR-Cas system type I-C. BMC Genomics., 2020; 21(1): 255. DOI: 10.1186/s12864-020-6650-9

40.

Hernández-García

, García-Castillo

, García-Fernández

, et al. Distinct epidemiology and resistance mechanisms affecting ceftolozane/tazobactam in Pseudomonas aeruginosa isolates recovered from ICU patients in Spain and Portugal depicted by WGS. J Antimicrob Chemother., 2021; 76(2): 370–379. DOI: 10.1093/jac/dkaa430

41.

Sastre-Femenia

, Fernández-Muñoz

, Gomis-Font

, et al. Pseudomonas aeruginosa antibiotic susceptibility profiles, genomic epidemiology and resistance mechanisms: A nation-wide five-year time lapse analysis. Lancet Reg Health Eur., 2023; 34: 100736. DOI: 10.1016/j.lanepe.2023.100736

42.

González de Aledo

, Blasco

, Lopez

, et al. Prophage identification and molecular analysis in the genomes of Pseudomonas aeruginosa strains isolated from critical care patients. mSphere., 2023; 8(4): e0012823. DOI: 10.1128/msphere.00128-23

43.

Brettin

, Davis

, Disz

, et al. RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep., 2015; 5: 8365. DOI: 10.1038/srep08365

44.

Wishart

, Han

, Saha

, et al. PHASTEST: Faster than PHASTER, better than PHAST. Nucleic Acids Res. 2023; 51(W1): W443–W450. DOI: 10.1093/nar/gkad382

45.

Hyatt

, Chen

, Locascio

, et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics., 2010; 11: 119. DOI: 10.1186/1471-2105-11-119

46.

Couvin

, Bernheim

, Toffano-Nioche

, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for cas proteins. Nucleic Acids Res., 2018; 46(W1): W246–W251. DOI: 10.1093/nar/gky425

47.

Huang

, Yang

, Yi

, et al. AcrDB: A database of anti-CRISPR operons in prokaryotes and viruses. Nucleic Acids Res. 2021; 49(D1): D622–D629. DOI: 10.1093/nar/gkaa857

48.

Yoon

, Park

, Kim

. PAIDB v2.0: exploration and analysis of pathogenicity and resistance islands. Nucleic Acids Res., 2015; 43(Database issue): D624–D30. DOI: 10.1093/nar/gku985

49.

Sayers

, Bolton

, Brister

, et al. Database resources of the national center for biotechnology information. Nucleic Acids Res., 2022; 50(D1): D20–D26. DOI: 10.1093/nar/gkab1112

50.

Chen

, Lu

. CSAR-web: A web server of contig scaffolding using algebraic rearrangements. Nucleic Acids Res., 2018; 46(W1): W55–W59. DOI: 10.1093/nar/gky337

51.

Altschul

, Gish

, Miller

, et al. Basic local alignment search tool. J Mol Biol., 1990; 215(3): 403–410. DOI: 10.1016/s0022-2836(05)80360-2

52.

Potter

, Luciani

, Eddy

, et al. HMMER web server: 2018 update. Nucleic Acids Res., 2018; 46(W1): W200–W204. DOI: 10.1093/nar/gky448

53.

Gabler

, Nam

, Till

, et al. Protein sequence analysis using the MPI bioinformatics toolkit. Curr Protoc Bioinformatics., 2020; 72(1): e108. DOI: 10.1002/cpbi.108

54.

Liu

, Li

, Xie

, et al. ICEberg 2.0: An updated database of bacterial integrative and conjugative elements. Nucleic Acids Res. 2019; 47(D1): D660–D665. DOI: 10.1093/nar/gky1123

55.

Olson

, Assaf

, Brettin

, et al. Introducing the bacterial and viral bioinformatics resource center (BV-BRC): A resource combining PATRIC, IRD and ViPR. Nucleic Acids Res., 2023; 51(D1): D678–D689. DOI: 10.1093/nar/gkac1003

56.

Sullivan

, Petty

, Beatson

. Easyfig: A genome comparison visualizer. Bioinformatics., 2011; 27(7): 1009–1010. DOI: 10.1093/bioinformatics/btr039

57.

Pierce

, Irber

, Reiter

, et al. Large-scale sequence comparisons with sourmash. F1000Res., 2019; 8: 1006. DOI: 10.12688/f1000research.19675.1

58.

Del Barrio-Tofiño

, López-Causapé

, Oliver

. Pseudomonas aeruginosa epidemic high-risk clones and their association with horizontally-acquired β-lactamases: 2020 update. Int J Antimicrob Agents., 2020; 56(6): 106196. DOI: 10.1016/j.ijantimicag.2020.106196

59.

Yin

, Yang

, Entwistle

. Bioinformatics identification of anti-CRISPR loci by using homology, guilt-by-association, and CRISPR self-targeting spacer approaches. mSystems., 2019; 4(5)DOI: 10.1128/mSystems.00455-19

60.

Roche

, Fléchard

, Lallier

, et al. ICEEc2, a new integrative and conjugative element belonging to the pKLC102/PAGI-2 family, identified in Escherichia coli strain BEN374. J Bacteriol., 2010; 192(19): 5026–5036. DOI: 10.1128/jb.00609-10

61.

van der Meer

, Sentchilo

. Genomic islands and the evolution of catabolic pathways in bacteria. Curr Opin Biotechnol., 2003; 14(3): 248–254. DOI: 10.1016/s0958-1669(03)00058-2

62.

