Determination of the small RNA GcvB regulon in the Gram-negative bacterial pathogen Pasteurella multocida and identification of the GcvB seed binding region

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

Identification and initial characterization of Hfq-associated sRNAs in Histophilus somni strain 2336

Small RNAs (sRNA), in association with the global chaperone regulator Hfq, positively or negatively regulate gene expression in bacteria. For this study, Histophilus somni sRNAs that bind to Hfq were identified and then partially characterized. The Hfq-associated sRNAs in H. somni were isolated and identified by co-immunoprecipitation using anti-Hfq antibody, followed by sRNA sequencing. Sequence analysis of the sRNA samples identified 100 putative sRNAs, out of which 16 were present in pathogenic strain 2336, but not in non-pathogenic strain 129Pt. Bioinformatic analyses suggested that the sRNAs HS9, HS79, and HS97 could bind to many genes putatively involved in virulence/biofilm formation. Furthermore, multi-sequence alignment of the sRNA regions in the genome revealed that HS9 and HS97 could interact with sigma 54, which is a transcription factor linked to important bacterial traits, including motility, virulence, and biofilm formation. Northern blotting was used to determine the approximate size, abundance and any processing events attributed to the sRNAs. Selected sRNA candidates were confirmed to bind Hfq, as determined by electrophoretic mobility shift assays using sRNAs synthesized by in vitro transcription and recombinant Hfq. The exact transcriptional start site of the sRNA candidates was determined by RNA ligase-mediated rapid amplification of cDNA ends, followed by cloning and sequencing. This is the first investigation of H. somni sRNAs that show they may have important regulatory roles in virulence and biofilm formation.

Central Role of Sibling Small RNAs NgncR_162 and NgncR_163 in Main Metabolic Pathways of Neisseria gonorrhoeae

Small bacterial regulatory RNAs (sRNAs) have been implicated in the regulation of numerous metabolic pathways. In most of these studies, sRNA-dependent regulation of mRNAs or proteins of enzymes in metabolic pathways has been predicted to affect the metabolism of these bacteria. However, only in a very few cases has the role in metabolism been demonstrated. Here, we performed a combined transcriptome and metabolome analysis to define the regulon of the sibling sRNAs NgncR_162 and NgncR_163 (NgncR_162/163) and their impact on the metabolism of Neisseria gonorrhoeae. These sRNAs have been reported to control genes of the citric acid and methylcitric acid cycles by posttranscriptional negative regulation. By transcriptome analysis, we now expand the NgncR_162/163 regulon by several new members and provide evidence that the sibling sRNAs act as both negative and positive regulators of target gene expression. Newly identified NgncR_162/163 targets are mostly involved in transport processes, especially in the uptake of glycine, phenylalanine, and branched-chain amino acids. NgncR_162/163 also play key roles in the control of serine-glycine metabolism and, hence, probably affect biosyntheses of nucleotides, vitamins, and other amino acids via the supply of one-carbon (C₁) units. Indeed, these roles were confirmed by metabolomics and metabolic flux analysis, which revealed a bipartite metabolic network with glucose degradation for the supply of anabolic pathways and the usage of amino acids via the citric acid cycle for energy metabolism. Thus, by combined deep RNA sequencing (RNA-seq) and metabolomics, we significantly extended the regulon of NgncR_162/163 and demonstrated the role of NgncR_162/163 in the regulation of central metabolic pathways of the gonococcus. IMPORTANCE Neisseria gonorrhoeae is a major human pathogen which infects more than 100 million people every year. An alarming development is the emergence of gonococcal strains that are resistant against virtually all antibiotics used for their treatment. Despite the medical importance and the vanishing treatment options of gonococcal infections, the bacterial metabolism and its regulation have been only weakly defined until today. Using RNA-seq, metabolomics, and ¹³C-guided metabolic flux analysis, we here investigated the gonococcal metabolism and its regulation by the previously studied sibling sRNAs NgncR_162/163. The results demonstrate the regulation of transport processes and metabolic pathways involved in the biosynthesis of nucleotides, vitamins, and amino acids by NgncR_162/163. In particular, the combination of transcriptome and metabolic flux analyses provides a heretofore unreached depth of understanding the core metabolic pathways and their regulation by the neisserial sibling sRNAs. This integrative approach may therefore also be suitable for the functional analysis of a growing number of other bacterial metabolic sRNA regulators.

