Phage Encoded H-NS: A Potential Achilles Heel in the Bacterial Defence System

Phage, viruses that infect bacteria and archaea, play a fundamental role in the environment through predation and lateral gene transfer. Uncultured environmental phage have been most extensively studied in marine ecosystems where they have been demonstrated to affect oceanic biogeochemistry.

Connor Skennerton
3 years ago
No Comments

Introduction

Phage, viruses that infect bacteria and archaea, play a fundamental role in the environment through predation and lateral gene transfer [1]. Uncultured environmental phage have been most extensively studied in marine ecosystems where they have been demonstrated to affect oceanic biogeochemistry [2]. The importance of phage has been recognized in other environments including many engineered systems that are often low diversity and susceptible to phage attack. For example in the dairy industry, phage induced collapse of the fermentation process cause significant economic loss [3]. Engineered systems also provide an ideal environment for investigation of phage-host dynamics in less complex communities under controlled conditions. One such system, wastewater treatment, relies on a process known as enhanced biological phosphorus removal (EBPR; [4]) to remove dissolved organic carbon and phosphorus. However, wastewater treatment plants performing EBPR can suffer from unpredictable loss of performance, which can lead to large discharges of phosphorus into waterways [5]. Recent culture-independent studies of EBPR have mostly focused on the biology of the dominant member of the community, Candidatus Accumulibacter phosphatis (CAP), including a complete genomic characterization [6]. Due to their dominance, CAP populations are susceptible to ‘kill-the-winner’ [7] predation by phage. However, despite the potential involvement of phage in the loss of EBPR performance [8], genomic characterization of EBPR phage populations to help understand phage-host interactions is lacking.

Microorganisms have developed a number of methods to defend against phage attack. Extracellular polysaccharides form a first layer of defense by providing a physical barrier against phage entry [9]. Phage can subvert this by using degradative enzymes to reach host cell receptors. Once the phage genome has been injected into the host cell, restriction modification systems can target and degrade phage DNA [10]. Phage have been shown to evade Type III restriction by corrupting recognition sequences in their genome [11]. CRISPRs (clustered regularly interspersed short palindromic repeats) are the most recently discovered phage defense mechanism and act as a type of adaptive immune system. Bacteria and archaea incorporate small fragments of phage genomes into their CRISPR loci as spacers between repeats, which are then used to direct degradative protein machinery against future infecting phage [12]. A CRISPR spacer must be identical to the phage genome sequence for resistance [13] and can therefore be a potent driving force for phage evolution.

Recent reports have revealed that CRISPR expression can be regulated by a histone-like nucleoid structuring (H-NS) protein in Escherichia coli [14], [15], [16]. H-NS is a global bacterial repressor protein primarily found in the Proteobacteria [37] and has been studied extensively due to its widespread effect on the transcriptome [17], [18]. Genome wide analysis has determined that H-NS preferentially binds to regions of high AT-content and it has been suggested that H-NS regulates the expression of variable elements in the genome such as transposases or horizontally acquired genes [19].

Here, we present the first analysis of a phage metagenome from an EPBR environment. One of the dominant phage genomes assembled from the metagenomic sequence data encodes H-NS, the first discovery of this repressor on a phage genome. The presence of H-NS suggests a previously unrecognized mechanism for evasion of host defense mechanisms, including CRISPRs.

Results and Discussion

Phage metagenome assembly and community composition

A conventionally operated enhanced biological phosphorus removal (EBPR) bioreactor was sampled by random shotgun sequencing on two occasions seven months apart [20]. At the first sampling time (t0), total microbial biomass (bacteria, archaea and phage) was sequenced and analyzed as previously reported [6], and at the second time point (t7) purified phage virions were sequenced. While the t0 microbial metagenome was extensively analyzed [6], the t7phage metagenome was only screened for molecular links between the phage and host populations [20]. Here we assembled the t7 dataset (16 Mbp of Sanger sequence) using two assembly methods (Table S1) and a consensus was built using overlapping contigs from each assembly. Despite the small size of the t7 dataset, two thirds of the data assembled into only 13 contigs, comprising three complete and six partial phage genomes (Table 1; Figure S1) out of ∼130 genotypes estimated by PHACCS [21]. This indicates the presence of a small number of dominant EBPR phage types consistent with the microbial community structure which is dominated by a single uncultured bacterial population, Candidatus Accumulibacter phosphatis (CAP) [6].

thumbnail

Table 1. Characteristics of assembled EBPR phage phage metagenome from sampling time t7.