Botelho

, Cazares

, Schulenburg

. The ESKAPE mobilome contributes to the spread of antimicrobial resistance and CRISPR-mediated conflict between mobile genetic elements. Nucleic Acids Res., 2023; 51(1): 236–252. DOI: 10.1093/nar/gkac1220

63.

Juhas

. Type IV secretion systems and genomic islands-mediated horizontal gene transfer in Pseudomonas and Haemophilus. Microbiol Res., 2015; 170: 10–17. DOI: 10.1016/j.micres.2014.06.007

64.

Pawluk

, Bondy-Denomy

, Cheung

, et al. A new group of phage anti-CRISPR genes inhibits the type I-E CRISPR-Cas system of Pseudomonas aeruginosa. mBio., 2014; 5(2): e00896. DOI: 10.1128/mBio.00896-14

65.

Rubio

, Garzón

, Pérez-Pulido

. Biological warfare between two bacterial viruses in a genomic archipelago sheds light on the spread of CRISPR-Cas systems. bioRxiv., 2023; 2023.09.20.558655,DOI: 10.1101/2023.09.20.558655

66.

Athukoralage

, McMahon

, Zhang

, et al. An anti-CRISPR viral ring nuclease subverts type III CRISPR immunity. Nature., 2020; 577(7791): 572–575. DOI: 10.1038/s41586-019-1909-5

67.

Figueroa

, Cazares

, et al. Distribution and molecular evolution of the anti-CRISPR family AcrIF7. PLoS Biol., 2023; 21(4): e3002072. DOI: 10.1371/journal.pbio.3002072

68.

Sanchez-Londono

, Rust

, Hernández-Tamayo

, et al. Visualization of type IV-A1 CRISPR-mediated repression of gene expression and plasmid replication. Nucleic Acids Res., 2024; 52(20): 12592–12603. DOI: 10.1093/nar/gkae879

69.

Wiedenheft

, Bondy-Denomy

. CRISPR control of virulence in Pseudomonas aeruginosa. Cell Res., 2017; 27(2): 163–164. DOI: 10.1038/cr.2017.6

70.

, Fang

, Tan

, et al. Type I CRISPR-Cas targets endogenous genes and regulates virulence to evade mammalian host immunity. Cell Res., 2016; 26(12): 1273–1287. DOI: 10.1038/cr.2016.135

71.

Pinilla-Redondo

, Russel

, Mayo-Muñoz

, et al. CRISPR-Cas systems are widespread accessory elements across bacterial and archaeal plasmids. Nucleic Acids Res., 2022; 50(8): 4315–4328. DOI: 10.1093/nar/gkab859

72.

van Belkum

, Soriaga

, LaFave

, et al. Phylogenetic distribution of CRISPR-Cas systems in antibiotic-resistant Pseudomonas aeruginosa. mBio., 2015; 6(6): e01796-15–e01715. DOI: 10.1128/mBio.01796-15

73.

Wheatley

, MacLean

. CRISPR-Cas systems restrict horizontal gene transfer in Pseudomonas aeruginosa. ISME J., 2021; 15(5): 1420–1433. DOI: 10.1038/s41396-020-00860-3

74.

de Oliveira Luz

, da Silva Junior

, do Nascimento Junior

, et al. Genetic characteristics and phylogenetic analysis of Brazilian clinical strains of Pseudomonas aeruginosa harboring CRISPR/cas systems. Curr Genet., 2021; 67(4): 663–672. DOI: 10.1007/s00294-021-01173-4

75.

Parra-Sánchez

, Antequera-Zambrano

, Martínez-Navarrete

, et al. Comparative analysis of CRISPR-Cas systems in Pseudomonas genomes. Genes (Basel)., 2023; 14(7)DOI: 10.3390/genes14071337

76.

Geuverink

, Houtman

, Retel Helmrich

IRA

, et al. A decade of public engagement regarding human germline gene editing: A systematic scoping review. Eur J Hum Genet. 2025; 33: 570–579. DOI: 10.1038/s41431-024-01740-6

77.

Gautier

. CRISPR in 3D: Innovations in disease modelling and personalized medicine. Biotechniques., 2024; 76: 473–478. DOI: 10.1080/07366205.2024.2418748

78.

Marsiglia

, Vaalavirta

, Knight

, et al. Computationally guided high-throughput engineering of an anti-CRISPR protein for precise genome editing in human cells. Cell Rep Methods., 2024; 4(10): 100882. DOI: 10.1016/j.crmeth.2024.100882

79.

Qin

, Liu

, Chen

, et al. Engineered bacteriophages containing anti-CRISPR suppress infection of antibiotic-resistant P. aeruginosa. Microbiol Spectr., 2022; 10(5): e0160222. DOI: 10.1128/spectrum.01602-22

80.

Redondo-Salvo

, Fernández-López

, Ruiz

, et al. Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids. Nat Commun., 2020; 11(1): 3602. DOI: 10.1038/s41467-020-17278-2

81.

Zheng

, Li

, Yin

, et al. Is ICE hot? A genomic comparative study reveals integrative and conjugative elements as “hot” vectors for the dissemination of antibiotic resistance genes. mSystems., 2023; 8(6): e0017823. DOI: 10.1128/msystems.00178-23

82.

Mahendra

, Christie

, Osuna

, et al. Broad-spectrum anti-CRISPR proteins facilitate horizontal gene transfer. Nat Microbiol., 2020; 5(4): 620–629. DOI: 10.1038/s41564-020-0692-2

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.11 MB