Identification of small RNAs associated with RNA chaperone Hfq reveals a new stress response regulator in Actinobacillus pleuropneumoniae

The RNA chaperone Hfq promotes the association of small RNAs (sRNAs) with cognate mRNAs, controlling the expression of bacterial phenotype. Actinobacillus pleuropneumoniae hfq mutants strains are attenuated for virulence in pigs, impaired in the ability to form biofilms, and more susceptible to stress, but knowledge of the extent of sRNA involvement is limited. Here, using A. pleuropneumoniae strain MIDG2331 (serovar 8), 14 sRNAs were identified by co-immunoprecipitation with Hfq and the expression of eight, identified as trans-acting sRNAs, were confirmed by Northern blotting. We focused on one of these sRNAs, named Rna01, containing a putative promoter for RpoE (stress regulon) recognition. Knockout mutants of rna01 and a double knockout mutant of rna01 and hfq, both had decreased biofilm formation and hemolytic activity, attenuation for virulence in Galleria mellonella, altered stress susceptibility, and an altered outer membrane protein profile. Rna01 affected extracellular vesicle production, size and toxicity in G. mellonella. qRT-PCR analysis of rna01 and putative cognate mRNA targets indicated that Rna01 is associated with the extracytoplasmic stress response. This work increases our understanding of the multilayered and complex nature of the influence of Hfq-dependent sRNAs on the physiology and virulence of A. pleuropneumoniae.

GcvB Regulon Revealed by Transcriptomic and Proteomic Analysis in Vibrio alginolyticus

Vibrio alginolyticus is a widely distributed marine bacterium that is a threat to the aquaculture industry as well as human health. Evidence has revealed critical roles for small RNAs (sRNAs) in bacterial physiology and cellular processes by modulating gene expression post-transcriptionally. GcvB is one of the most conserved sRNAs that is regarded as the master regulator of amino acid uptake and metabolism in a wide range of Gram-negative bacteria. However, little information about GcvB-mediated regulation in V. alginolyticus is available. Here we first characterized GcvB in V. alginolyticus ZJ-T and determined its regulon by integrated transcriptome and quantitative proteome analysis. Transcriptome analysis revealed 40 genes differentially expressed (DEGs) between wild-type ZJ-T and gcvB mutant ZJ-T-ΔgcvB, while proteome analysis identified 50 differentially expressed proteins (DEPs) between them, but only 4 of them displayed transcriptional differences, indicating that most DEPs are the result of post-transcriptional regulation of gcvB. Among the differently expressed proteins, 21 are supposed to be involved in amino acid biosynthesis and transport, and 11 are associated with type three secretion system (T3SS), suggesting that GcvB may play a role in the virulence besides amino acid metabolism. RNA-EMSA showed that Hfq binds to GcvB, which promotes its stability.

The Role and Targets of the RNA-Binding Protein ProQ in the Gram-Negative Bacterial Pathogen Pasteurella multocida

The Gram-negative pathogen Pasteurella multocida is the causative agent of many important animal diseases. While a number of P. multocida virulence factors have been identified, very little is known about how gene expression and protein production is regulated in this organism. One mechanism by which bacteria regulate transcript abundance and protein production is riboregulation, which involves the interaction of a small RNA (sRNA) with a target mRNA to alter transcript stability and/or translational efficiency. This interaction often requires stabilization by an RNA-binding protein such as ProQ or Hfq. In Escherichia coli and a small number of other species, ProQ has been shown to play a critical role in stabilizing sRNA-mRNA interactions and preferentially binds to the 3' stem-loop regions of the mRNA transcripts, characteristic of intrinsic transcriptional terminators. The aim of this study was to determine the role of ProQ in regulating P. multocida transcript abundance and identify the RNA targets to which it binds. We assessed differentially expressed transcripts in a proQ mutant and identified sites of direct ProQ-RNA interaction using in vivo UV-cross-linking and analysis of cDNA (CRAC). These analyses demonstrated that ProQ binds to, and stabilizes, ProQ-dependent sRNAs and transfer RNAs in P. multocida via adenosine-enriched, highly structured sequences. The binding of ProQ to two RNA molecules was characterized, and these analyses showed that ProQ bound within the coding sequence of the transcript PmVP161_1121, encoding an uncharacterized protein, and within the 3' region of the putative sRNA Prrc13. IMPORTANCE Regulation in P. multocida involving the RNA-binding protein Hfq is required for hyaluronic acid capsule production and virulence. This study further expands our understanding of riboregulation by examining the role of a second RNA-binding protein, ProQ, in transcript regulation and abundance in P. multocida.