Sequence similarity to reference phage genomes was used to classify the assembled EBPR phage, as three Podoviridae (EPV1–3) and six Siphoviridae (ESV1–6). EPV1 and EPV2 were inferred to be lysogenic, as they contain integrase proteins (Figure 1). Only EPV3 had detectable synteny to any previously sequenced phage (Pseudomonas phage 119X) (Figure S2). Further, only 30% of the t7 metagenome had a significant match (e-value≤10−4) in the NCBI viral refseq database, reflecting the general undersampling of environmental phage.

thumbnail

Figure 1. Genomic maps of the five largest genomes assembled from the phage metagenome (t7).

Annotated open reading frames (ORFs) are colored based on the taxonomy of the top BLAST hit. ORFs labeled as bacterial/host acquired matched only to bacterial genes in the NCBI nr database, these genes may represent genes of prophage present in bacteria that have not been sampled in the viral refseq database.

We investigated the presence of t7 phage in the t0 microbial metagenome and found that EPV1 was the only phage genome sampled at both time points. However, GAAS community analysis of the t7 metagenome showed that EPV1 was not the most abundant phage (Table 1). The high frequency of turnover in CAP populations and the high specificity of phage host range could account for such a large change in viral community composition over a relatively short time period [22]. Alternatively, this may be due to sampling bias in the microbial metagenome as phage were not specifically enriched at t0, contrary to the t7 sample. However, phage sequences have been detected in high numbers in bulk metagenomes not specifically enriched for virions [23], [24].

Selective pressures on EBPR phage populations

We investigated nucleotide variation within each t7 phage population to assess possible evolutionary pressures. Most populations were homogeneous with the exception of ESV1, which could be resolved into two distinct genotypes based on single nucleotide polymorphisms (SNP) patterns (Table 1; Figure 2). By reassembling reads from the t0 metagenome that mapped to EPV1, we identified a second genotype for this population only present at t0. The two EPV1 genotypes were significantly different in the region between the coat and stabilization genes where it appears that two hypothetical genes were replaced by three different hypothetical genes (Figure 2). Comparison of the ESV1 and EPV1 genotypes suggest similar selective pressures on these EBPR phage populations as the regions of highest variation in both populations were found in structural proteins such as tail or portal proteins (Figure 2). This is consistent with previous observations that phage structural proteins and their corresponding host receptors are in a constant state of co-evolution, an arms race, which can result in numerous new variants with altered outer membranes or coat proteins [25], [26]. The dN/dSratios for ESV1 and EPV1 show that the great majority of their genes were under purifying selection (dN/dS<1, Table S2). Purifying selection has been observed in many viruses where there is strong selective pressure to maintain small genome sizes and resist random changes [27].

thumbnail

Figure 2. SNP frequencies between (A) ESV1 and (B) EPV1 genotypes.

Frequencies were calculated using a sliding window and are expressed as a percentage of mismatching bases. The EPV1 genome is marked with a grey box showing the dominant genotype at each sampling point and the position of the H-NS gene is marked in bold. The position of the three H-NS binding sites in the EPV1 genome are marked with a black arrow underneath ORFs gp33, gp40 and gp48. The region of major divergence between the two EPV1 genotypes (shaded in grey) was not used in the calculation. Proposed transcriptional phases for each genome are labeled below as early, middle or late based on the presence of marker genes typically associated with the temporal classification of transcripts [55], [56]. A question mark indicates that the transcriptional phase was uncertain.

Discovery of a bacterial regulatory gene in EPV1

A gene encoding a homolog of the global transcriptional regulator, heat-stable nucleoid structuring (H-NS) protein, was detected in the EPV1 population (both genotypes, Figure 2). Transcriptomic studies in enteric bacteria have shown that host-encoded H-NS down regulates expression of up to 5% of host genes [17]. Although H-NS has been found on plasmids [28], this is the first report of a H-NS gene in a phage genome. A search of public phage metagenomes from MG-RAST [29] CAMERA [30] and IMG-M [31] only identified H-NS in a small number of wastewater phage sequences. A functional H-NS requires two domains, a C-terminal DNA binding domain and an N-terminal oligomerization domain [32], [33]. Some phage carry the H-NS oligomerization domain that de-represses genes under host H-NS transcriptional control required for infection [34]. However, it is likely that the H-NS of EPV1 increases the repression of H-NS controlled genes as it contains both the oligomerization and DNA binding domains (Figure S3). When compared against the NCBI nr database, the EPV1 H-NS was most similar to a H-NS gene in the CAP genome (47% amino acid identity; Figure S3). This suggests that EPV1 laterally acquired its full length H-NS gene from CAP and that CAP is a host of this phage.