Mining RNA-seq data reveals the massive regulon of GcvB small RNA and its physiological significance in maintaining amino acid homeostasis in Escherichia coli

Bacterial small RNAs regulate the expression of multiple genes through imperfect base-pairing with target mRNAs mediated by RNA chaperone proteins such as Hfq. GcvB is the master sRNA regulator of amino acid metabolism and transport in a wide range of Gram-negative bacteria. Recently, independent RNA-seq approaches identified a plethora of transcripts interacting with GcvB in Escherichia coli. In this study, the compilation of RIL-seq, CLASH, and MAPS data sets allowed us to identify GcvB targets with high accuracy. We validated 21 new GcvB targets repressed at the posttranscriptional level, raising the number of direct targets to >50 genes in E. coli. Among its multiple seed sequences, GcvB utilizes either R1 or R3 to regulate most of these targets. Furthermore, we demonstrated that both R1 and R3 seed sequences are required to fully repress the expression of gdhA, cstA, and sucC genes. In contrast, the ilvLXGMEDA polycistronic mRNA is targeted by GcvB through at least four individual binding sites in the mRNA. Finally, we revealed that GcvB is involved in the susceptibility of peptidase-deficient E. coli strain (Δpeps) to Ala-Gln dipeptide by regulating both Dpp dipeptide importer and YdeE dipeptide exporter via R1 and R3 seed sequences, respectively.

sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction

Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programsin terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

Polymyxin causes cell envelope remodeling and stress responses in mcr-1-harboring Escherichia coli

Polymyxins remain important last-line antibiotics against multidrug-resistant Gram-negative bacteria. Unfortunately, polymyxin resistance is emerging and the mobile polymyxin resistance gene, mcr is contributing to the wide dissemination of polymyxin resistance, especially among Escherichia coli, with mcr-1 being the most commonly found variant. The objective of this study was to provide mechanistic insights into concentration-dependent transcriptomic responses of mcr-harboring E. coli following polymyxin treatment. An mcr-1-carrying clinical isolate of E. coli (LH30) was treated with polymyxin B at 2 and 8 mg/L. Bacterial cultures were collected before and 1 h following treatment for viable counting and transcriptomic analysis. Growth of E. coli LH30 was unaffected by 2 mg/L polymyxin B, whereas killing of ∼2 log₁₀ cfu/mL occurred with 8 mg/L at 1 h. All four phosphoethanolamine (pEtN) transferase genes (mcr-1, eptA, eptB and eptC) were upregulated (FC=2.4-4.0) by 8 mg/L polymyxin B, indicating that pEtN modifications were the preferred polymyxin resistance mechanism. The higher polymyxin B concentration also affected the expression of genes involved in fatty acid, lipopolysaccharide, lipid A, phospholipid biosynthesis, iron homeostasis and oxidative stress pathways. Our transcriptomic analysis revealed that cell envelope remodeling, pEtN modification, iron acquisition and oxidative stress protective mechanisms play a key role in the survival of mcr-carrying E. coli treated with polymyxin. These findings provide new mechanistic information at the gene expression level to counter polymyxin resistance.