H-NS is a generic means for many bacterial species to down regulate newly acquired genes in hyper-variable regions of their genomes (e.g. transposons) [10]. H-NS binds preferentially to AT-rich sequences which are often characteristic of these dynamic regions [35]. To predict which genes are under H-NS control in CAP, putative binding sites were identified by comparative analysis with characterized H-NS binding profiles in related model proteobacteria (see methods). The predicted H-NS binding profile of CAP suggests that it too is able to repress hypervariable regions including polysaccharide biosynthesis gene cassettes, a Type III restriction-modification system and a CRISPR locus (Figure 3, Table S3). Notably, these are all key phage defense mechanisms.

thumbnail

Figure 3. Location of putative H-NS regulated genes in the CAP genome.

The GC-content of the CAP genome is represented by the outermost ring. Regions with less than 55% GC are highlighted in red. The genes on the posistive and negative strand of the CAP genome are represented by blue and orange rings, respectively. The innermost green ring marks the position of all the H-NS affected genes. The approximate positions of genes that may affect phage infection are highlighted by arrows in the inner most layer.

Although the host-encoded H-NS in CAP has not been shown to repress CRISPR expression, this functionality has been demonstrated in Enterobacteriaceae [14], [15], [16], [36]. We propose that the H-NS of EPV1 can repress the CAP CRISPR and other key phage defense mechanisms, giving EPV1 a selective advantage over other phage when infecting CAP. Phage repression of host genes under H-NS regulation could only occur after infection and would therefore only be effective against intracellular defense mechanisms. Moreover, the phage H-NS would need to be expressed quickly in order to be effective. The location of the H-NS gene in the early expressed cohort of phage genes (Figure 2) is consistent with this hypothesis. Repression of CRISPR and restriction-modification systems would be highly beneficial to phage as they would not have to go through repeated rounds of evolution every time a new CRISPR spacer is introduced in the host genome. More generally, repression of up to 6% of CAP genes (Table S3) would be favorable to a phage by making available more resources (e.g. nucleotides, ATP) for virion synthesis. The potential for EPV1 to dramatically alter its host’s gene expression to favor its own infection may help to explain the persistence of EPV1 in the EBPR bioreactor between t0 and t7 sampling. Three putative H-NS binding sites were also identified in the EPV1 genome adjacent to genes gp33, gp40, and gp48, the first of which falls in the variable region between the two EPV1 strains (Figure 2). These may play a role in controlling the expression of phage genes and associated virion production.

Phage are increasingly being recognized for their ability to manipulate their hosts using host-acquired genes. This has mostly been via up-regulation of key metabolic genes such as photosystem (psbA) and phosphate metabolic (phoH) genes in cyanobacteria [37]. By comparison, the interaction mediated by a phage-encoded H-NS would manipulate the host genome via down-regulation of many genes. H-NS is widely distributed in the Proteobacteria, particularly the Beta- and Gammaproteobacteria [38] (Figure S4). We speculate that ecosystems dominated by members of these groups, such as EBPR, will harbor and be susceptible to phage that carry host-derived H-NS. This previously unrecognized Achilles heel in bacterial defense systems may have significant implications in the phage-host arms race and warrants further investigation.

Methods

Microbial and phage metagenomic datasets

Two previously published metagenomic datasets obtained from the same lab-scale EBPR reactor operated in Madison, WI, USA, were used in the present study. The first (herein, t0) was a bulk microbial metagenome (acc. no. AATO00000000) leading to the reconstruction of the dominant bacterial population in the bioreactor, Candidatus Accumulibacter phosphatis, Type IIA [6]. The second dataset, sampled seven months later (herein t7), was obtained from phage as previously described [20]. Briefly, phage virions were purified using density gradient cesium chloride ultracentrifugation, and a linker-amplified shotgun library was constructed [20]. Sanger sequencing of 16,807 clones from a single end using the AmpL1 primer produced ∼16 Mb of sequence data.

Phage metagenome assembly

The t7 phage metagenomic data was quality trimmed using a Phred [39], [40] Q20 score and the reads were assembled using Velvet 1.0.05 [41] with a K-mer length of 37, expected coverage of 25 and a coverage cutoff of 2, which produced assemblies containing the longest contigs. CAP3 [42] was used to complement and confirm this assembly using an overlap of 35 bp with 85% identity. The Velvet and CAP3 assemblies were validated by tetranucleotide clustering [43], BLASTn [44] comparisons and manual inspection of contigs using Geneious 5.0.4 [45]. The largest contigs from each assembly were compared against each other with BLASTn to determine if one assembler had broken a contig into smaller parts. These breaks or inconsistencies were checked manually to determine whether this was due to misassembly. Tetranucleotide frequencies were generated for individual contigs and all contigs over 2 kb in size were subjected to tetranucleotide clustering using a k-means algorithm with a max of 20 iterations. Contig clustering allowed for multiple contigs to be assigned to a single phage genome. Assembled contigs belonging to the nine dominant phage have been deposited to the public databases under accession numbers JF412294-JF412302.