Metabolic Perturbations Caused by the Over-Expression of mcr-1 in Escherichia coli

Rapid dissemination of the plasmid-born polymyxin resistance gene mcr-1 poses a critical medical challenge. MCR-1 expression is tightly controlled and imposes a fitness cost on the bacteria. We used growth studies and metabolomics to examine growth and metabolic changes within E. coli TOP10 at 8 and 24 h in response to different levels of expression of mcr-1. Induction of mcr-1 greatly increased expression at 8 h and markedly reduced bacterial growth; membrane disruption and cell lysis were evident at this time. At 24 h, the expression of mcr-1 dramatically declined with restored growth and membrane integrity, indicating regulation of mcr-1 expression in bacteria to maintain membrane homeostasis. Intermediates of peptide and lipid biosynthesis were the most commonly affected metabolites when mcr-1 was overexpressed in E. coli. Cell wall biosynthesis was dramatically affected with the accumulation of lipids including fatty acids, glycerophospholipids and lysophosphatidylethanolamines, especially at 8 h. In contrast, levels of intermediate metabolites of peptides, amino sugars, carbohydrates and nucleotide metabolism and secondary metabolites significantly decreased. Moreover, the over-expression of mcr-1 resulted in a prolonged reduction in intermediates associated with pentose phosphate pathway and pantothenate and CoA biosynthesis. These findings indicate that over-expression of mcr-1 results in global metabolic perturbations that mainly involve disruption to the bacterial membrane, pentose phosphate pathway as well as pantothenate and CoA biosynthesis.

Polymyxin resistance in Klebsiella pneumoniae: multifaceted mechanisms utilized in the presence and absence of the plasmid-encoded phosphoethanolamine transferase gene mcr-1

OBJECTIVES

Until plasmid-mediated mcr-1 was discovered, it was believed that polymyxin resistance in Gram-negative bacteria was mainly mediated by the chromosomally-encoded EptA and ArnT, which modify lipid A with phosphoethanolamine (pEtN) and 4-amino-4-deoxy-l-arabinose (l-Ara4N), respectively. This study aimed to construct a markerless mcr-1 deletion mutant in Klebsiella pneumoniae, validate a reliable reference gene for reverse transcription quantitative PCR (RT-qPCR) and investigate the interactions among mcr-1, arnT and eptA, in response to polymyxin treatments using pharmacokinetics/pharmacodynamics (PK/PD).

METHODS

An isogenic markerless mcr-1 deletion mutant (II-503Δmcr-1) was generated from a clinical K. pneumoniae II-503 isolate. The efficacy of different polymyxin B dosage regimens was examined using an in vitro one-compartment PK/PD model and polymyxin resistance was assessed using population analysis profiles. The expression of mcr-1, eptA and arnT was examined using RT-qPCR with a reference gene pepQ, and lipid A was profiled using LC-MS. In vivo polymyxin B efficacy was investigated in a mouse thigh infection model.

RESULTS

In K. pneumoniae II-503, mcr-1 was constitutively expressed, irrespective of polymyxin exposure. Against II-503Δmcr-1, an initial bactericidal effect was observed within 4 h with polymyxin B at average steady-state concentrations of 1 and 3 mg/L, mimicking patient PK. However, substantial regrowth and concomitantly increased expression of eptA and arnT were detected. Predominant l-Ara4N-modified lipid A species were detected in II-503Δmcr-1 following polymyxin B treatment.

CONCLUSIONS

This is the first study demonstrating a unique markerless deletion of mcr-1 in a clinical polymyxin-resistant K. pneumoniae. The current polymyxin B dosage regimens are suboptimal against K. pneumoniae, regardless of mcr, and can lead to the emergence of resistance.

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.

IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation

By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to efficiently adapt to changes in their environment. To better understand how Escherichia coli acclimatizes to nutrient availability, we performed UV cross-linking, ligation and sequencing of hybrids (CLASH) to uncover Hfq-associated RNA-RNA interactions at specific growth stages. We demonstrate that Hfq CLASH robustly captures bona fide RNA-RNA interactions. We identified hundreds of novel sRNA base-pairing interactions, including many sRNA-sRNA interactions and involving 3’UTR-derived sRNAs. We rediscovered known and identified novel sRNA seed sequences. The sRNA-mRNA interactions identified by CLASH have strong base-pairing potential and are highly enriched for complementary sequence motifs, even those supported by only a few reads. Yet, steady state levels of most mRNA targets were not significantly affected upon over-expression of the sRNA regulator. Our results reinforce the idea that the reproducibility of the interaction, not base-pairing potential, is a stronger predictor for a regulatory outcome.

Biosynthesis and regulation mechanisms of the Pasteurella multocida capsule

Pasteurella multocida possesses a polysaccharide capsule composed of a viscous surface layer that acts as a critical structural component and virulence factor. Capsular polysaccharides are structurally similar to vertebrate glycosaminoglycans, providing an immunological mechanism for bacterial molecular mimicry, resistance to phagocytosis, and immune evasion during the infection process. In recent years, a series of important research advances have been made in understanding the biosynthesis and regulatory aspects of the P. multocida capsule. This review systematically examines the serogroups, polysaccharide composition and structures, biosynthetic loci and functions, biosynthesis pathways, and expression regulation mechanisms of the P. multocida capsule, supplying a theoretical basis for the molecular pathogenesis of the P. multocida capsule and the future development of capsular polysaccharide vaccines.

Differential quantitative proteomics reveals the functional difference of two yigP locus products, UbiJ and EsrE

The yigP (ubiJ) locus has been shown to be associated with many phenotypic changes in Escherichia coli, while the individual function of its two products, EsrE small RNA and UbiJ protein, is still elusive. In this study, we constructed two single-element mutants, EsrE mutant strain Mut and UbiJ mutant strain Ter, on the basis of the base substitution programs. The variable antibiotics resistance and ubiquinone (UQ, coenzyme Q) yield and the similar cell growth between mutants revealed the division of labor and collaboration of EsrE and UbiJ in JM83. Furthermore, we detected the concentration of intracellular proteins of Mut and Ter by stable isotope-labeled quantitative proteomics. The results demonstrate that both EsrE and UbiJ are involved in the aerobic growth of E. coli, while EsrE preferentially contributes to the amino acid-related pathway, and UbiJ is an indispensable factor in the biosynthesis of UQ. Moreover, we uncovered a potential regulatory circuit of d-cycloserine (DCS) that composed of EsrE, GcvA, and GcvB by proteomic analysis.

GcvB small RNA uses two distinct seed regions to regulate an extensive targetome

GcvB small RNA is described as post-transcriptional regulator of 1-2% of all mRNAs in Escherichia coli and Salmonella Typhimurium. At least 24 GcvB:mRNA interactions have been validated in vivo, establishing the largest characterized sRNA targetome. By performing MS2-affinity purification coupled with RNA sequencing (MAPS) technology, we identified seven additional mRNAs negatively regulated by GcvB in E. coli. Contrary to the vast majority of previously known targets, which pair to the well-conserved GcvB R1 region, we validated four mRNAs targeted by GcvB R3 region. This indicates that base-pairing through R3 seed sequence seems relatively common. We also noticed unusual GcvB pairing sites in the coding sequence of two target mRNAs. One of these target mRNAs has a pairing site displaying a unique ACA motif, suggesting that GcvB could hijack a translational enhancer element. The second target mRNA is likely regulated via an active RNase E-mediated mRNA degradation mechanism. Remarkably, we confirmed the importance of the sRNA sponge SroC in the fine-tuning control of GcvB activity in function of growth conditions such as growth phase and nutrient availability.

Identification of Novel Acinetobacter baumannii Type VI Secretion System Antibacterial Effector and Immunity Pairs

The type VI secretion system (T6SS) is a macromolecular machine that delivers protein effectors into host cells and/or competing bacteria. The effectors may be delivered as noncovalently bound cargo of T6SS needle proteins (VgrG/Hcp/PAAR) or as C-terminal extensions of these proteins. Many Acinetobacter baumannii strains produce a T6SS, but little is known about the specific effectors or how they are delivered. In this study, we show that A. baumannii AB307-0294 encodes three vgrG loci, each containing a vgrG gene, a T6SS toxic effector gene, and an antitoxin/immunity gene. Each of the T6SS toxic effectors could kill Escherichia coli when produced in trans unless the cognate immunity protein was coproduced. To determine the role of each VgrG in effector delivery, we performed interbacterial competitive killing assays using A. baumannii AB307-0294 vgrG mutants, together with Acinetobacter baylyi prey cells expressing pairs of immunity genes that protected against two toxic effectors but not a third. Using this approach, we showed that AB307-0294 produces only three T6SS toxic effectors capable of killing A. baylyi and that each VgrG protein is specific for the carriage of one effector. Finally, we analyzed a number of A. baumannii genomes and identified significant diversity in the range of encoded T6SS VgrG and effector proteins, with correlations between effector types and A. baumannii global clone lineages.