Annotation of phage contigs

Open Reading Frames (ORFs) were predicted using FGENESB (www.softberry.com) to call the position of the ORFs. Each ORF was extracted and compared against the NCBI non-redundant protein database (nr) using BLASTp. Each ORF that returned a match with e-value<10−3 was manually examined for potential function through homology to the most significant BLASTp similarity. The phylogeny of each annotated gene was determined by comparing them to all other virus genomes with BLASTx and assigning the phylogeny based on the highest similarity match with an e-value cutoff of 10−3. Contigs were then assigned an overall phylogeny by comparing the number of genes that fell into each recognized family of phage.

Analysis of phage contigs

Phage contigs were compared to the published EBPR microbial metagenome (acc. AATO00000000) [6] using BLASTn to determine whether any phage were also sampled at that time point. Contigs with alignment lengths greater than 5 kb were compared against each other using both Dotmatcher (http://emboss.sourceforge.net) (sliding window and probabilistic scoring matrix window size: 100, threshold: 75 and tile size: 10000) and the Mauve genome aligner [46](mauveAligner algorithm, match seed weight: 15, minimum LCB score: 69, using MUSCLE3.6 for the gapped alignment algorithm). Strain determination was performed using Strainer 1.0 [47]. MEGA 5 [48] was used to calculate dN/dS ratios and the SNP frequency between strains was calculated for 250 bp windows across genotypes. Phage contigs from the t0 metagenome were reassembled by mapping the entire t0 metagenome onto the contigs of the t7 phage genomes. The subset of reads that mapped to each contig were independently reassembled using CAP3.

Community structure and diversity analysis

The community structure of the t7 metagenome was analysed using PHACCS [21] and GAAS 0.15 [49] to generate estimates of the community composition. PHACCS estimates of richness the average genome size calculated by GAAS 0.15 and a contig spectrum generated by Circonspect 0.2.4 (10× coverage), using the Minimo assembler and default parameters. Furthermore GAAS was used to generate estimates of the community composition using parameters of 50% identity, 50% read coverage, minimum 10e−6 e-value, weighing all hits and normalizing for genome length. When t7 was compared solely to the NCBI refseq database only 1.5% of the metagenomic reads had similarities. However, by adding in the nine phage genomes from the assembly over 66% of the reads matched.

Heat-stable Nucleoid-Structuring (H-NS) gene analysis

A homolog of the H-NS gene found in the genome of EPV1 was aligned with H-NS homologs from the alpha-, beta- and gamma- subdivisions of the proteobacteria using ClustalW 2.0.11 [50]. A phylogenetic tree was made with ARB [51] using the maximum likelihood method RAxML and the Dayhoff substitution model. A genome wide scan of CAP for H-NS binding sites was performed by a pattern search, using the high affinity site of E. coli K12 [52], with a maximum of two mismatches. Over 1,000 sites were found and these were analyzed using Weblogo (http://weblogo.berkeley.edu/logo.cgi) to generate the CAP high affinity site. This new pattern, TCGANNAATT, was used to search for CAP and EBPR phage genes and operons that may be affected by H-NS. Since H-NS is strongly associated with regions of low GC-content [53], [54], operons associated with 1 kb regions that had a GC-content of less than 55% were combined with those found by the pattern matching.

References

  1. Canchaya C, Fournous G, Chibani-Chennoufi S, Dillmann M-L, Brussow H (2003) Phage as agents of lateral gene transfer. Current Opinion in Microbiology 6: 417–424.C. CanchayaG. FournousS. Chibani-ChennoufiM-L DillmannH. Brussow2003Phage as agents of lateral gene transfer.Current Opinion in Microbiology6417424
  2. Fuhrman JA (1999) Marine viruses and their biogeochemical and ecological effects. Nature 399: 541–548.JA Fuhrman1999Marine viruses and their biogeochemical and ecological effects.Nature399541548
  3. Brussow H, Bruttin A, Desiere F, Lucchini S, Foley S (1998) Molecular Ecology and Evolution of Streptococcus thermophilus Bacteriophages – a Review. Virus Genes 16: 95–109.H. BrussowA. BruttinF. DesiereS. LucchiniS. Foley1998Molecular Ecology and Evolution of Streptococcus thermophilus Bacteriophages – a Review.Virus Genes1695109
  4. Oehmen A, Lemos PC, Carvalho G, Yuanb Z, Keller J, et al. (2007) Advances in enhanced biological phosphorus removal: From micro to macro scale. Water Research 41: 2271–2300.A. OehmenPC LemosG. CarvalhoZ. YuanbJ. Keller2007Advances in enhanced biological phosphorus removal: From micro to macro scale.Water Research4122712300
  5. Neethling JB, Bakke B, Benisch M, Gu AZ, Stephens HM (2005) Factors influencing the reliability of enhanced biological phosphorus removal.JB NeethlingB. BakkeM. BenischAZ GuHM Stephens2005Factors influencing the reliability of enhanced biological phosphorus removal.
  6. Garcia Martin H, Ivanova N, Kunin V, Warnecke F, Barry KW, et al. (2006) Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities. Nature Biotechnology 24: 1263–1269.H. Garcia MartinN. IvanovaV. KuninF. WarneckeKW Barry2006Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities.Nature Biotechnology2412631269
  7. Thingstad TF (2000) Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnology and Oceanography 45: 1320–1328.TF Thingstad2000Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems.Limnology and Oceanography4513201328
  8. Barr JJ, Slater F, Fukushima T, Bond PL (2010) Evidence for bacteriophage activity causing community and performance changes in a phosphorus-removal activated sludge. FEMS Microbiology Ecology 74: 631–642.JJ BarrF. SlaterT. FukushimaPL Bond2010Evidence for bacteriophage activity causing community and performance changes in a phosphorus-removal activated sludge.FEMS Microbiology Ecology74631642
  9. Labrie SJ, Samson JE, Moineau S (2010) Bacteriophage resistance mechanisms. Nat Rev Micro 8: 317–327.SJ LabrieJE SamsonS. Moineau2010Bacteriophage resistance mechanisms.Nat Rev Micro8317327
  10. Dorman CJ (2004) H-NS: A universal regulator for a dynamic genome. Nature Reviews Microbiology 2: 391–400.CJ Dorman2004H-NS: A universal regulator for a dynamic genome.Nature Reviews Microbiology2391400
  11. Krüger D, Kupper D, Meisel A, Reuter M, Schroeder C (2005) The significance of distance and orientation of restriction endonuclease recognition sites in viral DNA genomes. FEMS Microbiology Reviews 1–2: 177–184.D. KrügerD. KupperA. MeiselM. ReuterC. Schroeder2005The significance of distance and orientation of restriction endonuclease recognition sites in viral DNA genomes.FEMS Microbiology Reviews1–2177184
  12. Horvath P, Barrangou R (2010) CRISPR/Cas, the Immune System of Bacteria and Archaea. Science 327: 167–170.P. HorvathR. Barrangou2010CRISPR/Cas, the Immune System of Bacteria and Archaea.Science327167170
  13. Marraffini LA, Sontheimer EJ (2010) Self versus non-self discrimination during CRISPR RNA-directed immunity. Nature 463: 568–571.LA MarraffiniEJ Sontheimer2010Self versus non-self discrimination during CRISPR RNA-directed immunity.Nature463568571
  14. Pougach K, Semenova E, Bogdanova E, Datsenko KA, Djordjevic M, et al. (2010) Transcription, processing and function of CRISPR cassettes in Escherichia coli. Molecular Microbiology 77: 1367–1379.K. PougachE. SemenovaE. BogdanovaKA DatsenkoM. Djordjevic2010Transcription, processing and function of CRISPR cassettes in Escherichia coli.Molecular Microbiology7713671379
  15. Pul U, Wurm R, Arslan Z, Geissen R, Hofmann N, et al. (2010) Identification and characterization of E. coli CRISPR-cas promoters and their silencing by H-NS. Molecular Microbiology 75: 1495–1512.U. PulR. WurmZ. ArslanR. GeissenN. Hofmann2010Identification and characterization of E. coli CRISPR-cas promoters and their silencing by H-NS.Molecular Microbiology7514951512
  16. Medina-Aparicio L, Rebollar-Flores JE, Gallego-Hernandez AL, Vazquez A, Olvera L, et al. (2011) The CRISPR/Cas immune system is an operon regulated by LeuO, H-NS and LRP in Salmonella enterica serovar Typhi. Journal of Bacteriology. L. Medina-AparicioJE Rebollar-FloresAL Gallego-HernandezA. VazquezL. Olvera2011The CRISPR/Cas immune system is an operon regulated by LeuO, H-NS and LRP in Salmonella enterica serovar Typhi.Journal of BacteriologyJB.01480-01410. JB.01480-01410.
  17. Hommais F, Krin E, Laurent-Winter C, Soutourina O, Malpertuy A, et al. (2001) Large-scale monitoring of pleiotropic regulation of gene expression by the prokaryotic nucleoid-associated protein, H-NS. Molecular Microbiology 40: 20–36.F. HommaisE. KrinC. Laurent-WinterO. SoutourinaA. Malpertuy2001Large-scale monitoring of pleiotropic regulation of gene expression by the prokaryotic nucleoid-associated protein, H-NS.Molecular Microbiology402036
  18. Dillon SC, Cameron ADS, Hokamp K, Lucchini S, Hinton JCD, et al. (2010) Genome-wide analysis of the H-NS and Sfh regulatory networks in Salmonella Typhimurium identifies a plasmid-encoded transcription silencing mechanism. Molecular Microbiology 76: 1250–1265.SC DillonADS CameronK. HokampS. LucchiniJCD Hinton2010Genome-wide analysis of the H-NS and Sfh regulatory networks in Salmonella Typhimurium identifies a plasmid-encoded transcription silencing mechanism.Molecular Microbiology7612501265
  19. Dorman CJ (2007) H-NS, the genome sentinel. Nature Reviews Microbiology 5: 157–161.CJ Dorman2007H-NS, the genome sentinel.Nature Reviews Microbiology5157161
  20. Kunin V, He S, Warnecke F, Peterson SB, Garcia Martin H, et al. (2008) A bacterial metapopulation adapts locally to phage predation despite global dispersal. Genome Research 18: 293–297.V. KuninS. HeF. WarneckeSB PetersonH. Garcia Martin2008A bacterial metapopulation adapts locally to phage predation despite global dispersal.Genome Research18293297
  21. Angly F, Rodriguez-Brito B, Bangor D, McNairnie P, Breitbart M, et al. (2005) PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information. BMC Bioinformatics 6: 41.F. AnglyB. Rodriguez-BritoD. BangorP. McNairnieM. Breitbart2005PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information.BMC Bioinformatics641
  22. He S, Bishop FI, McMahon K (2010) Bacterial community and “Candidatus Accumulibacter” population dynamics in laboratory-scale enhanced biological phosphorus removal reactors. Applied and Environmental Microbiology 76: 5479–5487.S. HeFI BishopK. McMahon2010Bacterial community and “Candidatus Accumulibacter” population dynamics in laboratory-scale enhanced biological phosphorus removal reactors.Applied and Environmental Microbiology7654795487
  23. DeLong EF, Preston CM, Mincer T, Rich V, Hallam SJ, et al. (2006) Community Genomics Among Stratified Microbial Assemblages in the Ocean’s Interior. Science 311: 496–503.EF DeLongCM PrestonT. MincerV. RichSJ Hallam2006Community Genomics Among Stratified Microbial Assemblages in the Ocean’s Interior.Science311496503
  24. Ghedin E, Claverie J-M (2005) Mimivirus relatives in the Sargasso sea. Virology Journal 2: 62.E. GhedinJ-M Claverie2005Mimivirus relatives in the Sargasso sea.Virology Journal262
  25. Buckling A, Rainey PB (2002) Antagonistic coevolution between a bacterium and a bacteriophage. Proceedings of the Royal Society London B 269: 931–936.A. BucklingPB Rainey2002Antagonistic coevolution between a bacterium and a bacteriophage.Proceedings of the Royal Society London B269931936
  26. Rodriguez-Valera F, Martin-Cuadrado A-B, Rodriguez-Brito B, Pasic L, Thingstad TF, et al. (2009) Explaining microbial population genomics through phage predation. Nat Rev Micro 7: 828–836.F. Rodriguez-ValeraA-B Martin-CuadradoB. Rodriguez-BritoL. PasicTF Thingstad2009Explaining microbial population genomics through phage predation.Nat Rev Micro7828836
  27. Wichman HA, Brown CJ (2010) Experimental evolution of viruses: Microviridae as a model system. Philosophical Transactions of the Royal Society B: Biological Sciences 365: 2495–2501.HA WichmanCJ Brown2010Experimental evolution of viruses: Microviridae as a model system.Philosophical Transactions of the Royal Society B: Biological Sciences36524952501
  28. Doyle M, Fookes M, Ivens A, Mangan MW, Wain J, et al. (2007) An H-NS-like Stealth Protein Aids Horizontal DNA Transmission in Bacteria. Science 315: 251–252.M. DoyleM. FookesA. IvensMW ManganJ. Wain2007An H-NS-like Stealth Protein Aids Horizontal DNA Transmission in Bacteria.Science315251252
  29. Meyer F, Paarmann D, D’Souza M, Olson R, Glass E, et al. (2008) The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9: 386.F. MeyerD. PaarmannM. D’SouzaR. OlsonE. Glass2008The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes.BMC Bioinformatics9386
  30. Sun S, Chen J, Li W, Altinatas I, Lin A, et al. Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource. Nucleic Acids Research. S. SunJ. ChenW. LiI. AltinatasA. LinCommunity cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource.Nucleic Acids Research
  31. Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, et al. (2008) IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Research 36: D534–D538.VM MarkowitzNN IvanovaE. SzetoK. PalaniappanK. Chu2008IMG/M: a data management and analysis system for metagenomes.Nucleic Acids Research36D534D538
  32. Stella S, Spurio R, Falconi M, Pon CL, Gualerzi CO (2005) Nature and mechanism of the in vivo oligomerization of nucleoid protein H-NS. The EMBO Journal 24: 2896–2905.S. StellaR. SpurioM. FalconiCL PonCO Gualerzi2005Nature and mechanism of the in vivo oligomerization of nucleoid protein H-NS.The EMBO Journal2428962905
  33. Sette M, Spurio R, Trotta E, Brandizi C, Brandi A, et al. (2009) Sequence-specific Recognition of DNA by the C-terminal Domain of Nucleoid-associated Protein H-NS. Journal of Biological Chemistry 284: 30453–30462.M. SetteR. SpurioE. TrottaC. BrandiziA. Brandi2009Sequence-specific Recognition of DNA by the C-terminal Domain of Nucleoid-associated Protein H-NS.Journal of Biological Chemistry2843045330462
  34. Liu Q, Richardson CC (1993) Gene 5.5 protein of bacteriophage T7 inhibits the nucleoid protein H-NS of Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America 90: 1761–1765.Q. LiuCC Richardson1993Gene 5.5 protein of bacteriophage T7 inhibits the nucleoid protein H-NS of Escherichia coli.Proceedings of the National Academy of Sciences of the United States of America9017611765
  35. Navarre WW, Porwollik S, Wang Y, McClelland M, Rosen H, et al. (2006) Selective Silencing of Foreign DNA with Low GC Content by the H-NS Protein in Salmonella. Science 313: 236–238.WW NavarreS. PorwollikY. WangM. McClellandH. Rosen2006Selective Silencing of Foreign DNA with Low GC Content by the H-NS Protein in Salmonella.Science313236238
  36. Westra ER, Pul Um, Heidrich N, Jore MM, Lundgren M, et al. (2010) H-NS mediated repression of CRISPR-based immunity in Escherichia coli K12 can be relieved by the transcription activator LeuO. Molecular Microbiology 77: 1380–1393.ER WestraUm PulN. HeidrichMM JoreM. Lundgren2010H-NS mediated repression of CRISPR-based immunity in Escherichia coli K12 can be relieved by the transcription activator LeuO.Molecular Microbiology7713801393
  37. Rohwer F, Thurber RV (2009) Viruses manipulate the marine environment. Nature 459: 207–212.F. RohwerRV Thurber2009Viruses manipulate the marine environment.Nature459207212
  38. Tendeng C, Bertin PN (2003) H-NS in Gram-negative bacteria: a family of multifaceted protein. Trends in Microbiology 11: 511–518.C. TendengPN Bertin2003H-NS in Gram-negative bacteria: a family of multifaceted protein.Trends in Microbiology11511518
  39. Ewing B, Hillier L, Wendl MC, Green P (1998) Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Research 8: 175–185.B. EwingL. HillierMC WendlP. Green1998Base-calling of automated sequencer traces using phred. I. Accuracy assessment.Genome Research8175185
  40. Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Research 8: 186–194.B. EwingP. Green1998Base-calling of automated sequencer traces using phred. II. Error probabilities.Genome Research8186194
  41. Zerbino DR, Birney E (2008) Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Research 18: 821–829.DR ZerbinoE. Birney2008Velvet: Algorithms for de novo short read assembly using de Bruijn graphs.Genome Research18821829
  42. Huang X, Madan A (1999) CAP3: A DNA Sequence Assembly Program. Genome Research 9: 868–877.X. HuangA. Madan1999CAP3: A DNA Sequence Assembly Program.Genome Research9868877
  43. Pride DT, Meinersmann RJ, Wassenaar TM, Blaser MJ (2003) Evolutionary Implications of Microbial Genome Tetranucleotide Frequency Biases. Genome Research 13: 145–158.DT PrideRJ MeinersmannTM WassenaarMJ Blaser2003Evolutionary Implications of Microbial Genome Tetranucleotide Frequency Biases.Genome Research13145158
  44. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. Journal of Molecular Biology 215: 403–410.SF AltschulW. GishW. MillerEW MyersDJ Lipman1990Basic local alignment search tool.Journal of Molecular Biology215403410
  45. Drummond AJ, Ashton B, Cheung M, Heled J, Kearse M, et al. (2010) Geneious. AJ DrummondB. AshtonM. CheungJ. HeledM. Kearse2010Geneious.5.0.4 ed. 5.0.4 ed.
  46. Darling ACE, Mau B, Blattner FR, Perna NT (2004) Mauve: Multiple alignment of conserved genomic sequence with rearrangements. Genome Research 14: 1394–1403.ACE DarlingB. MauFR BlattnerNT Perna2004Mauve: Multiple alignment of conserved genomic sequence with rearrangements.Genome Research1413941403
  47. Eppley JM, Tyson GW, Getz WM, Banfield JF (2007) Strainer: software for analysis of population variation in community genomic datasets. BMC Bioinformatics 8: 398.JM EppleyGW TysonWM GetzJF Banfield2007Strainer: software for analysis of population variation in community genomic datasets.BMC Bioinformatics8398
  48. Kumar S, Nei M, Dudley J, Tamura K (2008) MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences. Briefings in Bioinformatics 9: 299–306.S. KumarM. NeiJ. DudleyK. Tamura2008MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences.Briefings in Bioinformatics9299306
  49. Angly FE, Willner D, Prieto-Davo A, Edwards RA, Schmieder R, et al. (2009) The GAAS Metagenomic Tool and Its Estimations of Viral and Microbial Average Genome Size in Four Major Biomes. PLoS Computational Biology 5: e1000593.FE AnglyD. WillnerA. Prieto-DavoRA EdwardsR. Schmieder2009The GAAS Metagenomic Tool and Its Estimations of Viral and Microbial Average Genome Size in Four Major Biomes.PLoS Computational Biology5e1000593
  50. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, et al. (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23: 2947–2948.MA LarkinG. BlackshieldsNP BrownR. ChennaPA McGettigan2007Clustal W and Clustal X version 2.0.Bioinformatics2329472948
  51. Ludwig W, Strunk O, Westram R, Richter L, Meier H, et al. (2004) ARB: a software environment for sequence data. Nucleic Acids Research 32: 1363–1371.W. LudwigO. StrunkR. WestramL. RichterH. Meier2004ARB: a software environment for sequence data.Nucleic Acids Research3213631371
  52. Lang B, Blot N, Bouffartigues E, Buckle M, Geertz M, et al. (2007) High-affinity DNA binding sites for H-NS provide a molecular basis for selective silencing within proteobacterial genomes. Nucleic Acids Research 35: 6330–6337.B. LangN. BlotE. BouffartiguesM. BuckleM. Geertz2007High-affinity DNA binding sites for H-NS provide a molecular basis for selective silencing within proteobacterial genomes.Nucleic Acids Research3563306337
  53. Castang S, McManus HR, Turner KH, Dove SL (2008) H-NS family members function coordinately in an opportunistic pathogen. Proceedings of the National Academy of Sciences of the United States of America 105: 18947–18952.S. CastangHR McManusKH TurnerSL Dove2008H-NS family members function coordinately in an opportunistic pathogen.Proceedings of the National Academy of Sciences of the United States of America1051894718952
  54. Gordon BRG, Li Y, Wang L, Sintsova A, Bakel Hv, et al. (2010) Lsr2 is a nucleoid-associated protein that targets AT-rich sequences and virulence genes in Mycobacterium tuberculosis. Proceedings of the National Academy of Sciences of the United States of America 107: 5154–5159.BRG GordonY. LiL. WangA. SintsovaHv Bakel2010Lsr2 is a nucleoid-associated protein that targets AT-rich sequences and virulence genes in Mycobacterium tuberculosis.Proceedings of the National Academy of Sciences of the United States of America10751545159
  55. Duplessisa M, Russelld WM, Romerod DA, Moineau S (2005) Global gene expression analysis of two Streptococcus thermophilus bacteriophages using DNA microarray. Virology 340: 192–208.M. DuplessisaWM RusselldDA RomerodS. Moineau2005Global gene expression analysis of two Streptococcus thermophilus bacteriophages using DNA microarray.Virology340192208
  56. Miller ES, Kutter E, Mosig G, Arisaka F, Kunisawa T, et al. (2003) Bacteriophage T4 Genome. Microbiology and Molecular Biology Reviews 67: 86–156.ES MillerE. KutterG. MosigF. ArisakaT. Kunisawa2003Bacteriophage T4 Genome.Microbiology and Molecular Biology Reviews6786156
Post Views: 1,148

Leave a Reply

Phage Encoded H-NS: A Potential Achilles Heel in the Bacterial Defence System

Phage, viruses that infect bacteria and archaea, play a fundamental role in the environment through predation and lateral gene transfer. Uncultured environmental phage have been most extensively studied in marine ecosystems where they have been demonstrated to affect oceanic biogeochemistry.
Scroll to top
MENU